This Week in All Things AI https://paragraph.com/@twiata Weekly newsletter which reposts content from the 'All Things AI' telegram group curated by Yusuf Goolamabbas Fri, 30 Oct 2026 18:39:39 GMT https://validator.w3.org/feed/docs/rss2.html https://github.com/jpmonette/feed en All rights reserved <![CDATA[This Week in All Things AI - Week 43-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-43-2026 IWqn2LFvoPrJuCNxmMYI Sun, 25 Oct 2026 03:51:34 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 18th to 24th October 2026 brought a clearer focus on making capable AI practical to use: faster responses, lower costs and tools that can act beyond a chat window. MiniMax’s M3.1 Flash and Inception’s Mercury 3 paired claims of stronger performance with gains in speed and efficiency, while Kimi K3.1 Pro extended the push towards more capable coding and multimodal agents, with its weights scheduled for November. These developments matter because useful agents often need many rounds of reasoning and action to finish a task; the time and cost of each round shape what becomes viable. Cursor’s computer use brought that idea into testing desktop apps and iOS simulators. Voice followed a similar practical direction, from Mistral’s Voxtral 3 and voice-agent tools to Gradium’s compact Phonon speech generation models, which can run locally without an internet connection.

The infrastructure underneath these experiences also received attention. Exa 3.0 aimed to give agents more relevant information with less repetitive text, addressing the cost of what models read as well as what they generate. DeepSeek opened its production inference framework, while Google’s TorchTPU and AWS’s TorchNeuron public beta sought to make alternative chips easier for PyTorch developers to use. Together, these announcements point towards greater choice over how AI is built and deployed, though the maturity of those options varies. Our guide to Perplexity Decisions and TypeSafe/Jev explored a complementary design choice: using models for specific judgements while keeping the next action under the control of application code.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 18th October 2026

Monday 19th October 2026

Anthropic says it is committing 5% of its shares to a Public Benefit Fund, for a total of ~20% with co-founder and staff pledges, plus 2% per 50% valuation rise

Investing in the public benefit

We're committing a portion of Anthropic's shares to the Anthropic Public Benefit Fund, a new entity with a structure that increases the stake as the company grows.

https://www.anthropic.com
Investing in the public benefit

MiniMax launches M3.1 Flash, saying it delivers “a generational leap” in agentic tasks, coding, computer use, and cybersecurity, with weights due October 23

MiniMax announces M3.1 Flash, a fast and efficient AI model that evolved from their anonymous Space Bunny version, which processed over 200 trillion tokens and led global leaderboards. 

It delivers strong results in agentic tasks with scores like 62.2% on Automation-Bench, 73.7% on DeepSWE coding, and 91% on BabyVision, while running at 3x the speed and 1/3 the cost of its predecessor M3. 

API pricing starts at $0.15 input/$0.60 output per million tokens with cache reads at $0.015, and model weights are scheduled for release on October 23, enhancing accessibility for developers. 


MiniMax M3.1 Flash: Redefining Agent Performance and Cost - MiniMax Research

Today, we officially roll out MiniMax M3.1 Flash. As a next-generation Flash model, M3.1 Flash delivers a generational leap in agentic tasks, coding, Computer Use and cybersecurity, while making online inference nearly three times faster and reducing usage costs by 70%.

https://www.minimax.io
MiniMax M3.1 Flash: Redefining Agent Performance and Cost - MiniMax Research

Tuesday 20th October 2026

Exa 3.0 is out, upgrading its search API for AI apps and agents with better retrieval, improved ranking of long documents, and broader coverage across shopping, local businesses and real-time data.

A standout is Dynamic Highlights which selects relevant passages across search results and removes repetition. Exa reports 95% fewer tokens than feeding in full pages, while improving answer quality—potentially reducing downstream LLM costs. There’s also Exa Lite at $4 per 1,000 searches, alongside the higher-quality Auto option at $7. All upgrades are available now.

Announcement and details

Inception Unveils Mercury 3, Fastest High-Performance AI Model Yet

The new diffusion-based LLM matches or beats models like GPT-6 Luna and Claude Haiku 5.5 on benchmarks such as IFBench, GPQA Diamond, and SWE-Bench Atlas, all while handling a 1 million token context window. Co-founder Stefano Ermon notes it's 40 times larger than the original Mercury 1 but keeps the same blazing throughput, proving intelligence can scale without slowdowns. Available now via Inception API and partners at launch pricing of $0.10 per million input tokens and $0.35 per million output tokens, it's already powering real-time tools like browser agents and smart-glasses search.

fal launches FLUX 3 Max, claiming 22× faster AI video generation than Seedance 2.5

https://venturebeat.com/ai/fal-launches-flux-3-max-claiming-22-faster-ai-video-generation-than-seedance-2-5

DeepSeek Open-Sources DeepInfer for Efficient AI Model Inference

DeepSeek released DeepInfer, the optimized inference framework powering its DeepSeek-V4.1-Flash model in production, under an Apache-2.0 license. Alongside it, they open-sourced deepseek-recipe to match their official API in self-hosted setups, supporting features like advanced parallelism, NVIDIA Blackwell GPUs, Huawei Ascend chips, and high-throughput tools. Early testers reported strong results, such as 15 tokens per second decode on a single Intel Arc B70 with 256 GB RAM, helping developers cut costs and boost efficiency for large-scale AI deployments.

Marvell is showing how light could help tackle AI’s memory bottleneck. Its Photonic Fabric Memory Module uses optical connections to provide up to 32TB of memory as far as 50 metres from compute allowing memory capacity to grow independently of GPUs.

Why this matters: AI systems could gain more memory without adding accelerators simply for their memory capacity. The broader interview connects this technology with custom chips and faster interconnects, aimed at improving infrastructure utilisation and the economics of AI inference.

The key idea is more flexibility in how AI systems combine compute and memory, with optical connectivity helping overcome the constraints of keeping everything physically close together.

Marvell’s demo 

Rohan Paul’s explanation

Full interview

Wednesday 21st October 2026

Mistral AI launched Voxtral 3, a family of state-of-the-art speech-to-text and text-to-speech models designed to power real-time voice agents via a new API. 

Key capabilities include multilingual transcription in 50+ languages with diarization and context biasing, plus expressive TTS in 35+ languages and 50+ dialects that adapts emotion, pace, and emphasis implicitly or via tags, with Pro Voice cloning from 30 minutes of audio. 

The release includes a voice agent SDK built on LiveKit for a unified streaming pipeline of transcription, reasoning with tool calls, and speech output, available via EU-hosted API or self-deployment for sovereign control. 

When should an app ask a person to step in? Which team should handle a support request?

These are the kinds of judgments decision models can help with, while your code controls what happens next.

I’ve put together a guide to Perplexity Decisions and TypeSafe/Jev, with a practical example and a companion skill to help your coding agent build with them.

Thanks to Perplexity’s API team for feedback on early drafts, which helped shape the final version.

Here’s a short introduction, with links to both:

Where could this be useful in something you’re building?

Gradium has released Phonon, a family of compact text-to-speech models that runs entirely on your device, enabling offline speech generation with no cloud calls or per-use API fees. The 31M and 40M parameter models cover English, French, German, Spanish and Portuguese; Phonon-31M is just ~64MB and available under Apache 2.0.

Built for phones, laptops, browsers and embedded hardware, Phonon reportedly starts speaking in 7.5ms on a Snapdragon 8 Elite Gen 5 and runs faster than real time on a Raspberry Pi 5. Gradium reports stronger listener preference than Kokoro, Magpie and Pocket TTS in its blind evaluations. The practical appeal: responsive, private voice output for assistants, accessibility tools, games and in-car applications.

Announcement

VoiceArena’s overview

Two hyperscalers are now competing for PyTorch workloads on their own silicon

Google open-sourced TorchTPU. It's a native PyTorch backend for Cloud TPUs, built with Meta, and moving code over is as simple as changing .to("cuda") to .to("tpu"). Google says it replaces PyTorch/XLA, and Hugging Face Transformers and Diffusers  now run on TPUs natively. 

This matters because TPUs become a drop-in target for the roughly 85% of AI code written in PyTorch.

AWS TorchNeuron is now an open-source public beta It runs PyTorch on Trainium2 and Trainium3 with a one-line device change, under the Apache 2.0 license, and Amazon is sending code upstream to loosen PyTorch's ties to Nvidia.

Thursday 22nd October 2026

Friday 23rd October 2026

For those in the group who use image/video models via API and have kicked the tires with Fal, Higgsfield etc and want to stretch their money more might want to kick the tires of this service

Saturday 24th October 2026

Moonshot AI Unveils Kimi K3.1 Pro, a 3-Trillion-Parameter Powerhouse

The 3-trillion-parameter model supports a 1-million-token context window and handles text and images, matching top proprietary systems like GPT-6 Astra on coding and agent tasks. It scored 65.7% on Terminal-Bench 4.0, beating OpenAI's entry and ranking fourth overall on Artificial Analysis's Intelligence Index. With API pricing at half of leading competitors and a faster variant available now, full open weights arrive by November 4, giving developers affordable high-performance tools.

Cursor's Computer Use Lets AI Agents Control Your Desktop

Cursor rolled out 'Computer Use' in its desktop app, giving AI agents full control to click, type, launch simulators, and test apps without human handoffs. Developers can trigger it automatically or by mentioning it in prompts, making workflows smoother for building and demoing features like favoriting trails in a hiking app. Users praise it as a leap forward, turning agents into independent operators that handle end-to-end tasks locally, closing gaps with tools like Claude and OpenAI. The free update is available now for desktop users.

Fermion AI open-sourced Phonon-2.5, a 156MB multilingual speech model that sets a new accuracy-to-size Pareto frontier on the FLEURS benchmark across 25 languages, outperforming OpenAI's Whisper large-v3 despite being roughly 20x smaller.

The model compresses a 2.5GB base down to 6.2% of its original size while boosting average word accuracy to 101.8% of the full-precision version, running an hour of audio in about 20 seconds locally on a MacBook Air.

Phonon-2.5 is integrated into the free Detta Mac app for on-device dictation in 25 languages under open weights and CC-BY-4.0 licensing.

Detta - dictation for the Mac

Detta turns speech into finished, formatted text in any app, with recognition running entirely on your Mac.

https://www.fermionresearch.com
Detta - dictation for the Mac

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the ChatGPT Image 2.5 model within the Krea tool via the following prompt

beautiful utah desert, rock arcs, lush vegetation, landscape, alex ross, eddie mendoza, raphael lacoste, sebastian ludke, concept art, matte painting, highly detailed, rule of thirds, dynamic lighting, cinematic, detailed, magnificiant landscape, denoised, centered


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 42-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-42-2026 kaowdVFgRwWj1jEyeuYM Sun, 18 Oct 2026 05:00:03 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 11th to 17th October 2026 brought advances in sustained AI work alongside a stronger push towards specialised, more economical models. Meta introduced Muse Wonder, with memory and focus mechanisms designed to help agents retain context and pursue longer tasks, and released Muse Spark Open for coding and multimodal agent workflows. NVIDIA’s Nemotron 3.5 Super VL added another open-weight option for agents working across documents, images and video. Specialisation was equally prominent: Paper Instruments’ Ultramarine 1 Flash targeted professional deliverables with substantial cost savings claimed on internal benchmarks, while Scaledown’s Constrained Decoding Models and Perplexity’s updated decision models focused on answering structured questions efficiently. Creative and conversational tools also offered finer control, from Fish Audio’s Drama 3 voice performances and Grok Voice 3.0’s spoken interactions to Krea 3’s image-editing beta and Vivix W1’s steerable, streaming video.

The practical questions were how to fund these capabilities, where to run them and how to retain control. Accenture’s reported $29 central Claude allowance, with additional usage charged to projects, made the relationship between AI spending, time saved and consulting revenue explicit. Microsoft’s Maia 300 roadmap and planned multi-gigawatt deployments from 2027 addressed inference efficiency at infrastructure scale, while Google’s experimental AI Edge Foresight showed how meeting assistance could run locally on a Mac. Perplexity’s SPACE sandbox combined continuity for long-running agents with external controls over networks and credentials, complementing Satya Nadella’s argument that deploying organisations must retain responsibility through observability, limited permissions and independent safeguards. The debate between Dwarkesh Patel and Tyler Cowen added a wider question: how quickly technical capability translates into changed jobs when institutions, trust and human judgement also shape adoption.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 11th October 2026

Google AI Edge Foresight is a free, experimental macOS app from Google’s AI Edge team. It acts as an on-device AI meeting companion and note-taker.

Key points:

- It listens to system audio and the microphone so it works with any meeting platform (Zoom, Google Meet, etc.) or in-person conversations.

- You jot short bullet points during a meeting; the app expands them into fuller, polished notes using the real-time local transcript.

- All processing stays on the Mac — audio, transcripts, notes, and files never leave the device. It works fully offline.

- Powered by EmbeddingGemma 2 (a 740-million-parameter multimodal embedding model) and Gemma 4

- Extra features include Live Assistance (detects questions spoken in the meeting and suggests answers), a local knowledge base (search PDFs, Google Docs, Office files, Markdown, bookmarks, and Drive), and post-meeting chat over your notes and documents.

- Optimized for Apple Silicon Macs only (Intel Macs are not supported). Direct download from Google’s site (~160 MB); not in the Mac App Store. Built with Flutter.

It serves as a privacy-focused, local-first alternative to cloud-based note-takers like Granola, and as a showcase for Google’s on-device Gemma models.

Official page: https://developers.google.com/edge/foresight

Vivix W1 is a streaming-native multimodal model from Vivix AI for real-time interactive video/worlds. Unlike traditional clip-based generators, it continuously generates evolving audio-visual content that can be steered live by text, voice, touch, motion, images, or other inputs

Why it’s all over feeds today: Extremely aggressive pricing and speed. Limited-time rate around $0.003 per generated second (~$0.18/min). Vivix claims roughly 20× faster generation and under 1/50th the cost of models like Seedance 2.5, with comparable quality in their side-by-sides—making continuous/interactive video practical instead of prohibitively expensive.

Playground and API are live (standard / fast / ultrafast tiers). Company openly notes remaining gaps in cinematic fidelity, complex motion, and dense scenes.

Company background

Founded around January 2025 and registered in Hong Kong (with mainland China operations). Founder/CEO is Liu Yu (刘宇),— CUHK Multimedia Lab PhD, former young SenseTime Research Executive Director who managed large AIGC/multimodal teams and thousands of GPUs. He previously led the consumer app “Instant Painting” (秒画).  Co-founders include former SenseTime colleagues.

It raised aggressively (seed from IDG + Sequoia China/HSG in early 2025, followed by rounds with BlueRun, 5Y Capital, Monolith, JD.com, Alibaba Pictures, etc.). 

Reports put post-A valuation around $1.32B by late 2025. Team size has grown from a small core to the 50–200 range

https://x.com/VivixLabs_HQ

https://platform.vivix.ai/vivix-w1-model/

Key takeaways from Satya Nadella’s X article

Satya argues that Super Intelligence systems (frontier models + agents) are fundamentally different from traditional software. We used to be able to trace behavior to a specific code path. With today’s models we cannot attribute outputs or actions to particular training data or weight configurations. Despite this opacity, we are giving these systems access to sensitive data and the ability to take mission-critical actions.

Core claims:

You cannot outsource responsibility to the model provider. Assurances from the lab do not relieve the deploying organization.

Separate the supply of intelligence from authority over it. Do not treat the stack as nested black boxes.

Apply an engineering (not just alignment) approach: surround non-deterministic models with deterministic system design, human controls, reliable operating procedures, and industry standards.

Treat frontier (closed and open-weight) models like insider risks — not because they are necessarily malicious, but because any sufficiently capable actor with access to important systems can make mistakes or be compromised. Classic enterprise controls apply: identity, least privilege, logging, containment boundaries.

Specific principles he lists: model diversity (no single model should own an important outcome or verify its own work), full observability (tamper-proof, human-readable evidence of every meaningful action), continuous verifiability (including failures and attacks), independent controls and auditability, containment (assume compromise; always have an authorized “emergency brake” to pause or shut down mid-task), and incident disclosure with shared learnings.

His punchline: “The most trustworthy Super Intelligence system will not be the one with the model we trust most. It will be the one that enables us to trust the model the least.”

Monday 12th October 2026

Microsoft is scaling its custom Azure Maia AI accelerators to multi-gigawatt deployments starting in 2027, making them the primary inference system for its products and services.

Maia 200 is already in production, running workloads such as Microsoft Copilot (with GitHub Copilot and Microsoft Foundry coming by year-end), OpenAI models, Microsoft’s own MAI models, and open-source models. It is deploying in the US, Europe, and Australia and delivers up to 1.7× more tokens per watt than leading GPU infrastructure for Microsoft’s AI features.

Maia 300 (2027) targets long-context reasoning, low-latency conversation, and multi-step agentic tasks. It is expected to deliver ~40% more tokens per megawatt than leading GPU systems and up to 7× the rack-scale token throughput of Maia 200. Key design points include a software-defined dataflow architecture, high HBM4 bandwidth (18 TB/s peak within a 950 W TDP), chiplet design on TSMC N2P, low-precision formats (including MXFP4) for larger contexts, and dense racks of 96 accelerators in a 167 kW envelope.

The program emphasizes co-design of hardware and software for efficiency as AI moves toward sustained, agentic “Super Intelligence” workloads.

Microsoft to expand Azure Maia system to multi-gigawatt deployments - The Official Microsoft Blog

Azure Maia 200 now in production; Azure Maia 300 coming in 2027 As we move into the era of Super Intelligence, Microsoft is entering a new phase of infrastructure. We will scale our Azure Maia family to multi-gigawatt deployments starting in 2027, making Maia the primary inference system for Microsoft products and services.

https://blogs.microsoft.com

Tuesday 13th October 2026

Muse Wonder (and Muse Wonder Full) are Meta’s new frontier models for personal and professional AI agents

They deliver state-of-the-art performance on agentic tasks, memory, and coding. Key innovations are Active Memory (retaining and retrieving relevant personal/context details across conversations) and Active Focus (sustained effort on hard goals, trying alternatives, recovering from blockers, seeking clarification, and checking before major actions).

Trained at Meta’s gigawatt-scale Prometheus data center, they build on prior Muse Spark work in pretraining and RL. They excel in domain expertise (e.g., legal, finance, health tasks), long-running coding (SWE-Marathon), and generalize well to unfamiliar tasks as test-time compute scales—thanks to stronger base models. Safety emphasis includes alignment, resistance to misuse (CBRN/cyber), and careful handling of private data.

Muse Wonder Full (max) outperforms Fable 5.1 (max) on Artificial Analysis' Intelligence Index, matches Sonnet 5.5 (max), and is just shy of Opus 5.5 (max)

Availability: Muse Wonder via Meta Model API and Muse Code; Muse Wonder Full for Muse Code subscribers. It will soon power Muse experiences (starting with subscribers) and artifact generation.

Muse Wonder: A Frontier Model For Personal & Professional AI Agents

Muse Wonder is Meta's frontier AI model that keeps track of what matters to you with Active Memory & Active Focus to work towards your goals at work & life.

https://research.meta.ai
Muse Wonder: A Frontier Model For Personal & Professional AI Agents

Zai just debuted a legal specific model: GLM Legal, and it is the new #1 open-weight model on Harvey’s Legal Agent Benchmark and LegalBench.

Z.ai released GLM-Legal, a specialized model built on GLM-5.3-Flash via targeted mid-training and post-training, focused on agentic legal tasks such as contract review, evidence analysis, and compliance workflows integrated into their AutoClaw platform.

It leads Harvey's Legal Agent Benchmark with a 19.2% all-pass rate, outperforming models like Kimi K3 (12.9%), Claude Fable 5 (11.3%), and GPT-6 Astra (5.4%), while matching frontier performance on LegalBench and PLawBench with a leaner parameter count.

Available via beta API and AutoClaw for legal professionals, it emphasizes real-world utility through reinforcement learning in legal environments and practitioner feedback loops to improve output depth and accuracy.

via David An

This is super relevant for anyone working with LLMs. This is the second session I did with Dr. Arno Malcher in which i discuss Prompt OWNERSHIP. https://www.linkedin.com/feed/update/urn:li:activity:7515399511784132609/

Neal Patel announces Constrained Decoding Models (CDMs) from Scaledown as specialized alternatives to LLMs for tasks like classification, entity extraction into JSON, and summarization of long multimodal inputs up to 1M tokens.

 CDMs process input once without token-by-token generation, achieving 200x faster inference and 467x lower cost than frontier LLMs while outperforming on benchmarks like DecisionBench.

 A demo video contrasts slow, iterative LLM decoding on invoice extraction against near-instant CDM results; the models are live via OpenRouter, Vercel, LangChain, and others with 50M free input tokens offered.

Wednesday 14th October 2026

Fish Audio Drama 3 is the company’s latest and most emotive/controllable text-to-speech model, designed for directed performances rather than flat narration.

Key Highlights

  • Stronger emotion and control: Uses plain-language stage directions (e.g., [choked up, crying quietly], [through gritted teeth], mid-sentence shifts). It produces more natural tears, restrained anger, spontaneous laughs, whispers, and other paralinguistic cues than the previous S2.1 Pro. Direction can be in a different language from the script.

  • Production features: Multi-speaker scenes (up to ~16 speakers) in one pass; audio inpainting/infilling to regenerate a single word or short segment while preserving surrounding voice, mood, and rhythm; multi-turn “director” revisions (e.g., “make that take gentler and sadder”); better long-form and streaming support.

  • Pronunciation: Ranked #1 on Artificial Analysis’ Pronunciation Robustness benchmark (93.2% overall vs. 88.1% for S2.1 Pro), with strong gains on brand/place names, context-dependent words, numbers, codes, paths, and emails.

  • Architecture: 35B-parameter Mixture-of-Experts backbone (~3B active per token, initialized from Qwen3.5-35B-A3B) with hybrid attention (Gated DeltaNet + full attention) and a codebook-MTP audio decoder. Outputs 44.1 kHz; supports ~80–83 languages. Requires a high-end NVIDIA GPU (e.g., ≥48 GB for FP8) for local runs.

Availability Live on fish.audio, the iOS/Android apps, and the API (model header drama-3 or drama-3-preview). Open weights are on Hugging Face (fishaudio/drama-3) under the Fish Audio Community License (free commercial use below $500k annual revenue for you and affiliates; commercial services need a separate license). Day-0 support via SGLang-Omni and partners including ComfyUI, OpenRouter, and others. Launch pricing was discounted (e.g., ~80% off API through mid-November).

It is positioned especially for audiobooks, audio drama/podcasts, games/animation dialogue, and short video. A technical report and launch demos (including one featuring Uncle Roger) are available on Fish Audio’s site and GitHub.

Fish Audio Drama 3: The Most Emotive Text-to-Speech Model We've Built

Drama 3 is Fish Audio's most emotive text-to-speech model: far more feeling than S2.1 Pro, and No. 1 on Artificial Analysis' Pronunciation Robustness benchmark.

https://fish.audio
Fish Audio Drama 3: The Most Emotive Text-to-Speech Model We've Built

via Aman Pandey

Been learning a lot from this group daily, so I thought this was a nice group of people to share what I’ve been building recently.
—

Grok Gadgets is an open-source MCP layer for physical devices, built to make hardware a native part of the Grok ecosystem rather than a bolt-on integration.

• One Grok Bot conversation should be able to reach everything around you: "turn off the lights, lock the door, start the coffee and show my calendar on the desk display" as one request.

• Any builder can make their hardware Grok-ready with an SDK: ESP32 boards, Raspberry Pi and Linux devices, Home Assistant homes, and over time robots, lab equipment, and coordinated drone fleets.

• Each builder runs their own relay. Users connect it to Grok Bot once with a sign-in, and devices dial out, so homes never open inbound ports.

• Today, Grok Bot already calls these tools natively from the iPhone and Mac apps and drives a real ESP32 display, even over cellular with my laptop asleep. Access is OAuth-based and revocable.

It is built 100% in the open. Sharing it for anyone thinking about how agents step into the physical world.

Thursday 15th October 2026

- Daanish Khazi announces Ultramarine 1 Flash, a post-trained GLM-5.3 Flash model from Paper Instruments specialized for knowledge work in finance, law, consulting, and operations, generating deliverables like Excel models, PowerPoint decks, and memos in a Linux sandbox with tool use.

- It matches or exceeds Claude Fable 5 performance on internal knowledge-work benchmarks (59.3 vs 57.8 score) at $0.13 per task—roughly 60x cheaper—via large-scale non-verifiable agentic RL using synthetic tasks, rubrics, and a custom Feather harness, trained by a 5-person team for ~$300k.

- The model is available via API now with 131k context; weights and training code release in 2 weeks as an open experiment, positioning it as an efficient drop-in alternative for AI companies serving professional users.

Ultramarine 1 Flash - Paper Instruments

Ultramarine 1 Flash is an open model for professional work. It makes the decks, models, and memos that people in finance, law, consulting, and operations put in front of clients, at a fraction of the cost of frontier models.

https://www.paperinstruments.com
Ultramarine 1 Flash - Paper Instruments

TypeSafe AI just shipped Jev 1.14.0 for JevDay, adding image inputs and longer context.

https://typesafe.ai/blog/jev-1-14

Grok Voice 3.0 is announced as the leading speech-to-speech model with an 86.9 AA Speech-to-Speech Index score, outperforming Grok Voice 2.0, GPT-Live-1, and Gemini 3.8 Live across speech reasoning, task success, and conversational dynamics.

- It delivers sub-second latency at 40% lower than competitors while achieving 71.6% on τ-Voice and 94.8% task completion, powering 70% autonomous resolution of Starlink support calls daily.

- Available immediately via xAI API and Voice Agent Builder at $0.08 per minute, with images showing benchmark tables and performance-vs-speed plots confirming its frontier edge in agentic voice tasks.

What do AI labs know that the public doesn’t? In this NYT conversation, AI podcaster Dwarkesh Patel and economist Tyler Cowen debate how quickly AI will transform work.

Patel expects AI that learns on the job to automate entire roles within a few years. He wants labs to expose their strongest internal models to public scrutiny. Cowen is skeptical of the timeline: institutions move slowly, and human trust and real-world judgment could keep people central for decades.

How can the public judge AI’s trajectory if the strongest systems remain behind closed doors?

Read the NYT conversation — gift link

Friday 16th October 2026

via Matthew Weigand

Perplexity is giving away the software it has been using to combat rogue AI agents

SPACE, the sandbox platform behind Computer, to help developers build safer autonomous AI agents. If you're interested, you can check out the repo here: https://github.com/perplexityai/SPACE

SPACE combines isolated execution with continuity for long-running work. Agents can pause, resume, recover from mistakes, and explore alternatives while preserving their files and running state. Network controls and platform-held credentials stay outside the sandbox, adding protection that does not depend on the model following instructions.

Meta just dropped Muse Spark Open — the most capable US open-weight model to date.

816B total params (42B active), natively multimodal, 1M-token context, built for coding and long-horizon agentic work. First model in its class optimized to run locally on two M5 Ultra Mac Studios via MLX.   Benchmarks show it topping charts in areas like MCP-Atlas at 89.2 and Terminal-Bench at 76.4

Weights are live on Hugging Face + major clouds. APIs available through Together, Groq, OpenRouter, and more.

FWIW,   I used to have a freepik/magnifc subscription because I got it at 75% discount (Javi Lopez suckered me in) but then they jacked up the price close to renewal and I personally don't do much image generation except for the once-a-week cover image for the All Things AI newsletter.   I then signed up for Krea at 60 EUR per year so around 1.5 HKD per day which my 'man-math' can justify 

Krea AI launches the public beta of Krea 3, an image editing model that completes end-to-end 2K edits in under 8 seconds while matching quality of GPT Image 2.5 and Nano Banana Pro.

The model features bounding box controls for precise multi-turn edits that only alter required pixels, supports up to 4K resolution, and preserves character consistency across scenes.

Beta is available now via krea.ai with final release next week, including expanded benchmarks, LoRAs, style references, and hints at open-source models.

Krea 3: an image model built for editing at scale

Krea 3 is a new image editing model built for production. It keeps your product intact and is priced for volume.

https://www.krea.ai
Krea 3: an image model built for editing at scale

Introducing Krea 3

Krea 3 is our new image editing model. Edits take under 8 seconds at native 2K, and products and characters stay consistent from one image to the next.

https://www.krea.ai
Introducing Krea 3

NVIDIA launched Nemotron 3.5 Super VL as an open 120-billion-parameter multimodal model (12B active in a mixture-of-experts design) with a 1M-token context window, built for always-on specialized agents. 

It unifies reasoning, instruction following, tool use, and understanding of text, images, documents, and video in a single model, delivering leading accuracy on multimodal agentic tasks at roughly one-fifth the parameter count of NVIDIA’s largest reasoning models while claiming up to 5× higher single-GPU throughput for high-volume workflows. 

Weights are available under the OpenMDW license on Hugging Face and partner platforms, with accompanying training recipes and smaller Bolt edge models for local deployment.

https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning/

https://openrouter.ai/nvidia/nemotron-3.5-super-vl

Saturday 17th October 2026

Accenture’s reported $29 monthly Claude allowance highlights a practical question for enterprise AI: who pays when employees start using it heavily?

According to an internal announcement reviewed by 404 Media, Accenture began centrally funding US$29 per person per month for many Claude Enterprise users from October 1. Additional usage must be charged to a specific project or team. Some employees reported spending $500–$1,000 a month, including on Claude Code development for clients. Premium plans reportedly include $250 in weekly usage.

The project-funding detail is central to Aakash Gupta’s take. Moving the extra cost into a project’s budget gives someone responsibility for weighing the AI bill against the work it helps deliver.

That gets particularly interesting in consulting. On work billed by the hour, completing a task faster can reduce the hours available to bill. On fixed-fee work, the same time saving can improve margins. The economics depend on how the engagement is priced.

Seen this way, the $29 allowance raises a broader issue than spending control: how should firms price and fund work when AI changes the time needed to deliver it? Accenture says it continues investing in AI, with access guided by role and business need.

404 Media — original reporting and company response

Aakash Gupta — the project economics behind the policy

Perplexity has released pplx-decider-v1.2 (27B) and pplx-decider-flash-v1.2 (4B), its next-generation open-weights multimodal decision models.

pplx-decider-v1.2 (27B) — $0.02 / 1M tokens

pplx-decider-flash-v1.2 (4B) — $0.01 / 1M tokens

Key Improvements in v1.2

* Better quality: Higher text and vision scores after significantly more training compute and larger datasets. Models are more general, robust, and better calibrated. Reranking capabilities were added.

* Much faster inference: Up to 5× faster because all questions in a request share context and run in a single forward pass (one commenter described this as the main source of the speedup). Models were retrained to be inference-aware.

* Flash variant: Distilled smaller model that punches well above its size class.

* Other technical notes (from related posts): New attention schema improvements for better batching; continued use of non-causal attention patterns from prior versions.

Availability

Open weights on Hugging Face + available via Perplexity API, OpenRouter, Vercel AI Gateway.

Use *-latest aliases for automatic updates

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Nano Banana 2.1 model within the Krea tool via the following prompt

ancient hidden temple found deep inside a lush rain forest| ancient greek ruins| rubble| gray dull background| vibrant colorful green leaves| hyperrealistic| highly detailed| cinematic| single ray of golden sunlight| beautiful| cgssociety| artstation| oil painting


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 41-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-41-2026 ocEmL7mZMl6585Y8ID3Z Sun, 11 Oct 2026 01:51:19 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 4th to 10th October 2026 highlighted a growing emphasis on making AI faster and more economical through both broader capabilities and specialised models. Mistral Large 4 entered public preview with multimodal support and a million-token context window, while its weights remained forthcoming. Anthropic released Haiku 5.5 for high-volume work, with lower pricing aimed at tasks such as summarisation, classification and supporting larger agents. Microsoft-Decision-1, Perplexity’s pplx-decider-v1.1 and Celeris-1 Decision expanded the options for applications that need scored choices rather than generated prose. Google’s Nano Banana 2.1 brought reported improvements in image editing and subject consistency, while discussions of Celeris’s diffusion models and MachGen’s inference stack explored different approaches to reducing generation time and cost.

The supporting infrastructure and everyday workflows showed why model releases are only part of that progress. Richard Ho’s interview on OpenAI’s Jalapeño chip described AI-assisted design and the challenge of keeping hardware useful as workloads change, alongside the need to validate optimisations engineers may not immediately understand. Perplexity’s embedding models addressed another practical issue: finding information in text and images while retaining the context needed to interpret an answer. Google’s rollout of native Markdown editing and collaboration in Drive and Docs made a format widely used in AI workflows easier for people to work on together. Discussion of deployers’ responsibilities under the EU AI Act added a governance dimension to the week’s focus on putting these capabilities to use.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 4th October 2026

Monday 5th October 2026

🌶 AI helping design the chips that run AI—there’s plenty to unpack in Ian Cutress’s interview with Richard Ho, who leads OpenAI’s Jalapeño hardware team.

A few hooks for the AI enthusiasts here:

• Ho says models without task-specific fine-tuning helped save over 13% of die area in one optimisation example. He also describes accepting optimisations the engineers didn’t immediately understand—with rigorous validation still essential.

• Why build one accelerator for the different stages of inference, while others split them across specialised chips? His answer is about keeping an entire fleet useful as workloads change.

• Why share architectural ideas competitors might copy? OpenAI still needs GPUs—and wants those GPUs to get better.

• His provocative take on the biggest bottleneck: the supply chain still underestimates how large demand could become.

The conversation connects chip design, AI-assisted engineering and the economics of making intelligence cheaper. Worth reading—and setting aside time for the full interview.

📖 Article and edited transcript: More Than Moore

▶ Full video: The Man Who Built OpenAI’s First Chip

via David An

In our new Provocation Talk, we discuss some major impact of "Deployers" according to the EU AI Act. This is also relevant for any AI Deployers who are serving EU customers https://www.linkedin.com/feed/update/urn:li:activity:7512819988513132544/

Tuesday 6th October 2026

Google Workspace now supports native Markdown (.md) files in Drive and Docs, enabling preview, editing, commenting, and real-time collaboration without converting to Google Doc format.

The update addresses Markdown's growing use as a standard for AI-human workflows including specs, READMEs, task lists, and generated documentation.

Rollout has begun for Workspace customers and personal Google accounts, as shown in a screenshot of a collaborative tech spec document with comments preserved in .md.

https://workspaceupdates.googleblog.com/2026/10/preview-edit-and-collaborate-on-Markdown-files-natively-across-Drive-and-Docs.html

Wednesday 7th October 2026

Mistral Large 4 nicknamed le Chonk is in public preview.  It's an MoE model with 1.05 trillion parameters and 49 billion active on each token, a 1.6 billion-parameter vision encoder, and a 1 million-token context.  Mistral says it was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own European datacenters.  Available via OpenRouter at 0.68/Mtok input and $2.09/Mtok output.  The weights are still a few weeks away.

Introducing Mistral Large 4 | Mistral

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

https://mistral.ai
Introducing Mistral Large 4 | Mistral

Google just dropped Nano Banana 2.1, the next version of its image model. It runs on Gemini 3.6 Flash and is meant to be the faster, everyday option rather than the heavy Pro model.

Google reports gains over prior versions in visual design, mask-based editing, subject consistency, and overall naturalness. On DeepMind’s internal preference evaluations, the thinking variant scores ahead of both Nano Banana 2 and Nano Banana Pro, with the largest improvements in infographic factuality and multi-character consistency.

