Alternatives
Products that do what Multi-Tool Content API does
RSS, content extraction, sitemaps, and AI-readable files
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Howdy! RSSfeedASAP scratches my own itch. I run a regional podcasting directory which gets dozens of messy submission for podcasts. Often they don't even include an xml file and me being a good samaritan I sometimes do the manual work and find it myself. I got tired of that manual work and decided to build a microapp. RSSfeedASAP is this app and I decided to release it in case someone else finds any use in it.
2023 · rssfeedasap.com
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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RAG-ready web scraping that cuts your LLM token costs
Apr 2026 · geekflare.com
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2017 · choppingboard.recipes
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2017 · wrapapi.com
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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
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2014 · github.com
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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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