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Docs-to-Markdown – Convert Documentation URLs into Markdown

Hacker News

Docs-to-Markdown – Convert Documentation URLs into Markdown

Hey HN! I built *docs-to-markdown*, a CLI tool that automatically crawls a website’s documentation and converts it into organized Markdown files—handy for quickly feeding docs into LLMs. *Key Features:* - *Flexible Conversion:* Grab docs as one single file or multiple files reflecting the site’s structure. - *LLM-Optimized:* Includes an optional GPT-4 filtering step for cleaning up the text. - *Easy Setup:* `pip install docs-to-markdown`, then run a single command. *Quick Example:* ```bash pip install docs-to-markdown docs-to-markdown https://example.com/docs --doc_name example_doc # Generates a folder 'example_doc' with one or more Markdown files ``` For filtering with GPT-4, just add `--llm-filtering` (requires an OpenAI key). *Repo & More Info:* [GitHub – fdagostino/docs-to-markdown]( https://github.com/fdagostino/docs-to-markdown ) I’d love any feedback! Let me know what you think, or open an issue if you find any bugs or have feature requests.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, single, open · 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% 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
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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