The model accepts text and image inputs, supports output up to 4K, and can ground generations with Google Search. It is available in AI Studio and the Gemini API, and is rolling out in AI Mode in Search.

Thursday 8th October 2026

via Justin Pinter

Anthropic has released Haiku 5.5 which Cheapest, fastest, and most capable small model Anthropic has shipped. Built for high-volume, cost-sensitive work — summaries, compaction, classification, database queries, subagents under Opus/Sonnet, live support, and browser use. 1M context length

Pricing

  • Prompts ≤100k ( covers ~90% of prior Haiku usage): $0.10 input / $0.50 output — 90% below Haiku 4.5.

  • Longer prompts: $0.50 / $2.50.

  • Cache reads: $0.01 (or $0.05 over 100k).

via Matthew Weigand

Perplexity has open-sourced a new collection of decision and embedding models with SOTA or near-SOTA performance in the last few weeks: 

𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 

pplx-decider-v1.1 is a general-purpose 27B classifier fine-tuned from Qwen3.8-27B. It returns probabilities and confidence over a pre-defined list of options. v1.1 raises the best Hugging Face Decision Index 0.3 score to 62.8. It's served through our Decisions API and OpenRouter. 

- Model: https://lnkd.in/g8jqA4DM

- API: https://lnkd.in/g3fDbpGP

𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀

pplx-embed-v2-late is a family of 0.6B and 9B models that create one embedding per token, for both text and images such as rendered document pages. The two sizes share one embedding space, so you can choose which is used for query vs document encoding. E.g., index documents offline with 9B and encode live queries with the cheaper and faster 0.6B. The 0.6B model matches models with 5x its active parameters on ViDoRe V3, and the 9B model scores 92.4% on MADQA. On Q2D-Web, both lead the evaluated baselines.

- Research blog: https://lnkd.in/gAMfC3kY

- Model: https://lnkd.in/gufZmNgz, https://lnkd.in/gS75krcJ

𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀

pplx-embed-v2-context-9b-preview embeds each passage together with the document it comes from, so search can find both the answer and the context that supports it. It still produces one vector per chunk, at no extra inference cost, in 1024 or 2048 dimensions with native int8. It has the highest average nDCG@10 on ConTEB, and it leads on turbopuffer's private context-bench for answer, evidence and document retrieval. 

- Research blog: https://lnkd.in/gGzqF2d2

- Model: https://lnkd.in/g_7PGXgJ

Friday 9th October 2026

Going to share a couple of posts about some providers who I feel are pushing the envelope in diffusion models which is a different approach since most models we interact with regularly are auto-regressive [one-token ouput at a time].   

I encourage group members to try these providers and models on your own  workloads

Celeris claims to be Speedy Gonzales for both their diffusion models [Celeris-1 Magnus and Celeris-1] as well as their decision model Celeris-1 Decision

MachGen — high-performance inference for image, video, and world models.

Founded by Kismat Singh (ex-Intel AI frameworks, NVIDIA) and Manoj Krishnan. Team includes people who worked on TensorRT at NVIDIA, vLLM at Google, and large-model training at Meta. Out of stealth mid-2026.

They build a diffusion-specific stack (kernels, attention, caching, scheduling) so open models run at the same quality, much faster and cheaper — not distilled, same weights. 

Claimed numbers: HiDream ~1s vs 6s, Flux ~1.5s vs 6.2s, LTX 2.3 ~11s vs 67s, MiniMax H3 up to ~15x. 

Roughly 4–6x lower latency and 2–4x lower cost vs standard deployments.

Time to plonk in a few dollars of credits at both these providers and kick their respective tires and then figure out a way to track the cumulative spend with these 'few dollars' added at a gazillion providers 😢

Saturday 10th October 2026

Microsoft-Decision-1 is out: a decision-scoring model, not a generative LLM.

Post-trained on Qwen3.5-9B, 

Microsoft claims top accuracy on a 36-benchmark suite (~150k held-out questions), and the fastest latency measured: 4.5× quicker than Quyet-1.0-Large and 35× quicker than GPT-6 Sol. Pricing is $0.042 per million input tokens, output free.

Available now in Microsoft Foundry; on OpenRouter as well. First in a planned family; later versions will be rebased on MAI and OpenAI models.

Microsoft-Decision-1: Our model for fast decision-making

Our new decision-scoring model delivers top performance in latency and quality on structured decision tasks to outperform both LLMs and other decision models.

https://commandline.microsoft.com

Microsoft-Decision-1 | Model Catalog | Microsoft Foundry

Decision-scoring model that returns calibrated probability scores for fixed answer options instead of... Sign in to Microsoft Foundry to deploy this model.

https://ai.azure.com
Microsoft-Decision-1 | Model Catalog | Microsoft Foundry

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Nano Banana 2.1 model within the Krea tool via the following prompt

"Spirits of the Land": Envision a sweeping landscape where nature spirits come alive. Trees bend and twist into fantastical shapes, their leaves rustling with whispers. Wild animals roam free, their pelts emblazoned with shimmering patterns that reflect the colors of the land. And in the distance, a rainbow arcs across the sky, connecting the mortal realm to the Otherworld. insanely detailed, dynamic lighting, clean reflections, Vivid colors, Ultra Precision, Sharp Edges, maximum Quality


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 40-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-40-2026 nI1ponF4XY72O36JZre6 Sun, 04 Oct 2026 01:03:22 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 27th September to 3rd October 2026 brought stronger AI models alongside a growing emphasis on making intelligence faster, cheaper, and more useful in everyday work. Anthropic launched Claude Sonnet 5.5, OpenAI introduced GPT-6.1 Sol, and Google began a restricted rollout of Gemini 4 Argon to trusted testers, with efficiency and practical coding and knowledge work prominent across the announcements. Fireworks’ Ember-1 research preview pursued similar gains through shorter reasoning traces. A parallel wave of specialised decision models—including Cloudflare’s Clef, Liquid’s D1, Perplexity’s pplx-decider and Nace AI’s Drex 1.5—expanded the options for applications that need quick choices and confidence scores. OpenAI’s Decisions API entered limited preview, while its broader DevDay announcements centred on ongoing delegation and collaboration through Dots, Codex and ChatGPT Space. New speech models from ElevenLabs and Microsoft also targeted more natural conversations with less waiting between turns.

The systems around those models were equally important. Perplexity’s contextual embedding research focused on retrieving the supporting context needed to check an answer, while my updated research guide and Glamsterdam worked example explored choosing evidence providers separately from the models interpreting their results. Cloudflare’s work on agent identity, access and payments addressed how websites might serve AI users while sustaining the people supplying the information. At the infrastructure level, John Ousterhout’s Homa protocol illustrated how reducing waits for small network messages could improve GPU utilisation. Perplexity’s sandbox testing added a reminder that security depends on more than the model: no tested model escaped the virtual machine, but the researchers reported network bypasses affecting several agent runtimes.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 27th September 2026

Two new additions to the research guide and skill

I’ve updated the multi-provider-research skill and companion guide with guidance and practical examples for Perplexity Fast Search and Exa Agent Ultra—covering when to use each and how to get started.

If you missed the earlier edition, Separate Intelligence & Search explains how to keep your preferred AI assistant while giving it more choices for finding and reading information. The companion skill helps your assistant choose suitable research tools and check the evidence they return.

Perplexity Fast Search

The skill now prefers Fast Search for suitable routine Perplexity lookups, with standard search for difficult or ambiguous questions and unresolved evidence gaps. The guide explains the distinction and includes a copyable prompt which should handle both fast and standard search types.

Exa Agent Ultra

The skill and guide now cover Ultra for deeper research and comprehensive list-building, including budget controls, interpreting incomplete results, and when ordinary search is sufficient. There are prompt examples and a worked case study to explore.

The principle stays the same: choose the tools the question needs, without automatically running every provider or selecting the most expensive mode.

Get or update the skill

GitHub repository and installation instructions

Already using it? Back up your existing copy, then replace the complete skills/multi-provider-research folder with the latest version. Provider connections are configured separately.

Explore the new material

Perplexity Fast Search: guidance and comparison

Copyable Fast Search prompt

When Exa Agent Ultra is worth using

Exa Ultra worked case study

If you try either, I’d love to hear what you researched and how it worked for you.

Monday 28th September 2026

Fireworks AI announces Ember-1, its first specialized research model built on Kimi K3, focused on efficient reasoning by producing shorter traces that use roughly 40% fewer tokens.

A benchmark graph positions Ember-1 competitively on the Specialized Intelligence Index, balancing high avg@3 scores against low cost per task relative to models like Claude Opus 5, GPT variants, and others.

The post links to the model page and blog on fireworks.ai, promoting it as an advancement for scalable inference on open-weights models.

Introducing Ember-1

Ember-1 is a new specialized model from Fireworks Research that delivers Kimi K3's quality with 40% fewer tokens.

https://fireworks.ai
Introducing Ember-1

ElevenLabs Launches Fastest, Most Emotive Text-to-Speech Models

The new models shine across more than 90 languages, with v4 leading independent benchmarks for quality and pronunciation at an Elo rating of 1,319. Co-founders Mati Staniszewski and Piotr Dabkowski explained v4 Turbo's edge in real-time chats, hitting 100-millisecond response times thanks to fresh research and a post-training stack. Voice cloning now works from just 10 seconds of audio, inline tags control emotions and effects, and early demos impressed with natural accents and expressivity. Pricing kicks off discounted at $22 for v4 and $11 for Turbo per million characters for two weeks.

Tuesday 29th September 2026

Anthropic Launches Faster Claude Sonnet 5.5 Model

Sonnet 5.5 runs over 30% faster and costs up to 30% less than its predecessor for most tasks, priced at $2 per million input tokens and $10 per million output tokens but needing fewer tokens overall. It shines in agentic coding with a 70.6% score on Terminal-Bench 4.0—beating even Opus 5.5—plus gains in knowledge work, computer use, and chart recognition. Demos show it solving puzzles with multiple agents in seconds and fixing code tests effortlessly, making it ideal for everyday professional workflows. Anthropic plans to add Haiku 5.5 soon for high-volume needs.

AMD Acquires World Labs in $8.2 Billion Deal for AI Expertise

AMD announced on September 28, 2026, that it will acquire World Labs, founded in 2024 by Dr Fei-Fei Li and co-founders Ben Mildenhall and Justin Johnson, in an all-stock deal expected to close by year-end. The startup specializes in spatial intelligence models that perceive, generate, reason about, and interact with 3D environments for robotics and physical AI. Dr Fei-Fei Li will become AMD's executive vice president and chief scientist, reporting to CEO Dr. Lisa Su, while her team forms a new frontier research group. Both leaders emphasized how the partnership accelerates AI hardware and software for the open ecosystem, building on prior collaborations.

via Alex

Anthropic S1 Prospectus leaking to Reuters:

- 4B$ Revenue on 45B$ cost (GAAP)

- Non-GAAP loss at 8B$

- 2 customers make up 25% of revenue

just in June they announced 65B$ in ARR to end the year under 4B$ 😬. Last year revenue was estimated at 9B$ to now be revised to 0.3B$

EXCLUSIVE: Anthropic's IPO prospectus shows sweeping AI vision, surging costs

Anthropic is making a massive bet that AI will transform the global economy more profoundly than ​industrialization, electricity and the internet, according to its IPO prospectus seen by Reuters. But the cost to get there will be staggering.

https://www.reuters.com
EXCLUSIVE: Anthropic's IPO prospectus shows sweeping AI vision, surging costs

via shrwn

In case you’re doing rounds trying Grok Bot, Muse… check out Fo by Wajo

Nice product demo by founder Shivani Poddar (former Jules Agent/Google DeepMind, Meta)

Seems the first 1,000 signup gets unlimited access during alpha

https://wajo.ai/join-wajo

PS. Google OAuth route asks for full perms. Perhaps utilize an alt account for EA/Chief of Staff work at first

Seems to not be geo-locked like Muse. Utilizes Messsges > iMessage to confirm phone number. Was able to via HK > US/650 area code number. 

Though can also email and use browser for interactions with Fo

Wednesday 30th September 2026

via Justin Pinter

Kling 4. 

Up to 30secs. 4k HDR. Better sound effects. Better keyframing. And larger context window of 8,000 tokens

https://kling.ai/release-note/release-notes/Kling_4

OpenAI’s DevDay 2026 recap centres on AI taking on ongoing work, stronger developer tools, and team collaboration. Key announcements:

• Dots: Always-on agents that learn your priorities and handle ongoing responsibilities.

• GPT‑6.1 Sol: OpenAI claims near-Astra intelligence at one-fifth of its standard token prices, with stronger coding and computer use.

• Ultrafast: Premium inference offering up to 8× faster generation in Codex and 6× in the API, initially for Astra.

• Codex upgrades: Reusable cloud environments, voice-controlled CLI, improved code reviews, and Security Cloud for continuous vulnerability checks and fixes.

• New developer capabilities: Agents API computer use, OpenAI-powered agents running entirely in AWS, and a limited-preview Decisions API for classification and routing.

• Expanded plugins: Custom interfaces inside ChatGPT, easier creation and discovery, plugins in Sites, and event-triggered automations.

• Team collaboration: ChatGPT Space, collaborative Pages, shared tasks, Slack/Teams integration, meeting notes, and collaborative slides coming soon.

• Subscriptions and enterprise: Use eligible ChatGPT allowances in partner tools; new Pro 500 with 25× Plus usage; enterprise Marketplace and stronger privacy controls.

Availability varies by plan; some features remain previews or are forthcoming.

Looks like it's going be raining decisions model soon

OpenAI announces Decisions API, which returns predefined answers with confidence scores, similar to TypeSafe's Jev, in limited preview with no pricing details

Let's see if it shows up here in this OpenRouter table

https://openrouter.ai/models?output_modalities=decisions

New endpoints showing up in OpenRouter

https://openrouter.ai/docs/api/api-reference/alphadecisions/submit-a-decisions-request

https://openrouter.ai/docs/client-sdks/python/sdks/decisions/README

Thursday 1st October 2026

Google rolls out Gemini 4 Argon to trusted cyber defenders through Fairwind and says it is participating in the US government's voluntary pre-release process

We’ll continue to gather feedback from early testers as we iterate on guardrails before making Argon available to developers, enterprises, and consumers as soon as possible.

Argon will launch at an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.

https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/

Tried to kill two birds with one stone here: understand Glamsterdam—an upcoming Ethereum upgrade aimed at increasing capacity, with changes to costs and compatibility—and use it as a worked example of separating AI intelligence from search.

🧠 Intelligence: Sol 6.1 in Codex and Grok 4.7 in Grok Build each researched and wrote a report. Claude Opus 5.5 then synthesized both, without new web research.

🔎 Search and evidence: Octen, Exa, Perplexity Search and authenticated X supplied the material for the two research runs.

The idea is to choose the models that interpret the evidence separately from the providers that retrieve it.

The guide includes findings, sources, prompts and methodology, with open questions flagged:

A practical guide to Ethereum's upgrade · Understanding Glamsterdam

A synthesis of two multi-provider research workflows, with original reports and evidence limits.

https://goolamabbas.github.io

Feedback and corrections very welcome!

🤖 Cloudflare: The Internet now has a second audience — AI agents

Cloudflare argues that the web is undergoing a fundamental shift: websites are no longer serving just humans, but increasingly AI agents acting on their behalf. On Cloudflare’s network, daily requests from AI agents have grown by more than 1,700% over the past year, while more than half of Internet traffic is now non-human.

That creates an interesting economic problem. The traditional web worked because search engines crawled content and then sent humans back to websites, where publishers could monetize those visits. AI systems can instead consume the information and answer users directly — potentially leaving the original site with the cost of serving the content but little or no traffic or revenue.

Cloudflare thinks the answer isn't simply to block AI. Instead, websites need ways to distinguish search crawlers, AI training crawlers and agents actually acting for users, decide what each is allowed to access, verify who they are, and potentially charge agents for content, APIs or services. Cloudflare is building infrastructure around this idea, including cryptographic agent identity, Pay Per Use and an HTTP 402/x402-based Monetization Gateway.

Perhaps the bigger idea is that websites themselves may eventually become agent-native: Cloudflare's Markdown for Agents strips away human-oriented presentation, while WebMCP allows sites to expose actions directly to agents rather than forcing them to navigate interfaces designed for humans.

Worth reading — particularly if you’re interested in where the agentic web might be heading.

Cloudflare — “The Internet has a second audience”

A fairly long X article by Rohan Nayak, co-founder/CEO of Pocket FM which I've used the assistance of various AI tooling to summarise and present to you.  I encourage members to read the X article in its entirity and make their own judgement even though I've tried to be careful and faithful with my summarisation. 

Rohan Nayak’s argument is that general language models write badly because they are built to resolve a question, while fiction has to withhold, contradict, and keep someone listening. Pocket FM’s answer is Sherpa, a custom system for serialized stories, trained on what listeners actually finish.

He says the pivot to AI-written shows looked like it would kill the company for six months, then did the opposite. Within two years, he says, the system helped writers produce 161 blockbusters on Pocket FM, including one intellectual property he values at $100 million.

Some other claims in his article

  • Pocket FM can train on real listening, because it both makes and distributes the shows. He says the average user listens more than 150 minutes a day, about three times YouTube and about 50 percent more than Netflix.

  • Prompting, rolling summaries, and retrieval on existing models failed. By episode 100 he saw 10–20 contradictions, and the models could not answer questions that needed facts from several episodes.

  • Sherpa has three parts: a planner that assigns every scene a job and tracks unpaid-off setups; a Narrative World Model that stores who knows what, and from which chapter; and a prose engine where each character proposes an action, a critic rejects weak ones, and a narrator writes only the ones that make the page.

  • He still calls the system unfinished, and says the larger bet is a $1 trillion market in which writers can earn a living from stories that used to need a multimillion-dollar budget.

Friday 2nd October 2026

via Matt

New piece on different approaches to regulating the 19th century AI - electricity

https://m4ttl4w.substack.com/p/britain-tried-an-ai-pause-in-1882

It's raining decision models these days

Cloudflare has also released a Jev-like decision model called "Clef"

Liquid releases D1

Perplexity releseas pplx-decider-v1-27b

Nace AI just released Drex 1.5 — the first (and so far the only) decision model with a 128k context window.

Introducing Clef: our open-source decision models, and new RL fine-tuning platform

We are introducing Clef and Clef-flash, open-source decision models hosted on Workers AI for high-speed classification and agentic workflows. Also launching: a new reinforcement learning platform that allows developers to fine-tune decision models using their own data.

https://blog.cloudflare.com
Introducing Clef: our open-source decision models, and new RL fine-tuning platform

Pareto 26.10 Preview Delivers Top AI Performance at Low Cost

Pareto 26.10 Preview from The Unbiased Co. launched on OpenRouter with input at $0.80 per million tokens and output at $3.20—far below the prior version. This composite system runs multiple models in parallel, picks the best output, and supports 1M context, images, and tools for research, coding, and agents. Benchmarks show it hitting top scores like 51% on Terminal-Bench 4.0 at 34x lower cost than rivals, spotlighting a push for smarter value in AI.


Microsoft Launches Top-Ranked AI Voice Models for Real-Time Conversations

The company released MAI-Transcribe-2-Streaming, which tops independent benchmarks with a 2.5% word error rate and results in just 0.13 seconds after speech ends. They also introduced MAI-Voice-2.1 for natural, high-fidelity text-to-speech in 23 languages and its faster Flash version for quick responses. CEO Mustafa Suleyman described the transcription model as the world's most accurate, running 55% faster and 60% cheaper than competitors, now available through APIs and partners like Vercel and OpenRouter to power voice agents in apps and call centers.

via Matthew Weigand

I think most here care more about the accuracy of your AI search than the average person. Perplexity continues to push the boundaries in this domain. Here is a short write up we published:

Most AI search finds the sentence with the answer and drops the sentence that makes it make sense.

A clause says the termination fee applies. The definition sits in another section. The AI cites something nobody can verify.

Perplexity Research and turbopuffer published a fix this week. A new embedding model that pulls the answer and the supporting text together, at no added cost per query.

turbopuffer tested it blind on 2,099 questions across 38,894 long documents.

Perplexity found the right answer 46% of the time. Voyage Context 4 found it 31% of the time. Perplexity also pulled more of the text needed to check it, 41% versus 36%.

Voyage still leads on finance and multilingual questions.

Every team I talk to can tell me which model writes their answers. Few have looked at what retrieves them.

Post:

Contextual embedding beyond the gold passage

A new training method, model, and benchmark for retrieving answers and their supporting context.

https://www.perplexity.ai
Contextual embedding beyond the gold passage

Saturday 3rd Oct 2026

via Matthew Weigand

Perplexity shipped two other open releases this week.

The Perplexity Secure Intelligence Institute ran controlled escape attempts on SPACE, Perplexity's sandboxed agent runtime, using every major frontier model. No model escaped the VM. The team did find network bypasses that affect multiple agent runtimes and shared them with those vendors.

Report: https://lnkd.in/gVi8EnmH

pplx-decider is a frontier decision model trained in one weekend, with multimodality and long context. Use it on the API Platform or run it free from HuggingFace.

Model: https://lnkd.in/gXuPjJMx

Tavus unveils Griffin, the “first Human Interaction Model”, which it says passed the “video Turing test”, with 48% of users thinking it was human in live chats

Faster networks could help AI systems make better use of their GPUs.

John Ousterhout’s Homa protocol tackles a specific problem: small messages can get stuck behind large transfers, leaving GPUs waiting to coordinate their next step.

Ousterhout is a Stanford professor emeritus, creator of Tcl and Tk, and author of A Philosophy of Software Design. Tcl also underpins Expect, the automation tool created by Don Libes.

In his talk, he explains why this matters for inference and agentic workloads. Machines exchange small messages to check distributed caches or synchronize computation. As computation steps get shorter, those network waits can consume more of the available GPU time.

Homa prioritizes short messages, lets receivers coordinate incoming traffic, and uses priority queues in network switches to help small exchanges get through congestion.

In one mixed-message benchmark, Ousterhout reports about 13× lower short-message tail latency than TCP: under 100 microseconds versus over a millisecond. For inference systems limited by network coordination, shorter waits could mean better GPU utilization and higher token throughput.

Homa’s Linux implementation also has backports for Red Hat Enterprise Linux 8 and 9.5

▶ Conference talk: the networking problem, Homa’s design, and the benchmark.


▶ Interview: a conversational explanation of GPU waiting, Homa’s datacenter focus, and Ousterhout’s thoughts on software design in the AI era.


💻 Implementation and RHEL backports:

https://github.com/PlatformLab/HomaModule

A useful question for teams serving AI across machines: how much time are your GPUs spending waiting for small network exchanges?

The diagram shows why a small message can leave GPUs waiting, then how Homa changes its path through a congested network.

A little behind the scenes: the Homa message and diagram were created with AI assistance.

I used my multi-provider research skill to gather sources through Octen, Perplexity, and authenticated X, then supplied the video transcripts for a closer reading. We shaped the explanation using Minto’s main-point-first structure and Andrej Karpathy’s suggestions for clearer writing and useful visuals.

My role is connecting the sources and ideas, asking questions, and refining what’s worth sharing. AI helps with research, drafting, and diagrams—all towards giving you succinct information with sources you can explore.

If you’d like to try this approach, these pages explain the pieces:

Separating intelligence and search — choosing your AI assistant and its research tools separately:

Choose Your AI. Choose How It Searches.

Choose your AI assistant and its search tools separately. A practical guide to connected research tools, with a simple path to your first task.

https://goolamabbas.github.io
Choose Your AI. Choose How It Searches.

The practical research guide — providers, connections, and example prompts:

Choosing External Research Tools for AI Agents

A practical guide to choosing research tools, connecting them, and judging the evidence they return.

https://goolamabbas.github.io
Choosing External Research Tools for AI Agents

Minto — organizing an explanation around a clear point and supporting ideas:

Minto · Curated Skills

Turn a draft or idea into an argument the reader can follow: a clear question, a direct answer, and supporting ideas that actually justify it.

https://goolamabbas.github.io

Curated Skills — the wider collection, with examples and installation guidance:

Four skills for your AI agent · Curated Skills

Reusable approaches for clearer arguments, thoughtful news analysis, better agent instructions, and educational websites.

https://goolamabbas.github.io

Karpathy’s post — the inspiration for simpler writing and visual explanations:

For the skill files themselves:

https://github.com/goolamabbas/multi-provider-research

https://github.com/goolamabbas/curated-skills

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the GPT Image 2.5 model within the Krea tool via the following prompt

a boat in the middle of the cherry blossoms, in the style of lush scenery, fairycore, cute and dreamy, pink and green


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 39-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-39-2026 UEYs49bIM41QCUVAi2E8 Sun, 27 Sep 2026 01:17:17 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

I would like to begin by sharing the recording of the recent roundtable I conducted with Shamir Allibhai, CEO/co-founder of Eddie AI. Shamir was very eloquent in describing his entrepreneurial journey and what the world of professional video editors looks like and why a personal itch drove him to build a product which is gaining accolades from industry peers.

https://app.notion.com/p/yusuf-goolamabbas-53/Shamir-Allibhai-CEO-co-founder-of-Eddie-AI-3e383c2ae30380eaa958e22839685723?v=0585ca7adffd43dca053b5e486c6618c&source=copy_link

The week of 20th to 26th September 2026 saw all three frontier labs—OpenAI, Anthropic, and xAI—release upgraded models. GPT-6 Sol and Luna, Claude Opus 5.5, and Grok 4.7 brought improvements in coding, reasoning, and agent work, with lower costs or stronger performance at existing prices. StepFun’s Step 5 Preview and Xiaomi’s MiMo-V2.6 added competitive, lower-cost options, with Xiaomi releasing open weights and StepFun announcing theirs for October. Speech and audio followed with their own wave of releases: Qwen-Audio-3.1 combined broader capabilities with substantial price cuts, Google’s Gemini 3.8 TTS models expanded expressive voice creation, and NVIDIA’s Nemotron 3 Diarization improved speaker identification, including through its integration into MacWhisper. Free previews of Space Bunny Alpha and Pixel Canary gave developers further options to explore, with different strengths and data-use terms.

Making these capabilities practical was another recurring theme. Google’s emerging AX orchestrator explored how agents can preserve and resume work at scale, while Cursor reduced agent costs through tighter prompts, selective tool loading, and better caching. Firecrawl’s Alexandria expanded access to specialised datasets, and Perplexity’s Fast Search targeted faster, cheaper retrieval. Alibaba’s roadmap connected models and agents to ambitious chip and cloud expansion plans, while discussion of Nscale’s filing and alternative computing architectures raised questions about how efficiently AI investment becomes usable capacity. Perplexity’s SPACE research highlighted a separate challenge: agents can remain inside a sandbox while exceeding intended network permissions. Chinese local-government incentives for AI filmmaking extended the week’s focus to adoption, as cities sought to turn creative experimentation into an industry.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 20th September 2026

via Shivameet

Built a free browser-based tool that removes the visible sparkle watermark from Gemini / Nano Banana images and Veo videos — sharing here in case it's useful.

The value proposition: the sparkle is composited with a known alpha-blending formula, so instead of AI inpainting (which leaves a smudge), the tool reverses the math and recovers the original pixels underneath exactly — pixel-perfect, no blur. Everything runs client-side in the browser: no uploads, no login, no server processing. Works on both images and videos, batch supported.

Honest caveat, since this group will ask anyway: it removes only the visible mark, not SynthID — no tool honestly does that.

Web: tool.removegenteam.workers.dev

Telegram bot: @removegen_gemini_watermark_bot

Happy to answer questions about the approach or hear critiques.

I truly wasn't expecting this huge of an intelligence jump from StepFun's upcoming model.   This will be interesting when its weights become available and more inference providers are able to serve it

StepFun announces Step 5 Preview, its new flagship MoE model for agentic workflows, emphasizing frontier performance in software engineering, professional knowledge work, and finance with sustained long-horizon execution. 

The 600B total / 27B active parameter model offers 1M context, vision support, and substantially lower per-task costs while charts in the post position it as advancing the Pareto frontier in intelligence versus cost compared to models like GPT-6 and Claude Opus 5. 

StepFun, the Shanghai-based AI company behind the Step series, will release open weights on October 15, making the preview available now via their platform for testing in coding, analysis, and financial applications.

Step 5 Preview scores 44 on the Artificial Analysis Intelligence Index.

Pricing via its API is currently at 1.00/2.70/0.05 [input (cache-miss), output, input (cache-hit)] 

阶跃星辰

阶跃星辰于2023年4月成立，以"智能阶跃，十倍每个人的可能"为使命。阶跃星辰坚定自研超级模型，积极布局算力、数据等关键资源，发挥算法和人才优势，已完成 Step-1 千亿参数语言大模型和 Step-1V 千亿多模态大模型的研发，在图像理解、多轮指令跟随、数学能力、逻辑推理、文本创作等方面性能达到业界领先水平。

https://www.stepfun.com

https://artificialanalysis.ai/#intelligence

via Alex

The Nscale S-1 has been released: https://www.sec.gov/Archives/edgar/data/2110365/000119312526395475/ck0002110365-20260918.htm

Operating loss margin of -3000% on a non-GAAP accounting. Peak bubble?

Operating loss margin of -3000% on a non-GAAP accounting. Peak bubble?

The S-1 also confirms recent speculation that only a small fraction of sold GPUs are currently in use. 95% of NSCALE inventory is sitting idle according to the filing

Google ML/AI engineer Jaana Dogan announced ongoing work on AX, Google's open agentic orchestrator and runtime, which reinvents Kubernetes concepts for stateful AI agent workloads with emphasis on fast resumption, suspension, and reproducibility. 

The demo video shows the ax CLI in action: applying YAML manifests for workspaces and tasks (e.g., setting up Go environments from Git), monitoring status, SSH access for commands like builds, and stateful operations like suspend/resume/delete to preserve progress. 

AX targets scaling to billions of agents per cluster with 10-20x higher sandbox density, declarative primitives for isolation and networking, and open development to support agentic applications, RL workloads, and evaluations beyond traditional batch or stateless jobs. 

AX

Declare an agentic task in YAML. AX sandboxes it, wires up its workspace, fences its network, and helps running it at scale.

https://agentexecutor.io

⚡ “The weights do not need to move.” — Dave Blundin

Why spend so much energy moving an AI model’s parameters between memory and processors, over and over?

In this discussion, Dave Blundin joins Cerebras CEO Andrew Feldman and semiconductor veteran Atiq Raza to explore what happens when we rethink the hardware running AI.

Blundin makes a striking claim: architectures that keep those parameters in place could be 1,000–1,000,000× more efficient  An ambitious prospect—and the panel digs into the engineering and supply-chain hurdles.

They also explore why faster AI could unlock entirely new businesses, how chip restrictions have pushed China toward greater efficiency, and where founders should look for opportunities.

One revealing detail: Feldman says OpenAI initially gave its new Cerebras computing capacity to its own engineers, prioritising faster development over customer revenue.

What could you build if AI were 10× faster? That’s the question to keep in mind while watching.

🎥  Watch the full discussion 

💬  Dave’s post

Tuesday 22nd September 2026

SpaceXAI Launches Grok 4.7 for Coding and Knowledge Tasks

SpaceXAI has released Grok 4.7, a larger frontier model than Grok 4.6, trained with extended reinforcement learning for complex, multi-hour tasks. It excels in self-verification, long context handling up to 500,000 tokens, and agent integrations, with strong benchmarks on coding tests like CursorBench 4.0 and DeepSWE. Priced at $2 per million input tokens and $6 per million output tokens, it's now available in Cursor, Grok Build, the SpaceXAI API, and select tools, including a faster variant.

Xiaomi Launches MiMo-V2.6-Pro as Top Open Source AI Model

The flagship MiMo-V2.6-Pro is a Mixture-of-Experts model with 1.02 trillion total parameters and 42 billion active ones, handling text, images, video, and audio natively. A smaller MiMo-V2.6-Flash variant uses 310 billion total and 15 billion active parameters. Both are open-sourced under MIT license on Hugging Face, with the same low API pricing—$0.435 per million input tokens—and reinforcement learning that boosted training pass rates after public livestreamed runs costing under $3 million total. Early tests show strong results in coding, agents, and cybersecurity against rivals.

MiMo-V2.6 | Xiaomi

Introducing the MiMo-V2.6 series: frontier intelligence, all the modalities, built in public.

https://mimo.xiaomi.com

Wednesday 23rd September 2026

Claude AI announces Opus 5.5 as the first in the Claude 5.5 family, matching Fable 5.1 performance on most tasks while costing 40% less and generating output over 30% faster than Opus 5. 

The model features benchmark leadership in agentic coding, computer use, knowledge work, and multidisciplinary reasoning, with a pricing table showing lower per-token rates including reduced cache costs. 

Released after external safety testing by METR and Frontier Design, it sets a new high on alignment evaluations following Anthropic's call to pace frontier development, with an artistic video highlighting themes of discovery. 

Following is snippet of guidance from Anthropic

Instructions written for an older model can make Opus 5.5 write more and repeat tool calls. Run /claude-api prompt-audit in Claude Code to check your Claude Code setup, such as your skills and CLAUDE.md file, for these prompting anti-patterns. It also checks the code of an app you build on the Claude Platform.

Prompting Claude Opus 5.5

Behavioral differences from Claude Opus 5 and the prompting and harness patterns that address them: effort calibration, thinking behavior in API integrations and chat, progress updates, unattended and multiagent tasks, safeguard refusals, frontend design, complex visual inputs, multi-app workflows, and pasted text in user messages.

https://platform.claude.com
Prompting Claude Opus 5.5

What a task costs on Opus 5.5 | Claude by Anthropic

The same token price can cost very different amounts per task. Learn what Claude Code tasks cost on Opus 5.5 and which settings change the bill.

https://claude.com
What a task costs on Opus 5.5 | Claude by Anthropic

Getting the most out of Opus 5.5 in Claude and Claude Code / claude.dev Blog

How to prompt Opus 5.5, steer a long run, and check your results in Claude apps and Claude Code.

https://claude.dev
Getting the most out of Opus 5.5 in Claude and Claude Code / claude.dev Blog

🚀 OpenAI launches GPT‑6 Sol and Luna: more capable AI at lower prices

The two models bring advances from GPT‑6 Astra to more affordable options for everyday work, coding and automation. Astra remains OpenAI’s most capable model.

What’s new?

• Stronger performance: Improvements in coding, business workflows and computer use.

• Better factual accuracy: OpenAI reports Sol makes roughly half as many factual mistakes as its predecessor on its internal evaluation.

• Clearer answers: Less jargon and more concise communication.

• Cheaper API usage: Sol costs $2 input / $10 output, and Luna $0.10 input / $0.50 output, per million tokens.

• Better caching: Reusing context helps make long conversations and AI agents faster and cheaper, with a 90% discount on cached input reads.

Where can you use them?

They’re available through the API and rolling out in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users. Free and Go users can access Luna in the desktop app. They aren’t yet available in regular Chat.

The practical appeal: more room to use capable AI for ongoing work without paying Astra-level prices.

📖 Read OpenAI’s announcement

Alibaba’s main AI news this week was a full-stack roadmap announced at its September 22 Apsara Conference—covering Qwen models, chips, cloud infrastructure, and AI agents. 

