Co

Cosmos47, a public chronological feed without algorithms

Hacker News

Cosmos47, a public chronological feed without algorithms

I’ve worked as a B2C product manager and co-founded two social-media-related startups (one acquired and one closed down), but I was never particularly good at conventional social networking. I don’t enjoy cultivating followers, optimizing for engagement, or wondering how a post will perform. Yet sometimes I want to share something and I want people to get a reasonable chance of finding it. Sure, I can start a blog or an X account (which obviously I have) but when I post something, usually nobody sees it or interacts with it. So... I've built Cosmos47 - a public social feed that shows all posts chronologically. One feed that appears the same to all users, from all over the world. No algorithm or ranking people by popularity. All posts are analyzed with AI and each post gets tagged with its language, topics, tone, format, length, location, and other characteristics. Readers can then filter the feed themselves (if they wish). The idea is distribution through relevance rather than popularity. Full Disclosure The entire website was built using AI tools, including the frontend, backend, and much of the work required to make a client-rendered app accessible to search engines. The main platform was Base44, but I've used their MCP (with ChatGPT 5.6 Sol) and added a Cloudflare layer that returns crawler-readable public pages, metadata and sitemaps. It’s still an early experiment. Most of the content currently on the platform is actually AI slop test content that I created to test multilingual feeds, filtering, moderation, embeds, search and indexing. I’m not trying to present this as an existing community and my next challenge is to attract real people who genuinely want to try it out. I’d appreciate any feedback you want to share!

Share card

Actual performance

2points
10comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, user, chatgpt · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, real people · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

A
A public feed of website changes51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A public feed of website changes

Hacker News4
Fe
Feed.news – A public news feed for anything you care about43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Feed.news – A public news feed for anything you care about

Hacker News5
Fe
Feed the Quine41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Feed the Quine

Hacker News1
Dj
Django Filtered Feed40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Django Filtered Feed

Hacker News1
GT
GTFS (transit) feed normalization41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GTFS (transit) feed normalization

Hacker News8
Rundown
Rundown32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Your feed, synthesized.

Indie Hackers1ai
Parsed Filings
Parsed Filings36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Streaming feed of public US financial disclosures

Indie Hackers1investing
AI
AI Feed26%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Feed

Hacker News1
Ho
Hoppy – Public IPv4 and IPv6 Addresses over WireGuard58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hoppy – Public IPv4 and IPv6 Addresses over WireGuard

Hacker News40
In
Inkling's Habitat (now public)47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Inkling's Habitat (now public)

Hacker News2