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Products that do what LLMs.txt File Generator does
Build a spec-correct llms.txt file in minutes
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Generate llms.txt from any sitemap. No CMS required.
Feb 2026 · sitemaptollms.com
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2024 · adamgrant.info
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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
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Make your website visible to AI in less than 30 seconds
Jan 2026 · llmstxtdirectory.org
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2024 · github.com
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Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
2023 · automorphic.ai
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I found that I am using ChatGPT more and more to get the FFmpeg command I need, but the process can be a bit tedious: copy-pasting commands, dealing with input file names and locations, making sure the prompt contains enough info about the input files. This site attempts to solve that. You just describe what you want to do, pick the input files and an LLM (currently DeepSeek) generates the FFmpeg command. You can then run it directly in your browser or use the command elsewhere.
2025 · vidmix.app
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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
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2023 · github.com
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