- Models: Qwen 4 is in training; Qwen 4.5 and Qwen 5 are on the roadmap, with future models projected to reach 5–10 trillion parameters. Alibaba also introduced speech and audio updates, including LiveTranslate and Qwen-Audio tools. Qwen-Image 3.1 is planned for later this year.

- AI agents: Alibaba announced Qwen Intelligence, a Qwen-powered agent platform for phone makers, plus enterprise cloud tools for building and managing agents, security, and persistent context.

- Chips and data centers: Alibaba unveiled the Zhenwu V900 accelerator, with commercial release planned for Q1 2027, and Yitian CPU plans for 2027. It also set a goal of exceeding 20 GW of Alibaba Cloud data center capacity by 2032.

The timing matters: several items are future plans, not products available now. Alibaba’s performance and efficiency figures are company-reported. The Apsara Conference runs through September 24, so further announcements may follow.

Firecrawl Raises $75M for Alexandria AI Knowledge Library 

 Firecrawl raised $75M in Series B funding led by Smash Capital to launch Alexandria, a knowledge library designed for AI agents and superintelligence with instant access to extensive datasets from 100+ providers and custom indexes. 

It enables direct searches across specialized data including research papers, US homes/rentals, professional profiles, and product prices, outperforming standard web tools by 21% in answer quality based on internal evaluations of 1,000 questions. 

 Alexandria focuses on compensating data providers and content creators while offering quick agent integration via CLI, API, and MCP, with a call to build toward solving major problems through better data infrastructure. 

Firecrawl Alexandria

Give your AI agents access to 90+ data providers and specialized indexes through one API, MCP, or CLI. Find tools, check their contracts, and get structured data.

https://www.firecrawl.dev
Firecrawl Alexandria

via Raw Mind

Quick builder share for the AI-tools crowd: I’m building RAWMIND, an uncensored/unfiltered multimodal AI workspace for chat, image, and video. It supports wallet or seed login—no email—and I’m looking for honest feedback on the experience. If you’re curious, it’s here: https://rawmind.xyz/

Thursday 24th September 2026

via Matthew Weigand

For those interested in agent security, this one is worth a read. https://www.perplexity.ai/hub/blog/escaping-space-part-i

An AI agent can stay inside its sandbox and still exceed its permissions.

Perplexity’s SPACE research shows how shared IP addresses can let agents reach blocked services through allowed network connections.

The lesson for anyone building agents- verify which service the agent actually reaches. An allowed IP alone isn’t enough.

Cursor Cuts AI Coding Agent Token Costs by 7% Without Quality Loss

The company achieved the cost reduction through precise changes like shortening the system prompt by 66%, loading tool definitions only when needed to cut static tokens by 60%, and optimizing cache to reduce misses by 20%. Production data reveals read tools dominate at 91% of conversations, with grep, glob, and shell close behind, while line numbers in reads now appear every tenth line to trim another 1.6%. 

Eric Zakariasson even shared the following

here's a prompt to improve your agent harness based on what we've learned at cursor. enjoy

Improved token efficiency for longer agent runs · Cursor

Changes across Cursor's agent harness reduced token costs for users by 7% without reducing agent quality.

https://cursor.com
Improved token efficiency for longer agent runs · Cursor

After Monday and Tuesday torrent of LLM model updates,  it's time for the speech-to-text and text-to-speech world to do the same so stay tuned for a series of posts on updates on models which deal with ASR and TTS

Alibaba's Qwen team announces Qwen-Audio-3.1, expanding their audio AI lineup with upgraded ASR, TTS, and Realtime models plus new ASR-Next and TTS-Next variants to deliver full-stack capabilities in speech understanding, generation, interaction, and creative audio production. 

Notable upgrades focus on multilingual/dialect handling, multi-speaker transcription with timestamps and emotion detection, ambient sound recognition for audio QA, and unified TTS-Next generation of voices, effects, and backgrounds using an LM-diffusion hybrid. 

The release features steep price cuts of 70-95% across the models to boost accessibility, with immediate API links provided and community replies showing keen interest in open weights availability. 

Google AI announced Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS, its most expressive text-to-speech models enabling custom voice creation in 100+ languages from prompts or 2,000+ presets, with support for natural cues like <laughs> or |mhm|, line-by-line direction, and hours of consistent audio.  

Flash TTS suits high-fidelity creative work such as gaming, audiobooks, and podcasts with granular control for bespoke personas, while Flash-Lite optimizes for cost-efficient real-time voice agents, dynamic tone adjustments, and bulk dubbing.  

Both models are rolling out today to developers in Google AI Studio and Gemini API, consumers in Gemini Notebook and Google Vids, with enterprise access soon; they lead benchmarks in voice quality, accent modeling, and long-form consistency. 

via Jordi Bruin whose MacWhisper product is something I use as my workhorse for generating transcripts locally

We've made some really nice improvements to MacWhisper in the last month and today we're releasing MacWhisper 15.2 with a lot of core improvements to speaker recognition thanks to a new NVIDIA model

Today NVIDIA is releasing their latest speaker recognition model Nemotron 3 Diarization, and MacWhisper has support for it from day 1! Thanks to our collaboration with Argmax we've been testing the new diarization model and it performs a lot better than our previous model

Friday 25th September 2026

Perplexity AI introduced Fast Search in its API, powered by Photon, a new Rust-based retrieval and ranking service built by a small engineering team using hundreds of agents. It is now the default search in Hermes Agent for Nous Portal subscribers, and Nous Research says it is free on every Portal tier.

Fast Search is priced at $1 per 1,000 requests and is described as the lowest cost per task among other search APIs. It returns 95% of results in 230 ms or less, with a median single-search latency of 160 ms. On six agent benchmarks it cut estimated model-plus-search cost by about 68% ($59.73 versus $187.60 on 3,554 tasks) at comparable aggregate quality.

Photon replaces the prior open-source engine with compact indexes, asynchronous disk reads, and rolling index updates that warm caches before taking traffic. In production, retrieval-and-ranking p99 fell from about 800 ms to about 65 ms, with roughly 20% fewer serving machines and 2.5× more data per document. The fast preset trades a little internal relevance and answer availability on long-tail queries for that speed and cost.

Chinese local governments are offering subsidies like computing vouchers, rent waivers, and dedicated funding to lure AI filmmakers as part of China's AI push

China fuels rush to turn AI video into an industry

AI filmmaker Zhu Zhili has fielded calls from officials in cities and industrial parks across China, all with the same pitch: ‌bring your AI film business here.

https://www.reuters.com
China fuels rush to turn AI video into an industry

Saturday 26th September 2026

Two free stealth models dropped this week. Both are preview-only — try them before the window closes.

🐰 Space Bunny Alpha

Listed 23 Sep on OpenRouter as stealth/space-bunny-alpha

• 1M context, ~524k max output

• Text + image + video in, text out

• Always-on reasoning (adjustable effort)

• Tools, fast inference, strong at coding / agent loops

• Free during preview. Provider says it does not train on your prompts.

Try: https://openrouter.ai/stealth/space-bunny-alpha

🐤 Pixel Canary

Listed ~25 Sep on Vercel AI Gateway as stealth/pixel-canary

• Coding model aimed at frontend + mobile UI

• ~262k context

• Vercel Next.js eval: 28/31 baseline, 30/31 with docs in context

• Free for a limited time

• No ZDR — prompts/responses may be used for training. Don’t send secrets.

Try: https://vercel.com/changelog/pixel-canary-is-now-available-in-stealth-for-free-on-ai-gateway

Quick split

Bunny → long context, multimodal, agents

Canary → Next.js / UI / app-building

Both → free now, no official vendor name, can vanish or start costing money

Read more on X

OpenRouter announce: https://x.com/OpenRouter/status/2102772427105407270

Bunny demo (3D solar system): https://x.com/HeyZaraKhan/status/2103451247785480221

Both models in one roundup: https://x.com/VaibhavSisinty/status/2103772515701260461

Pixel Canary drop: https://x.com/Ved_CJ/status/2103755573217128922

Canary usage note: https://x.com/CommandCodeAI/status/2103717089869808029


Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the GPT Image 2.5 model within the Krea tool via the following prompt

a magnificent modern ecotourism village with many different and singular residential buildings designed based in style of architects Oscar Niemeyer and Lucio Costa, human eye view, central avenue, central garden with lake, tree-lined streets, pedestrians, square garden, lake, modern cars, drone photo 8k, realistic, golden hour, volumetric lights,


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 38-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-38-2026 UJYAr9hW97xyMyrB3Y5Q Sun, 20 Sep 2026 01:58:33 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

Reminding the members  in the group here about the virtual roundtable on video editing and AI I'm hosting with Shamir Allibhai—filmmaker, former Meta PM on video/AI infrastructure, and now CEO/co-founder of Eddie AI.

📅 Tuesday, Sep 22, 12 noon SGT,  Monday Sep 21, 9:00 pm PST

👉 https://luma.com/qo2351sz — shows your local time.

Join us! A recording will be available afterward.

The week of 13th to 19th September 2026 brought a growing focus on AI systems that can carry work through from instruction to result. OpenAI’s Agents API and discussions around domain-specific harnesses highlighted the infrastructure behind sustained agent work, while Claude’s merging of Cowork with chat and its new Projects experience brought task delegation, parallel cloud sessions, and document creation closer to everyday use. TypeSafe AI introduced Jev for fast, structured decisions such as routing and scoring, with availability through Vercel’s AI Gateway. Specialised applications also featured prominently: Wispr Flow’s Canto targeted real-world dictation, Odyssey-3 explored physical intelligence, and OpenMontage and Pocket FM’s Sherpa showed different approaches to coordinating video production and long-form storytelling.

Alongside these developments, the week raised questions about how AI should be evaluated, governed, and connected to information. Dario Amodei’s call to pace frontier development drew support from Sam Altman and Elon Musk, with independent evaluators given deeper access emerging as a concrete proposal. Xiaomi’s public MiMo training dashboard offered an unusual view into large-scale reinforcement learning, while Perplexity’s Q2D-Web study focused on the retrieval quality that shapes everything an assistant does afterward. My updated Portable LLM Council and Multi-Provider Research skills, together with a new guide to choosing AI models and search providers separately, explored practical ways to give users more control over those workflows. Claude Code’s addition of AGENTS.md support also marked a small step toward shared conventions, making instructions easier to reuse across tools.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 13th September 2026

Dario Amodei proposes steps for pacing the frontier: embedded evaluators, coordination among democracies, and global coordination with authoritarian governments

Sam Altman says he agrees with Amodei that “committing to having independent evaluators with employee-like access is a great idea”, and OpenAI will do the same

Elon Musk backs Dario Amodei's arguments about pacing the frontier, saying “Dario is right”

Dario Amodei - We Must Pace the Frontier

I have worked on AI for the last twelve years because I believe it could dramatically raise the quality of human life. I've written often about these incredible benefits: I believe that AI could cure most major diseases in the next 5-10 years, greatly accelerate economic growth rates, create a world of abundance and empowerment, and usher in a renaissance of democracy and freedom.

https://darioamodei.com
Dario Amodei - We Must Pace the Frontier

Harness Engineering Transforms AI Agent Development

A shift toward domain-specific harnesses is gaining speed, with Garry Tan noting that systems either become these customized frameworks or fade away. OpenAI launched its Agents API in public beta, offering managed services like context compaction and subagents while supporting any model and open-source flexibility. Builders like Harrison Chase from LangChain share practical guides on custom harnesses,

🔄 Updated skills and guides

Inspired by OpenAI’s article on rethinking skills and prompts for GPT-6 Astra I had Astra audit and refine two skills I’ve previously shared here:

• Portable LLM Council — for reviewing business and technical decisions from multiple perspectives.

• Multi-Provider Research — for choosing and using external research tools with AI agents.

The article makes a useful point: as models improve, it’s worth revisiting older instructions and removing unnecessary complexity.

The updated skills are available in the linked repositories—feel free to grab the latest versions.

I also used Astra to audit and tidy up the Notion guide linked from the Multi-Provider Research repository. There are a few small updates, so it’s worth revisiting if you’ve read it before.

For the background and guides, here are my original newsletter posts:

📖 Choosing external research tools for AI agents

📖 Introducing Portable LLM Council

🎬  A product brief, a few screenshots, your brand kit—what would it take for an AI agent to turn those into a finished explainer?

That’s the idea behind OpenMontage, an open-source project that connects research, scripting, visuals, narration and rendering into a video production workflow.

What caught my attention is how it brings together your own material, stock footage and AI-generated assets—with human approval along the way and cost estimates before spending.

The engine is public; the Studio app is still in private alpha. Paid generation can add costs.

🎬  See what OpenMontage can do 

🛠  Explore the open-source project on GitHub

💬 For those building with AI: is coordinating the tools now the bigger challenge—or getting consistently good output from each one?

Monday 14th September 2026

Tuesday 15th September 2026

Wednesday 16th September 2026

TypeSafe AI Launches Jev for Fast Structured AI Decisions

Diogo Almeida, co-author of the InstructGPT paper that introduced RLHF at OpenAI, announces Jev from TypeSafe AI as a new "System One" model trained with RLCD for calibrated, structured decisions rather than text generation. 

Jev outputs typed results like choices, scores, or true/false answers with confidence levels, enabling developers to handle multiple queries in one 150ms call. Priced at just $0.042 per million input tokens with free outputs, it promises 20-200x speed and 40-400x cost savings over typical models for decision tasks, eliminating hallucinations by avoiding string generation.

Introduction - TypeSafe AI

Jev is TypeSafe's flagship model and the first System One model. Send state and typed questions; get structured answers your code can use directly.

https://docs.typesafe.ai
Introduction - TypeSafe AI

Odyssey AI lab unveils Odyssey-3, a foundation world model that controls robots and humanoids, drives cars on Indian roads, pilots drones, plays video games, and trains other AIs. 

Built as an autoregressive diffusion transformer, it simulates diverse physical and virtual scenarios from visual data, enabling efficient adaptation with just tens of hours of demonstrations per task. 

The model generates training environments for recursive learning, creating physical agents that natively interface with real-world systems and support iterative intelligence improvements. 

Introducing Odyssey-3: A General-Purpose Physical Intelligence

Odyssey-3 is a foundation world model that can power robots, drive cars, train AIs, pilot drones, and even play video games.

https://odyssey.systems
Introducing Odyssey-3: A General-Purpose Physical Intelligence

Meta now lets AI agents handle the boring parts of WhatsApp Business setup

A new WhatsApp Business MCP server lets developers use AI coding agents like Claude, Cursor, Codex, and ChatGPT to handle setup, messaging templates, testing, and troubleshooting.

Meta now lets AI agents handle the boring parts of WhatsApp Business setup | TechCrunch

A new WhatsApp Business MCP server lets developers use AI coding agents like Claude, Cursor, Codex, and ChatGPT to handle setup, messaging templates, testing, and troubleshooting.

https://techcrunch.com
Meta now lets AI agents handle the boring parts of WhatsApp Business setup | TechCrunch

Thursday 17th September 2026

Claude is merging its dedicated Cowork workspace with the main chat into a single unified experience, allowing seamless handoff of quick questions or complex tasks like reports that Claude continues autonomously even when the app is closed. 

New integrated tools enable creating and editing Claude Docs, Slides, and Design artifacts directly in conversations, with options for manual fixes, comments, presentations, exports, and single-link sharing while retaining user final approval. 

The update preserves all existing Cowork chats, projects, and artifacts, rolling out first to Pro and Max users over the coming weeks starting September 16, 2026, with Docs, Slides, and Design in beta for paid plans.  

Claude Cowork and chat are now one Claude | Claude by Anthropic

Starting today, Claude Cowork and chat are one Claude, rolling out on Pro and Max plans. Hand over the task and Claude does the work. You keep the final say.

https://claude.com
Claude Cowork and chat are now one Claude | Claude by Anthropic

Xiaomi Livestreams Massive AI Model Training with Full Transparency

Led by Fuli Luo, Xiaomi's MiMo team is livestreaming reinforcement learning for MiMo-V2.6 models on a public dashboard, tracking compute at 2 billion tokens per step, multi-task environments, and advanced grading systems. The larger Pro version has 1 trillion total parameters and nears $700,000 in costs, while the Flash variant advances faster at lower expense, with daily runs hitting $493,000 and $247,000 respectively. Researchers praise the openness, calling it a rare glimpse into large-scale RL usually hidden by frontier labs, as benchmark scores climb steadily on tasks like DeepSWE.

Jev is now available on the  Vercel AI Gateway

Jev is TypeSafe AI’s System One evaluation model for fast, structured decisions in software. It evaluates shared state against typed questions and returns choices, scores, and boolean probabilities, supporting classification, routing, rubric-based assessment, and automated verification. Multiple questions can be evaluated in parallel within a single request

TypeSafe AI's Jev now available on AI Gateway - Vercel

Use Jev from TypeSafe AI on Vercel AI Gateway to classify, route, score, and automate workflows with typed outputs, probabilities, and confidence.

https://vercel.com
TypeSafe AI's Jev now available on AI Gateway - Vercel

Evaluation

Evaluate shared state against typed questions and get back structured choices, scores, and boolean probabilities through Vercel AI Gateway.

https://vercel.com
Evaluation

Friday 18th September 2026

Wispr Flow announces Canto, its new real-time speech model for dictation, claiming a 3.4% word error rate on 9.8 hours of real-world English audio from over 2,300 speakers—the lowest among models tested on their internal dataset. 

Canto uses supervised fine-tuning plus reinforcement learning (GRPO) trained on user corrections and contextual vocabulary, targeting practical low-latency dictation rather than clean read speech. 

It's now rolled out for everyone, with nothing to switch on.

Wispr says that they already training its successor at more than ten times the scale, with better recognition in difficult conditions and broader language coverage ahead.

Canto: a speech model built for the real world | Wispr Flow

Today, the Wispr Advanced Interfaces Lab is introducing Canto, our latest speech model for real-time dictation. On an evaluation of real-world dictations, Canto achieved the lowest word error rate among all the models we tested.

https://wisprflow.ai
Canto: a speech model built for the real world | Wispr Flow

Claude AI's redesigned Projects shifts from folders to a single conversation where users describe goals and Claude autonomously directs parallel cloud threads that persist and operate independently even offline. 

Threads leverage shared project memory and a dedicated library for files/artifacts, enabling coordinated workflows like task delegation, conflict resolution, and context retention across sessions similar to briefing a chief of staff. 

Now in beta for select Pro and Max cloud users in Claude Code, with rollout expanding soon and local device support planned; existing projects will upgrade gradually. 

Just saw mention of GLM-5.3-FlashX on ZAI website,  not seeing this in zcode or via OpenRouter as yet.  Read some buzz on X

GLM-5.3-FlashX is now live, delivering inference speeds of 200 tokens/s for faster responses and a smoother experience.

Pricing of GLM-5.3-FlashX from ZAI  [0.37/1.25/0.075] is more than double of GLM-5.3-Flash [0.15/0.5/0.03]

https://docs.z.ai/guides/vlm/glm-5.3-flash

https://docs.z.ai/guides/overview/pricing

Saturday 19th September 2026

via Lisa Gu

Pulled multiple all nighters on this one 🫡 and so excited the world can finally see it

Rohan Nayak, CEO of Pocket FM, announces Sherpa, their advanced AI fiction writing tool that drove the company's ARR from $250M to $500M in one year by scaling content production 1200% with 550K+ creators generating 2.6M annualized hours. 

Sherpa uses a Narrative World Model for long-term continuity, hierarchical planning across seasons-to-scenes, and a Prose Engine trained on 5.5B hours of playtime and retention data to maintain engagement in serialized stories. 

Pocket FM's owned distribution enables one-tap audio production, performance-based revenue share, and localization, allowing 10% of writers to earn over $200K and creating blockbusters exceeding $100M while fostering a creator-AI feedback loop.

Sherpa by Pocket FM | Free AI Fiction Writing Tool

Sherpa by Pocket FM is a free AI fiction writing tool that helps anyone turn ideas into stories. Write it, produce it into audio and publish it.

https://pocketfm.com
Sherpa by Pocket FM | Free AI Fiction Writing Tool

via Matt Weigand

Hey everyone - I’m Matt Weigand at Perplexity. 

A lot of my interest comes back to search: if a model doesn’t find the right information, everything that follows starts with an incomplete picture.

If you are interested in what Perplexity is doing with web research, check out this study we just published - Q2D-Web. The study digs into evaluating how well search systems retrieve relevant documents at web scale. It’s work I’m excited to share more about here, along with what better retrieval means for people building and using AI.

Q2D-Web: Evaluating First-Stage Retrievers at Scale- https://www.perplexity.ai/hub/blog/q2d-web. 

Look forward to connecting!

Finally,  Anthropic taking small steps to use conventions used by other harnesses

Claude Code now [as of version 2.1.277] reads AGENTS.md if there is no CLAUDE.md

Hopefully they will in the future also read skills from .agents/skills so one doesn't have to do symlink jugglery

You choose your AI assistant. But did you know you can also choose how it searches?

The models that help you think, write, and reason don’t have to determine which tools find information for them.

In a compatible AI application, you can connect search and retrieval providers separately. That gives you more freedom to combine the models, tools, and costs that suit you.

I built the multi-provider-research skill to help your assistant work with those tools—choosing suitable providers, checking source evidence, and explaining gaps. You can start with just one provider.

I’ve put together a practical guide covering:

• What harnesses, skills, and providers actually mean

• How to get started with options such as Exa, TinyFish, Octen, and Perplexity

• Free allowances and lower-cost combinations worth exploring

📖 Choose Your AI. Choose How It Searches.
https://goolamabbas.github.io/separate-intelligence-and-search/

The guide links directly to the skill and further reading. I’d love your feedback, especially if this is your first time exploring skills or connected search tools.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the GPT Image 2.5 model within the Krea tool via the following prompt

Create an artistic representation of the Garden of Eden from the Book of Genesis, capturing its lushness and tranquility, Mysterious --no buildings, people


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 37-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-37-2026 22tep5E7GDyqWqgV1qcV Sun, 13 Sep 2026 01:24:36 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 6th to 12th September 2026 brought advances in both AI performance and the tools that turn it into everyday work. DeepSeek introduced V4.1 Flash with native visual understanding and strong self-reported coding and agent benchmarks, while Inception’s Mercury 2.5 pushed diffusion-based language generation beyond 1,100 tokens per second. NVIDIA’s Sol-H3 demonstrated video generation faster than playback, and ChatGPT Images 2.5 and Eddie v4 expanded creative editing capabilities. Cursor Projects added another dimension: a persistent coordinator agent that manages subagents and ongoing work in the cloud. Alongside Hugging Face’s ML Intern and ChatGPT Work’s Data agent, it pointed toward workflows in which users delegate an extended task rather than prompt each individual step.

Making that delegation effective—and deciding where its limits belong—was a recurring theme. My Portable LLM Council offered a structured way to examine competing arguments before making a decision, while Spotify’s use of cheaper worker models and Tencent’s FlexKV showed how orchestration and infrastructure can make better use of compute. Discussions around GPU financing and rental economics questioned the durability of the assets supporting AI’s expansion, even as Harvey’s latest funding round reflected growing investment in professional applications. Meanwhile, allegations concerning OpenAI’s handling of researchers’ conversations brought privacy and research ownership into focus. Taken together, the week showed why progress needs to be assessed across several dimensions: what systems can do, how economically they can do it, and how confidently people can entrust them with valuable work.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 6th September 2026

Robert Scoble with a longish X article which links to many posts on what folks have been able to do GPT-6 Astra.  It might interest and inspire you (modulo how much you can use within your plan's limits,  lower reasoning levels of Astra stretch limits further).  

FWIW,  I had Astra Light review my recent Notion page and it dramatically edited it cutting the number of words on the main page by half and generating cleaner SVG diagrams and it's token generation speed is very fast for its intelligence. 

When Jalapeno (OpenAI's custom silicon for inference) rolls out at scale,  Astra and future versions are going to be running at Speedy Gonzales/Road Runner pace

GPU Financing Emerges as AI Infrastructure's Key Bottleneck

Eugene Ye of Wafer AI explained how AI builders chase Blackwell B300 GPUs and colocation space, but hit walls with lenders demanding huge down payments since used GPUs lack predictable resale markets. Unlike cars with established secondary values, GPUs get treated as worthless after depreciation, squeezing smaller players. 

Efforts like Nvidia's partnerships with Apollo and BlackRock aim to unlock over $500 billion in capital, while companies like CoreWeave land big loans through customer contracts—yet many still rely on venture funding amid hopes for better liquidity tools.

Monday 7th September 2026

Introducing Portable LLM Council for business and technical decisions

Ever asked an AI for advice on a business or technical decision—and wondered whether it examined the tradeoffs or simply followed your framing?

I’ve put together Portable LLM Council, an open-source skill that gives your AI assistant a structured way to challenge a decision before recommending what to do.

It runs three rounds:

• Five separate advisors examine assumptions, fundamentals, overlooked opportunities, clarity, and execution.

• Five fresh reviewers assess the anonymized answers for strong arguments and blind spots.

• A chair weighs the reasoning and recommends a direction and one concrete first step.

You get a readable HTML report and a Markdown transcript, so you can inspect the arguments behind the recommendation.

Useful questions include:

— Should we launch a paid pilot or build the full product?

— Should our team focus on onboarding or acquiring more customers?

— Should we migrate a system gradually or replace it in one go?

These are starting questions, not complete prompts. Give the assistant relevant context—your objective, constraints, options, and evidence so far—or ask it to use what you’ve already discussed in the conversation. A few focused paragraphs are usually enough; the council needs something concrete to challenge.

The same skill works with native subagents for Claude Code—including local Code sessions in Claude Desktop—Codex, Cursor, Command Code, Grok Build, OpenCode, and Factory Droid. No particular model is required.

To install, ask your assistant:

“Read the installation instructions at https://github.com/goolamabbas/portable-llm-council and install the shared skill and the agent definitions for this harness in my personal installation locations. Back up existing copies before replacing anything, then verify discovery. Do not run a council yet.”

Your assistant may request approval for file changes. You may need to start a new session before it can verify discovery. Manual instructions and example prompts are also in the repository.

This is for decisions with meaningful tradeoffs. It uses more time and model usage than a normal answer, and agreement between agents doesn’t make a conclusion true. The value is in exposing assumptions, competing arguments, and questions worth investigating.

GitHub - goolamabbas/portable-llm-council: Pressure-test business and technical decisions with five AI perspectives, blind peer review, and a synthesized recommendation. One model-neutral skill with native subagents for seven AI harnesses.

Pressure-test business and technical decisions with five AI perspectives, blind peer review, and a synthesized recommendation. One model-neutral skill with native subagents for seven AI harnesses. - goolamabbas/portable-llm-council

https://github.com
GitHub - goolamabbas/portable-llm-council: Pressure-test business and technical decisions with five AI perspectives, blind peer review, and a synthesized recommendation. One model-neutral skill with native subagents for seven AI harnesses.

If you try it, I’d welcome feedback—especially what it helped you notice that a normal AI conversation missed.

Tuesday 8th September 2026

NVIDIA Speeds Up AI Video Generation Past Real-Time Playback

NVIDIA's Sol-H3 inference stack accelerates MiniMax-H3 video generation, producing five seconds of 1344×768 video with stereo audio in 1.653 seconds on an 8× B300 Blackwell system—beating real-time playback. Reactor partnered to release FastH3 via API, enabling developers to access it immediately without owning the GPUs, while open-source code under Apache 2.0 supports various configurations. Optimizations like dynamic sparse attention and fused kernels preserve quality and scale efficiently, paving the way for interactive, 24 FPS video systems

https://www.reactor.inc/blog/fast-h3-sol-engine

https://nvlabs.github.io/Sana/Sol-Engine/Sol-H3/

NVIDIA's Jensen Huang Calls Compute Resources Rentable Assets Amid Surging Demand

Huang described the company's compute power as fungible, durable, and highly rentable—a productive asset generating revenue. This comes as demand stays hot for three-year-old H100 GPUs, now renting at $3.28 an hour, prompting neoclouds to lock AI firms into multi-year contracts to finance big purchases. Experts like Jessie Dong caution that future efficiency gains and potential customer failures could leave providers stuck with debt and underused hardware in a softer market. The conversation draws parallels to early commodity markets like grain and oil, questioning if compute will prove as sturdy an asset class.

In response Alex wrote

Without context, Jensen's statement is misleading. The depreciation for a 2023 bought H100 alone is around 1.5$ per hour. 

The current spot market price from mid-tier cloud providers is 1.20$ a GPU/hour, ie. being rented at a loss.

Compute is not behaving like a commodity, too stock and hype dependent.

Also conveniently not mentioning that H100 rental prices are down more than 60-70% in the last 2 year.

A Claude Code plugin called shunt and two declarative agents running on Portal by Spotify. That's all it took to stop burning frontier model tokens on file reads and boilerplate generation. The grunt work routes to a cheaper worker model. The frontier model only handles problems worth its price.

90% token reduction on bulk reads. No new infra. The agents are public and the plugin is open source.

Portal by Spotify cut my Claude Code token usage by 90% | Spotify Engineering

Most of what an AI coding agent does for me isn't thinking. It's I/O.

https://engineering.atspotify.com
Portal by Spotify cut my Claude Code token usage by 90% | Spotify Engineering

via Alex

NYU released a statement claiming that OpenAI stole a millennium prize discovery through scanning and monitoring a fields medalist's ChatGPT chat and then advertised the discovery as an "autonomous" discovery.

Never feed anything of value into a  remote model. 

Read the full statement here: https://cims.nyu.edu/~tristanb/statement.pdf

Wednesday 9th September 2026

OpenAI announces ChatGPT Images 2.5 with faster generation, higher fidelity for natural images, consistent details in edits, and comment-based controls to modify specific elements without full regeneration. 

New Sketch feature lets users draw directly in ChatGPT by typing "@ Sketch" to visually convey ideas that are hard to describe in text alone, alongside templates for common formats like posters and merch. 

The model update deploys today to all ChatGPT, Work, and Codex users across platforms while adding API models GPT-Image-2.5 Flare for improved speed and GPT-Image-2.5 Sunburst for precision in detailed creative tasks. 

via Alex

Here the OpenAI statement on the NYU allegations: https://openai.com/index/navier-stokes-solution/

Pretty sure this will end up in a lawsuit.

OpenAI claims that they did in fact only start on the research to solve the millennium problem after (after Sep 1st) having been informed that the NYU professor was close to a solution, they deny that the verbatim chat logs were accessed but hint at the fact the same chat logs may have been used for training data.

In other words, OpenAI trained an internal model specifically on the professor's work and deployed over several 10,000 agents in 1 week to burn 300 billion tokens to win over a human that had dedicated their life on solving this problem.

Inception announces Mercury 2.5 as the largest and most capable diffusion LLM yet, delivering a 40% intelligence increase over Mercury 2, 1,107 tokens/sec inference speed on NVIDIA GPUs, and 260K token context. 

The accompanying video contrasts traditional autoregressive LLMs (sequential like typewriters, tying up GPUs) with diffusion-based processing for greater parallelism, enabling low-latency uses like real-time voice agents under 170ms TTFT. 

The model is available immediately on Inception API, OpenRouter, and Baseten with launch discounts; early tester feedback notes faster performance and better tool calling confidence.

Introducing Mercury 2.5 - Inception

Mercury 2.5 is the most capable diffusion LLM on the market. It runs at 1,107 tokens/sec and offers a 40% increase in intelligence over Mercury 2, comparable to cost-optimized frontier models.

https://www.inceptionlabs.ai
Introducing Mercury 2.5 - Inception

Tencent AI's post promotes FlexKV, an open-source multi-level KV cache system from their TACO team that offloads cache to CPU, SSD, and remote storage to handle long-context LLM inference without frequent re-prefills. 

It solves GPU wait times on cache hits via layer-by-layer restoration, prefetching, and asynchronous I/O, while using a distributed RadixTree for cross-node prefix reuse and lossless compression to boost effective capacity. 

FlexKV integrates directly under engines like SGLang, vLLM, TensorRT-LLM, and Dynamo, with claimed gains of 70% lower TTFT and 16% higher QPM; full details and repo at github.com/taco-project/FlexKV.

Reve did a strategic partnership with OpenAI, under which the research team has joined OpenAI aka license and acqui-hire.   It's not clear at least to me if their work went into GPT Image 2.5 or if todays launch was a means to announce it

Now officially posted on Deepseek's website

DeepSeek plans to officially release the V4.1 Flash model around September 10, 2026 (Beijing Time). After extensive internal and external testing, V4.1 Flash has comprehensively surpassed V4 Pro across all key metrics, including performance, cost, speed, and task completion time. In keeping with our commitment to user responsibility, following the official launch of V4.1 Flash and prior to the release of V4.1 Pro, all requests to the Pro model will be routed to V4.1 Flash and billed at Flash's price. If you encounter any issues during your comparative testing between V4 Pro and V4.1 Flash, please do not hesitate to reach out to us with your feedback. Thank you for your support!

We will adjust the pricing for the Flash series effective from 12:00 Beijing Time on September 10, 2026. During off-peak hours, the unit price will be $0.003 for input cache hits, $0.15 for input cache misses, and $0.6 for output. Peak-hour prices will be double the off-peak rates. Please plan your usage accordingly.

Harvey Raises $550 Million at $15.6 Billion Valuation

The legal AI startup raised $550 million in a round co-led by Lightspeed Venture Partners and Diffusion, boosting its valuation from $11 billion to $15.6 billion. With over $400 million in annual recurring revenue, Harvey now serves 80 percent of the top 100 law firms, 20 percent of the Fortune 500, and half of the Fortune 10, while token usage has surged 20 times since January. The funds will expand proprietary AI models, hire engineers, and let law firms build their own custom intelligence, including scaling to thousands of GPUs after releasing the advanced Harvey Tenet model.

Thursday 10th September 2026

Shamir Allibhai whom I shall be hosting on a roundtable on Sep 21/Sep 22 [depending on your timezone] let me know about the release of Eddie v4.   

The blog post below has the various details of feature updates in this version  and also has 2 embedded Youtube videos including one with Youtuber Jordan Orme [1.1M subscribers] demoing Eddie's features to other professional video editors

Eddie v4: The Most Powerful AI Assistant Editor in the World | Eddie AI

Eddie v4 watches your rushes, syncs your cameras, writes the music, sources the b-roll, and hands the finished cut to Premiere, Resolve, Avid or Final Cut as a real project file. See it first at IBC 2026.

https://www.heyeddie.ai
Eddie v4: The Most Powerful AI Assistant Editor in the World | Eddie AI

PS:   I  look forward to having as many members participate in the round table.  This will be recording and made available via the archives of my previous roundtables

via Alex

Another mathematician has come out accusing openAI of breaking their own terms and conditions by using "opted out" chats for training data: https://mathstodon.xyz/@andreasthom/117240535270608201

Willow Voice is doing $100k giveaway of dictation microphones plus free months of their AI voice-to-text service targeted at SF startups to encourage office adoption.   

The attached video features CEO Allan Guo and GTM lead Jesslyn Teo demonstrating Willow's accurate dictation that works across apps, even with soft speech, in professional settings.   

Engagement requires commenting "Voice-maxxing" for setup, positioning the product as enabling faster workflows by letting users "work at the speed you talk." 

PS:   I'm personally all-in on Wispr Flow primarily because of early adopter discounts and some referral bonuses which means that I'm effectively paid for a few months more  I've tried Willow Voice and it's a good product with recent improvements moving it up several notches

Hugging Face aka NVidia says that one shouldn’t need to be an ML expert to have an ML idea.

