Alternatives
Products that do what Context Weaver does
tool to merge documents for LLMs
- 1BT
2024 · github.com
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- 3GF
Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
Oct 2025 · twigg.ai
- 4TA
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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- 8IJ
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
- 9LS
2024 · github.com
- 10FC
2024 · github.com
- 11QP
Hey HN! We've just launched Quilt, a robust RAG (Retrieval-Augmented Generation) UI that revolutionizes how you interact with your documents. Key features: - Multi-user setup with private/public document collections - Advanced hybrid RAG pipeline combining full-text & vector search - Smart citations with in-browser PDF preview and highlights - Fully customizable settings and prompts through the UI Making an account is free, no need to even use a strong password: this is only to ensure your documents are separate from the rest. We're keen to hear your thoughts and feedback. What features…
2024 · quilt.fly.dev
- 12LP
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
- 13LD
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
- 14AF
Hello HN! I wanted to share a side project of mine: a fully standalone app that uses a local LLM (specifically Mistral 7B) for document interaction using RAG. It supports a wide variety of document types and is designed to run right after installation. There are still quite a few bugs to iron out and many features I'm excited to add. Considering this is my first time working on such a project, I'm really happy with the progress and am learning a ton! The project is open source, so I please feel free to check out my work and leave any feedback :)
2024 · github.com
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I built this browser extension to solve a daily frustration: copying documentation from websites to feed into AI coding assistants like Cursor, Windsurf, or ChatGPT, only to get a mess of ads, popups, and navigation junk mixed in with the actual content. LLMFeeder uses Mozilla's Readability.js (same tech as Firefox Reader Mode) to extract just the main article content, converts it to clean markdown with Turndown.js, and copies it to your clipboard with a single keyboard shortcut (Alt+Shift+M or ⌥ ⇧ M). No clicking through popups, no selecting around ads, no fighting with modern web clutter.…
2025 · github.com
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Hello HN! One of the most common uses of LLMs is to go beyond what traditional RPA or IDP can do when it comes to structuring unstructured documents. However, there are a lot of challenges in getting this done right from extraction of text data from PDFs, scanned images or other formats, prompt engineering, evaluation and integration with existing systems. This very specific use case is where Unstract can help teams move really fast, leveraging LLMs. By doing the heavy-lifting in this fast-changing ecosystem it lets engineers concentrate on implementing core business workflow automations.…
2024 · github.com
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- 20BA
Hey HN, solo dev here. After years of frustration with how LLMs handle complex documents, especially PDFs with tables, I decided to build a solution myself. My approach uses a Markdown conversion step to preserve the table structure, which seems to work surprisingly well for chunking. This little parser is the first public piece of a much larger, privacy-focused AI platform I'm building. I'm pretty much running on fumes financially, so any feedback, critique, or support is massively appreciated. Happy to answer any questions about the approach!
Nov 2025 · github.com
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- 22LA
Hi HN, I am Jan, CTO and co-founder of Pathway.com. We’ve built a LLM microservice that answers questions about a corpus of documents, while automatically reacting to additions of new docs. The single, self-contained service fully replaces a complex multi-system pipeline that scans in real-time for new documents, indexes them into a specialized database and queries it to generate answers. Everyone can have their own real-time vector now. Github: https://github.com/pathwaycom/llm-app Demo video: https://youtu.be/kcrJSk00duw I am eager to hear your thoughts…
2023 · github.com
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2024 · github.com
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