I built a search engine for llms.txt sites

Hacker News

More and more developer tools are adopting the llms.txt standard to build AI-friendly versions of their docs. The problem is that it's very hard to search across them. So I crawled millions of webpages to build Statespace, a search engine for llms.txt sites. And it's free, no API keys required. You can run plain queries to search across all sites: mcp server setup vector database embeddings oauth2 refresh token Or scope your queries to a specific site with "site: query" stripe: webhook verification mistral.ai: function calling docs.supabase.com: edge functions auth Quotes work like Google for exact phrases: "context window limit" vector database "semantic search" stripe: "webhook signature verification" This is still a work in progress, so looking for feedback and beta testers! - Search for humans (website): https://statespace.com/ - Search for agents (CLI, SDK, MCP, skill): https://github.com/statespace-tech/statespace

Visit product View on Hacker NewsApr 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

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 · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%50% 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: google · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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