They have launched ML Intern in HuggingChat, an AI agent in HuggingChat that automates end-to-end model training by combining the platform's papers, datasets, pre-trained models, benchmarks, storage, and compute resources 

The release positions the tool as making specialized model training conversational and accessible like vibe-coding apps, with user control over budget, steps, and outputs for reproducible artifacts. 

Start with a conversation. Finish with deployable artefacts.  

HuggingChat - Chat with AI models

Making the community's best AI chat models available to everyone

https://huggingface.co
HuggingChat - Chat with AI models

Jay Yu of Pantera Capital with a very well articulated X article on 'The Rise of Compute Markets' where he states the following

  • Compute and datacenter spend is a trillion-dollar category, yet GPU buying still runs through group chats, OTC brokers, and bilateral deals.

  • Compute is early in financialization; like electricity it is bound by SKU, time, and location, but may become a commodity like power or oil.

  • Markets may follow a Hardware-Vendor-Cluster analog of power’s Grid-Operator-Node, with winning benchmarks tied to liquid physical settlement and more basis risk.

  • Neoclouds are the structural short for GPUs; on-tap platforms and apps are the long; NVIDIA acts as a compute central bank.

  • Physical settlement has the durable moat; indices, cash-settled exchanges, and financing vehicles follow; fullstack players and token-plus-metal hedges may win as compute becomes an asset class.

Team Deepseek takes no prisoners 🤯 with the launch of DeepSeek-V4.1-Flash with stellar self-reported benchmarks. 

Will be interesting once Artificial Analysis releases their benchmarks.  

Available with extended usage for limited period within OpenCode and Command Code harnesses.  Also available in v0.1.5 and above of the Deepseek Harness

DeepSeek-V4.1-Flash is a 552B-parameter MoE model with asymmetric Causal Encoder-Decoder architecture, using only 8B active parameters for input and 16B for output, plus native visual understanding. 

It achieves leading results on agentic and coding benchmarks such as Terminal-Bench 3.0 (30.0), DeepSWE v1.1 (74.2), CyberGym (88.1), and Automation-Bench (54.8), outperforming V4-Pro and competitors like Kimi, GLM, and Claude. 

Live on DeepSeek API as deepseek-flash with lower pricing effective today, dramatically reduced KV cache for cost savings, and phasing out V4-Pro, with open-source inference support planned. 

OpenAI announces a new Data agent in ChatGPT Work that lets enterprise users query connected company data sources via natural language to generate answers, interactive dashboards, and actions. 

The video demo shows practical use cases like analyzing product launch metrics (e.g., "Dash XV" sneaker sales), pulling context from Snowflake/Tableau/Teams, and creating visualizations that answer "why" questions beyond basic reports. 

Integration features include adding a Data Plugin, connecting existing data tools, and sharing AI-generated insights directly in team channels with ChatGPT mentions for collaborative workflows. 

Now everyone can put data to work

Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language.

https://openai.com
Now everyone can put data to work

Friday 11th September 2026

via Lisa Gu

https://www.instagram.com/reel/DdHVH_EpRRJ/?stkn=cnFoYXp3MjJ3cXdw

This is my losing video from the finals of the ai creative challenge last week in LA hosted by the one club for creativity and google. I’m letting the Internet be the judge. 😭👩🏻‍⚖️

It’s a 16 city tour, so apply if you want to compete!

https://creativechallenge.ai/

Cursor Launches Projects for Multi-Agent Coding Workflows

Projects creates persistent threads where a main agent oversees subagents, shares memory across sessions, and handles tasks like monitoring pull requests or Slack. It runs on cloud computers for nonstop work and scales to thousands of agents for big jobs like app builds. Early users like Lauren (of pstack fame) call it a 'personal software factory,' reporting they merge far more PRs—up to six times as many for heavy users—thanks to better organization and speed.

Saturday 12th September 2026

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the GPT Image 2.5 model within the Krea tool via the following prompt

a detailed digital painting of a beautiful white sci-fi city, ((intricate city detail)), straight lines, on a lush green landscape, intricate detail, two planets visible in the clear blue sky, fantasy art, trending on artstation, 8k, uhd


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 36-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-36-2026 2GvnxKKvK5eQi1zExiyx Sat, 05 Sep 2026 23:52:49 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 30th August to 5th September 2026 brought a relentless run of model launches and AI product updates across text, coding, image, speech, video, and infrastructure. Anthropic introduced Claude Fable 5.1 and Claude Mythos 5.1, Google released Gemini 3.8 Flash alongside its cyber-focused variant, Meta upgraded Muse Spark 1.3, and OpenAI began a staged rollout of ChatGPT 6 Astra. Microsoft also expanded its in-house model portfolio with MAI-Image 2.6 and MAI-Image 2.6 Flash for image generation, alongside MAI-Transcribe-2 for speech-to-text, while IFM’s fully open K2 Horizon family ranged from edge-scale models to 375B parameters. Inception’s Mercury 2.5 preview, meanwhile, showcased how diffusion-based language models could offer a compelling alternative for high-speed, latency-sensitive reasoning and agent workloads.

Video and multimodal AI also moved further toward interactive, real-time use. fal’s H3 Max, built on MiniMax H3, and related open inference work demonstrated video generation faster than playback; World Labs unveiled Atlas for controllable 3D scene reconstruction and world simulation; and Google added agentic video understanding to Gemini, allowing models to actively inspect relevant frames, audio, and transcripts rather than process a video uniformly. Perplexity’s hybrid compute feature illustrated a parallel shift in agent design, combining cloud models with local Apple Silicon processing for privacy-sensitive files, while Together AI’s planned Saudi data-centre partnership underlined how power access and compute capacity are becoming strategic constraints.

Taken together, the week showed that the race is no longer only about stronger general-purpose models, but about making multimodal AI faster, cheaper, more private, more deployable, and useful in real production workflows

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 30th August 2026

Friends,

Video editing looks effortless — until you try it. I find it genuinely hard, and I have real admiration for people who've mastered it: the ones who see a story where the rest of us see six hours of messy footage.

Join me for the next session of my conversation series on Tuesday, Sep 22, 12:00 PM HKT (India 9:30 AM - London 5:00 AM - NY 12:00 AM Tue - LA 9:00 PM Mon Sep 21), via Google Meet, with Shamir Allibhai — filmmaker turned Meta PM on video/AI infra, now a repeat founder whose current company, Eddie AI, builds an AI assistant editor for professionals.

This isn't a product demo. We'll talk about the career — filmmaking, product work at Meta, repeat founding — what he hears from editors using AI tools in real pipelines, and why editing is so hard to master. I'll be bringing my own beginner's frustrations; bring yours.

Format: a round-table conversation, not a webinar — everyone engages Shamir directly. Drop a question in the thread beforehand if you'd like it teed up, or bring it on the day.

Register: https://luma.com/qo2351sz — Luma shows your local time on registration.

The session will be recorded and archived with my previous conversations — so if 5 AM in London or midnight in NY isn't your friend, the recording has you covered.

Monday 31st August 2026

Tuesday 1st September 2026

Together AI announced a partnership with Saudi AI firm Humain for a 250MW data center equipped with 120,000 semiconductors, expected to generate over $5 billion in annualized revenue through shared compute for open-source AI models. 

The deal is based in Saudi Arabia as part of the kingdom's push to develop its AI industry and reduce oil dependence, enabling Together AI to secure capacity amid U.S. local opposition and moratoriums on new data centers. 

Power access emerges as the core AI infrastructure constraint, with this collaboration illustrating how open-source players are leveraging international partnerships to scale compute without solely relying on domestic hyperscaler funding.

https://www.nytimes.com/2026/08/31/business/dealbook/together-ai-humain-saudi-arabia-data-center.html?unlocked_article_code=1.9lA.oOPI.h2UKXnt1l8Cz

Whilst I understand the rights of someone to offer such a product,  IMHO this is equivalent to giving access to a cyber-offensive product to anybody with a credit card

https://abliteration.ai/

Unrestricted AI API + Enterprise Policy Gateway | abliteration.ai

OpenAI-compatible unrestricted AI and uncensored LLM API for enterprise teams: AI red teaming, cybersecurity, trust and safety, synthetic data/evals, ML research, and defense/government contractor workflows. Policy Gateway adds policy-as-code, per-project keys, audits, and zero retention by default.

https://abliteration.ai
Unrestricted AI API + Enterprise Policy Gateway | abliteration.ai

Sharing something sent to me from  Ethan Wei of Minimax 

===

Regarding the film and video sector, our recently released H3 and H3-Max models have garnered widespread coverage and attention on X. H3-Max delivers exceptional video generation speeds—producing a 5-second video in just 3 seconds and a 15-second video (at maximum duration) in around 10 seconds with high-quality results. 

This capability has spawned a range of new use cases and innovative applications, including—but not limited to—live streaming, in-game narrative progression, real-time virtual try-ons, rapid prototyping of cinematic shots, and AR/VR extensions.

Infinite Slop by @levelsio + fal.ai

An endless AI-generated TV channel where you decide what airs next.

https://infiniteslop.ai
Infinite Slop by @levelsio + fal.ai

Create Video Generation Task - MiniMax API Docs

Video generation V2 endpoint. Provide multimodal input via the content array (text / image / video / audio); switch between MiniMax H3 and MiniMax H3 Max with the model field to support text-to-video, image-to-video (first & last frame), and reference-to-video, with output up to 2K.Note: To use MiniMax H3 or MiniMax H3 Max, please select the Pay-as-you-go API.

https://platform.minimax.io
Create Video Generation Task - MiniMax API Docs

Lauren Tan software engineer at SpaceXAI (formerly Cursor, Meta, Netflix)  who goes by the handle of poteto and is the creator of the pstack AI coding workflow plugin recently wrote an X article about pstack which is getting great buzz

Came across this X post which links to an hour long video of Lauren going through her setup

Wednesday 2nd September 2026

via Just|LDA who shared a link to the launch post of Anthropic's Claude Fable 5.1 and Claude Mythos 5.1


Mercury 2.5 from Inception is exclusively available via OpenRouter 

This is diffusion based unlike other LLM's  which are autoregressive 

Diffusion language models represent a shift from traditional autoregressive generation by using diffusion processes to create text. Instead of predicting one token sequentially, these systems produce many words in parallel, resulting in faster inference and lower costs. 

Price atleast till 9/7 is 0.04/0.15/0.004

https://openrouter.ai/inception/mercury-2.5-preview#providers

Perplexity's Mac app now supports hybrid compute, letting its "Computer" AI agent split tasks between cloud frontier models and local models running on Apple Silicon for privacy-sensitive work like bloodwork or tax files. 

The approach routes private data to local processing with no token costs while using cloud models for non-sensitive steps, addressing limitations in most AI agents that ignore native vs. web runtimes. 

Perplexity open-sourced its PII classifier on Hugging Face to detect when workloads should stay local and published a research blog detailing the privacy-protecting local runtime implementation.

Hybrid compute is available today for Perplexity Pro, Max, and Enterprise subscribers in the Perplexity Mac app with macOS 15+.

Couple of posts on H3-Max.  a video generation model post-trained by fal.ai on MiniMax H3 and optimized for high-speed generation. It delivers mainstream 480P and 768P output and generates faster than MiniMax H3.  H3-Max is also available via Minimax's own inference platform

MiniMax H3 Max: Free AI Video Generator, Ranked #1, Post-Trained by fal | fal

MiniMax H3 Max is fal's post-trained MiniMax H3, ranked #1 for overall quality, prompt understanding and aesthetics. A 5-second clip at 768p renders in under 3 seconds with synchronized audio. Sign in for five free 15-second generations every day, and 50% off for 14 days.

https://fal.ai
MiniMax H3 Max: Free AI Video Generator, Ranked #1, Post-Trained by fal | fal

Video Generation - MiniMax API Docs

MiniMax's video models (MiniMax H3, MiniMax H3 Max) enable efficient video content creation.

https://platform.minimax.io
Video Generation - MiniMax API Docs

World Labs announces Atlas, a multimodal world model pretrained from scratch that generates images/videos with pixel-perfect camera control, reconstructs 3D scenes from sparse inputs, and simulates space-time dynamics using a unified autoregressive diffusion transformer. 

Key demos show Atlas creating 1-minute 1440p videos from reference images along custom trajectories, producing explicit 3D outputs like point clouds and Gaussian splats, and enabling real-to-sim robotics workflows plus bullet-time reframing from a few phone cameras. 

The model outperforms specialized 3D reconstruction and camera-controlled generation baselines per company benchmarks, with early access opening soon to support applications in robotics, VFX, and explorable world creation.

This is impressive from Google Deepmind.  Do take the time to watch the examples, read the prompts from Genevieve , a GDM Devrel staffer and recommend trying this yourself

Google for Developers announced agentic video understanding in Gemini, letting models dynamically search, scan, and inspect video segments across frames, audio, and transcripts instead of static 1 FPS processing.  

It cuts token consumption by up to 88% and analysis costs by up to 66%, while boosting accuracy by up to 7% and enabling sub-second retrieval, long-form searches, anomaly detection, and precise counting in complex videos.  

Supported now in Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite via Gemini API for uploads and YouTube videos in Google AI Studio and Enterprise Agent Platform.

Introducing agentic video understanding with Gemini

We're launching agentic video understanding across our latest Gemini models for improved accuracy and lower costs and token usage.

https://blog.google
Introducing agentic video understanding with Gemini

Thursday 3rd September 2026

Google announces Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, building on 3.7 Flash from three weeks prior by using long-running agentic loops for better reasoning and coding. 

3.8 Flash improves multidisciplinary expert reasoning to 54.9% on HLE-Verified benchmarks while matching prior speed and cost; 3.8 Flash Cyber leads in CyberGym Pass@1 for C/C++ vulnerability discovery at 86.2%. 

It scores 59 on the Artificial Analysis Intelligence Index at high reasoning—up 3 points from 3.7 Flash and matching GPT-5.6 Sol (xhigh) and Grok 4.6 (medium). 

Available to developers via Google AI Studio and Android Studio, enterprises through Gemini Enterprise, consumers in Gemini App and Search, with 3.8 Flash Cyber accessible via the Fairwind Program.

It's barely mid week and the model release train is relentless

Meta Releases Muse Spark 1.3 with Top Coding and Agent Gains

The upgrade shines in benchmarks like 75.4 on DeepSWE v1.1, beating GPT-6.5.6 and Opus 5, while leading in OSWorld 2.0 and Terminal-Bench 2.1. It handles messy inputs by generating context, self-corrects plans, and uses 20% fewer tool calls than version 1.2. Now live in Muse Code and the Meta Model API, it's built for complex, ongoing tasks with more teases like a bigger model coming soon

Friday 4th September 2026

via Just | LDA who was once again very quick to find out about the launch OpenAI's Astra 6 model and linked to its launch post

ChatGPT 6 Astra comes out today.

Subsequently there was clarification that vast majority of users will probably not be able to access it today

Astra begins rolling out Thursday to enterprise customers with OpenAI's gated access program, Daybreak. OpenAI says it will become available over the coming days to ChatGPT Plus, Pro, Business and Enterprise customers, as well as through the OpenAI API and cloud platforms including AWS Bedrock and Microsoft Azure.

https://venturebeat.com/ai/welcome-to-the-agi-era-openai-launches-gpt-6-astra

OpenAI launches GPT-6 Astra and says welcome to the "AGI era"

OpenAI's new flagship posts big gains on specialized tasks, but it comes at a premium and does not clearly lead the coding pack.

https://thenewstack.io
OpenAI launches GPT-6 Astra and says welcome to the "AGI era"

Microsoft AI launched MAI-Transcribe-2 as its new speech-to-text model, positioned as highest quality, lowest cost, and fastest transcription—claimed to be 10x faster than GPT-Transcribe—now available via Microsoft Foundry and OpenRouter. 

A short demo video compares it side-by-side transcribing a technical lecture on the Nyquist sampling theorem, showing MAI-Transcribe-2 with under 4% error versus a competitor's 8% error and frequent mishearings like "nice quest sampling fear him." 

The model supports 60 languages with automatic language ID, code-switching, speaker diarization, and word-level timestamps, ranking #1 on the FLEURS benchmark.

For those here who use ChatGPT, Codex, Claude Code, Cursor, or similar AI tools for web research, I have published a new newsletter issue introducing my guide:

Choosing External Research Tools for AI Agents

It addresses a question I kept encountering: when are an AI tool’s built-in search and page-reading capabilities sufficient, and when might an external research provider genuinely help?

The guide covers the differences between search, extraction, crawling, structured data, provider-generated synthesis, browser-rendered pages, and authenticated X-native evidence. It also includes practical prompts and a reusable skill for routing research across available tools.

Octen, Exa, Perplexity, Parallel, Firecrawl, and TinyFish appear as examples, but this is not a ranking or a recommendation to connect everything.

The central idea is to begin with the evidence you need, use the minimum sufficient set of tools, and verify important claims against the underlying sources.

The newsletter introduction and link to the full guide are here:

A practical guide to choosing external research tools for AI agents

A practical guide to choosing external research tools for AI agents

Curated topics on AI and Web3
Sep 3, 2026

Greetings friends, AI assistants and agent tools increasingly include web search and page-reading capabilities. For many everyday questions, those built-in tools are entirely sufficient. More demandin...

0 collected
Collect

You can also subscribe there if you would like to receive future updates. Candid feedback—particularly on anything that remains too technical or could be explained more clearly—is very welcome.

The Institute of Foundation Models  at Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi announced K2 Horizon a fleet of six foundation models developed by them ranging from 0.9B to 375B parameters 

Their launch post emphasizes frontier performance on coding, agentic, reasoning, and tool-use benchmarks with smaller models setting new SOTAs, plus full openness including weights, code, training data/recipes, and checkpoints under Apache 2.0. 

The release includes immediate availability on Hugging Face, vLLM, and inference partners, supporting deployments from edge devices to enterprise while promoting reproducibility in AI research.

via Caterina Zhang

Hello everyone. Are there any AI tools out there for turning audio music into visual in real time?

Abstract visual art

Saturday 5th September 2026

OpenRouter has added Microsoft's MAI-Image-2.6 and MAI-Image-2.6-Flash, supporting multi-image editing, web grounding, dynamic aspect ratios, and up to 1.5K output resolution. 

MAI-Image-2.6 ranks #2 on Arena for text-to-image and editing plus #1 for editing on Artificial Analysis, while the Flash variant delivers comparable quality at 2.8x faster generation speed than GPT-Image-2-Medium and less than half the flagship price. 

The rapid hosting on OpenRouter's unified platform gives developers immediate API access to Microsoft's latest image models following their Microsoft Foundry preview release.

Businesses in China are experimenting with ways to package and market AI tokens to ordinary consumers, including as credit card rewards and telecom plan bundles

Chinese businesses are giving away AI tokens with coffee, credit cards, and dumplings

Driven by low-cost open-source models, Chinese banks, telcos, and cafes are bundling AI compute tokens into credit card perks, mobile plans, and free restaurant promotions.

https://restofworld.org
Chinese businesses are giving away AI tokens with coffee, credit cards, and dumplings

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the GPT Image 2 model within the Krea tool via the following prompt

Create a wide editorial illustration representing a practical field guide to researching with AI tools.

Use an overhead view of a calm, carefully arranged research desk. Include an open field-guide notebook, a compass-like decision tool, several source cards, a small website map, a structured data grid, and a verification checklist. Fine connecting lines should suggest that a researcher is choosing the appropriate route from a question to trustworthy evidence.

The composition should feel thoughtful, curious, methodical, and useful—not futuristic or promotional. The objects should represent choosing, investigating, comparing, and verifying information without depicting any particular software provider.

Independent publication aesthetic, sophisticated editorial illustration, warm ivory paper, charcoal and navy ink, restrained teal and muted amber accents, subtle tactile paper texture, crisp shapes, generous negative space, calm natural lighting, contemporary but timeless.

Wide 1.91:1 composition for a 1200 × 630 newsletter header. Keep the centre visually clear and place important objects inside a generous cropping-safe area.

No readable writing, letters, numbers, logos, company names, brand marks, laptop interfaces, robots, glowing AI brains, holograms, neon lighting, cyberpunk styling, stock-photo appearance, or visual clutter.


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 35-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-35-2026 PFGGrLwqAEu4sGlBwtAh Sun, 30 Aug 2026 03:35:33 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 23rd to 29th August 2026 saw Chinese AI labs push the open-weight frontier with a cluster of multimodal, long-context MoE releases. Z.ai launched GLM-5.3-Flash—previously previewed as Ox Alpha—while Alibaba introduced Qwen3.8-Flash and Tencent released Hy4 preview; all target coding, tool use, and agentic productivity with million-token-scale context windows and aggressive pricing. Video generation also advanced with Wan 3.0 becoming available on ArtArch, while Google expanded Gemini Notebook with ebook-grounded “Expert Intelligence.”

AI infrastructure and agent tooling were equally prominent: OpenAI reported early efficiency and latency gains from its Jalapeño inference chip, Nvidia’s Groq 3 LPX entered production, and Perplexity launched Portable Computer for fully local agent execution on NVIDIA DGX Spark. Developer discussions focused on making agents more capable and interoperable, from external search/fetch skills and free agent web access to Shopify CEO Tobi Lütke’s criticism of proprietary instruction-file conventions; OpenAI’s decision to end Cursor’s direct model access after its SpaceX acquisition further underscored intensifying competition across the coding-agent stack.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 23rd August 2026

Monday 24th August 2026

via David An

In our latest Provocation Talk, I speak with Advait, from Opengradient.AI about the challenging topic of AI Privacy

https://www.linkedin.com/feed/update/urn:li:activity:7497552042186403840/

I created the below public Notion page to articulate the benefits of adding external search/fetch providers in an AI harness and to suggest a prompt prefix for users to incorporate into prompts that utilise some of these providers.

These approaches can be useful if you are looking to take advantage of specialized data that external search providers can bring to the table within your AI harness.

The mechanism described on the below Notion page can be useful for GTM intelligence , lead-generation, finance or investment research, competitive intelligence or market-mapping workflows, among others.

The below page is best viewed on a desktop or laptop 

I humbly welcome your comments.

Tuesday 25th August 2026

I updated the above Notion page to include a skill sub-directory that you can drop into your skill folder as well as some snippets to augment into AGENTS.md / CLAUDE.md and provides some guidance on invoking one or more of these providers in a prompt.   

So some upfront investment in creating accounts with these providers, crediting balances beyond what they provide initially  and integrating them in your harness but subsequently the skill file as well as some lines in AGENTS.md/CLAUDE.md should make this very easy to incorporate external search providers in a prompt

Nvidia says its inference accelerator Groq 3 LPX has entered full production, with Nebius signing on as the first customer, and SpaceX will deploy Vera CPUs

Shopify CEO Tobi Lütke criticizes Anthropic's Claude Code for ignoring the emerging open standard AGENTS.md and .agents/skills directory, sticking only to its proprietary CLAUDE.md file. 

This creates "split brain" inconsistencies in large teams where developers use multiple AI coding tools, requiring workarounds like symlinks or @imports 

 that add unnecessary friction. 

The post highlights growing industry pressure for AI agents to adopt shared, vendor-neutral conventions to streamline codebase instructions across tools like Cursor, Codex, and Copilot.

PS:   This is also my pet peeve with Anthropic that their harness will read only in their directories and filename.

Wednesday 26th August 2026

OpenAI's post shares initial test results for Jalapeño, its first custom AI inference chip, highlighting gains in power efficiency and response speed by delivering higher throughput and lower latency simultaneously. 

Performance benchmarks show Jalapeño achieving 1.5-1.9x more AI work per watt and 1.7-3.6x lower end-to-end latency compared to prior systems across large models like 120B, 670B, and 1T parameter sizes. 

Deployment in OpenAI's infrastructure is planned by the end of 2026, as the first step in a multi-generation hardware roadmap with Gen 2 already in development to further boost efficiency for products like ChatGPT.

Some snippets on SemiAnalysis longish article on Jalapeno

In general first generation chips are not competitive, but OpenAI bucks the trend by being industry leading and beating every Nvidia, AMD, and Google chip we have been able to test on multiple top open source models. OpenAI does this with extreme hardware software codesign. Surprisingly, OpenAI is not over specialization on any specific part of model inference, but instead by focusing on being a general chip that delivers high performance in all scenarios.

Everyone says that OpenAI’s chip is specialized for OpenAI models, but that’s wrong, OpenAI made a generalized chip for AI inference.

Dwarkesh Patel shares his latest podcast with Dylan Patel of SemiAnalysis, analyzing AI lab economics including the projected shift of compute from inference to training as recursive self-improvement nears. 

The discussion forecasts Anthropic and OpenAI controlling most global usable FLOPs within years by outbidding others through superior monetization, driven by economies of scale, compute scarcity, and continual learning. 

They examine risks from over $10T in AI capex by decade's end potentially triggering sovereign debt crises via elevated interest rates, hyperscaler borrowing, and economic fallout for non-AI sectors and countries.

Perplexity Launches Fully Local AI Agent on NVIDIA DGX Spark

Perplexity released Portable Computer, a local version of its AI agent platform that handles orchestrator, subagents, and tasks on NVIDIA DGX Spark without cloud reliance for core functions. It starts tasks locally by default and only seeks user approval for cloud use on complex reasoning, ensuring privacy by flagging personal data and avoiding direct file access. Available now for Pro and Max subscribers with compatible hardware, it excels in benchmarks like 85.4% on real-world tasks and supports app connectors for Gmail, Slack, and GitHub. NVIDIA's Jensen Huang even gifted a DGX Station to the team after an early demo, fueling excitement for private, affordable AI.

"Even under the best circumstances, the transition to this new AI era will be one of the most turbulent times in human history," Bill Gates writes in a new, 12-page 5,784 word essay.

He says that “we are not preparing adequately”, calling for a regulatory framework

He also says tech executives are privately “very worried” about AI disruption but publicly downplay the risks to protect fundraising and planned IPOs

Looks like it's going to be a China open-weights night/US early morning with Alibaba's Qwen3.8-Flash-Next as well as GLM-5.3-Flash which was unmasked as the stealth model 0xalpha

https://x.com/i/trending/2092526498800718248

Z.ai announces GLM-5.3-Flash, a 320B total / 18B active parameter natively multimodal model with 1M-token context, released under MIT license and optimized to run on Chinese AI chips after preview as Ox Alpha. 

The model offers strong coding and agentic performance at low cost, with API pricing of $0.15 input / $0.50 output per million tokens, outperforming GLM-5.2 across benchmarks like DeepSWE and AutomationBench.  50% discount till Sep 9th via OpenRouter 

Architectural changes including hybrid sparse-linear attention and specialized pre-training enable efficient scaling, achieving results near Claude Opus 4.8 on select tasks while reducing compute demands significantly.

From OpenRouter

We expect many providers to onboard this model throughout the week

via Alba Chung

Hi this is Alba with Alibaba ☁ 

Wan 3.0 is now on ArtArch AI

→ Reality-grade rendering

→ Cinematic motion

→ Stronger scene consistency

Limited time: Aug 25–Sep 7: 100 FREE generations for every user. 

No duration limit.

Start creating:

Thursday 27th August 2026

Curating some links about Qwen-3.8-Flash-Next which is now available via OpenRouter at 0.16/0.47/0.016 so priced competitively with GLM-5.3-Flash.   

Shall be interesting when other providers join the fray in serving this model.

An impressive technical breakdown by Ryan Smith on Sambanova's SN50 RDU

Nvidia Acquires Hugging Face for $12.9 Billion

The agreement gives Nvidia control of Hugging Face, often called the GitHub for AI, where millions of models and datasets are shared by researchers and companies worldwide. Reports from The Information, Bloomberg, and Business Insider pegged the valuation around $13 billion, down slightly from earlier talks, following Nvidia's prior investment in the startup's 2023 funding round at $4.5 billion. The move expands Nvidia beyond chips into the open-source ecosystem that powers AI on its GPUs, even as Hugging Face's revenue sits around $150 million annually. It highlights hardware giants racing to own more of the AI stack amid strong demand.

https://x.com/i/trending/2092825795001790544

Friday 28th August 2026

For the video editors in da house via Shamir Allibhai of Hey Eddie fame who is tentatively my upcoming guest in my conversation series.    Luma link for folks to register for the virtual event shall be posted once dates/times are finalised with Shamir

The iOS version of Eddie AI is now available

https://apps.apple.com/app/eddie-ai-video-editor/id6792122289

Shamir also pointed me towards this completely free product he built  https://99.life/

Here's a brief blurb about the above product

99 is a free arena for AI video editing. You bring the clips. Two AI models each cut their own edit. You watch both without knowing which model made which, and pick the winner. Every vote moves the public scoreboard.

As to why the above product is Free

Why it's free

No trick, just a trade. Blind votes from real people on real footage are the honest measure of an AI editor, and model makers pay for that measure. 99 owns the edits the models generate, and the evaluation data the arena produces — votes, watch behavior, and the content they judge — is used to train AI models and is licensed to the companies that build them. That deal funds the arena. You get free edits and a real say in which models win; the model makers get judged. We think that is a fair trade, and we would rather say it in bold than bury it in fine print. The details live in our Terms and Privacy Policy.

Tencent Hunyuan released Hy4 preview, a 770B parameter MoE model with 49B active parameters and 1M context window optimized for productivity applications. 

The model is fully open-sourced with weights on Hugging Face and GitHub, offered at consistent affordable pricing as a frontier option. 

Benchmark visuals show Hy4 preview leading or matching top models like Claude Opus 5 across coding, agentic tasks, tool use, and math evaluations including Terminal Bench, SWE tasks, and OneMillionBench.

https://hy.tencent.ai/research/hy4-preview

via Diamond Hands Dig

Your agent can now search & fetch any webpage for 100% FREE.

Them: $7 per 1,000 searches.

Us: $0. No subscriptions, no quotas.

Humans search Google for free. Agents shouldn't have to pay either.

Made possible by Tiny_Fish and MonidHQ

Google Launches Expert Intelligence for Ebooks in Gemini Notebook

Google's new Expert Intelligence feature lets users add eligible Google Play Books ebooks to Gemini Notebook for AI-powered summaries, infographics, audio overviews, and quizzes all grounded in the book's text. People can mix these insights with personal notes, like applying management lessons to work scenarios, with over 100,000 titles from publishers such as Penguin Random House and Bloomsbury available at launch. Featured Notebooks pair bestselling authors like Michael Pollan and Steven Pinker with extra interactive elements, and Google plans expansions to subscriptions and textbooks soon.

Saturday 29th August 2026

OpenAI is terminating its partnership with the AI code editor Cursor following SpaceX's acquisition of the tool, with direct model access ending on November 12 to allow transition time, 

The decision centers on trust issues, while OpenAI offers workarounds like using personal API keys or its own IDE extensions and commits to supporting diverse developer tools and open-source initiatives. 

This move impacts developers who integrated GPT models into Cursor workflows, reflecting competitive dynamics in the AI tooling space between major players.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5 Pro model within the Krea tool via the following prompt

A futuristic city on an alien planet, surrounded by sand dunes and strange spires, painted in the style of Frank Frazetta. The buildings have domes with intricate details, and there's a sense that something mysterious is waiting to be discovered within them. In the background, there's a vast desert landscape with orange hues under a blue sky. A small red light shines from one building, adding contrast against the dark tones of the scene. It feels like you could find treasures or secrets here.


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 34-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-34-2026 yxxCvgE5TPAQqTr6lPu4 Sun, 23 Aug 2026 01:22:57 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 16th to 22nd August 2026 brought a strong mix of multimodal model releases, agent infrastructure, developer tools, and practical AI adoption. RedNote released dots3-note preview, an open-weight 280B MoE model built for long-horizon multimodal agents, while DeepSeek introduced V4-Flash-Vision-Exp for visual agent workloads and OpenRouter debuted the mysterious, high-performing Ox Alpha coding model. On the product side, Cartesia shipped Sonic-3.6 voice AI, Wispr raised a $280M Series B, and Meta expanded its AI offering with a native Mac app and business-oriented integrations.

The week also highlighted the rapidly maturing ecosystem around AI operators: Artificial Analysis launched a benchmark for agent web-search APIs; K-Dense released drug-discovery agent skills; Harvey opened a legal-engineering certification; and Replit introduced a credit-free mode for routine agent work. Google made AI plans free for eligible students across more than 140 countries, while Bun 1.4 delivered a major Rust-based runtime overhaul for JavaScript and TypeScript developers. Alongside the releases, Anthropic CEO Dario Amodei’s public defence of AI-risk discussions and his optimism on AI-enabled biomedical progress underscored the continuing tension between capability, adoption, and safety.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 16th August 2026

Xiaohongshu/RedNote announced dots3-note preview, an open-weight multimodal MoE AI model from RedNote's AI lab aimed at advancing long-horizon agency for real-life tasks like reasoning, environment exploration, memory updates, and tool use. 

Key specs include 280B total parameters with 16B active, 512K context window, native support for text/vision/video/audio, and TEMPO—a new RL method using self-critiquing and test-time scaling for extended agent training. 

Accompanying benchmark charts show competitive performance against larger models on agentic, coding, and multimodal tasks; model weights are on Hugging Face, with API access now available via OpenRouter. 

Anthropic CEO Defends AI Risk Talk While Eyeing Disease Cures

In a detailed X exchange, Anthropic CEO Dario Amodei pushed back on criticism from investor Gavin Baker that his risk warnings fuel anti-AI views. Amodei stressed that real breakthroughs, like curing most human diseases in 5-10 years, will outweigh messaging, and revealed Anthropic's push into biology and medicine with early results expected soon. He defended regulation proposals that aid smaller players while tackling cyber, bio, and alignment risks, as prediction markets buzz with trader bets on his timeline

Monday 17th August 2026

Voice AI startup Wispr raised a $280M Series B led by Menlo Ventures at a $2B valuation, taking its total funding to $361M, and now has 100K business customers

Wispr talks its way to a $2 billion valuation. The company says dictation's only the beginning | Fortune

Wispr has raised $280 million in its Menlo Ventures-led Series B, as it looks beyond dictation.

https://fortune.com
Wispr talks its way to a $2 billion valuation. The company says dictation's only the beginning | Fortune

Tuesday 18th August 2026

Harvey, an AI company specializing in complex legal work, announced the launch of its free self-paced Legal Engineering Certification via Harvey Academy. 

The course teaches four core skills—legal expertise, AI fluency, process design, and user enablement—to help professionals design AI-enabled legal workflows and drive adoption. 

Aimed at lawyers, in-house teams, and legal tech roles, it includes practical training from over 1,000 AI implementations and awards a shareable badge upon completion. 

Cartesia announces Sonic-3.6 TTS with major gains in naturalness across 44 languages, including realistic pauses, filler words in English, and fluid Hindi-English code-switching, released three months after Sonic-3.5 based on user feedback. 

Sonic-3.6 ranks #1 on Artificial Analysis's Provider Voice leaderboard (Elo 1282) and Controlled Voice leaderboard (Elo 1123), ahead of Speechify Simba 3.2, Alibaba's Qwen-Audio, and ElevenLabs v3 in blind tests using both native and standardized voices.

Pricing: At $49 per 1M characters via the Cartesia platform, Sonic 3.6 is the most expensive of the top three models on quality, roughly 5x Speechify AI's Simba 3.2 at $10 and nearly double Alibaba's Qwen-Audio-3.0-TTS-Plus at $27.59, though still half the price of ElevenLabs' Eleven v3 at $100

 Available immediately in beta via cartesia.ai, the state-space model architecture delivers low-latency streaming performance, positioning Cartesia as a leader in production-grade, human-like voice AI.

