Alternatives
Products that do what Clean docs for LLMs does
Crawl docs. Compose concise llms.txt.
- 1BT
2024 · github.com
- 2
- 3PT
I've developed a Python API service that uses GPT-4o for OCR on PDFs. It features parallel processing and batch handling for improved performance. Not only does it convert PDF to markdown, but it also describes the images within the PDF using captions like `[Image: This picture shows 4 people waving]`. In testing with NASA's Apollo 17 flight documents, it successfully converted complex, multi-oriented pages into well-structured Markdown. The project is open-source and available on GitHub. Feedback is welcome.
2024 · github.com
- 4
- 5
- 6MJ
2025 · github.com
- 7
- 8LD
I was inspired by a recent tweet by Andrej Karpathy, as well as my own experience copying and pasting a bunch of html docs into Claude yesterday and bemoaning how long-winded and poorly formatted it was. I’m trying to decide if I should make it into a full-fledged service and completely automate the process of generating the distilled documentation. Problem is that it would cost a lot in API tokens and wouldn’t generate any revenue (plus it would have to be updated as documentation changes significantly). Maybe Anthropic wants to fund it as a public good? Let me know!
2025 · github.com
- 9
- 10
- 11
- 12RL
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com
- 13LS
2024 · github.com
- 14
- 15
- 16
- 17
- 18
- 19
- 20IJ
Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
2024 · github.com
- 21
- 22LP
Hey HN! Over the last few months, we’ve seen many tools here trying to tackle the problem of making complex, unstructured documents ready for LLMs. The complexity primarily includes parsing highly complex documents in terms of format, layout, design, complex tables, checkboxes, etc, with high accuracy and reliability. LLMWhisperer is our take on the problem. LLMwhisperer solves most of the document complexity with reliable accuracy. With our user-friendly playground (https://pg.llmwhisperer.unstract.com/), you can effortlessly test your document use case. No sign-up is…
2024 · llmwhisperer.unstract.com
- 23
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →