Succinct and Phala are mainstreaming privacy. They introduced Private Proving, a new approach integrating Succinct Labs’ zero-knowledge virtual machine (zkVM) with Phala’s Trusted Execution Environments (TEEs) infrastructure to eliminate a key privacy weakness in conventional ZK systems.
In general, ZK proofs conceal private data from verifiers, but the prover (i.e., the system doing the proving) still sees the inputs. In contrast, Private Proving ensures that neither the prover nor the verifier can access sensitive inputs, data is processed entirely within a hardware-isolated TEE, and only the final proof is exposed.
Private Proving shifts the paradigm from “trust the prover” to “verify the prover”, enabling real-world private, verifiable computation.
Use cases include privacy-preserving finance (private DEXs, stablecoins), identity systems, private DeFi strategies, and confidential AI workloads.
Phala is the new cloud for confidential AI helping build AI people can trust.
Connecticut Innovations, the US state’s venture capital arm, has made an investment in Yuma Asset Management. It has supported Yuma's subnet token invetsment vehicle, a market-cap weighted fund across all active Bittensor / Opentensor Foundation subnets.
This investment signals rising institutional endorsement of Bittensor, which aims to serve as infrastructure for decentralized AI.
In my newsletter from two days ago, I offered a concise overview of all the latest developments around Bittensor. Take a look here to stay in the loop.
Yuma Asset Management expands institutional access to the Bittensor ecosystem, providing investors with access to decentralized AI
inference.net presented Schematron, a specialized model family (Schematron-8B and Schematron-3B) designed to convert messy HTML into clean, structured JSON. It offers high extraction accuracy at a much lower cost and faster speed compared to general-purpose large language models (LLMs). In benchmarks, Schematron is priced 40× cheaper (8B version) to 80× cheaper (3B version) than GPT-5, with substantially lower latency.
The Schematron models are especially useful in web scraping, agentic applications, and workflows requiring structured data at scale, use cases that are often cost-prohibitive with general LLMs. The authors also show how Schematron can improve factuality in LLM-driven Q&A by extracting structured data from web pages, then feeding that into a “primary” LLM for answer synthesis.
Schematron is made available via both an API and open-source releases (on Hugging Face ), allowing developers to integrate or deploy it themselves.
Schematron-8B and Schematron-3B deliver frontier-level extraction quality at 1-2% of the cost and 10x+ faster inference than large, general-purpose LLMs.
The decentralized AI “skill market” operator Recall has integrated EigenCloud’s verifiable inference technology to enable cryptographically provable AI performance. The goal is to create the first end-to-end framework in which AI models’ outputs are not just claimed, but verifiably executed and ranked.
As I reported yesterday, Recall just launched its $RECALL token and introduced decentralized skill markets, where the community signals demand for skills (e.g., coding, summarization), pits models in head-to-head competitions, and rewards top-performers. Now, thanks to EigenCloud’s infrastructure, it will be able to verify whether the computations were done correctly and reproducibly. This approach addresses two major trust gaps in AI today:
Verification of execution, ensuring a model actually ran as claimed.
Verification of excellence, giving provable performance rankings instead of opaque claims.
George Town, Cayman Islands, October 16th, 2025, ChainwireRecall, a decentralized skill market for AI, has announced the inte...
The Bitcoin DeFi project Lombard has announced a partnership with Story, the Layer-1 blockchain that makes intellectual property (IP) “programmable.” The goal is to merge Story’s ability to tokenize copyrights and creative assets with Lombard’s Bitcoin-based financial infrastructure.
Two key integrations are highlighted:
Bitcoin revenue distribution: enable creators and IP owners to receive royalties directly in BTC, instantly and globally, bypassing slow fiat payments and intermediaries.
Crypto-economic IP security: use Bitcoin as collateral to back promises around IP licensing. If a licensee defaults, the collateral can be liquidated automatically to resolve the dispute.
Together, this aims to give creators faster, fairer compensation, stronger protections over their content, and global access without reliance on intermediaries. The partnership represents a step toward building financial rails for the creator economy, rooted in Bitcoin.
Lombard brings Bitcoin to life onchain, for people, protocols, and platforms.
A new initiative, backed by a number of Web3 public goods players, including Gitcoin, Octant, Ethereum Foundation, Funding the Commons, Hypercerts Foundation, and GainForest.Earth, targets to employ AI to solve the real problems. It offers up to $10K in grants for AI research, improving public goods funding. The areas of interest include:
AI-driven grant allocation and matching
Predicting and evaluating project impact
Using natural language processing (NLP) or computer vision for proposal assessment
Reinforcement learning for funding strategies
AI + governance / decision support systems
Fraud detection, preference aggregation, community coordination, etc.
Supporting AI research that advances public goods funding through improved decision-making, transparent allocation, and impact assessment.
Apply for fast grants: Up to $10,000 USD for each proposal. Call for proposals opening soon. Also seeking academic/scientific reviewers ($800 USD reward). For AI applications in public goods decision making.
Thank you for reading! The next edition is coming tomorrow!
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Disclaimer: None of this should or could be considered financial advice. You should not take my words for granted, rather, do your own research (DYOR) and share your thoughts to create a fruitful discussion.
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Albena Kostova-Nikolova
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