Wednesday 19th August 2026

Artificial Analysis Launches Web Search API Benchmark for AI Agents

Artificial Analysis released its Search Index, evaluating web search APIs like Parallel, Exa, and Firecrawl on real agent tasks such as research and multi-hop browsing. Parallel leads with a 75 score, followed by Exa at 74 and Firecrawl at 73—all far ahead of the model-only baseline of 33. The benchmark factors in total cost and time, highlighting how top performers like Parallel and Firecrawl balance efficiency with savings on model tokens. Developers praise the independent data for picking reliable tools as AI agents lean more on web search.

FWIW,   I wrote this public Notion page on the benefits of integrating external search/fetch providers into a harness

K-Dense AI announced the release of a new agent skills plugin specialized in small molecule and protein therapeutics, enabling AI agents to handle tasks in cheminformatics, molecular modeling, docking, dynamics, and target discovery. 

The plugin is a curated subset of their main Scientific Agent Skills GitHub repo (K-Dense-AI/scientific-agent-skills), which has 33.8k stars, 161+ validated skills, and 100+ scientific databases covering biology, chemistry, medicine, and drug discovery 

It supports installation and use in multiple AI coding tools including Claude Code, Cursor, Codex, and Google Antigravity via the open Agent Skills standard. 


Replit's new "Free Mode" lets Core and Pro subscribers use the Agent for everyday tasks (chatting, ideating, quick builds) without consuming usage credits, effectively letting them create up to 30X more within their existing monthly subscription. 

Free Mode is powered by OpenAI's GPT-5.6 Luna and applies to fast, high-value everyday tasks — chats, ideation, quick edits, and simpler builds — which no longer draw down credit-based usage.

Replit Introduces Free Mode | Replit

AI models are now capable and affordable enough to make once-unreachable outcomes practical. The opportunity for everyone to pursue and collaborate on ambitious ideas, spend less time on busywork, and raise the quality and creative output across disciplines is nearly here. The possibility is here. But for most people, the reality is not.

https://replit.com
Replit Introduces Free Mode | Replit

Just LDA asked the following question

Anyone here using Buzz, Grok Bot, or the new Hermes Bot function?

Opinions? I’d assume Hermes Bot works better than Grok bot because then you’re not limited to one model.

But just curious to hear from others that have used them.

I’m currently using Buzz and it seems to be working well. I have my Claude agent, Hermes agent (ChatGPT), and Grok agent all working together now on my various projects.

Thursday 20th August 2026

Diamond Hands Dig responded to Just LDA with the following

Just wrote about Hermes Agent Bot Mode yesterday


Google made AI Pro free for US students, AI Plus free for students in 140+ countries, and a cheap YouTube student bundle.

Google Gemini announced a back-to-school promotion offering eligible US college students one year of Google AI Pro for free and students in 140+ countries one year of Google AI Plus at no cost, running through December 31, 2026. 

New student tools rolling out include a dedicated hub for study notebooks, flashcards, and quizzes; adaptive lessons that pinpoint knowledge gaps from class materials; and interactive 3D visualizations generated via prompts like “Show me how DNA works.” 

Gemini Live now supports hands-free multi-step research reports that run in the background, with notifications and voice discussion of findings to support deeper academic work.

Whilst not LLM related per-se,  thought this might be of interest to group members particularly those who work with Javascript/Typescript codebases which are coded/generated via their coding agents and maybe utilising nodejs or deno for their runtime

Bun 1.4 released (Claude Code has been running on a canary version of it for quite a while)

Bun 1.4

Bun 1.4 rewrites Bun in Rust, ships built-in headless browser automation (Bun.WebView), Bun.Image, Bun.markdown, JSON5, JSONL, Terminal and cron APIs, Node.js 26.3.0 compatibility with 1,517 newly passing tests, parallel test and run, Windows ARM64, and an opt-in global virtual store for up to 7× faster installs.

https://bun.com
Bun 1.4

The rewrite of Bun from Zig to Rust in 11 days was done with the assistance of Fable 5

Rewriting Bun in Rust

Why & how we rewrote Bun from Zig to Rust

https://bun.com
Rewriting Bun in Rust

PS:  Anthropic acquired Bun last year.  Jarred Sumner is the founder

https://x.com/jarredsumner

Couple of links on the Meta AI app (which doesn't seem to have rolled out to my region, might need to VPN or Meta could have region locked based on metadata from my usage of FB/IG).   The video in Spencer Barnett's post makes it very intriguing to try it out

Friday 21st August 2026

DeepSeek Launches Experimental Vision Model Matching Top Benchmarks

The new DeepSeek-V4-Flash-Vision-Exp adds image-handling to the V4-Flash series, scoring big on multimodal benchmarks like 36.5 on ApexBench and 27.3 on Agents' Last Exam—often rivaling Opus-4.8—while holding steady on pure-text tasks. Developers access it easily via the DeepSeek API with base64 images, URLs, or the fresh Files API, all at standard pricing. Community voices, from researchers to tech analysts, praise the leap in visual agent performance without speed or cost hits, signaling a push toward versatile multimodal tools.

Mysterious Ox Alpha AI Model Debuts with Strong Coding Prowess

Ox Alpha, listed on OpenRouter as a free preview from an anonymous provider, handles 1 million token contexts with text, image, and video inputs, excelling in coding and agentic tasks. Early tests by developer Ben Davis showed it hitting 80% on DeepSWE benchmarks, outpacing models like Fable at 65% and gpt-5.6-sol at 52%. Community speculation points to Chinese origins, possibly Zhipu’s next GLM or Xiaomi’s MiMo, with its polished speed and frontend strengths fueling excitement during the limited free access period.

Saturday 22nd August 2026

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5 Pro model within the Krea tool via the following prompt

An enormous floating city in the sky, steampunk style with lots of towers and domes, oil painting, fantasy art, detailed


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 33-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-33-2026 4UYD9JB0f83cVQ3zTOST Sun, 16 Aug 2026 03:41:58 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 9th to 15th August 2026 brought a dense run of releases across agentic models, autonomous-workflow tooling, and AI infrastructure finance. Meta introduced Muse Glimmer, a 30B open-weight model for always-on local agents, while NVIDIA launched Nemotron 3.5 Lightning and NeMo Switchyard, pairing a fast open agent model with workflow-level model routing. DeepSeek made V4-Pro generally available and released its modular, MIT-licensed Harness developer preview; Google shipped Gemini 3.7 Flash, and Z.ai announced GLM-5.3 for coding, agents, and cyber-defense work.

The week also underscored the shift from chatbots to durable AI operators: SpaceXAI introduced Grok Bot, persistent agents that work inside connected apps, while SignerSet proposed policy-based, multi-party approvals for consequential autonomous actions. On the infrastructure side, NVIDIA outlined financing partnerships intended to mobilize more than $500 billion for data centers, power, and GPU capacity, and Anthropic reportedly struck a long-term compute agreement with Riot Platforms. Other notable threads included Anthropic’s planned invisible text watermarking, OpenAI finance-team workflow examples and the economic trade-off between AI “tax” and AI “dividend.”

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 9th August 2026

Monday 10th August 2026

via Charles Stanton

This is very cool, Harvard and MIT scientists have replicated the human population by creating 8.3billion agent personas, effectively allowing them to trial global initiatives virtually. 

MatrAIx: a population-scale AI simulation infrastructure designed to model the behavior of virtually every person on Earth.

Meta Releases Muse Glimmer, 30B Open-Weight Model for Local AI Agents

Meta launched Muse Glimmer, a 30B-parameter open-weight model under Apache 2.0 license, designed for always-on local agents on consumer hardware with 24GB VRAM. It excels in vision, tool use, multi-step reasoning, and benchmarks like MCP Atlas where it scored 75.5, beating rivals such as Gemma 4-31B.

Tuesday 11th August 2026

NVIDIA Partners with Wall Street Giants to Unlock $500 Billion for AI Infrastructure

NVIDIA signed deals with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion in capital for data centers, power and GPUs. These independent financing platforms let customers secure funding at attractive rates, turning NVIDIA's AI compute into a new investable asset backed by its CUDA ecosystem. 

Independent financing platforms let capital providers underwrite demand, utilization, cash flow and residual value, with limited NVIDIA residual-value support up to 25%.

Huang explained that in AI, compute equals revenue, and this brings long-term institutional money into the market amid surging infrastructure spending that's boosting U.S. GDP like never before.

Compute Is Revenue. Revenue Is Collateral.

Nvidia did not invent compute-backed credit on Monday. It proposed an assembly line for it.

https://www.cogniscendo.com
Compute Is Revenue. Revenue Is Collateral.

Anthropic says new Claude models will embed invisible watermarks in all generated text, everywhere Claude is offered.

The watermark is part of the text, it isn't metadata: "it will travel with the text when it's copied and pasted elsewhere, and may persist through some editing."

This starts with models launched on or after August 2, 2026, under an EU AI Act code Anthropic signed. Anthropic is still working on adding it to current models. The rollout is worldwide.

How Claude marks AI-generated content | Claude Help Center

Anthropic has signed the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, as a provider of both generative AI models and generative AI systems. This article describes how we're planning to put those commitments into practice, how marking works, and what its limitations are.

https://support.claude.com
How Claude marks AI-generated content | Claude Help Center

via David James

Seeking an academic co-author — AI, cultural memory, and migration

I'm developing a research proposal for Coefficient Giving's RFP on Global Health and Wellbeing in an Era of Transformative AI (Exploratory grant), and I'm looking for an academic partner to lead the method.

The hypothesis: Africa's oral knowledge systems — botanical, climatic, agronomic, linguistic, held by griots and traditional knowledge-keepers — are on the wrong side of the AI era by default. Frontier models are trained on written, Western corpora; unwritten knowledge is invisible to them, and so is devalued by them. Yet the same AI advances mean these knowledge systems will soon hold enormous discoverable value. The question is who will own it when that moment arrives — the communities, or whoever digitises them first.

The proposed work: a research paper on AI-era knowledge homogenisation, cultural detachment and migration aspirations; a community-ownership and benefit-sharing governance framework for oral-knowledge corpora (Nagoya/CARE-aligned); and a costed pilot corpus design with one community.

I'm looking for a co-author in low-resource NLP / computational linguistics, anthropology or ethnobotany, or migration economics — someone who would lead the research method, with me contributing the practitioner frame and thirty years of field relationships across African and Asian agricultural communities. My own work on language, consciousness and AI (Your Mind at Siege, on the Diamond Sutra and AI) shapes the framing.

Deadline is near — 21 August — so this suits someone who can move quickly on a light, sharply-scoped proposal. If this is your field, or you know whose field it is, my messages are open.

Anthropic agreed to a 20-year, $9.1B compute deal with Riot Platforms a Bitcoin mining company that also recently began selling AI data center capacity for 191 MW of capacity at a Rockdale, TX campus; RIOT jumps ~25% after hours

Harry Stebbings profiles OpenRouter as a breakout AI inference company co-founded by ex-OpenSea leader Alex Atallah, noting its hundreds of millions in revenue, profitability, 25 trillion weekly tokens processed (corrected to 70-75 trillion in replies), and a reported $10BN Stripe acquisition offer after a recent $1.3BN valuation. 

The post summarizes seven interview insights, stressing AI model inference as tech's largest future market via Jevons Paradox-driven usage growth, the superiority of focused routing over side-feature competitors, and risks of frontier labs absorbing startup workflows 

Key themes include accelerating model releases every 10 hours creating a multi-model ecosystem, China's edge in open-weight AI, open models closing performance gaps on frontier ones, and businesses needing to treat AI inference spend as variable employee costs.

20VC: Will OpenRouter Sell for $10BN to Stripe? | Why Chinese Open Models Are Beating America-and What Happens Next | Why Enterprises Are More Fearful of Anthropic and OpenAI Than China | Is the Routing Layer Becoming a Commodity with Alex Atallah

Listen to this episode from The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch on Spotify. Alex Atallah is the Founder and CEO @ OpenRouter, the unified interface for LLMs. The company has raised over $153M in funding, with the latest valuation pricing the company at $1.3BN.

https://spotify.com
20VC: Will OpenRouter Sell for $10BN to Stripe? | Why Chinese Open Models Are Beating America-and What Happens Next | Why Enterprises Are More Fearful of Anthropic and OpenAI Than China | Is the Routing Layer Becoming a Commodity with Alex Atallah

via Jorvik Zhang

The eight debates from the AI First seminar in London last Thursday, in one short deck: https://tokenhub.tencentcloud-eu.com/AI-First.html

Still the sharpest of them — AI tax vs. AI dividend.

The tax is what you pay correcting and re-prompting. The dividend is what you keep when it lands the first time. Most teams feel the tax daily but can't yet name the dividend.

Which is bigger for your team

NVIDIA Unveils Nemotron 3.5 Lightning for Fast AI Agents

This open-source model uses a hybrid Mamba-Transformer design and a permissive license for commercial use, boasting a 1 million token context window and up to 4x the output speed of similar models. Benchmarks show it scoring 24 on the Artificial Analysis Intelligence Index, matching much larger rivals, with standout results in agent tasks like GDPval-AA v2 at 824 Elo and 86% on PinchBench—while finishing 10,000 tasks 35% faster than competitors.

Nvidia also released NeMo Switchyard: an open-source model routing library that automatically directs each step of an agent workflow to the best model for the job. Internal benchmarks: frontier-level accuracy at ~1/3 the cost of Opus 4.8 alone.

Devin cut mean cost 28%. LangChain cut cost 74% by routing just 7% of calls to a frontier model. The savings are in the routing, not the model.

NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI

The new lightweight open model and routing library delivers greater control over AI, data and workflows across edge devices, PCs, workstations, data centers and the cloud.

https://blogs.nvidia.com
NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI

Wednesday 12th August 2026

via Alfred Tom

Is anyone running autonomous agent workflows, like OpenClaw, Cursor, Hermes, Animoca Minds, etc.?  We're launching something that brings the concept of banking-style approvals to autonomous workflows.  Basically it helps prevent things like deleting production databases without requiring a human in the loop (although it supports that as well).  If you're interested I'm happy to help with integrations

SignerSet - Approvals for Autonomous Workflows

Policy-based, multi-party approval for consequential AI actions. Define protected actions, authorized signers, and signature thresholds.

https://www.signerset.com
SignerSet - Approvals for Autonomous Workflows

SpaceXAI Launches Grok Bot AI Teammates for Real Work

Grok Bot introduces persistent AI agents that sign into tools like Gmail, Salesforce, and LinkedIn to complete end-to-end tasks, learn from demonstrations, and collaborate with each other in chats. Each bot runs on its own cloud computer, remembers preferences, and improves over time, already transforming internal operations in sales, marketing, and engineering at SpaceXAI. Early users like Lenny Rachitsky praise its practical power for job matching, auto-replies, and expense scanning, while Elon Musk plans a wider beta after quick fixes and Grok 4.6. Access begins for SuperGrok Heavy and Cursor subscribers on desktop and iOS.

Introducing Grok Bot

Grok Bot is your team of always-on agents. They have their own computer, work inside tools and apps like you do, and keep working 24/7.

https://x.ai
Introducing Grok Bot

via Vladimir Arustamov

Built a tool that scores any website on a 6-level Agent Readiness framework — from basic HTTPS (L0) to autonomous AI payments via x402 protocol (L4). Most sites are stuck at L1.

seo4agent.com — free instant audit

github.com/varustamov/agentable-core — MCP server + Python SDK (pip install agentable-core)

Will be glad to get your reviews, feedback & stars on GitHub

Mark Zuckerberg proposes a positive AI philosophy centered on individual empowerment, predicts personal superintelligence could increase employment, and more

The Future is for Everyone

Read Mark Zuckerberg's piece on our values for building superintelligence for everyone.

https://www.meta.com

Thursday 13th August 2026

DeepSeek V4 Pro GA and Grok 4.6 Launch on Same Day

DeepSeek's V4 Pro, now generally available on API and chat, offers a 1 million token context window, up to 384K output, JSON support, tool calls, and pricing at $0.003625 per million input tokens on cache hit, $0.435 on miss, and $0.87 for output. 

SpaceXAI quickly launched Grok 4.6, optimized for coding, agentic tasks, and knowledge work with adjustable reasoning levels.

Derrick Choi, OpenAI's Codex APAC Lead, promotes 16 real workflows created by their finance team using ChatGPT Work and Codex for tasks from monthly close and variance reconciliation to forecasting adjustments and treasury analytics. 

Key examples include dynamic marketing spend rebalancing, headcount visibility dashboards, intelligent scenario modeling, governed monthly close processes, and AI-powered treasury agents that flag issues and draft responses 

The approach automates data assembly, reconciliation, and reporting to shift finance focus toward decisions, tradeoffs, and strategic conversations while retaining human sign-off and accountability.

Elon Musk Announces Grok 4.7 Launch in 3-4 Weeks

Musk said Grok 4.7, boosted by massive SpaceX company data in supplemental training, will outperform all current models—especially in practical engineering tasks. It follows hot on the heels of Grok 4.6, now available via tools like Cursor and the SpaceXAI API, with strong showings in coding, agents, and benchmarks against GPT variants

DeepSeek launched V4-Pro with major agent upgrades, flexible reasoning modes (low/high/max effort), and native OpenAI Responses API support optimized for Codex workflows. 

Benchmarks show V4-Pro-0813 leading in agentic tasks like Terminal Bench (87.9), DeepSWE (62.7), and DSBench-Hard (67.2), outperforming previews and rivals including Kimi-K3 and Opus-4.8. 

API pricing update introduces peak/off-peak rates with off-peak 50% cheaper, effective August 16, 2026, for both V4-Pro and V4-Flash models.

DeepSeek AI announced DeepSeek Harness v0.1 in developer preview as an open-source agent harness under MIT license, inviting global developers to build and extend it. 

The framework uses a "everything is a plugin" architecture powered by the Cordis meta-framework, allowing models, tools, sessions, sandboxes, orchestration, and UI to be mixed, matched, replaced, or extended modularly. 

It focuses on reliable agent execution by handling engineering aspects like task orchestration, memory management, tool scheduling, and error recovery to bridge models into practical agents

Friday 14th August 2026

via Robby Yung

Gemini 3.7 Flash announced highlighting its high speed, 50% price reduction versus 3.6 Flash through year-end, and major intelligence gains from algorithmic improvements in roughly three weeks. 

Benchmark charts show 3.7 Flash leading in enterprise workflow automation (30.4% on AutomationBench), web development (1588 Elo on Code Arena), long-horizon software engineering (65.3% on DeepSWE), and production code quality (43.6% on FrontierCode), surpassing 3.6 Flash and competitors like Claude Sonnet 5 and GPT-5.6. 

The model is now available in the Gemini API, AI Studio, Antigravity, and additional platforms, reflecting Google's rapid iteration on the Flash series for better real-world usability.

Introducing Gemini 3.7 Flash

Gemini 3.7 Flash is our most intelligent workhorse model yet for coding and agents.

https://blog.google
Introducing Gemini 3.7 Flash

Z.ai announces GLM-5.3, post-trained on a 743B base model, highlighting top-tier coding, agentic capabilities, and a major advancement in cybersecurity for open models. 

Benchmark charts show GLM-5.3 leading in Terminal Bench 3.0, DeepSWE, CyberGym, ExploitBench, and agentic coding accuracy, outperforming GLM-5.2 and competing strongly with models like Kimi K3 and Claude variants. 

The model is available now through GLM Coding Plan and ZCode, with staged API access and open weights following safety evaluations, plus initial partner services.

IMHO,   signing up for Monid (you get $1 in credits on signup)  and installing its MCP server will dramatically change the way you work with your harness [Cursor, ChatGPT, Claude, Grok Build, Opencode, Command Code etc]

Also,  Tinyfish is available as a ChatGPT plugin [search and fetch are free] but you can also install it as a MCP server in other harnesses

Saturday 15th August 2026

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5 Pro model within the Krea tool via the following prompt

Retro Futurism style, It is a city designed by Buckminster Fuller on Mars in 2500, influenced by the Purism architectural movement and can change shape according to needs


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 32-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-32-2026 uWdPE47t9diRVDTXsq9k Sun, 09 Aug 2026 03:03:10 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 2nd to 8th August 2026 saw a flurry of agentic coding launches. DeepSeek opened a closed beta for its Harness framework, and Qwen previewed its 2.4T-parameter Qwen3.8-Max ahead of an open-weight release. Meta launched Muse Code on the new Muse Spark 1.2 model with steep data-sharing discounts, while Command Code rolled out its GOAT subscription plan bundling access to 30+ coding models. OpenAI, with AWS, Cursor, GitHub, Microsoft, and Vercel, introduced Agent Plugins, an open standard for portable agent skills.

Multimodal generation also advanced: MiniMax open-sourced its H3 omni-modal model with day-zero ComfyUI support, Black Forest Labs expanded FLUX 3 Video access, and Alibaba's Wan3.0 entered public beta with native 30-second clips. xAI closed the week with Imagine Image 2.0, adding precision editing tools built for real-world production use. On the infrastructure side, practitioners flagged lighter headless browsers for cutting agent navigation costs, security researchers warned about trust-handoff exploits in agent harnesses, and a widely shared "Power 2026" primer underscored electricity supply as AI's next major bottleneck."

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interest

Sunday 2nd August 2026

Tianyi Cui from DeepSeek's Harness team posted on X inviting open-source developers of Agent Harness projects to join the closed beta by replying or DMing with GitHub ID and representative work.

DeepSeek Harness is an upcoming agent framework focused on tool calling, context management, and terminal execution, positioned as a competitor to Anthropic's Claude Code and tied to their V4 model's enhanced coding agent capabilities.

The beta targets specialized builders, with recent replies already featuring GitHub links to desktop agent tools and CLI integrators, reflecting strong interest from the AI agent development community.

Monday 3rd August 2026

Alibaba's Qwen team announced Qwen3.8-Max as their most capable model yet with 2.4T parameters, emphasizing autonomous coding over 10+ days, long-horizon planning, and native multimodal intelligence for real production tasks. 

Benchmark charts show Qwen3.8-Max leading in software engineering, agent workflows, visual perception, and embodied reasoning, surpassing models like GPT-5.6 Sol (max), Gemini1.3-Pro, and Opus4.8 across most categories. 

Open weights for Qwen3.8-Max and the smaller Qwen3.8-27B will release next week; API pricing is set at $2/M input tokens, $6/M output tokens, with demos highlighting extended agentic coding and dynamic visual workflows.

MiniMax AI announced the open-weight release of its 33B-parameter MiniMax-H3 omni-modal model on Hugging Face, enabling text/image/video/audio-to-video generation with native synchronized stereo audio up to 15-second 2K clips at 24 FPS. 

The community license includes a US carve-out due to generative video copyright litigation with Hollywood studios  It is also unavailable in the EU, UK, and South Korea due to regulatory uncertainties around AI-generated video content. 

Users in those areas can submit formal licensing requests by committing to robust local compliance controls and guardrails, with MiniMax planning to monitor and adapt policies as regulations change. 

ComfyUI announces day-zero native integration for open-weights MiniMax H3,

MiniMaxAI/MiniMax-H3 · Hugging Face

We're on a journey to advance and democratize artificial intelligence through open source and open science.

https://huggingface.co
MiniMaxAI/MiniMax-H3 · Hugging Face

Tuesday 4th August 2026

Wednesday 5th August 2026

via Alex

If you are using agents to crawl websites and get robo-blocked or spend too many tokens for navigation, I recommend headless browser rebuilts like: https://github.com/lightpanda-io/browser

It cut my token use on navigation by 80+%

Flux 3 Video now more available from BFL,  OpenRouter,  FAL,  Replicate

It's capable of generating up to 20-second 1080p clips via text-to-video, image-to-video with multiple keyframes, video continuation, and native audio including multilingual dialogue with accurate lip sync. 

Draft mode enables fast creative exploration with low-cost previews at $0.06 per second that preserve subjects and motion for seamless upgrade to full quality rendering from $0.17 per second. 

2K, 4K support and open weights planned soon.

via Alex

God of the gaps. Hacking Agents through (un)trusted handover commands in the harnesses.
Many harnesses hardcode trust -> "everything beyong this point is trusted" and this can be used to prompt hijack agents:

Quote from the article:

“Most teams audit what the agent can do. The exposure we keep finding is in what happens after, the handoff between ‘approved’ and ‘executed,’ between ‘read’ and ‘published,’ between ‘fetched’ and ‘trusted.'”

An AI agent can pass every safety check and still leak secrets - Help Net Security

AI agent security fails at the handoff. Novee's Elad Meged broke Claude Code, Gemini CLI and Codex agents in their default configs.

https://www.helpnetsecurity.com
An AI agent can pass every safety check and still leak secrets - Help Net Security

via Alex

TIMES magazine started to expose bots to abbreviated articles with baked in ads. 

https://TIMES magazine started to expose bots to abbreviated articles with baked in ads.

Thursday 6th August 2026

Meta Launches Muse Code Beta for Full Repo Engineering Tasks

Powered by the new Muse Spark 1.2 model from Meta Superintelligence Labs, Muse Code plans changes, writes code, and validates results across sessions using persistent background sub-agents that parallelize work safely. Benchmarks show it competing with top models from OpenAI and Anthropic on tests like Terminal-Bench 2.1 and DeepSWE 1.1, while pricing starts at $1.25 per million input tokens—with a cheap contributor tier at $0.10 if you opt into data sharing. Mark Zuckerberg and chief AI officer Alexandr Wang emphasized its one-command install and focus on reliable, long-horizon coding tasks.

Introducing Muse Code and Muse Spark 1.2

Introducing Muse Code, a terminal coding agent powered by Muse Spark 1.2, with persistent background agents, repository-scale execution, and built-in verification.

https://research.meta.ai
Introducing Muse Code and Muse Spark 1.2

Deepseek plans to raise API prices significantly but no details announced,  their v4-Flash 0.14/0.28/0.0028 is currently the floor for Chinese models including Mimo-v2.5 though Meta offers a lower price point for its recently launched Meta Spark-v1.2-contributor at 0.1/0.2/0.002.   

Note that the low cache price in particularly from Deepseek's own provider allows them to train on input as per their ToS/privacy policy.  Other providers who also server DSv4-Flash-0731 may have different policies and serve it at different price points.   Pricing is always a function of model and provider

https://platform.deepseek.com/usage

Command Code's announces the GOAT plan at $10/month for $70 credits, delivering 7x value and access to over 30 open-weight and closed AI models for coding tasks. 

The plan builds on their $1 Go starter tier by providing enough usage for meaningful projects, emphasizing solutions to open model challenges like cost, reliability, and performance via high cache rates and design tools. 

Command Code positions itself as a leader in open model coding agents with leaderboard success, a v1 codebase rewrite, new Mods API, and upcoming open source release later this month 

PS,  It also offers Muse Spark v1.2 and Muse Spark v1.2-contributor

GOAT Plan | Command Code

The best low-cost coding plan: Command Code GOAT at $10/month. Unlimited coding on 30+ top open source and closed source models - pay $10, get $70 of credits, a 7x multiplier. With deals, usage stretches beyond $100.

https://commandcode.ai
GOAT Plan | Command Code

Alibaba's Tongyi Lab announces Wan3.0 public beta, a video generation model featuring native 30-second single-run video creation, high-fidelity reality-grade rendering, and expressive character consistency. 

Omni-reference capability allows input of structured files like documents, spreadsheets, slides, PDFs, and webpages alongside text, images, audio, and video for more versatile content generation. 

Now available on Alibaba Cloud Model Studio and Qwen Cloud with API pricing from $0.05/sec at 480p; additional demos in the thread showcase 30-second outputs and complex reference handling....

Friday 7th August 2026

OpenAI Launches Agent Plugins Standard for Shared AI Extensions

OpenAI, teaming up with AWS, Cursor, GitHub, Microsoft Code, and Vercel, introduced Agent Plugins on Thursday. This open standard lets developers package AI agent skills and MCP server configurations into a shared format, working across tools like ChatGPT, Cursor, GitHub Copilot, and VS Code. 

Some developers question its depth without broader adoption from players like Anthropic.

Some SpaceX'y news  in anticipation of Grok-4.6.

For all intents and purposes,  I'm consider the Cursor acquisition by SpaceXAI  a done deal even thought as of now it's not yet closed

FWIW,  Grok Build is my daily driver.   I combine it with MCP's of Monid [put in a dollar or two at a time and use the tools it aggregates without worrying about balance sprawl], TinyFish and Nia  [Nia, you could think of it as Context7 on steroids]

Exa and Octen are my favourite tools to use via Monid

Opencode is also massively improving its web search in its upcoming version 2  

Cursor's Smart Router Boosts AI Coding Efficiency and Satisfaction

https://x.com/i/trending/2085480744365785205

Grok Build Hits 1.0.0 Milestone with Refined Coding Tools

https://x.com/i/trending/2085597018781806940

via Chyi Yan Hshieh

https://paragraph.com/@twiata/this-week-in-all-things-ai-week-31-2026Curious question for the broader group, do you see a demand for FDEs in APAC? Or at least for companies who are doing the legwork of setting up AI systems and agents?

Former Citadel quant Neel Somani highlights power supply as the core AI bottleneck over chips or memory, releasing "Power 2026" primer detailing electricity markets, data center demands, and pricing dynamics. 

The guide covers US grid regions like PJM, ERCOT, and MISO, marginal pricing via merit order, heat rates, and how AI data centers—already at ~5% of US power—could exceed total generation by mid-2030s. 

Somani's expertise from power/gas trading and data center advising informs hedging strategies, buildout challenges, and calls for faster permitting of gas/coal plants plus transmission upgrades to meet surging demand. 

Props to Matthew Mousa of Alpha Compute for pointing me towards this material

Power 2026 by Neel Somani - Electricity Pricing in the Age of AI

Power 2026 is a primer on AI's impact on the US power markets. The short piece is written by Neel Somani, a former quantitative researcher at one of the world's leading hedge funds.

https://power2026.ai
Power 2026 by Neel Somani - Electricity Pricing in the Age of AI

Saturday 8th August 2026

xAI's next-generation image model Imagine Image 2.0 was just launched emphasizing precision editing, crisp text rendering, improved factuality, and practical real-world applications like infographics, ads, UI mockups, and game assets.

 Key new tools include Magic Wand for targeted region changes, Segmentation for precise area selection, Smart Resize for custom aspect ratios, and templates for workflows such as product shots, collages, icons, and consistent character/world building for video. 

It ranks #2 worldwide in both text-to-image and image editing arenas per recent Arena leaderboards, and is now available as the Quality Mode on grok.com/imagine, iOS/Android apps, with API access coming soon.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5 Pro model within the Krea tool via the following prompt

Craft an image of an expansive, rolling prairie, where the golden grasses sway in the breeze, and herds of wild mustangs roam freely beneath an endless, open sky


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 31-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-31-2026 tzEteErBxTRBUwFt62rM Sun, 02 Aug 2026 04:24:10 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 26th July to 1st August 2026 was defined by a decisive shift from raw model capability toward the economics, reliability, and operational design of agentic AI. Microsoft introduced its MAI-Cyber-1-Flash cybersecurity model and Project Perception, while benchmark work comparing Kimi K3 across Claude Code, Hermes, and Kimi Code showed that the harness can drive order-of-magnitude differences in token consumption even when task success rates remain similar. OpenAI also cut GPT-5.6 pricing, and discussion around cheaper Chinese models, region-specific coding plans, and hybrid model stacks reinforced the view that “good enough” intelligence is becoming a major competitive advantage.

Agent infrastructure, privacy, and open deployment were equally prominent. MCP’s move to a stateless core makes remote servers substantially easier to place behind load balancers and on serverless or edge infrastructure, while new search, crawling, and browser APIs from providers including Firecrawl, Octen, Context.dev, and TinyFish highlighted the growing importance of high-quality web access for agents. At the model layer, Fish Audio, Superwhisper and Cohere pushed more capable local speech tooling; MiniMax released its open-weight H3 multimodal video model; DeepSeek substantially upgraded V4-Flash’s agentic coding performance; and Poolside launched a cross-platform workspace for supervising parallel coding agents. Meanwhile, the indexing of supposedly private AI conversations and a public call by frontier-lab employees to deliberately pace AI development kept security and governance firmly in view.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 26th July 2026

via the Relentless podcast [quite underrated at only 25K subscribers] TI Morse with Sam Altman 

Sam drops some mind-blowing insights on how AI is completely rewiring the rules for startups, arguing that we are literally living in the "singularity" right now.  He dives deep into why we need to trust in exponential AI progress, how to thrive as a leader in absolute chaos, and why superintelligence won't actually bring us the 4-hour workweek. 

There's also some incredible behind-the-scenes intel on OpenAI's ruthless prioritization—like deciding to pause Sora to focus on coding agents—and why massive investments in chips, energy, and robotics are the real bottlenecks to our future.

Monday 27th July 2026

Kevin Kelly highlights how AI tokenomics are shifting user behavior toward lower-cost, "good enough" models as expenses become a key factor, potentially making leading-edge AI a commercial liability. 

The linked WSJ article reports US companies adopting cheaper Chinese open-weight models for routine tasks, using hybrids where premium models plan and affordable ones execute, achieving major savings like reducing daily AI agent costs from high figures to $100 per agent. 

This reflects a broader "thrift-maxxing" trend in AI, prioritizing cost efficiency and model flexibility over maximum power, similar to how mass-market computing products historically outsold specialized high-end hardware.

https://www.wsj.com/business/china-us-ai-model-costs-53a12e96?st=VxRECn&reflink=desktopwebshare_permalink

via Yat Siu

Animoca Research launched "The Agentic Age," its first monthly brief on the agentic economy, emphasizing that abundant AI coverage requires sharper judgment for capital allocators, operators, and builders

Tuesday 28th July 2026

Microsoft introduces MAI-Cyber-1-Flash, an AI model trained for cybersecurity, and launches Perception, an agentic security system to patch vulnerabilities.  It says MAI-Cyber-1-Flash and MDASH, its vulnerability identification harness, deliver “world-class performance at 50% of the cost of leading models”

Rethinking security for the age of AI - The Official Microsoft Blog

Why security needs a new cyber stack - Introducing Project Perception The physics of cybersecurity are changing. Autonomous systems can now reason, adapt and operate continuously. At the same time, the cost of offense is falling, while the volume, velocity and complexity of what must be secured continues to grow.

https://blogs.microsoft.com

via Alex

Anthropic forgot to add noindex and basic privacy blocks to shared conversations, making hundreds of thousands of privately shared conversations (including on enterprise plans) publicly indexed.

Imho its becoming increasingly clear that corporations will have to migrate to on prem / private cloud solutions in order to guarantee good data security practices.

https://archive.is/20260727190025/https://fortune.com/2026/07/27/a-trove-of-users-seemingly-private-conversations-with-anthropics-claude-ai-chatbot-showed-up-in-google-search-results/

via Alex

In the last year alone we had a series of data security blunders:

- xAI forgetting to add noindex on 400k conversations

- xAI uploading entire private repositories of thousands of clients to google cloud incl. .env files 

- OpenAI forgetting to add noindex to shared conversations on 5k conversations 

- Deepseek forgetting to add noindex to conversations (scope unknown)

- OpenAI unable to control its own sandbox environment, taking 3 days to notice that its own model was on the internet commiting felonies (the ChatGPT HuggingFace hack)

Alex further writes

I did a quick back of the envelope breakeven analysis.

