Convert any sitemap into LLM-friendly llms.txt to help with SEO

Hacker News

Been experimenting with how LLMs crawl and interpret content. Realized most sites aren’t optimized for them at all. BUT! the popular ones have already added llms txt file So I built a free tool that converts a site’s sitemap.xml into a markdown-style llms.txt file — kind of like robots.txt but for LLMs. It lists useful pages for AI agents to learn from or link to, in plain readable format. Try it out: https://keploy.io/llmstxt-generator Inspired by the idea that the next SEO battle isn’t just human-readable content, but AI-readable structure. Would love feedback or ideas on what to add next.

Visit product View on Hacker NewsMay 21, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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33%33% 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

16%16% 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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