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Llms.txt directory with automated submission and rough draft generator

Hacker News

Llms.txt directory with automated submission and rough draft generator

I have been noticing AI websites adding support for llms.txt standard, which inspired me to read more about it. llms.txt is similar to robots.txt but for LLMs so they can better understand a website with less tokens. I have seen a few directories, but submission is typically through a pull request to a Github repo so I went ahead and created one with automated submission and a rough draft llms.txt generator. I plan to keep improving it as more websites get added. Take a look, and let me know what you think!

Visit product View on Hacker NewsFeb 14, 2025

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, 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

47%47% 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 · Missing: mobile apps, ios, personal

34%34% 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

15%15% 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

15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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