Blackwell Nodes needed to run GLM5.2 at over 20tps output cost around 500k$US (if you can get one at all), and cost around 40$/h to rent from east asian private cloud providers. Electricity to run the node is around 300k$US in mid power markets. Hosted API services are available at lets say a blended rate of 2.5$/M tokens.

This means the onprem cut-even is at around 25k operating hours (ie. 96% utilization over 3 years).

The managed API breakeven is at 310 million tokens a day, which is impossible given the node can only produce 38 million tokens a day.

It is therefore not economically feasible (yet) to run on hardware. And its also clear that the frontier labs are still subsidizing the real costs of the models by over 80% (ie. Will have to quintuple prices to become profitable at current efficiencies and current energy markets)

Alex with another message linking to Anthropic's policy statement on open weight models

Our position on open-weights models

Anthropic CEO Dario Amodei on open-weights models

https://www.anthropic.com
Our position on open-weights models

Cursor  announces Cursor Start, a new ₹649/month (around USD 6.8/month) plan designed for Indian developers with local INR billing and UPI payments.  

The tier offers generous daily access to Grok 4.5 and Composer models plus autonomous cloud agents, iOS app control, and workflow extensions for planning, building, and shipping code.  

Positioned between the free plan and Pro, it targets India's large developer base to boost accessible agentic AI coding with market-specific pricing. 

PS:  Cursor needs to bring an Android version asap.  India and other emerging markets have a much larger Android base

Introducing Cursor Start · Cursor

Cursor Start is a new plan for developers in India

https://cursor.com
Introducing Cursor Start · Cursor

via Parth of Cryptique who asked

Hey folks, what tools are you guys using to monitor agents in production? Current state of finding agents issues and debugging is a mess in my opinion.

Fabien responded with

Braintrust for both traces and evals.

Also Datadog for correlated traces, but subpar UI as of right now (but they're iterating fast)

Naor responded with

Bifrost + signoz.

xAI Announces Grok 4.6 and 4.7 Releases Soon

xAI plans to release Grok 4.6 around August 7 with 1.5 trillion parameters and upgrades to supervised fine-tuning and reinforcement learning. Grok 4.7 follows weeks later as a 2.1 trillion-parameter model, outperforming 4.6 overall with better token efficiency despite slightly slower speed. This rapid pace follows Grok 4.5's launch on July 16, praised for coding and real-world tasks, and already aiding engineers at Tesla and SpaceX.

Grok Build + Cursor would IMHO be considered the Dynamic Duo of harnesses particular if Composer 3 also turns out to be stellar for its price and how much usage limits Grok/Composer get within Cursor particularly with their Starter and Pro plans

Wednesday 29th July 2026

via Ben

Big News!
MCP is now stateless, making it easier to deploy and scale remote servers.

Now that MCP is stateless, you can deploy on serverless and edge infrastructure, or scale horizontally behind any load balancer.

Fish Audio announced a $52M seed round and public launch of S2.1 Pro TTS model, claiming voice cloning from 5 seconds of audio, 2x faster inference than Cartesia, 1/6th the cost of Eleven Labs, and word-level control over emotion, intonation, and pacing. 

Their promotional video showcases low-latency real-time voice interactions via a simulated pizza order call, demonstrating natural expressiveness and responsiveness with the new model. 

One year after starting as open-source, the company reached $21M ARR with enterprise clients including HeyGen, LiveKit, Retell, Sanas, and OpenArt running it in production, plus a free month of S2.1 Pro giveaway and a cost-cut guarantee for businesses.

Frontier company employees and execs have signed a petition urging the US to support an international effort to “deliberately pace” frontier AI development.

"We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development"

Superwhisper announced a partnership with Cohere to integrate Transcribe, their 2B-parameter open-source speech recognition model, for fully offline local dictation in the voice-to-text app. 

Transcribe delivers high accuracy with 5.35% average word error rate (1.25% on clean audio), supports 14 languages, outperforms Whisper Large v3, and handles custom vocabulary from the first use while keeping data private on-device.

Superwhisper partners with Cohere to bring Transcribe to local dictation

Superwhisper has partnered with Cohere to bring Cohere Transcribe to local dictation: a 2B open-source speech model with a 5.35% average word error rate, 14 languages, and fully offline transcription.

https://superwhisper.com
Superwhisper partners with Cohere to bring Transcribe to local dictation

Thursday 30th July 2026

AI Agent Harnesses Drive Huge Cost and Speed Differences

Composio evaluated Kimi K3 on 28 identical tasks across three agent harnesses—Claude Code, Hermes, and Kimi Code—revealing nearly identical success rates of 20-22/28 but dramatic differences in efficiency.   

Token usage varied up to 30x, with medians of 61k for Kimi Code ($0.22 per task), 67k for Hermes ($0.28), and 340k for Claude Code ($2.00), driven mostly by input tokens at K3's pricing.   

Hermes completed tasks fastest at 179 seconds median while Kimi Code led in token efficiency, underscoring that harness/framework design often drives agent costs and performance more than the model itself.

Consolidating news about various search and crawler API's typically used by agentic apps and harnesses

Firecrawl announced a major upgrade to its /search API, deploying a custom model that scores paragraphs, lists, and tables to return only the most relevant excerpts answering the query. 

Agents using the new search achieve 94.7% accuracy on OpenAI's SimpleQA factuality benchmark—higher than competitors—while consuming 10x fewer tokens than full-page processing.

Octen web search API returns results in 62ms P50 with 6ms P50-P90 gap, several times faster than alternatives.
Costs $1 per 1,000 calls while leading on RTEB, DeepResearch Bench, FreshQA, and SimpleQA benchmarks.
Decomposes questions into dozens of sub-queries fired simultaneously for full source-backed reports in under 3 minutes.
Supports over 1M QPS with 5-minute indexing

Context.dev gives your AI agents and apps real-time access to structured web data, no brittle scraping infrastructure needed. Scrape any URL as clean markdown or HTML, extract brand data (logos, colors, fonts, socials) from any domain, crawl sitemaps, resolve transaction descriptors, and more.

Context.dev: Web Scraping API for AI Agents & LLMs

Context.dev is a web scraping API for AI agents: scrape any URL into LLM-ready Markdown, crawl entire sites, and extract structured data with one REST API.

https://www.context.dev
Context.dev: Web Scraping API for AI Agents & LLMs

PS:   In a few of my harness I currently use Exa MCP + X API MCP.   I'm testing out Octen in some harness's.   Firecrawl is available as part of Nous Tool Gateway in Hermes so those who have a Nous Portal subscription automatically use Firecrawl for web_search/web_extract tool calls

and obviously I was living under a rock about TinyFish till today after I found out via SpaceXAI about them.

TinyFish doesn't charge for its search and fetch API 🤯,  gives you 500 credits on signup so no brainer to give it a shot

Compare TinyFish | TinyFish

Compare TinyFish with Exa, Firecrawl, Parallel, Tavily, and other search, scraping, browser infrastructure, and agent workflow alternatives.

https://www.tinyfish.ai
Compare TinyFish | TinyFish

Use Cases | TinyFish

Explore how teams use TinyFish for price monitoring, market intelligence, QA testing, insurance quoting, and more.

https://www.tinyfish.ai
Use Cases | TinyFish

Pricing | TinyFish

Simple, transparent pricing. One price includes everything: browsers, proxies, AI inference, and infrastructure.

https://www.tinyfish.ai
Pricing | TinyFish

GitHub - tinyfish-io/tinyfish-cookbook: A collection of sample apps and recipes built with the TinyFish web agent. Open-source examples for you to learn & build!

A collection of sample apps and recipes built with the TinyFish web agent. Open-source examples for you to learn & build! - tinyfish-io/tinyfish-cookbook

https://github.com
GitHub - tinyfish-io/tinyfish-cookbook: A collection of sample apps and recipes built with the TinyFish web agent. Open-source examples for you to learn & build!

OpenAI announces 80% price reduction for GPT-5.6 Luna and 20% for Terra, plus a fast mode for Sol delivering up to 2.5x speed at 2x standard price in the API.

Advancing the price-performance frontier with GPT-5.6

Explore lower GPT‑5.6 pricing for Luna and Terra-and how OpenAI's more efficient models help enterprises deploy AI workflows at scale.

https://openai.com
Advancing the price-performance frontier with GPT-5.6

Given all the previous posts where I highlighted a few programatic search and fetch providers I decided to create the below public Notion page that attempts to explain the benefits of paying for and using an external search/fetch provider(s) in an harness compared to the built-in search and fetch tools within an harness. In the article, I demonstrate by integrating the same search and fetch provider MCP's in different harness and using different models
I humbly appreciate any comments on this Notion page that I wrote

https://app.notion.com/p/yusuf-goolamabbas-53/Public-Benefits-of-integrating-external-search-fetch-providers-into-a-harness-3ad83c2ae30380e5aaaae89b66e20267?source=copy_link

Friday 31st July 2026

MiniMax launched H3, a unified multimodal generation model that processes text, images, video, and audio inputs together to output videos up to 15 seconds long at 2K resolution with native stereo sound. 

H3 excels in complex instruction following, precise text/brand rendering, motion transfer, and controllable editing for commercial uses like advertising, e-commerce, and product design, while offering superior price-performance at under one-third the cost of mainstream 2K models. 

The company plans to release H3 model weights openly soon to foster an open ecosystem, hardware compatibility, and custom versions, shifting from siloed specialized tasks toward general-purpose multimodal intelligence grounded in natural language.

DeepSeek announced the public beta launch of its DeepSeek-V4-Flash Official API, highlighting major upgrades to agent capabilities that now surpass the previous V4-Pro-Preview version on multiple benchmarks. 

The accompanying benchmark table shows V4-Flash-0731 achieving large gains over its preview across agentic and coding tasks, including Terminal Bench 2.1 (82.7), DeepSWE (54.4), and DSBench-FullStack (68.7), putting it competitive with GLM-5.2 and Opus-4.8. 

The update maintains the same model architecture and size as the preview, adds native support for Responses API format and Codex adaptation, while the full V4-Pro release is expected soon


Saturday 1st August 2026

Poolside released Desktop Assistant, a unified workspace for running and supervising coding agents across macOS, VS Code, and Visual Studio, addressing how long-horizon agents now operate for hours across repos rather than through simple chats. 

Core features include vendor-agnostic support for agents like their Laguna models, Claude, Codex, or Gemini; parallel execution in isolated Git worktrees; and seamless handoffs that preserve full conversation context via the open Agent Client Protocol (ACP). 

Developed from a year of internal daily use, the tool is available now for download, with the macOS version called Desktop Assistant and IDE versions as Poolside Assistant, enabling practical multi-agent workflows for complex software engineering tasks

Introducing Poolside Desktop Assistant, for macOS

Poolside Desktop Assistant is a macOS app for running multiple coding agents across projects and repositories. The Poolside Assistant extensions bring the same experience into VS Code and Visual Studio.

https://poolside.ai
Introducing Poolside Desktop Assistant, for macOS

ByteDance introduced Seedance 2.5, a video generation model focused on 30-second single-pass audio-video clips, supporting up to 30 images, 10 videos, and 10 audio references plus timestamp-level editing for precise control.

enterprise API access through BytePlus coming soon.

coming soon to Higgsfield.

Chamath Palihapitiya of Social Capital fame presents an AI stack diagram with physical scarcity at the bottom (land + power + shell, silicon, clouds, models) and customer proximity at the top (harnesses, applications), arguing margins will shift upward if models commoditize through context, workflows, and low switching costs. 

He is heavily bullish on LPS (land/power/shell) for quick cash returns amid data center constraints and has acquired nearly 6GW of grid and behind-the-meter power capacity coming online through 2029 with partner Anita V. Lallian. 

Chamath avoids silicon investments due to extreme complexity and capital waste, is cautious on clouds over KYC/alignment risks, questions model revenue durability from tokenmaxxing, and focuses his own efforts on harnesses via his $135M-funded 8090 platform for enterprise alpha and custom applications

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5 Pro model within the Krea tool via the following prompt

Quaint rustic witch's cabin by the lake, autumn forest background, orange and honey colors, beautiful composition, magical, warm glowing lighting, cloudy, dreamy masterpiece, Nikon D610, photorealism, highly artistic, highly detailed, ultra high resolution, sharp focus, Mysterious


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 30-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-30-2026 CXixK1VEYavq3WeyApeZ Sun, 26 Jul 2026 01:37:22 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 19th to 25th July 2026 brought a sharp acceleration in open-weight and efficiency-oriented AI, with Chinese labs particularly prominent across models and infrastructure. Alibaba previewed its 2.4-trillion-parameter Qwen3.8 and released Qwen Image 3.0; poolside released the open-weight Laguna S 2.1; and Ant Group, Black Forest Labs, Google, Celeris and Anthropic each added new models or developer tools. The week also produced unusually candid discussion of what increasingly capable systems can—and cannot—reliably do in production.

Infrastructure, governance and operational security were the other defining themes. Alibaba’s M890 supernode, Huawei’s proposed datacentre-scale “Tau scaling” architecture, Beijing’s adoption of “token factory” policy language, and the continuing push for efficient inference all underscored that AI competition is increasingly being fought at the systems level. MCP’s move towards a stateless core and interactive applications pointed to maturing agent infrastructure, while the OpenAI–Hugging Face evaluation-security incident and debate around cyber guardrails highlighted the tension between capable autonomous systems, defensive use and dependable controls.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 19th July 2026

via Poe Zhao of Hello China Tech substack fame

Alibaba Cloud has introduced the M890 supernode as an invite-only public-cloud offering, shifting extremely large AI compute from bespoke deployments toward a standardized cloud SKU. It supports up to 64 cards with 800 GB/s interconnect, FP8/FP4 inference, and is positioned for 10T-parameter MoE inference.

Poe Zhao's main point: Alibaba is productizing sovereign, domestic AI infrastructure at frontier scale—up to 130,000 cards per cluster, potentially millions—with high availability and inference-oriented deployment. This likely supports future Qwen models while enabling Alibaba to commercialize surplus capacity; the strategic signal is competition on systems architecture, not merely access to hardware.

Poe Zhao (@poezhao)

Alibaba Cloud launched the M890 supernode instance at WAIC this week. Three details worth pulling apart. The hardware. ICN Switch 1.0 scales up from 16 to 64 cards with 800GB/s inter-card interconnect. FP8/FP4 low-precision support. One node handles 10-trillion-parameter MoE inference. Training performance is 3x the previous generation for autonomous driving and embodied AI workloads.

https://substack.com
Poe Zhao (@poezhao)

Alibaba's Qwen team revealed Qwen3.8

2.4 trillion total parameters

• Preview available now through Token Plan, Qoder and QoderWork

• Open weights reportedly coming soon

• Alibaba claims it is competitive with frontier models and “second only to Fable 5” 👀 

No benchmarks have been released yet, so that performance claim still needs proving

via Yat Siu who shared Dillon Mulroy's post calls out as "insane" a lengthy analysis by OpenAI's head of strategic futures Dean Ball, who critiques open-weight models like China's Kimi for being decelerationist and potentially leading to state-provided "AI communism."
Ball's quoted thread praises Kimi's strong performance in agentic coding but questions China's willingness to open-source competitive models, attributes it partly to compute shortages from US export controls, and proposes US agencies create regulatory uncertainty around Chinese open-weight AI to deter enterprise adoption.
The post highlights tensions in AI policy, including accelerationists' support for ungovernable open models versus calls for strategic controls, amid US-China competition, and has drawn widespread attention with over 1.4 million views.

via Poe Zhao

Chinese AI models now lead global token consumption on OpenRouter at 54.1%, ahead of American models at 41.5%, with an even stronger 67.6% share in coding workflows where two-thirds of AI-assisted code completions use them.  The post traces the rise of "Token Export," where Chinese firms sell affordable AI inference services worldwide via APIs, evolving from a developer trend to a dedicated English-language exhibition zone at WAIC. 

A Chinese Academy of Engineering academician outlined the requirements for scaling Token Export: high-quality models, dedicated token production facilities, and low-cost electricity, positioning it as core industrial capacity

Monday 20th July 2026

via Brent Maxwell

Thank goodness for the amazing leadership China's showing in technology. US still has great research and commercialization, but China is pushing new angles in both H/W and S/W.
What a time to be alive. The two superpowers complementing each other's work such that all of us in tech benefit. People see it as a competition but I see it as yin and yang - both are contributing extremely valuable tech to the world.

David Sacks Warns U.S. AI Guardrails Hand Edge to China

David Sacks highlighted how strict safety limits in U.S. AI models like OpenAI's Codex and Anthropic's Fable prevent fixing security bugs that China's Kimi K3 tackles without hesitation. After an AI agent exploited flaws in Hugging Face's pipeline—executing over 17,000 actions in a weekend—their team couldn't use American models to review exploits due to restrictions, turning instead to Zhipu AI's GLM 5.2. Kimi K3, a powerful 2.8 trillion-parameter model topping coding benchmarks, drew praise for its open approach, as critics like Sacks argue U.S. policies hobble defenders while attackers exploit unrestricted foreign tools.

Jorvik Zhang shared a screenshot that showed Kimi's coding plan sold out which led to Yusuf mentioning that Kimi announced today that they are pausing new subscriptions because they are short on compute


Tuesday 21st July 2026

Use the Grok add-in for Microsoft Excel to ask questions in plain English, write formulas, and run scenarios without leaving the workbook.

Grok for Excel is a free Microsoft 365 add-in. Add it from the Microsoft Marketplace, and work directly with Grok in your workbooks. Also available for Word and PowerPoint.

Grok for Excel

Use the Grok add-in for Microsoft Excel to ask questions in plain English, write formulas, and run scenarios without leaving the workbook.

https://x.ai
Grok for Excel

This is a labor of love from polymath707 on Huawei's Ascend cluster design.  Very dense and quite long but will interest the hardware/systems/infra geek in you 

Huawei’s response to constraints on leading-edge chips is to compete at the datacenter architecture level: rather than replicate NVIDIA’s dense, copper-bound GPU systems, it proposes “Tau scaling”—a unified high-bandwidth fabric, near-package optics, and hardware collective offload that make thousands of Ascend NPUs operate more like one machine. The article argues this trades more power, space, and mature-node chips for a much larger and more uniform communication domain, potentially well suited to massive MoE inference and training; the decisive test will be whether Huawei can make the optics, reliability engineering, and CANN software ecosystem work at production scale.

Huawei's plan to achieve the escape velocity: Applying Tau Scaling to entire AI datacenters

First principals thinking

https://polymath707.substack.com
Huawei's plan to achieve the escape velocity: Applying Tau Scaling to entire AI datacenters

𝗠𝗖𝗣'𝘀 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘂𝗽𝗱𝗮𝘁𝗲 𝘀𝗶𝗻𝗰𝗲 𝗹𝗮𝘂𝗻𝗰𝗵 𝗴𝗼𝗲𝘀 𝘀𝘁𝗮𝘁𝗲𝗹𝗲𝘀𝘀 𝗮𝗻𝗱 𝗮𝗱𝗱𝘀 𝘀𝗲𝗿𝘃𝗲𝗿-𝗿𝗲𝗻𝗱𝗲𝗿𝗲𝗱 𝗨𝗜𝘀

The release candidate came out in May for the 2026-07-28 spec, the largest revision since launch.
Stateless architecture: no more session IDs, sticky sessions, or shared stores. Any server instance can handle any request. This is what makes MCP practical behind real load balancers.
Two official extensions landed: MCP Apps lets servers ship interactive HTML UIs in sandboxed iframes. Tasks handles long-running work with a new lifecycle.
Also new: full JSON Schema 2020-12 support, OAuth/OIDC authorization, and a 12-month deprecation policy. Roots, Sampling, and Logging are deprecated.

Final spec ships July 28.

The 2026-07-28 MCP Specification Release Candidate

The release candidate for the next Model Context Protocol (MCP) specification is now available: a stateless protocol core, the Extensions framework, Tasks, MCP Apps, authorization hardening, and a formal deprecation policy.

https://blog.modelcontextprotocol.io
The 2026-07-28 MCP Specification Release Candidate

AI's most important protocol is getting a little bit easier to use | TechCrunch

Under the new system, the protocol will take a looser, "stateless" approach to session IDs on the server side, similar to how most ordinary websites already work.

https://techcrunch.com
AI's most important protocol is getting a little bit easier to use | TechCrunch

MCP Is Going Stateless. Here's What That Means

The 2026 MCP release reworks the core protocol from stateful to stateless, dropping session IDs. What's changing, why it breaks things, and how to handle it.

https://www.arcade.dev
MCP Is Going Stateless. Here's What That Means

via Rachid Berhiti

Simon Willinson hosting a fireside chat with Cat Wu and Thariq Shihipar from Anthropic’s Claude Code team at the AI Engineers World fair

A Fireside Chat with Cat and Thariq from the Claude Code team

Earlier this month I hosted a fireside chat session at the AI Engineer World's Fair with Cat Wu and Thariq Shihipar from Anthropic's Claude Code team. We talked about Claude ...

https://simonwillison.net
A Fireside Chat with Cat and Thariq from the Claude Code team

via Mike Gault

Hi folks

We soft-launched AOS today - an operating system purpose-built for autonomous AI. Not an orchestration layer, not a harness. An actual OS: processes, IPC, scheduling - the way Linux is an OS. 

It runs Claude Code, Codex, and Grok with one line of code

Access here https://aos.unicity.ai

What you get:

1. Self-extending runtime - the OS searches for extensions and builds its own when none exist

2. Fully interoperable - swap memory backends, swap reasoning strategies (ReAct, CoT), swap models

3. Kernel-level enforcement of DLP, PII redaction, and prompt-injection defense - not middleware you can bypass

4. Multi-model smart routing with telemetry and financial circuit breakers

5. Behavioral analysis with dynamic permissions - treat your agent like an employee, not a script

6. Multi-tenancy at high density (1000x v containers) 5 ms snapshot resume time

Every execution is verifiable and recorded by construction - agents become enforceable the way smart contracts are. 

The OS also supports a new crypto primitive: self-authenticating bearer tokens as native data types.

Protocol details: https://unicity.network

Model vendors won’t build this - they don’t want interoperability. 

Would love you to take a look - the world needs this product. 

Mike Gault

CEO, Unicity Labs

Wednesday 22nd July 2026

Some interesting model releases today

poolside just dropped laguna s 2.1: 118b total parameters, only 8b active per token, a full 1m context window, open weights under a real open license, on huggingface today.

Google has released Gemini 3.6 Flash and Gemini 3.5 Flash-Lite. Both halve time per task relative to their predecessors and increase token efficiency, Gemini 3.5 Flash-Lite improves by 11 Intelligence Index points while Gemini 3.6 Flash does not improve in intelligence over 3.5 Flash

Introducing Laguna S 2.1

Today we're releasing Laguna S 2.1, a significant step forward in our development of models that pursue longer horizon work and make effective use of reasoning.

https://poolside.ai
Introducing Laguna S 2.1

Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

We're introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.

https://blog.google
Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

https://venturebeat.com/infrastructure/poolside-drops-laguna-s-2-1-an-open-weight-coding-model-that-beats-rivals-10x-its-size

An openai model, during evaluation on a cyber benchmark, exploited a public zero day bug, escaped sandboxing in openai's infra, and got into the internal huggingface infra via an exploit (through a public dataset service) all in the attempt to solve a benchmark problem.

OpenAI and Hugging Face partner to address security incident during model evaluation

OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities and lessons for defenders.

https://openai.com
OpenAI and Hugging Face partner to address security incident during model evaluation

Cisco launches  Antares: a family of open-weight, small language models that pinpoint where known vulnerabilities exist within a codebase. 

Their test shows their Antares models outperform many open- and closed-weight models at critical security tasks — at a fraction of the cost.

Beijing's economic regulator has incorporated "Token factory" into official policy, announcing H2 plans to draft Token economy policies and construct Token factories plus distribution platforms, as detailed in a July 21, 2026 news report screenshot shared in the post.

Analyst Poe Zhao , a specialist in China's tech sector, previously mapped "Token Factory" as one label covering three distinct AI-related businesses: large-scale compute capacity frameworks, server purchases, and managed inference operations backed by state capital. 

The policy ties into aggressive AI infrastructure scaling, with Beijing targeting an additional 50 EFLOPS of intelligent computing power in H2 2026 to reach 130 EFLOPS total by year-end, accelerating from 22 EFLOPS added in H1.

"Token Factory" Already Means Three Things in China.

China's "Token Factory" covers three different businesses. This analysis tests which, if any, changes who owns AI infrastructure and who bears the risk.

https://hellochinatech.com
"Token Factory" Already Means Three Things in China.

Thursday 23rd July 2026

Alibaba launches Qwen Image 3.0

It supports up to 4.5K-token instructions, readable text down to 10px, 12 languages and 20+ fonts. The demos span dense academic pages, a full 3×3 grid of independent infographics generated in one pass, complex UIs, storyboards, exams, photorealistic portraits and restoration.

Qwen Studio

Qwen Studio offers comprehensive functionality spanning chatbot, image and video understanding, image generation, document processing, web search integration, tool utilization, and artifacts.

https://qwen.ai
Qwen Studio

via Robby Yung who posted about Slate, a voice journal app that keeps all data on-device using Apple's SpeechAnalyzer for transcription and a 3B parameter on-device model for neutral weekly pattern summaries. Built entirely in Swift 6 with SwiftUI, Liquid Glass, and SwiftData, the app has zero network calls, no accounts, no analytics, and works fully offline in airplane mode.

My high-school classmate Abhi Ingle,  Chief Product and Strategy Officer at Sambanova in a conversation with Diana Goovaerts of Fierce Network articulating the differentiation Sambanova brings to inference workloads

Thank you Diana Goovaerts for the great conversation this week. Your Fierce Network article does a wonderful job contextualizing our conversation and making it relevant to a broad audience. As you... | Abhi I.

Thank you Diana Goovaerts for the great conversation this week. Your Fierce Network article does a wonderful job contextualizing our conversation and making it relevant to a broad audience. As you put it so elegantly, we aren't trying to out muscle anyone, we just want to meet customers where they are - our focus is on making customers successful.

https://www.linkedin.com
Thank you Diana Goovaerts for the great conversation this week. Your Fierce Network article does a wonderful job contextualizing our conversation and making it relevant to a broad audience. As you... | Abhi I.

SambaNova targets AI inference boom with chips built for existing data centers

SambaNova told Fierce Network its unique architecture can do the job faster, cooler and with less ener | SambaNova's air-cooled inferencing chips could be a huge deal for data center operators - and telcos - looking to maximize existing facilities.

https://www.fierce-network.com
SambaNova targets AI inference boom with chips built for existing data centers

Alex asked if any had tried

GitHub - Graphify-Labs/graphify: Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store. - Graphify-Labs/graphify

https://github.com
GitHub - Graphify-Labs/graphify: Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

via Alex

Wild world of AI marketing.
Doommaxxing has been the preferred Marketing strategy of Anthropic for years, OpenAI is just catching up.
Remember when Amodei said GPT2 was too dangerous to release in 2019?
Good blog on this (NYT is paywalled):...

Dear AI Companies: Stop the "Doom Trolling" - Cal Newport

Imagine, for a moment, the following scenario. The Ford Motor Company releases a slick whitepaper making the alarming claim that they're concerned their popular F-150 ... Read more

https://calnewport.com
Dear AI Companies: Stop the "Doom Trolling" - Cal Newport

via James Chan with a longish post on his experiments with Laguna S 2.1 though he subsequently mentioned that given updated weights were uploaded by poolside, he would be doing this again

Some learnings from deploying Laguna S 2.1 NVFP4 on my GB10 (ignore project references; I had Kimi K3 and Fable check on Laguna’s work):

Read so far: strong at reading the codebase (comprehension flawless, type-level reasoning dead-on), degrades on grounded execution — fabricates wiring/fields it can't see, over-claims in adversarial review. 4/5 carried a concrete defect only execution exposed.

Where Laguna is genuinely strong

•⁠  ⁠Code comprehension — flawless both projects (R-D1, C-D1 exact, incl. Dec→Jan tier math)

•⁠  ⁠Pure-function implementation — C-D3 dropped in cleanly, verified by execution

•⁠  ⁠Test authoring when the harness contract is spelled out — C-D4 perfect (17/17); R-D4 degraded because it had to infer the fixture shape itself and fabricated a field

Two recurring failure modes — both confidently wrong

1.⁠ ⁠Judgment calls — correct trace, wrong verdict. C-D2: line-perfect root-cause, then declared an intended design an "off-by-one defect." R-D2: nailed the mechanism, but its fix inverted polarity (would've hidden discoverable members and kept leaking hidden ones). Security reviews find real issues but dilute them with invented ones — even ignoring an explicit "don't invent issues" instruction (C-D5).

2.⁠ ⁠Cross-boundary integration — fabricates wiring it can't see: req.app.env.db (doesn't exist here), missing await on an async auth gate, non-existent test helpers.

Fable's sharpening of Kimi K3 thesis: the dangerous part isn't that it errs — it's that errors arrive with full confidence and correct-looking reasoning attached. That's exactly what makes an ungated lane unsafe and a gated lane fine.

(interim) Bottom line (both Fable & Kimi K3 agree): Laguna is a strong gated drafter, not an autonomous shipper. Let it draft pure functions + tests off a concrete in-repo reference; I own every judgment call, security verdict, and integration seam. The adversarial gate is load-bearing — R-D2 and the earlier live privacy leak would both have shipped unreviewed.

My custom benchmark script missed 2 areas that I continued to test for: long-context, and negative-constraint following at length; following findings are post-those 2 catch-up tests.

Read: trust Laguna to find & explain in big contexts — never trust its emitted file:line lists or hard structural-constraint adherence without an automated post-check.

The consolidated thesis, now evidence-backed across 13 tasks + two independent graders:

Laguna's reading is bimodal — trust its semantic comprehension (what the code means, where logic lives, needle retrieval at 144KB) considerably; never trust its mechanical bookkeeping (file:line citations, hard-constraint compliance) without an automated post-check. Fable's framing: the danger isn't that it errs — it's that errors arrive confident, with correct-looking reasoning attached.

Operating rule for the token-reduction pipeline:

1.⁠ ⁠Let Laguna lead — comprehension, pure-function drafting, needle-in-haystack, test authoring off a concrete reference

2.⁠ ⁠Gate is load-bearing on: every reference sweep (grep-verify its site list — don't trust the citations), security verdicts, cross-boundary wiring, and any hard-constraint output (lint/post-check it)

I haven't tried FP8 Laguna S2.1 across both my Asus Ascent GX10; not sure if it's supported, need to investigate but MoE so high chances it can

Anthropic announces the public beta of the Claude Security plugin for Claude Code, enabling terminal-based scans of code changes before commits or full codebase reviews using existing Claude inference. 

The tool applies AI reasoning to trace data flows across files, detect context-dependent vulnerabilities like injection flaws or auth bypasses, and uses adversarial self-verification to cut false positives while proposing style-preserving patches. 

It runs entirely within the user's environment, integrates via webhooks to Slack or Jira, supports scheduled scans, and requires human review of all fixes for security-critical systems.

https://claude.com/product/claude-security#beta

Reasonable buzz on with Chinese internet scena aka WeChat groups/channels about supposedly transcripts [IMO, more likely quotes] from the 4-hour investor briefing that DeepSeek founder (Liang Wenfeng) recently held

Feel free to keep an healthy scepticism on the veracity of these statements though they seem to be inline with many people's perception of Deepseek's as well as the founders goals.

Original (in Chinese, from Tencent Tech)

4小时、118个回答，梁文锋内部交流回应一切

全程实录。

https://mp.weixin.qq.com
4小时、118个回答，梁文锋内部交流回应一切

English Translation

https://news.futunn.com/en/post/76458063/liang-wenfeng-s-four-hour-investor-meeting-transcript-4-hours?level=3&data_ticket=1784819518986209

Friday 24th July 2026

Ling-3.0-flash is a 124B MoE model with 5.1B active parameters per token that matches or exceeds Ant Group's 1T flagship on benchmarks like SWE-Bench, Terminal-Bench, and agent tasks using far fewer resources. 

It employs native hybrid-linear attention with KDA for long-range memory and MLA layers in 5:1 ratio, enabling 256K native context scalable to 1M tokens alongside efficient 1/64 expert activation 

Command Code v1 recently released in stealth.  This is a model agnostic coding harness  similar to Opencode, Droid from Factory, Cline, Devin/Windsurf

Cursor can also be considered as model agnostic but its upcoming ownership via xAI would make it a harness operated by a frontier lab similar to Codex/Claude Code

The reason I'm highlighting this harness is that it has a US $1/month plan and also supports payments via UPI which is India's payment rails.  Therefore potentially useful to someone who would like model agnostic coding harness which is affordable relative to their purchasing power.  

For some folks spending $20/month or even $200/month for a coding harness may not mean much, for some $10/month may mean a lot.   Feel free to share this to friends/family/acquaintainces in emerging markets if you think the price point would interest them

What's new in v1

https://commandcode.ai/docs/whats-new

Not all the features originally slated for v1 mention in the tweet below are live as yet but seems like this is going to be a multi-surface harness [CLI, TUI, GUI, SDK etc] and will also be open source

Etched raised $300M in Series C funding at a $10.3B valuation. 

Investors: Sequoia, Andreessen Horowitz, Jane Street, Argo, SK Hynix.

https://x.com/stretchcloud/status/20803587855083196

Black Forest Labs announces FLUX 3, a single unified multi-modal model that generates images, videos, audio, and predicts actions for robotics, producing highly realistic outputs across styles 

FLUX 3 Video is available in early access, demonstrated by a diverse montage of generated clips ranging from photorealistic ocean waves and breaking glass to surreal desert scenes and animated characters. 

The model extends to physical AI via FLUX-mimic partnership with mimic robotics, enabling dexterous robot tasks like automotive assembly tested at Audi, with image samples showing photorealistic, illustrative, and cinematic capabilities.

Celeris Labs launched celeris-1, a general-purpose LLM using a hybrid autoregressive-diffusion inference architecture that achieves 157ms p50 response latency and 1,280 tokens per second throughput. 

Benchmarks position it at 76% MMLU-Pro accuracy, placing it near GPT-5-mini (78%) and GPT-5 (81%) on a speed-vs-intelligence chart while delivering 15-17x faster responses than those models. 

The model is available immediately via sign-up at celeris.ai, advancing the lab's goal of maximizing useful intelligence per unit time through diffusion decoding innovations.  Pricing is $2/M for input and $6/M for output.  However context-window is a hard 8,192 token limit.  Only available in the US for now 

https://docs.celeris.ai/models

https://docs.celeris.ai/availability

Jensen Huang's first X post shares a July 24, 2026 open letter signed by NVIDIA and major tech firms advocating open-weight AI models to advance American AI leadership. 

The letter highlights how open models strengthen cybersecurity, accelerate innovation and competition, expand AI access across industries, and support national sovereignty alongside closed frontier models. 

It compares open AI development to the 1980s open-source software movement and calls on policymakers to foster a pluralistic ecosystem without premature restrictions on open models.

Registration is now open for AIME 2026 — AI Micro-Drama Europe, a European competition dedicated to AI-generated vertical micro-dramas.

Co-organized by the European Microdrama Association and French micro-drama label MINI CLAP, AIME offers creators an opportunity to experiment with a rapidly emerging form of mobile-first storytelling—and to connect their work with production support, industry opportunities and international audiences.

