Alternatives
Products that do what Open KB: Open LLM Knowledge Base does
We release an open source version of Andrej Karparthy's open knowledge base, and we scale it to support long PDFs with Pageindex. Any feedback is welcome to help us improve this project! Github repo: https://github.com/VectifyAI/OpenKB
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2014 · github.com
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2021 · archivy.github.io
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I've been working on Lurnby for 2 years. It's kind of like a mix of pocket + kindle + anki. It lets you => add add epubs, pdfs, and web articles to the app => highlight and add comments => tag and organize highlights => review them with a spaced repetition system Today I made the decision to open source the project. I'm passionate about helping other people learn to learn better and hope that this will allow a lot more innovation in the tool and the space. I'm very new to open source and development in general really, but looking forward to receiving the guidance of the community.
2022 · github.com
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2020 · github.com
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Apr 2026 · llmwiki.app
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2016 · github.com
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Automated documentation for developers, users, and AI Tools
Jul 2026 · moxiedocs.com
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Open-sourced SaasRock KB! A Knowledge Base starter kit built with Remix, Tailwind CSS, and Prisma. All features - Intercom/Crisp-like knowledge base structure - WYSIWYG editor with @tiptap_editor - Markdown editor - Multi-knowlege-base support - Multi-language support - Simple Analytics: Track Views and Votes - Image Storage with @Supabase - Article Duplication: Don't write from scratch - Article Drafts: draft and published copies - Import and Export: Quickly transfer knowledge bases - SEO meta tags Watch the introduction demo:…
2023 · github.com
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Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
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I found myself jumping between ChatGPT, tabs, and docs, but never building real understanding. This is my attempt at fixing that — for researchers, curious readers, and lifelong learners. Would love your thoughts on the interface, and whether this would be useful in your own work or otherwise.
2025 · proread.ai
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Hi ! I'm excited to share the Raku Knowledge Base v1.0, a comprehensive resource for the Raku programming language. Key Features: * Full-text search across all documents * Auto-generated global term index * Breadcrumb navigation for easy exploration * Integration of official Raku docs and community modules The Raku Knowledge Base is open source (Artistic 2.0 license): https://github.com/zag/raku-knowledge-base This project is built entirely using the Podlite markup language, showcasing its capabilities for creating documentation systems. Podlite itself announce post:…
2024 · raku-knowledge-base.podlite.org
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Hey HN, I'm excited to announce a new release of OntoCast — an open-source framework for extracting semantic triples and building knowledge graphs (KG) from unstructured documents (PDF, JSON, Markdown, and more). Before extracting facts, OntoCast automatically selects or creates a relevant ontology and iteratively refines it, leading to much more accurate and context-aware fact extraction. This is especially valuable for cross-domain or complex documents where a static ontology falls short. - Agentic workflow: Uses LLMs (OpenAI/Ollama) to drive the extraction and ontology refinement…
2025 · github.com
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Hey, I've made a pdf reader with ai assistance, you can quickly ask questions about the text and the images, and it will have context about all of your reading material so you can ask questions freely, try it now, I'm in open beta to find bug and get feedback. upcoming features: - Highlighting - Highlighting with Notes - Bibliography - Automatic Reference file opening - Improved UX and robustness currenlty alot of bugs so please let me know if you found any. feedback appreciated
2025 · pdf-hub.com
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2018 · en.tgr.am
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Hi all, I threw together a small prototype I am calling “Notepad.ai”. A new take on UIs for interacting with LLMs. While I enjoy using LLM’s in the chat format I wanted to see what it would be like to do it in a more long form style. It let’s you write in a pretty free form, much like Window’s Notepad, but you can choose to hit ctrl+[ to analyze the text with a preset prompt of your choosing. It has a few other small features. It’s WIP and very experimental. I would appreciate any feedback or thoughts. Video: https://youtu.be/ntdlgFmSxQY Live Demo:…
2024 · github.com
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We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes).…
2025 · github.com
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Hey HN! I've spent the past year full-time building Knowing, a tool for interacting with LLMs directly inside hierarchical structures instead of the usual prompt-response format. The idea started because I realized how much more intuitive it felt to build concept hierarchies continuously—no more endless copy-pasting or wondering how everything connects. The journey’s been a struggle. While I see huge potential in structuring AI interactions this way (writing books fast, planning projects, or organizing ideas), it’s been hard to pin down clear use cases in the market. I’m also working in near…
2024
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Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
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