# Nouns x Farcaster

*A path to delivering a great app*

By [Disperse Data by Tudor (two-door)](https://paragraph.com/@tudorizer) · 2024-03-27

nouns, farcaster, delivery, managing complexity

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### How can we best ensure that 3 months from Friday Nouns has at least one amazing Farcaster client?

Delivering a great app is not trivial and complexity creeps up easily from behind punchy feature names, wireframes, sketches and concept art.

**I value pragmatism**, so allow me to be boring for a moment.

[Nouns x Farcaster](https://nounsfarcaster.com?source=tudorizer) clients have layers:

*   data layer: aggregate, filter and store the Nounish content
    
*   presentation layer: display the data in an engaging and aesthetic manner
    
*   governance layer: decisions on client features, curation, contributing
    

Data
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The data layer is mostly already out there, because [/nouns](https://warpcast.com/~/channel/nouns) have historically done a cracking job at creating and putting-it-out-there! _Algonouns_ enhances this layer, by collecting and sort it, based on transparent and customisable criteria. It introduces a new primitive fit for the ethos of [Nouns](https://nouns.build/). Unlike most social apps, Algonouns optimises for discovery and connection, instead of ads and [“fame”](https://www.lisnewsletter.com/p/love-vs-fame-a-framework-for-social).

Presentation
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Presentation matters. "[Good UX](https://ia902800.us.archive.org/3/items/thedesignofeverydaythingsbydonnorman/The%20Design%20of%20Everyday%20Things%20by%20Don%20Norman.pdf)" is relative and unpacks based on type of content, user habits and accesibility. [Illustrations](https://imagedelivery.net/BXluQx4ige9GuW0Ia56BHw/a11e6094-5229-40fa-7021-d5d6f40eeb00/original) ([source](https://warpcast.com/momotsuki/0xdf4fb370)), [videos](https://zora.co/collect/zora:0x72d07beebb80f084329da88063f5e52f70f020a3/1?referrer=0xf3517EaeEC9772BB66F3a35Ff99f238822caEfcf), [stories](https://www.youtube.com/watch?v=jPTVtUAhVo0) and [gifs](https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExa2M5anU3d2VvZjBlczB3azYydmc0eGxzOTl0Y3Y3bnJpc25qa3cyNSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/dqUAj2tZHKnj8DIBDS/giphy.gif) are meant to shine in _NounTok_. The familiar feed + voting in _Nouncaster_ aims to quickly pop nuggets of news, while nurturing reactions by voting or replying. More [Kiwi News](https://news.kiwistand.com/), less Facebook. **Nounishness** is part of the content and adds or subtracts weight to how readers perceive the latest DAO launch (as one example).

![](https://paragraph.xyz/editor/callout/information-icon.png)

**Tangent:** Kiwi News introduced [**Feedbot**](https://paragraph.xyz/@kiwi-updates/kiwi-feedbot-submissions-open?referrer=0x3e6c23CdAa52B1B6621dBb30c367d16ace21F760) recently, with a similar goal.

Governance
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Finally, the governance layer should be concise and permissive. A lot of issues are solved by [CC0](https://creativecommons.org/public-domain/cc0/) and open-source, models which in the long run outlive even the coolest [BDFLs](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life). You don't have to be **Nounish** to read the spec and contribute your own curation buy assembling an algo in _Algonouns_.

**References and context**

A video deep-dive in Algonouns: [https://www.loom.com/share/b14cf7b8792249aeabe91b1489c59c93?sid=fc473216-4101-457a-bc07-61c5ce897dc9](https://www.loom.com/share/b14cf7b8792249aeabe91b1489c59c93?sid=fc473216-4101-457a-bc07-61c5ce897dc9)

Nouncaster in the prop.house: [https://prop.house/0x767a3bdf2aa3b3201b794927a997fcf4e50d4702](https://prop.house/0x767a3bdf2aa3b3201b794927a997fcf4e50d4702)

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*Originally published on [Disperse Data by Tudor (two-door)](https://paragraph.com/@tudorizer/delivering-nouns-x-farcaster)*