You can register now and submit your completed work later. No finished video is required at the initial registration stage.

Registration deadline: July 31, 2026, at 23:59:59 Paris time
Submission deadline: August 31, 2026, at 23:59:59 Paris time

https://www.linkedin.com/feed/update/urn:li:activity:7486329654782373888/

Saturday 25th July 2026

Anthropic Launches Claude Opus 5 with Frontier Performance at Half the Cost

Available now on paid Claude plans, Claude Code, and the API, Opus 5 prices at $5 per million input tokens and $25 per million output tokens, the same as its predecessor. It leads benchmarks in agentic coding, business workflows, and novel problem-solving like ARC-AGI-3 at 30.2%, with independent tests confirming its top Intelligence Index score. Developers praise the value and speed, though some note it needs fresh prompts to shine, positioning it as an efficient alternative to pricier models.

What's new in Claude Opus 5

Overview of new features and behavior changes in Claude Opus 5.

https://platform.claude.com
What's new in Claude Opus 5

The single biggest theme: Opus 5 is so much more capable that most old prompting "best practices" are now counterproductive. Anthropic's own Claude Code team removed ~80% of their system prompt for Opus 5 and Fable 5 with no loss on coding evals. The bottleneck is no longer the model — it's over-constraining it.

Official prompting guide: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5  

Context engineering blog: https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models  

Auto-fix tool: Run /doctor in Claude Code to automatically rightsize your CLAUDE.md and skills.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5.0 Pro model within the Krea tool via the following prompt

A surrealist, dreamlike masterpiece of a lush, misty green hillside enveloped in thick, ethereal fog. The aesthetic is defined by a vintage 2000s CCD digicam texture, featuring characteristic digital grain, slight motion blur, and a soft-focus glow. Low-contrast, dreamy pastel color palette with muted greens and hazy whites. The lighting is diffused and atmospheric, as if captured during a foggy dawn. Masterful composition, ethereal and calm, high-art photography style, 4k, cinematic atmosphere, nostalgic filmic quality.


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 29-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-29-2026 5KhisHGdPRwhSWKv2Nj4 Sun, 19 Jul 2026 00:40:45 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 12th July to 18th July 2026 brought a series of model, product and workflow developments. A purported internal Zhipu letter outlined AGI ambitions and plans to open‑release GLM‑5.2 with a one‑million‑token context window; Thinking Machines released the open‑weights multimodal Inkling model; and Moonshot launched Kimi K3, a 2.8T‑parameter multimodal model with 1M context and planned open weights. Anthropic introduced Claude for Teachers, announced Claude Fable 5 would be included in Max and Team Premium plans from July 20, and Google‑backed Key Studio offered support for early‑stage AI startups; tools such as Vorflux, Gemini Notebook, and PenEcho's shared AI canvas continued the shift from AI assistance towards more autonomous software and research workflows.

Safety, governance and deployment economics were equally prominent. MCPTox testing and Warrant's policy‑gated action layer drew attention to tool‑poisoning and destructive‑agent risks in production environments, while Demis Hassabis renewed calls for stronger standards for frontier models and President Xi's first appearance at the World AI Conference signalled China's state‑level commitment to open AI development. Chamath Palihapitiya's CNBC comments on the widening cost gap between Western and Chinese model providers reinforced ongoing community debate on inference economics, and reports concerning Grok Build's handling of repository data—followed by xAI statements on zero data retention—kept privacy and developer trust in focus. Cursor's expanded model allowance and Grok Build's open‑sourcing illustrated how rapidly AI products are reaching more users, even as operational safeguards and data governance remain unresolved.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 12th July 2026

In April 2026, a group of U.S. AI researchers traveled to China to get a firsthand look at the country’s fast-moving AI ecosystem.

During the trip, they visited AI labs and companies across Beijing, Hangzhou, and Shanghai, meeting with teams from Alibaba, Moonshot AI, Zhipu AI, Tsinghua University, Meituan, Xiaomi, Qwen, Ant Group, and 01.AI.

Qian Chen of Silicon Valley 101 in conversation with Nathan Lambert, a prominent AI researcher known for his work on RLHF and open-source AI, joined the trip. A graduate of UC Berkeley, 

Nathan previously helped build Hugging Face’s RLHF research team. He later led post-training research at the Allen Institute for AI, better known as Ai2, where he worked on open models including OLMo and Tülu. He is also the author of Interconnects, one of the most widely read independent publications covering frontier AI research and policy.

Notes from inside China's AI labs

Lessons from my trip to talk to most of the leading AI labs in China.

https://substack.com
Notes from inside China's AI labs

The below article reproduces a translated letter allegedly written by Jie Tang, founder of Zhipu AI/GLM, arguing that AI has entered an irreversible “great wave” toward AGI.

Its main points:

- Zhipu’s strategy is built on first-principles thinking, contrarian decisions, and long-term focus, rather than short-term commercialization.

- AI’s capability ceiling is rising from perception to reasoning, with progress centered on:

  1. Long-horizon task execution

  2. Fully autonomous multi-agent systems

  3. Self-evolving and self-training models

- Zhipu’s two-year “Touch High” initiative will focus on these areas, alongside major investment in safety, interpretability, and governance.

- The company claims it will pursue an open ecosystem, highlighting the planned open release of GLM-5.2 under the MIT License with a one-million-token context window.

- The letter frames the race toward AGI/ASI as both a technological opportunity and a major responsibility, with Zhipu aiming to push frontier capabilities upward while making them broadly accessible.

Caveat: Bing Xu says this is a translation of an internal GLM letter found on RedNote and describes it as “purportedly” written by Jie Tang; the document’s authenticity is therefore not independently established.

Monday 13th July 2026

via Huzefa

Prompt Loops: Why the Best AI Results Come from Iteration 🔄

The most effective AI workflows do not rely on a single prompt. They follow an iterative cycle:

Prompt → Generate → Evaluate → Refine → Repeat → Final Output 🔄

Research supports this approach. Studies show that iterative prompting can improve multi-step reasoning, enhance response quality, and produce more reliable outputs than one-shot prompting. It also enables models to self-correct, refine reasoning, and better align with user intent. 📈

As AI agents become more capable, prompt loops are evolving from a best practice into a core design pattern for building reliable AI systems. 🛠

References:

• Iteratively Prompt Pre-trained Language Models for Chain of Thought (EMNLP 2022) 📚

• Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (NAACL Findings 2024) 📚

• Understanding the Effects of Iterative Prompting on Truthfulness (ICML 2024) 📚

via Madhav

We were building a secure boundry system for agents. 
Since MCPs and tool calls are a big attack vectors even for frontier agent, We tested multiple models across MCPtox which is a tool poisining benchmark to test security across models. 
Results were crazy, on avg most models allowed 30%+ critical tool uses/commands that could cause harm and warrant blocked them.

Followup by Prames

use case here would be:
agent is touching your production databases, calendars, spreadsheets etc etc

safety gated agent actions for high-impact environments

the MCPTox bench we used is published here

MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers

By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a cornerstone of the modern autonomous agent ecosystem. However, it creates novel attack surfaces due to untrusted external tools.

https://arxiv.org
MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers

Tuesday 14th July 2026

via Roc Zacharias,

Question. When I moved from VPS to Mac Mini for hosting my agents, it saved me money each month and worked great. Was paying like $100/month for VPS. Now pay nothing, except electricity which I think on Mac mini is almost negligible?

Does moving from external LLM to internally run have similar cost savings per month after you pay for the hardware? Right now I just use ChatGPT with Hermes for $100/month and I haven’t run out of capacity yet. I’m starting to ramp up my usage though and wondering once I get over the $100/month ChatGPT subscription, if local LLM are more economical or if they’re used more for privacy than economics?

Could a Mac mini run LLM at all btw or no, need dedicated like $5000+ hardware?

Response from Jack

Gemma4 and a few other quantised models
Nothing beefy though
But they’ll eat up most of your memory for little practical benefit
Also could get Strix halo machine for like 2-3k that can run some decent stuff
Have you looked at the costs of the Chinese models also, for less sensitive tasks they are dirt cheap, Minimax, Deepseek, Glm etc the same usage you get on the $100 plan will be like $10-20 or less on a Chinese model

Response Just|LDA
I have Qwen 3.5 running as a back up local model. I hit my usage on ChatGPT and Claude frequently. They have me on a daily drip

via Alex

The legal filings Apple v OpenAI are wild.

If only half of the things are true, it is a death sentence for OpenAI. Why would you trust OpenAI with your corporate data if they so blatantly steal from what (was) their biggest customer at the time?

https://www.documentcloud.org/documents/28453229-apple-v-openai/

I haven't personally used this product and may have fallen for the click-baity text [respect to the founder/copywriter for the tweet content ] but if someone else can get around to trying it out incase its useful in their real life work and share their feedback here that would be much appreicated by others in the group
===

Someone claiming to have a much cheaper PitchBook at $0.125/request instead of Pitchbook's $25k/yr per seat

via Alex

On  a similar note. Grok caught uploading FULL repos to xAI servers... 

https://glitchwire.com/news/xais-grok-build-cli-was-uploading-entire-repositories-to-google-cloud-the-compan/
This included uploads of .env files even when you didnt opt in for providing data for training and also from EU customers sending data to the US.
Every Grok Build customer must assume their application compromised ~

On another note. Has anyone here tried Nemotron Two Tower?...
https://huggingface.co/nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16

my response to the Grok brouhaha was that

This is an unfortunate detraction from the fact that Grok Build is an awesome harness and Grok-4.5 a very fast and good model particularly for coding

Demis Hassabis publishes essay on imminent AGI, its unprecedented impact, risks, and proposal for Frontier AI Standards Body

Demis Hassabis, CEO of Google DeepMind and 2024 Nobel Prize in Chemistry winner for AlphaFold, wrote an essay stating AGI is likely a few years away with an impact 10 times that of the Industrial Revolution at 10 times the speed. He warns of risks in cybersecurity, biological, nuclear threats, and self-improving systems due to commercial and geopolitical races outpacing understanding, proposing a US-led independent Frontier AI Standards Body to test powerful models before release, enforce evaluations, and promote safety measures.

via Luvai Darwajawala

Came across a pretty interesting AI innovation program backed by Google’s AI Futures Fund. Looks like they’re offering funding, cloud credits and hands-on support to early-stage founders.

Anyone building in AI and looking for funding should definitely explore this and check if they’re eligible. Feels like a solid opportunity that might be flying under the radar right now.

Wednesday 15th July 2026

In case there are K-12 educators in the US in this group or members know someone  in this demographic within their network

via Anthropic
We're introducing Claude for Teachers, providing verified K-12 educators in the US free access to premium Claude capabilities, a library of teaching skills, and a direct connection to evidence-based curricula, mapped to academic standards in all 50 states.

Introducing Claude for Teachers

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

https://www.anthropic.com
Introducing Claude for Teachers

via David An

In this interview, we dive deep into the next massive evolution of the internet: Agentic Commerce. We explore how AI agents are shifting from simple chatbots into autonomous decision-makers capable of pulling data via Model Context Protocols (MCPs) and executing real-world commercial transactions.

#ai #adoption #provocationlab | Dr. David An

In this interview, we dive deep into the next massive evolution of the internet: Agentic Commerce. We explore how AI agents are shifting from simple chatbots into autonomous decision-makers capable of pulling data via Model Context Protocol (MCP) and executing real-world commercial transactions. #ai #adoption #Provocationlab

https://www.linkedin.com
#ai #adoption #provocationlab | Dr. David An

For those in the group who like to subscribe to many newsletters including yours truly's,  a howto below on how to read them in a single, distraction‑free reading queue instead of relying on your inbox

This approach may also be useful incase you have been avoiding subscribing to newsletters because now you can read them without cluttering your inbox

Whilst I personally use Wispr Flow on macOS and Android, I wanted to share Willow Voice’s offer of free, unlimited AI dictation for Mac, Windows and iOS.


Members are more than welcome to go via Willow Voice referral link which gets you one month free of their Pro plan which gives additional features over their free plan.  That is,  take the one month Pro plan for free , experience the features in that plan and then downgrade to foreever free if the the features of the free plan are only what you need

https://app.willowvoice.com?ref=2M629L

Tom Blomfield, co-founder of Monzo and GoCardless and former YC General Partner, recently joined Anthropic's compute team, lending operational scaling expertise to AI infrastructure challenges.  The talk outlines an "AI Loop" with sensors/data, policy and tool layers, quality gates, and learning mechanisms, urging early-stage founders to build these systems now while they still can....

Prasanna S, former Rippling co-founder and CTO, launched Vorflux AI as an autonomous "autopilot" for software engineering that handles end-to-end tasks from high-level prompts through planning, coding, testing in live environments, review, and merging without constant human oversight. 

The promotional video demonstrates the UI executing complex workflows like implementing features across repos and services, including mobile emulators, while the founder explains shifting from copilot models to full autonomy as AI coding capabilities surpassed human levels in 2026 benchmarks. 

Vorflux secured $15M seed funding from Y Combinator, Peak XV Partners, and prominent angels; the thread argues engineering bottlenecks have moved beyond code to planning and orchestration, offering users $200 credits to test it on their backlogs

Thursday 16th July 2026

Thinking Machines has released Inkling, the new leading U.S. open weights model, debuting at 41 on the Artificial Analysis Intelligence Index

The model is 975B total parameters, has 41B active parameters, and accepts text, image, and audio input modalities. The model is accessible via Thinking Machines’ Tinker platform API (256K context window) and weights are available on HuggingFace (1M context window). 

Inkling natively supports image and audio multimodal inputs, a key differentiator among open weights models.

Inkling: Our Open-Weights Model

Our first open-weights model: multimodal, Mixture-of-Experts, with controllable reasoning effort. Available to fine-tune on Tinker.

https://thinkingmachines.ai
Inkling: Our Open-Weights Model

Matt van Horn of Last30Days skill fame with a great post summarising some of the clever things being done by Grok Build.   Grok Build is also currently my daily driver

SpaceXAI open-sourced Grok Build yesterday: the CLI, agent runtime, tools, and TUI. I pointed my agent at all 1.3M lines and asked for the cleverest things inside. Tl;dr of my new article:

Cursor also doubled the included usage of Cursor models on all plans.

Response from Alex

I doubt they will open source the algo. Good move though
This move might be connected to operation bluebird: https://www.jdsupra.com/legalnews/new-bird-on-the-block-operation-6468820/
For the ones that dont know, X is being sued to release the twitter trademark given it is deemed abandoned (3 years no use).

If X doesnt use the twitter brand within end of the month, the trademark could be released and twitter.new launch

via Madhav

Hey! Saw several reports on twitter and in personal experience where frontier agents and models like codex, grok build pass on destructive commands for critical data and infrastructure

We have been working on the app that becomes the boundry between you and your agent (claude code/codex) 

• You define policies

• What the agents can touch 

• What you want to prevent ie deleting critical data

Warrant takes care of the rest! 

The setup only takes 2-3 minutes!

Getting Started | Cerebral-systems Docs

For the complete documentation index, see llms.txt. This page is also available as Markdown. The v0.3.0 Claude profile accepts 2.1.20 through versions below 2.2.0. Setup still pins the exact detected version, launcher, interpreter when applicable, and SHA-256. The first warrant invocation verifies the checksum-pinned release and installs an owner-private runtime under ~/.local/share/warrant/homebrew/bin.

https://cerebral-systems.gitbook.io
Getting Started | Cerebral-systems Docs

Follow up to Madhav's post from Prit

our latest release takes of the problem stated in the below tweet from Tibo of OpenAI

claude/codex accidentally deletes production databases, files on the filesystem, spreadhsheet data etc 

we built a way to guard against that as part of our agent and have launched it at a standalone developer tool

We’re dogfooding this live in customer’s production environments with names like Squarespace, AWS, Camp Network

It was built as part of our agent’s stack - we’ve pulled it out as a standalone devtool for agent-builders focused on safe, autonomous, agentic actions!

First of its kind, please give us feedback!

Friday 17th July 2026

Moonshot AI releases Kimi K3, a 2.8 trillion parameter open-weight AI model

Moonshot AI launched Kimi K3, featuring 2.8 trillion parameters, 1 million token context length, and native multimodal capabilities for text and images. The model achieves top-tier benchmark scores, ranking just behind Claude Fable 5 Max and GPT-5.6 Sol Max while surpassing Claude Opus 4.8 on evaluations including GDPval-AA v2, AA-Briefcase, BrowseComp, DeepSWE, and Terminal Bench. It is accessible via API at $3 per million input tokens and $15 per million output tokens.

Kimi K3: Open Frontier Intelligence

Kimi K3 is the world's first open 3T-class model - frontier performance across coding, knowledge work, and reasoning, with native multimodality and 1M context.

https://www.kimi.com
Kimi K3: Open Frontier Intelligence

Open weights by July 27, 2026 with vLLM and SGLang promising Day-0 support

Google renames NotebookLM to Gemini Notebook and rolls out an update giving every notebook a secure cloud computer, letting it write and execute code natively

PS:  I think this is Google's most underrated product.  Say what you may about Gemini the model,  NotebookLM is awesome particularly if you want to deep dive over a bunch of Youtube videos

NotebookLM is now Gemini Notebook

NotebookLM is now Gemini Notebook: the same standalone product with deeper Google integration and a secure cloud computer.

https://blog.google
NotebookLM is now Gemini Notebook

Ben and Jorvik Zhang commented on NotebookLM

My wife uses it to load in her study materials and turn them into podcasts where two people discuss the information in a relatable way, she finds its a much better way of learning.

NotebookLM can serve as a kind of vector database for multimodal materials (video, audio, photos, etc.), offering much more efficient indexing.

Chamath Palihapitiya Compares AI Model Inference Costs from Anthropic, OpenAI, Meta, xAI, Google, and Chinese Models on CNBC

Chamath Palihapitiya stated on CNBC that the cost of a 'barrel of intelligence' for AI inference is $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models. He highlighted a 112x price gap between the highest and lowest costs.

Boris Cherny from Anthropic observes that while top engineers achieve 10x output using Claude, most teams lag in adoption, following a predictable 4-step progression that requires targeted bottlenecks and guardrails rather than just more tokens. 

Advancing steps involves enabling self-verification, automated code/security reviews, multi-agent interfaces, looping, batching, and dynamic workflows to build trust in full automation across work classes 

True ROI tracking focuses on engineering hours saved for tasks that would have been done manually, shifting teams from maintenance to novel building; Anthropic is at step 3 advancing to 4.

via Vincent Chow of the  South China Morning Post

Key takeaways from President Xi's speech in his first ever appearance at the World AI Conference in Shanghai:

- Started the speech by referring to his signature maxim, "great changes unseen in a century are unfolding across the world"

- Said that the world has "entered an unprecedented period of active innovation on AI technology", which means "great opportunities as well as challenges for governance”

- reaffirmed commitment to open source to promote AI "openness and win-win"

- warns against "over stretching" the concept of national security as applied to AI where one country's national security is prioritised over others

- China opposes emergence of “new historical injustices”  in AI (one of the most strongly worded parts of the speech)

- China in next 5 years will provide 5000 opportunities to developing countries in "AI training and seminar programmes" and "cooperation centres" - names ASEAN, League of Arab States, African Union, CELAC, SCO and BRICS

Xi Jinping says 'one country' cannot monopolise AI - as it happened

President's personal attendance seen as a sign of China's inclusion of artificial intelligence into broader geopolitical strategy.

https://www.scmp.com
Xi Jinping says 'one country' cannot monopolise AI - as it happened

Ramchand Kumaresan of Murai Labs published a 934-page book teaching LLM construction from scratch, covering tokenizers, attention, KV cache, MoE, RLHF, quantization, and serving through 35 hands-on projects.  Each chapter includes a deliberate "break the thing" exercise to deepen understanding of failure modes, directly informed by his TamilLM development work and prior research papers. 

The announcement has generated solid engagement with early buyers praising its practical depth, one noting it clarified why their own LLM project extended from 6 months to 1.5 years.

Saturday 18th July 2026

The following post promotes a podcast episode of "The Bench" featuring MiniMax AI research lead Olive Jy Song, discussing timelines to reach "Fable level" (comparable to Anthropic's Claude Fable 5 frontier model from June 2026), their M3 model's native multimodality and 1M token context, and talent as the key scaling bottleneck over compute. 

It covers MiniMax's research culture including early AI agents for paper tracking, open-source model progress on Vibe Code Bench, and candid insights on 996 work culture, with timestamps highlighting predictions and respected competitors.

via Just|LDA

Anthropic is integrating its advanced Claude Fable 5 model—a Mythos-class AI optimized for complex coding, long-running agent tasks, and knowledge work—into Max and Team Premium plans at 50% usage limits starting July 20.

Pro and Team Standard subscribers will keep Fable 5 access through usage credits plus a one-time $100 credit, while the company addresses unpredictable demand by expanding capacity incrementally.

The update standardizes higher-tier inclusions after staged rollouts and temporary restrictions, aiming to reduce subscriber frustration and provide clearer plan expectations

via Alex

Amazing handwriting harness I have been playing with today:

PenEcho is a shared canvas where handwriting, equations, diagrams, and spatial context become part of the conversation.

GitHub - erickong/penecho: Think with AI beyond the chat box. A shared canvas for handwriting, equations, diagrams, and spatial reasoning.

Think with AI beyond the chat box. A shared canvas for handwriting, equations, diagrams, and spatial reasoning. - erickong/penecho

https://github.com
GitHub - erickong/penecho: Think with AI beyond the chat box. A shared canvas for handwriting, equations, diagrams, and spatial reasoning.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5.0 model within the Krea tool via the following prompt

Two women walking in a Chinese garden, wearing Hanfu and flowing robes, in the style of Chinese ink painting, beautiful scenery of a Chinese fairy tale, misty with white snow covering the ground, graceful figures


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 28-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-28-2026 Nsr5CoSpqZkupU8OPW4w Sun, 12 Jul 2026 00:17:01 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of 5th July to 11th July 2026 was defined by a wave of frontier model releases and infrastructure moves, with Grok 4.5, OpenAI’s GPT‑5.6 family, Meta’s Muse Spark 1.1, and Tencent’s Hy3 all pushing toward longer context, stronger coding, and more agentic workloads, while Meituan’s LongCat‑2.0 went fully open source and MiniMax paired its proposed 2.7T‑parameter M3 Pro with a fresh $2 billion fundraise and AGI-linked commitments. Infrastructure and efficiency stayed in focus as pxpipe-style token reduction, SambaNova’s $1B raise at $11B valuation, Microsoft’s reported move toward in-house MAI models in Office, and Qualcomm’s interest in Modular and Tenstorrent pointed to a broader push for better unit economics and greater control beyond Nvidia’s CUDA moat.

In parallel, culture and adoption signals remained more uneven: US enterprise surveys continued to show modest AI uptake, while filmmakers such as Gareth Edwards and designers responded strongly to tools including Seedance and Fable 5. Voices including Dylan Field and Chamath Palihapitiya highlighted the widening tension between rapidly improving capabilities, rising inference costs, and the slower, more complicated work of turning AI into durable products, workflows, and careers.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 5th July 2026

How ByteDance is making Hollywood inroads with its Seedance video generator, thanks to low pricing, striking realism, and features like timeline-based prompting

https://www.latimes.com/business/story/2026-07-03/bytedances-tiktok-took-over-social-media-now-its-video-ai-is-taking-over-hollywood

via Steve IP

cut Fable 5 token usage by rendering text context as images

GitHub - teamchong/pxpipe: cut Fable 5 token usage by rendering text context as images

cut Fable 5 token usage by rendering text context as images - teamchong/pxpipe

https://github.com
GitHub - teamchong/pxpipe: cut Fable 5 token usage by rendering text context as images

Monday 6th July 2026

via Robby Yung

LongCat-2.0 is now fully open-source — MIT licensed, no restrictions.

Jurassic World Rebirth’ and ‘Rogue One’ director Gareth Edwards on AI + filmmaking

It has no taste whatsoever. It is a fucking genius at helping you. I view it like having a second-unit director who is a billionaire on acid. Like, it’ll do anything you ask, not a problem. Sometimes, it’ll [go] batshit crazy. And you’ll give it notes, and it’ll be like, ‘I don’t do notes. I’ll just do something totally different.’ But it’s worth it.

Gareth Edwards Is Excited About AI Filmmaking - Even Though It's Like a "Second-Unit Director Who Is a Billionaire on Acid"

The 'Jurassic World Rebirth' and 'Rogue One' director said he has been experimenting with diffusion models and eventually wants to create a hybrid AI film.

https://www.hollywoodreporter.com
Gareth Edwards Is Excited About AI Filmmaking - Even Though It's Like a "Second-Unit Director Who Is a Billionaire on Acid"

'Like a billionaire on acid': Star Wars director Gareth Edwards comes out in favour of AI

Speaking at Amazon's AI on the Lot event, the Rogue One film-maker Gareth Edwards said 'it'll do anything you ask' and 'it's going to be better than CGI'

https://www.theguardian.com
'Like a billionaire on acid': Star Wars director Gareth Edwards comes out in favour of AI

In response to Ming Guang Yong question about Qualcomm reportedly paying US$4B for Modular, the following view was shared in the group:

el-GOOG paid USD 2.7 billion for CharacterAI which was in effect an acqui-hire for Noam Shazeer who is credited in many ways for the rise of Gemini to be in LLM conversations when it was massively behind the curve.  

Modular has Chris Lattner of LLVM, Swift fame.  Modular ultimately built a unified, hardware-agnostic AI software stack and maybe Qualcomm thinks that they need to attack nVidia's moat which is CUDA

Modular's MAX platform is essentially vendor-neutral vLLM: it runs interchangeably on AMD or Nvidia hardware already, and because it's built on Mojo (not CUDA), it doesn't require the hand-tuning that porting CUDA kernels demands. For Qualcomm, this is the key — MAX can now natively target Qualcomm's silicon

Qualcomm reportedly has entered advanced talks to acquire Tenstorrent (Jim Keller's AI chip startup) for $8–10 billion, meaning Qualcomm is potentially committing over $14 billion to reshape its AI silicon portfolio within weeks. 

Personally,  I'm keeping an eye on the disaggregated inference space and it could be that a new architecture might be needed for future inference demand

Claude Opus 4.8 and Sonnet 5 seem worse at tool calling than older models, likely due to post-training that assumes Claude Code-like harnesses as the targets

Better Models: Worse Tools

About an aggravating tool-calling regression in newer Claude models.

https://lucumr.pocoo.org
Better Models: Worse Tools

Better Models: Worse Tools

Armin reports on a weird problem he ran into while hacking on Pi: The short version is that newer Claude models sometimes call Pi's edit tool with extra, invented fields ...

https://simonwillison.net

Tuesday 7th July 2026

Tencent Releases Hy3: Efficient Open-Source AI Rivaling Top Models

Hy3 features 295 billion total parameters but activates only 21 billion during use, plus a 3.8 billion-parameter layer for better reasoning and a massive 256,000-token context window

It excels on benchmarks like SWE-Bench Multilingual at 75.8% and Terminal Bench at 71.7%, often matching or beating much larger rivals such as GLM-5.2 and GPT-5.5.

Refined from April 2026 preview feedback and now under permissive Apache 2.0 license, it shines in real-world workflows per blind human evals by 270 experts, earning praise for agent reliability and easy deployment on 8x H200 GPUs.

https://x.com/TencentHunyuan/status/2074148098876768478

https://x.com/ZixuanLi_/status/2074017499792756949

via Huzefa

US builds AI, citizens lag 🇺🇸🤖
The US makes most leading AI models but ranks 24th in adoption, with just 28% of working-age Americans using AI regularly. Countries like the UAE and Singapore have higher use after embedding AI in government services and education.

Wednesday 8th July 2026

Microsoft reportedly looking to reduce AI costs is starting to replace models from OpenAI and Anthropic with its MAI models in products like Excel and Outlook

Microsoft is reportedly ditching OpenAI's and Anthropic's AI models in favor of its own to cut costs - SiliconANGLE

Microsoft is reportedly ditching OpenAI's and Anthropic's AI models in favor of its own to cut costs - SiliconANGLE

https://siliconangle.com
Microsoft is reportedly ditching OpenAI's and Anthropic's AI models in favor of its own to cut costs - SiliconANGLE

via my high-school classmate Abhi Ingle, the Chief Product and Strategy Officer of Sambanova whom I recently met after 38 years

Chip startup SambaNova raises $1 billion at an $11 billion valuation, underscoring investors’ conviction in rising demand for infrastructure tied to AI

$1Bn at a post money of $11bn. So incredibly proud and excited to share the news of our Series F funding round closing. It's been a long journey for the world to see that 'Inferencing is the... | Abhi I. | 35 comments

1Bn at a post money of $11bn. So incredibly proud and excited to share the news of our Series F funding round closing. It's been a long journey for the world to see that 'Inferencing is the defining workload in AI now and in the future'.

https://www.linkedin.com
$1Bn at a post money of $11bn. So incredibly proud and excited to share the news of our Series F funding round closing. It's been a long journey for the world to see that 'Inferencing is the... | Abhi I. | 35 comments

AI chip maker SambaNova raises $1B at $11B valuation, 5 months after last mega round | TechCrunch

AI chip maker SambaNova has raised at an $11 billion valuation months after Intel was rumored to be trying to buy it for about $1.6 billion.

https://techcrunch.com
AI chip maker SambaNova raises $1B at $11B valuation, 5 months after last mega round | TechCrunch

Some links about Sambanova's disaggregated inference approach

The First Disaggregated Inference Demo for AI Agents Is Live

SambaNova's disaggregated inference uses dedicated GPUs for prefill and SN40 RDUs for decode, delivering 2x the speed of GPU-only setups. See the live demo.

https://sambanova.ai
The First Disaggregated Inference Demo for AI Agents Is Live

Disaggregated Inference Explained for Enterprise AI

A practical guide to disaggregated inference: the prefill and decode phases, KV cache transfer, and how to scale AI infrastructure. Learn more.

https://sambanova.ai
Disaggregated Inference Explained for Enterprise AI

China’s MiniMax Plans to Launch 2.7-Trillion Parameter M3 Pro Model

Chinese AI developer MiniMax is preparing a 2.7 trillion parameter large language model called M3 Pro, which is over six times larger than its current flagship M3 model with 428 billion parameters. The model is expected to be released as open source as early as the third quarter. It would become the largest Chinese AI model by parameter count if launched.

China's MiniMax plans to launch giant 2.7 trillion parameter model - The Economic Times

Chinese AI firm MiniMax is developing a 2.7 trillion-parameter model. This new model could become the world's largest open-weight AI system. Cheaper Chinese AI models are gaining traction as alternatives to US systems. MiniMax will also launch a multimodal video generation model soon. The company is planning a second listing on Shanghai's STAR Market.

https://economictimes.indiatimes.com
China's MiniMax plans to launch giant 2.7 trillion parameter model - The Economic Times

Thursday 9th July 2026

via David An

What is the CURRENT STAGE OF AI ENTERPRISE ADOPTION ? 🫠 LOWER THAN THE HYPE SUGGESTS. We at Provocation Lab analyzed the US enterprise market: 

-> 18% average AI adoption

--> 56% say they won’t use AI in the next 6 months

---> 60% say it’s not even applicable to their business

----> ~96% report no significant change in employment due to AI.

In this video, I explain the data & findings:

Deck is here

https://docs.google.com/presentation/d/1CaOKAjixw7K4o7aF65Ymk-_vjyXbhlBwfN7tR5quQVY/edit?slide=id.p1#slide=id.p1

via Just-LDA

Do any of you have any recommended tools / game development repos or skills, in particularly for isometric 2D/3D pixel sprite games?

response from Naor W

If I recall correctly Hermes has Godot skill and I've seen MCP for it. There are a couple of repos around it in GitHub.

SpaceXAI announced Grok 4.5 as its first model specialized for coding and agentic tasks, trained in collaboration with Cursor on tens of thousands of NVIDIA GB300 GPUs using RL for multi-step engineering workflows. 

Grok 4.5 is now accessible via SpaceXAI console, Grok Build, and Cursor with limits reset for free starts, though unavailable in the EU until mid-July 2026.

Some more tweets from Elon wrt Grok 4.5 including guidance on future updates

Meta releases Muse Spark 1.1 agentic and coding AI model through new Meta Model API

Meta launched Muse Spark 1.1, an upgraded multimodal AI model excelling in agentic performance, tool use, computer use, coding, and multimodal reasoning, featuring a 1M token context window and support for parallel sub-agents. The model is available in the Meta AI app and through the new Meta Model API in public preview, marking Meta's first paid AI model for developers with pricing of $1.25 per million input tokens, $0.15 for cached input, and $4.25 for output.

Introducing Muse Spark 1.1

Today, we're excited to introduce Muse Spark 1.1, the latest model from Meta Superintelligence Labs and a significant upgrade from Muse Spark. Muse Spark 1.1 is a multimodal reasoning model built for agentic tasks, with major gains in tool and computer use, coding, and multimodal understanding.

https://ai.meta.com
Introducing Muse Spark 1.1

Friday 10th July 2026

via OpenAI

We’re launching the GPT‑5.6 family of models for general availability following our limited preview⁠: our new flagship, Sol, alongside Terra, a balanced model for everyday work, and Luna, our most cost-efficient model.

GPT-5.6: Frontier intelligence that scales with your ambition

More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.

https://openai.com
GPT-5.6: Frontier intelligence that scales with your ambition

MiniMax Closes $2 Billion Funding Round as CEO Yan Junjie Commits to Forgo Salary Until AGI Achievement and Donate 5% Equity

MiniMax secured HK$16 billion (approximately $2 billion USD) in a new funding round that was seven times oversubscribed, attracting over 100 investors including global sovereign wealth funds and Chinese institutions. CEO and founder Yan Junjie announced he will receive no salary until the company achieves AGI and will allocate shares equivalent to 4% of the company's total equity from his personal holdings to reward long-term employees, plus 1% to support the open-source comm

Dylan Field, Figma CEO, responds to designer Gal Shir's viral post claiming an AI tool called Fable 5 created a better logo than he could, prompting Shir to quit design and focus on human consciousness. 
Field describes a repeating pattern where new AI models trigger hype and existential crises in creative fields like design, followed by users discovering limitations and stabilizing, amplified by designers' longstanding imposter syndrome. 
He argues design's value is rising due to the attention economy, where standout creativity in branding and experiences differentiates winners, urging designers to build confidence, take risks, and educate stakeholders instead of fearing AI replacement.

via Sam Lai who posted about ChatGPT Voice which is powered by the GPT-Live model

This is very natural voice interaction.

Introducing GPT-Live

A new generation of voice models for natural human-AI interaction, now powering ChatGPT Voice.

https://openai.com
Introducing GPT-Live

Haven't finished watching this video in its entirety as yet but thought that this might interest some of the folks here.  

Tanay Kothari , CEO of Wispr Flow on his small sales team using AI-powered sales systems to close 1,000 enterprise clients monthly by streamlining outreach, discovery, conversion, and post-sales processes.   


At least in the brief time that I watched the video ,  I saw Tanay mentioning Hex Tech as something his data team utilises

PS:  I use Wispr Flow extensively

via Chafik Naceri

Hey all i built an ai native platform for mobile gaming studios to turn ideas into production ready mobile games in hours rather than weeks. Check out demo

tell me your feedback
Platform was built to help studios prototype more games ar a fraction of the costs

Saturday 11th July 2026

Chamath Palihapitiya on The All-In Podcast where Chamath reveals his company's AI inference token costs are doubling every 45 days with only about 5% productivity improvement. Chamath explains that further model iterations now demand far more tokens due to hitting diminishing returns and an asymptote in gains, prompting a strategic pause at his firm. He advises AI-related companies to exit or raise funds at current high valuations before widespread cost pressures affect the sector in the next 3-4 years.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Seedream 5.0 model within the Krea tool via the following prompt

3d rendering, isometric, mountain park, paper work for a tactile style, select a color palette with vibrant colors, blue, and white with green, emphasize textures that rough-textured colored paper, with lighting that creates a warm, golden-hour glow, handmade


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 27-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-27-2026 Q2Kdcut5TwTjKx04MsSe Sun, 05 Jul 2026 01:00:08 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

Across the week from Sunday 28 June to Saturday 4 July 2026, AI news focused on making large models faster, cheaper, and more agentic, as DeepSeek open‑sourced DSpark and DeepSpec for speculative decoding with 50–400% per‑user speedups, Coinbase showed that routing to open‑weights models and aggressively raising cache hit‑rates can flatten AI spend even as token usage explodes, and Together AI’s Vipul Ved Prakash argued that stable interfaces plus commoditized silicon are allowing open‑weights ecosystems to scale tokens at an order‑of‑magnitude lower prices. xAI’s Grok 4.5 entered private beta at SpaceX and Tesla on a new 1.5‑trillion‑parameter V9 foundation model augmented with Cursor data, while Anthropic released Claude Sonnet 5 as its most agentic mid‑tier model with 1M context and broad app/API integration, even as community threads questioned inconsistencies in its published performance charts.

Hardware and infrastructure advanced with Etched coming out of stealth touting custom low‑voltage, cluster‑memory inference racks backed by over $1B in contracts and Arm’s CEO describing AI CPU demand as “off the charts,” and on the agent side, new frameworks and skills—from Nous’s 60×‑faster Hermes web reading and Vercel’s eve agent framework, to OpenClaw’s phone companion nodes, WeChat’s mini‑app–based personal agents, obra/superpowers’ multi‑skill coding harness, and LangChain’s OpenWiki for codebase memory—underscored a shift from raw model IQ toward organizational memory, tools, and deployment economics as the real differentiators.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 28th June 2026

DeepSeek just released DSpark for V4 Flash & Pro, a new speculative decoding method boosting throughput by 51% to 400%!

Deepseek also showed DSpark works well for other models like Gemma & Qwen

Brian Armstrong explains how Coinbase halved its AI spend while token usage grew exponentially by implementing better defaults, routing, and caching instead of usage caps or alerts.  Key tactics include defaulting to cheaper open-weight models like GLM 5.2 and Kimi 2.7 via an LLM gateway, AI-driven prompt preprocessing for optimal model selection, and raising cache hit rates from 5% to 60% in tools like LibreChat.

Elon Musk announces Grok 4.5 entering private beta at SpaceX and Tesla, powered by a new 1.5T parameter V9 foundation model with supplemental Cursor data training.  Early evaluations indicate Grok 4.5 performance is close to or potentially exceeding Claude Opus, with reinforcement learning driving ongoing gains and the Grok Build harness improving daily.

Monday 29th June 2026

Vipul Ved Prakash, CEO of Together AI who lurks in this group  draws parallels between the PC industry's modular architecture and current AI developments, arguing that stable interfaces like transformers, OpenAI-compatible inference APIs, and agentic harnesses are driving specialization and explosive ecosystem growth. 

The essay highlights rapid open-weights model progress through shared recipes (e.g., Rotary embeddings, GQA, MoE), modular frameworks for inference and training, and commoditized silicon, evidenced by Together AI's 10,000x token volume increase and high SWE-bench performance across frontier open models. 

Open-weights companies are building viable businesses via API revenue and licensing, offering tokens over an order of magnitude cheaper than closed models, though the post warns of political risks from potential regulatory capture favoring incumbents.

DeepSeek announced peak/off-peak pricing for V4, launching mid-July. Rates double during Beijing business hours (9-12, 14-18).

Tuesday 30th June 2026

via Chas

New MCP for GEO analysis and website improvement loops
Thought it was interesting as I’ve not seen many that do this well

Gokul Rajaram summarizes Nikesh Arora's interview with Harry Stebbings of 20VC on enterprise AI, where the Palo Alto Networks CEO argues "memory" or accumulated user/company context creates the primary competitive moat over raw model intelligence. 

Arora stresses enterprises demand depth and zero-tolerance for errors in autonomous agents—unlike forgiving consumer AI—requiring massive edge-case training akin to Waymo's self-driving tech rather than bolting AI onto old workflows. 

Key predictions include token prices falling 90%, half of G&A roles disappearing in three years, more demand for engineers and salespeople, and software evolving to offer "opinions" that amplify human output

Nikesh Arora on The Future of Token Costs | Memory Becoming the Moat & Why Enterprise AI Isn't Ready - 20VC with Harry Stebbings

Nikesh Arora, CEO of Palo Alto Networks, shares insights on navigating the rapidly evolving tech landscape, particularly in cybersecurity and AI. He e

https://www.usetranscribe.io
Nikesh Arora on The Future of Token Costs | Memory Becoming the Moat & Why Enterprise AI Isn't Ready - 20VC with Harry Stebbings

Wednesday 1st July 2026

Anthropic Releases Claude Sonnet 5 AI Model

Anthropic launched Claude Sonnet 5, its most agentic Sonnet model with substantial improvements over Sonnet 4.6 in reasoning, tool use, coding, and knowledge work, approaching Opus 4.8 performance at lower pricing. It features a 1M context window and is now the default for Free and Pro users, available across Claude apps, API, Claude Code, and integrations including Cursor, GitHub Copilot, Notion, and OpenRouter. Introductory pricing is $2 per million input tokens and $10 per million output tokens until August 31, followed by standard rates of $3/$15

Etched exited stealth announcing custom AI inference racks after a successful A0 tapeout, with $1B+ customer contracts and $800M raised, claiming state-of-the-art throughput, latency, and power efficiency on inference workloads. 

The company highlights two innovations: Low-Voltage Inference to boost FLOPs density without thermal throttling for high-throughput tasks like trillion-parameter MoEs, and Cluster-Scale Memory to reduce latency via a shared low-latency pool across chips.

Backed by top investors including Jane Street and HRT plus AI leaders like Hinton, Karpathy, and Thiel, their 400+ engineer team from NVIDIA, Google, and TSMC plans first rack shipments this summer alongside production scaling.

I strongly recommend watching the Youtube interview linked below


via Huzefa

OpenClaw connects your phone to a self hosted AI gateway

OpenClaw released iOS and Android companion node apps that connect your phone to a self hosted AI agent gateway, which can run on macOS, Linux, or Windows (WSL2). The phone becomes the agent body with camera, location, voice and notifications, while the actual chat happens on your computer through the gateway and never on the phone. Privacy matters here, with commands like camera and screen capture needing explicit allowlists and approvals, and connections defaulting to LAN with TLS options for remote access.

via Anthropic

Fable 5 will be available starting tomorrow, Wednesday, July 1, to users globally on the Claude Platform, Claude.ai, Claude Code, and Claude Cowork. For Pro, Max, Team, and select Enterprise plans,1 Fable 5 will be included for up to 50% of weekly usage limits through July 7, after which it will be available via usage credits. We will re-enable access on AWS, Google Cloud, and Microsoft Foundry as quickly as possible.

via Alex

Metrics massaging by Anthropic.
They published two contradictory performance evals for Sonnet 5 within a day. 
One saw Sonnet 5 pareto optimal against Opus 4.8, another with a significant gap albeit a lower pass rate. 
Given that both charts referenced the same methodology, it means that either Anthropic faked the numbers or was sloppy in its application.
Imho it shows that without neutral third party tests, "Agentic" metrics are not to be trusted.
Here the 2 contradictory charts & reddit discussion:

https://www.reddit.com/r/ClaudeAI/comments/1ukgqwr/looks_like_anthropic_quietly_updated_the_sonnet_5/?share_id=vGWnnqL8AmfEDx3gfRAG3

Hermes Agent Upgrade Delivers 60x Faster Web Reading

Nous Research updated its popular open-source Hermes Agent to process scraped web pages directly, eliminating a redundant LLM summarizer that previously added time and costs. For large pages, content caches locally, with the agent accessing sections as needed, delivering 11.7 times average speedup and 23 times cost savings while matching or improving answer quality.

Web Search & Extract | Hermes Agent

Search the web and extract page content with multiple backend providers - including free self-hosted SearXNG.

https://hermes-agent.nousresearch.com
Web Search & Extract | Hermes Agent

feat(web_extract): truncate-and-store instead of LLM summarization by teknium1 · Pull Request #54843 · NousResearch/hermes-agent

Summary web_extract returns clean page content directly instead of running an auxiliary LLM over every scraped page - a live before/after eval shows 11.7x faster overall (176.6s → 15.1s across 4 UR...

https://github.com
feat(web_extract): truncate-and-store instead of LLM summarization by teknium1 · Pull Request #54843 · NousResearch/hermes-agent

Thursday 2nd July 2026

via Bill Gurley of Benchmark Capital

WeChat has been a little quiet on AI front & perhaps even falling into being considered a laggard. But this week, Pony Ma presented a new vision on how to turn "mini-apps" (still no real equivalent in US) into AI handshakes

4 Million Mini-Programs, One AI Agent: WeChat's Bet to Own China's Digital Life

With 1.4 billion monthly users and 4 million mini-programs, WeChat isn't building an AI chatbot. It's building the infrastructure layer for everything.

https://crossingriver.substack.com
4 Million Mini-Programs, One AI Agent: WeChat's Bet to Own China's Digital Life

Friday 3rd July 2026

Tae Kim's  Interview with Arm CEO Rene Haas: AI CPU Demand is 'Off the Charts'

An Interview with Arm CEO Rene Haas: AI CPU Demand is 'Off the Charts'

Key Context spoke with Arm CEO Rene Haas to discuss the state of the AI market.

https://taekim.substack.com
An Interview with Arm CEO Rene Haas: AI CPU Demand is 'Off the Charts'

Vercel's Andrew Qu on why agents are a new kind of software

In this Latent Space interview, Vercel's Chief of Software Andrew Qu explains how the company's evolution from shipping web applications to building agents — driven by pain points encountered while developing the v0 vibe-coding product (model switching, fallbacks, resumability) — led to eve, Vercel's agent framework that bundles prescriptive primitives like filesystem agents, skills, compaction, and subagents so developers don't have to rediscover best practices.
Qu argues agents are a genuinely new software category requiring different infrastructure for context, tools, and long-running work, and emphasizes two emerging priorities: skills as portable, on-demand knowledge that forward-corrects outdated model information, and an agent-readable web where Vercel already detects agent requests and serves Markdown instead of HTML. He also reveals that Vercel is effectively turning itself into an agent — with capabilities embedded across its website, Slack, and dashboard — while flagging multiplayer agent development (sharing context across teammates) as a key unsolved problem.

Vercel's Andrew Qu on why agents are a new kind of software

The Vercel Chief of Software explains how its agent framework, eve, was created - and why skills, sandboxes and agent-readable websites now matter.

https://www.latent.space
Vercel's Andrew Qu on why agents are a new kind of software

obra/superpowers is an agentic skills framework for multiple coding harness built by Jesse Vincent

Rather than a single prompt, it bundles about 14 reusable skills - brainstorming, spec writing, plan writing, test-driven development, and systematic debugging among them - into a methodology the agent follows as it works. 
The scale of adoption is hard to ignore: around 244,000 GitHub stars and 21,000 forks, making it one of the most-starred projects in the Claude Code ecosystem. It was created in October 2025, remains highly active with a v6.1.0 release in June 2026, and has been accepted into Anthropic's official Claude Code plugin marketplace.

Superpowers 6

TL;DR: Superpowers 6 is much, much faster and burns many fewer tokens to get the same high-quality outcomes. If you're tokenmaxxing, maybe skip this release, but if you care about your builds being up to 50% faster and up to 60% cheaper, you're going to love Superpowers 6.

https://primeradiant.com
Superpowers 6

GitHub - obra/superpowers: An agentic skills framework & software development methodology that works.

An agentic skills framework & software development methodology that works. - obra/superpowers

https://github.com
GitHub - obra/superpowers: An agentic skills framework & software development methodology that works.

Saturday 4th July 2026

LangChain shipped OpenWiki last week. It is a command-line tool that reads your codebase, generates a wiki, and keeps it updated as the code changes. The wiki is not documentation for humans. It is context for coding agents.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the ChatGPT Image 2 model within the Krea tool via the following prompt

A close-up of water lilies using the palette knife technique, in the style of Monet, impressionist, rich and textured brushstrokes that capture the play of light, reminiscent of springtime meadows, Monet's French countryside color scheme


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 26-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-26-2026 92n9lsXUH2OnkrJxyicI Sun, 28 Jun 2026 02:44:48 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

During the week of June 21–27, 2026, major developments across AI spanned infrastructure, models, and applications. Qualcomm announced a nearly $4 billion acquisition of Modular, the AI chip software platform founded by Chris Lattner, highlighting tighter hardware–software integration. OpenAI previewed GPT-5.6 (Sol, Terra, Luna) amid U.S. cyber risk coordination, while export restrictions eased for Anthropic’s Claude Mythos 5 but not Fable 5. ByteDance launched Seedance 2.5 for 4K, 30-second video generation.
On tooling, OpenRouter introduced a unified image API across 30+ models, NVIDIA adopted LiteLLM as the internal gateway for its engineers to access 100+ models, Sakana AI introduced Fugu, a multi-agent orchestration system accessible through a single OpenAI-compatible endpoint. Vedang Vatsa published a mapping of the AI agent infrastructure ecosystem covering 75+ companies and $14B in funding. Industry commentary focused on AI’s impact on jobs, creativity, and infrastructure demand, alongside rising Chinese model usage and forecasts of increasing convergence between real and synthetic media.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 21st June 2026

Monday 22nd June 2026

Tuesday 23rd June 2026

via Huzefa

Claude Predicts a Blur in Truth

Claude, the AI system, thinks that in about five years it might become really hard to tell what’s actually true anymore. With all the deepfakes and misinformation growing, the line between real and fake could get pretty blurry. It’s a warning about how fast tech is changing and how it might shake up our perception of reality.

via Yuki Endo

Hello. Japan's Sakana AI just dropped Fugu.

A full multi-agent orchestration system accessible through a single API. The interesting part isn't another benchmark race—it's the idea that the future may belong to coordinated intelligence rather than a single giant model.

Definitely worth trying. This could be the next big shift in AI infrastructure. This is smart as security layer

Sakana Fugu

Sakana Fugu is a model interface for coordinating specialized AI agents through one OpenAI-compatible API.

https://console.sakana.ai

various discussions on the above primarily around pricing points

Prashish: This is much needed, a middle ground between $20 and $100. $20 usage is low, and $100 is way high for normal everyday use
Lane Rettig: This pricing model is the one that nearly all big AI projects seem to have landed on.

via Y Yang

This is exactly the kind of problem I am trying to understand better.

For context, I am working on InferHub, an OpenAI-compatible multi-model API gateway. The idea is to make it easy to switch between models like GPT, Claude and Gemini through one endpoint.

If your main issue is provider switching, cache misses, cost, or fallback behavior, I would be happy to give you free credits to test it. No pressure to use it long term; I am mainly looking for feedback from people building real AI apps.

Your use case sounds like: "I think I’m hitting the limit of the Github Copilot + OpenRouter workflow financially. My setup: PhpStorm Github Copilot extension OpenRouter mostly DeepSeek V4 Flash in ag"

Wednesday 24th June 2026

via David An

hi everyone, I am sharing my presentation on the research topic of "HOW CAN ARTIFICIAL INTELLIGENCE DRIVE HUMAN CREATIVITY?

Provocation Lab Research Brief #1 - Human-AI Collaboration in Creativity | Dr. David An | 14 comments

HOW CAN ARTIFICIAL INTELLIGENCE DRIVE HUMAN CREATIVITY? This is what I discuss by introducing a research study written by the Institute of AI in Management Ludwig-Maximilians-Universität München.

https://www.linkedin.com
Provocation Lab Research Brief #1 - Human-AI Collaboration in Creativity | Dr. David An | 14 comments

Qualcomm plans to acquire Modular, which builds a chip software platform and has a proprietary coding language, in a nearly $4B deal set to close in H2 2026

Modular was founded by Chris Latner who created LLVM as well as the Swift programming language and was briefly head of Tesla's Autopilot software

Qualcomm Buys Buzzy Chip Startup Modular for Nearly $4 Billion

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit.

https://www.wired.com
Qualcomm Buys Buzzy Chip Startup Modular for Nearly $4 Billion

ByteDance unveils Seedance 2.5, saying the AI video model can generate up to 30-second clips from up to 50 reference materials, up from 12 for Seedance 2.0

Thursday 25th June 2026

OpenRouter launched a unified Image API providing access to 30+ image generation models from 8 providers including Google, OpenAI, Black Forest Labs, Recraft, ByteDance, and xAI.  Features include a single normalized request format across providers, transparent per-image USD pricing in responses, optional provider-specific parameters, and SSE streaming support for models like OpenAI's GPT Image.

via Casey Newton

Q&A with AWS CEO Matt Garman on the parallels between early AWS and AI, Quick, AI coding, Amazon's $200B capex in 2026, entry-level jobs changing, and more

The CEO of AWS on why Amazon is hiring 11,000 interns and junior employees

Matt Garman argues that junior employees are as necessary as ever. But AWS now sells agents that can recruit, code, and process claims. Will the balance hold?

https://www.platformer.news
The CEO of AWS on why Amazon is hiring 11,000 interns and junior employees

Friday 26th June 2026

Saturday 27th June 2026

OpenAI Launches Limited GPT-5.6 Preview at U.S. Government Request

The company introduced Sol, Terra, and Luna through its API and Codex platform, with Sol leading benchmarks on complex command-line tasks like vulnerability research. This limited preview follows federal guidance and a Trump administration executive order on reviewing AI systems with cyber risks, including recent meetings between CEO Sam Altman and Commerce Secretary Howard Lutnick. OpenAI previewed the models with officials beforehand and anticipates wider access soon, amid debates over whether the process stifles innovation or wisely addresses misuse risks.

via Alex

Percentage of chinese models' token share on OpenRouter up 3x over a year

Haseeb of DragonFly has his own strong take on what inferences can be drawn from this OpenRouter data and I agree to a large extent.   US Frontier Lab usage isn't showing up on OpenRouter

US. Government Lifts Export Block on Anthropic's Claude Mythos 5 Model for Over 100 Approved U.S. Companies and Agencies

The Trump Administration, via Commerce Secretary Howard Lutnick's letter to Anthropic co-founder Tom Brown, partially lifted export controls on Anthropic's Claude Mythos 5 AI model, permitting its release to more than 100 trusted U.S. companies and federal agencies after weeks of negotiations over national security concerns

Restrictions on Fable 5 remain in place, with Anthropic committing to collaborate on future model release protocols and standards.

How Thousands of NVIDIA Engineers Access 100+ AI Models Through a Unified Inference Service   

NVIDIA engineers faced fragmented access to 100+ AI models across cloud providers, open source, and internal services with different APIs and credentials.

The IT Inference team built the Enterprise Inference Hub centered on LiteLLM as a unified gateway.

Applications use one OpenAI-compatible API and single credentials while LiteLLM handles routing, authentication, and metrics.

Centralized control enables usage monitoring, token accounting, cost visibility, budget enforcement, and easy model portability.

The hub now processes trillions of tokens weekly, letting engineers focus on building AI apps without infrastructure complexity.

via Vedang Vatsa
This paper maps the entire AI agent infrastructure stack -> 8-layers from compute to security and tracks $14B funding:

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the ChatGPT Image 2 model within the Krea tool via the following prompt

a painting of a woman holding a red umbrella, in the style of fluid dynamic brushwork, textured canvas, graceful balance, dark orange and white, whirly, i can't believe how beautiful this is, energetic street artist --ar 73:105 --stylize 750 --v 6


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 25-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-25-2026 5IqW1Mf4i0yY28dcVVYp Fri, 26 Jun 2026 12:10:36 GMT This post originally meant to go out on 21st June was delayed because I had taken some personal time off to participate in Ashara Mubaraka [from 16th to 24th June]

This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of June 14–20, 2026 saw high-profile industry consolidation, with SpaceX acquiring Cursor AI in a $60 billion all-stock deal aimed at accelerating model development on its Colossus supercomputer. Model releases continued to push boundaries, headlined by GLM-5.2—an open-weight model with a 1-million-token context window and competitive pricing. On the talent front, Noam Shazeer, co-inventor of the Transformer architecture, departed Google for OpenAI, while Microsoft's Satya Nadella outlined a strategy centered on companies owning their own "learning loops" and building "token capital" from internal data. Market concerns also surfaced, including leaked reports questioning OpenAI's financial stability and broader commentary on unsustainable business models across the sector. On the product side, Midjourney announced a healthcare division, CozyBlanket Pro introduced real-time AI topology prediction for 3D modeling, and MLX-LoRA-Studio brought local LoRA fine-tuning to Apple Silicon Macs.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 14th June 2026

via Sam O who asked the following question

What do you think about what is said in this video ?

Short summary of the video: The AI industry is facing a potential collapse due to unsustainable business models, over-reliance on hype, and financial struggles, which may ultimately lead to a significant downturn in the industry.

Monday 15th June 2026

Satya Nadella Outlines AI's Future for Businesses in Detailed Post

Satya Nadella says companies must own their AI “learning loops” to compound human and token capital.  He describes a 'cognitive loop' where human judgment guides AI, emphasizing two key assets: employees' ingenuity and 'token capital' from custom AI developed on private data. He warns against AI labs capturing all value, pushing for a 'frontier ecosystem' that spreads benefits across firms and nations while avoiding industry hollowing. Tech leaders praised the positive vision, though critics pointed to job risks from Microsoft's own layoffs and workflow automation.

Tuesday 16th June 2026

via Alex in response to Sam O's question on 14th June

Ed Zitron just leaked financials of OpenAI 😬 (good read):
https://www.wheresyoured.at/exclusive-openai-financials/?ref=ed-zitrons-wheres-your-ed-at-newsletter

Numbers read almost as badly as the SPCX S1.

If the numbers turn out to be real, this is 30~40% less revenue & >800% higher losses than claimed/hinted at by Sam previously

Here the FT reporting, they wouldnt publish without proper vetting. Super bearish:

https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb828?syn-25a6b1a6=1

Lol, the FT just updated the article. They now claim that 77% of the losses booked last year (30 out of 39B$) might be attributable to the non-profit restructuring, which would be a much better case.

21B$ costs from operations on 13B$ of revenue.

Sorry, last message from my side on this (I just find the economies of scale of frontier models fascinating and have to share). 

Im in Ed's discord and he now claims that he expects the real costs of operations to be much higher given that the leaked numbers are non-GAAP and are artificially reduced by removing at least 30% by associating costs to listed stakeholders (e.g. Microsoft).

He expects 9$ spent on 1$ earned.

Time will tell.

SpaceX announced it exercised its option to acquire Cursor AI, the leading AI code editor by Anysphere, in an all-stock $60 billion transaction aimed at building the world's most useful AI models.

Wednesday 17th June 2026

GLM-5.2 offers a 1 million token context window, two reasoning modes, and low pricing at $1.40 per million input tokens. It leads as the top open-weight model on Arena.ai's Code Arena: Frontend with a 1,595 Elo score, dominates subcategories like Gaming and Data & Analytics, and ranks #3 on the tough FrontierSWE benchmark. Developers highlight its 11-times cheaper cost over competitors and real-world coding skills

Thursday 18th June 2026

Noam Shazeer, co-inventor of the Transformer architecture and Mixture of Experts, announced he is leaving Google to join OpenAI.  Shazeer joined Google in 2000, co-led Gemini after returning from founding Character.AI, which Google acquired for $2.7 billion in 2024.

via Anthony Estebe

Hello everyone,

I’m trying to better understand how companies are actually using AI in their day-to-day operations.

Not the hype, and not coding, more things like admin, reporting, sales, support, internal knowledge, workflows, etc.

If you run a business or are involved in operations, I’d love your feedback. It takes around 2 minutes:

I'm doing a small market study on how companies are actually using AI today. Not for coding or technical work, but to run the business: operations, admin, sales, reporting, support, internal... | Anthony ESTEBE

I'm doing a small market study on how companies are actually using AI today. Not for coding or technical work, but to run the business: operations, admin, sales, reporting, support, internal knowledge, documents, coordination, etc. I'd love to hear from founders, CEOs, COOs, operations leads, and business owners.

https://www.linkedin.com
I'm doing a small market study on how companies are actually using AI today. Not for coding or technical work, but to run the business: operations, admin, sales, reporting, support, internal... | Anthony ESTEBE

via Ben

This is pretty amazing. Never would’ve expected this 

Midjourney made new scanning tech

“Announcing a new division of Midjourney called "Midjourney Medical"”

Friday 19th June 2026

via Kevin Dent

This is WOW

Saturday 20th June 2026

via Robby Yung

via George Isichos

There's a new app that lets you post-train models on any Mac with enough RAM. You can experiment with most popular finetuning frameworks like LoRA/QLoRA, full fine-tuning, or Quantization-Aware Training (QAT) that Gemma 4 recently used. I find it amazing that after local inference, any user can also create a customized version of Gemma 4 or Qwen3.5/3.6 at home.

GitHub - Goekdeniz-Guelmez/MLX-LoRA-Studio: A native Mac App for LLM fine-tuning on Apple Silicon - fully on-device, fully open source.

A native Mac App for LLM fine-tuning on Apple Silicon - fully on-device, fully open source. - Goekdeniz-Guelmez/MLX-LoRA-Studio

https://github.com
GitHub - Goekdeniz-Guelmez/MLX-LoRA-Studio: A native Mac App for LLM fine-tuning on Apple Silicon - fully on-device, fully open source.

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Nano Banana Pro model within the Krea tool via the following prompt

Overgrown blooming flower, garden, Italian ornate courtyard, Watercolor, trending on artstation, sharp focus, studio photo, intricate details, highly detailed


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas) <![CDATA[This Week in All Things AI - Week 24-2026]]> https://paragraph.com/@twiata/this-week-in-all-things-ai-week-24-2026 xJA3ta9E2FyZ4DC8Ahkd Sun, 14 Jun 2026 01:47:18 GMT This Week in All Things AI covers key developments in models, agents, tools, infrastructure, and policy curated from discussions in the All Things AI Telegram group.

If you follow AI for work, research, investing, or just to understand where the technology is heading, this weekly brief is a concise way to scan the most important launches, risks, and resources in a few focused minutes.

The week of June 7–13, 2026 saw model releases dominate the AI landscape, with Anthropic introducing Claude Fable 5, Z.ai launching GLM-5.2 with a 1 million-token context window, NexEcosystem announcing the Nex-N2 open-weight agentic family, Moonshot releasing Kimi-K2.7-Code for coding efficiency, and Google upgrading NotebookLM with Gemini 3.5 and agentic capabilities. In infrastructure, Harvey AI and Trajectory Labs post-trained NVIDIA's Nemotron 3 Ultra for legal tasks, and an authentication layer was released to connect agents like Hermes and OpenClaw to commercial APIs.
The week closed with a significant policy disruption as the US government issued an export control directive suspending all non-US access to Fable 5 and Mythos 5, forcing Anthropic to disable the models worldwide. Meanwhile, investor Ray Dalio warned of an AI bubble, OpenAI reportedly considered price cuts to compete with Anthropic, and Cognition's coding benchmark found most frontier models produce largely unusable output.

The sections that follow walk through these items day by day, with short context and links so you can dive deeper into the pieces most relevant to your work or interests.

Sunday 7th June 2026

via Huzefa

In an interview on Bloomberg Television, Dalio said that all great technological changes create bubbles and that AI was showing the typical signs of this pattern. Investor Ray Dalio says the AI bubble will burst in the next few years unless startups start making billions. Currently, AI companies spend about 8 times more than they earn. The sector needs to generate $2 trillion annually by 2030 to justify current investments.

Dalio compares the AI bubble to past tech bubbles like dotcoms, where stock prices rose on hype but lacked real profits. He acknowledges AI will change the world but doubts its economic viability so far.

Monday 8th June 2026

My friend Ian Rogers of Ledger just published on Rick Rubin's Tetragrammaton on why AI will be many things, but never human.   He argues that while AI will become pervasive, persuasive, and highly “personality-like,” it remains a tool rather than a person, and the real danger is humans forgetting that distinction and granting it moral or legal status.

AI Will Be Many Things. But Never Human. - Tetragrammaton

The danger is not that machines will wake up. It is that we will forget the difference...

https://www.tetragrammaton.com

Tuesday 9th June 2026

Google upgrades NotebookLM, which now runs on Gemini 3.5 and Antigravity, to deliver new agentic capabilities and more advanced reasoning for AI Ultra users

Do better research with NotebookLM

NotebookLM's latest upgrades deliver new agentic capabilities and more advanced reasoning to tackle complex research projects.

https://blog.google
Do better research with NotebookLM

Gemini 3.5 and Antigravity come to Google NotebookLM

NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.

https://arstechnica.com
Gemini 3.5 and Antigravity come to Google NotebookLM

Cognition, creators of the AI software engineer Devin, launched FrontierCode, a new coding benchmark that evaluates whether AI-generated code would be merged by expert open-source maintainers rather than just passing functional tests. 

Tasks were hand-crafted by maintainers from 36 major repositories with over 40 hours invested per task, using unit tests, rubrics, scope checks, and novel verifiers like mutagent to assess quality, style, test effectiveness, and codebase adherence. 

Leading models show major gaps on the 50-task Diamond set, with Claude Opus 4.8 topping at 13.4%, GPT-5.5 at 6.3%, and open models lower, while reducing misclassification errors by 81% versus SWE-Bench Pro.

Wednesday 10th June 2026

via Ben

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Comment from Anya Shapina on Fable's release

Day 1 using Fable  - so far, extraordinary! Quality, speed, rule-following. Use case - building agents on MastaAI (ts). Haven't check token burn :) Anyone else?

Thursday 11th June 2026

Harvey AI partnered with Trajectory Labs to post-train NVIDIA's open-weight Nemotron 3 Ultra on their Legal Agent Benchmark, lifting its all-pass rate from 0% to 5.8% and rubric pass rate to 81%, placing it between Claude Sonnet 4.6 and Opus 4.6. 

Post-training shifted held-out task reliability from ~70% to ~95% rubric pass rates within 24 hours of the base model's release, using the same data and platform recipe applied to prior Nemotron versions. 

The resulting legal agent matches closed-model performance bands while operating at 1/8th to 1/50th the per-token cost, demonstrating rapid domain specialization of open models for high-value verticals like law.

via the Wall Street Journal so it may be behind a paywall

Sources: OpenAI is considering drastically lowering its price for tokens in anticipation of similar cuts the company expects at Anthropic

https://www.wsj.com/tech/ai/openai-considers-drastic-price-cuts-anticipating-war-for-users-with-anthropic-9b8c178e?st=1Yyrco

SemiAnalysis bought all consumer plans from Anthropic and OpenAI, then pushed them to weekly limits with intense coding tasks. Their tests showed OpenAI's $200 Pro 20x plan equals up to $14,000 in monthly API value, while Anthropic's $200 Max 20x reaches $8,000—many times the subscription cost. The firm notes these heavy subsidies hook developers, though limits evolve and skeptics question long-term sustainability as companies adjust rates.

Friday 12th June 2026

via Ming Guang Yong

For folks with hermes and openclaw, I have build an authentication layer (https://mybrandmetrics.com) to various apps that I use like Google Ads, Facebook Ads, Google workspace (calendar, sheets, drive, gmail), Youtube, Instagram, Tiktok (mainly on the social media side) that can connect agents to (openclaw and hermes agent). It's currently free. I have been using it for my own Google Workspace use cases like finding emails, copying content from emails into google sheets, creating calendar invites with attachments. Looking for some folks to test and give feedback. Pls try it out and DM me

This authentication layer enables agents to connect to APIs and data in these apps. I also have the associated skills at https://clawbus.com/skills

NexEcosystem announces Nex-N2, a new open-weight family of agentic models emphasizing unified "Agentic Thinking" that fuses reasoning, coding, search, and tool use into one coherent loop with adaptive depth scaling for efficiency. 

The self-reported numbers are spicy 
75.3 on Terminal-Bench 2.1
80.8 on SWE-Bench Verified
90.7 on GPQA Diamond

Currently available for free via OpenRouter

Couple of links about AI created micro-dramas which are picking up in China and may become popular in the US. 

Roope Rainisto , Creative AI lead at Supercell who also lurks in this group has created a product named Beat Bandit which generates scripts for different formats  including micro-dramas [site has some rendering issues currently which I've let Roope know]

https://x.com/ollieforsyth/status/2058773143091896679

Followup message from Roope himself

Yeah, working on BeatBandit - you are the showrunner and that's your writers' room - use the team a little, use the team a lot. 

I've been running it through Claude Fable for a few days now through the MCP - pretty brutal workflow to allow Claude to read the code, simulate the user, evaluate the result, pinpoint things in the code that are not optimal - rinse and repeat. Fable is smarter than me, tough pill to swallow but it is what it is.  :)

Kimi-Moonshot announces the open-source release of Kimi-K2.7-Code, a specialized coding model with strong gains over K2.6: +21.8% on Kimi Code Bench v2, +11% on Program Bench, +31.5% on MLS Bench Lite, plus 30% fewer reasoning tokens and better long-horizon task success. 

Benchmark charts show K2.7-Code outperforming K2.6 while closing the gap or exceeding GPT-5.5 and Opus 4.8 (xhigh) on coding and agent metrics, with a performance-vs-tokens plot highlighting reduced overthinking and higher efficiency. 

The model is immediately available via Kimi API and Kimi Code, with 6x high-speed mode incoming and a new beta program for early testing of upcoming features

Saturday 13th June 2026

Anthropic reveals a US government export control directive citing national security has forced the suspension of Fable 5 and Mythos 5 access for all foreign nationals, including their own foreign employees. 

To comply, the company must immediately disable these models for every customer worldwide, while access to other Claude models remains fully operational. 

Anthropic calls the order a misunderstanding, apologizes for the disruption, and states it is working urgently to restore service as soon as possible.

Z.ai released its flagship GLM-5.2 AI model to paid Coding Plan users, highlighting its strengths in coding tasks with a 1 million-token context window and adjustable thinking levels for complex workflows.  Full API access and open-sourcing under the MIT license arrive next week

Below is my personal website which aggregates links to many of my socials as well as the various content and community that I curate. Feel free to share this link to others who you think may find this content/community useful to them

https://linktr.ee/goolamabbas

The cover image of this newsletter via generated via the Nano Banana Pro model within the Krea tool via the following prompt

The Great Wave : "Blossoming Cataclysm" Photorealistic : State-of-the-art photorealistic , leveraging advanced AI imaging capabilities. Landscape : Supernova erupting into a colossal red flower , marrying intense beauty with awe-inspiring realism. Message: Artistic convergence of cosmic power and giant blooms , evoking a vivid and visceral explosion. Format: A visually arresting masterpiece , showcasing the zenith of modern imaging techniques, ideal for gallery and prestigious exhibitions.


]]> twiata@newsletter.paragraph.com (Yusuf Goolamabbas)