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Thin AI layer on your storage, semantic search on S3 buckets

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  17. 17GO

    Small experiment of visualization of wikipedia articles as a graph using d3.js.<p>Articles with more traffic are bigger. I computed the semantic similarity using LSI with python (gensim) You have to scroll down/right a bit!<p>http://similarityapi.appspot.com/graph/?title=blade%20runner<p>There is also a JSON api: http://similarityapi.appspot.com/api/v1/?limit=100&title=blade%20runner<p>All feedback is appreciated:<p>@lucamartinetti [email protected]

    2012 · similarityapi.appspot.com

  18. 18VA

    Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document -…

    2025 · aisearch.vpuna.com

  19. 19

    Stop typing. Start autofilling with Superfill.AI.

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  20. 20IM

    When your embedding provider is good, but could be better for your use-case.

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  21. 21HA

    Hi all, I find literature searches to be slow and despite all the tools nowadays, it is difficult to map out vast areas of research and then connect my ideas to what I’m trying to do (e.g. writing a review, learning something new, planning a new project, assignments, etc). Some of my peers and teachers also found this to be a problem so I built this MVP app to help people not only leverage AI search capabilities but also go on to connect their ideas, map out research projects, cite various results and organise their “stream of consciousness” as they work. I’d love to get some feedback on it…

    2025 · hexle.ai

  22. 22MA

    Hey HN, We’ve been heads-down building MOSS - a semantic memory layer that brings AI-powered search and personalization fully on-device (No cloud | No latency | No data leaving the user’s device) We just launched a live demo showing MOSS running entirely in-browser, performing lightning-fast semantic search over local in-browser VectorDB. This unlocks a new class of privacy-first, hybrid AI experiences that work even without a server connection. If you’re curious about: - how to run AI search right inside the browser - the technical challenges behind on-device vector search - why we believe…

    2025 · twitter.com

  23. 23SB

    Hey HN! My brothers and I have worked on this for the last 2 weeks. We use OpenAI's `text-embedding-ada-002` model to embed queries and a vector database to search for similar verses / blocks of verses. We'd like to see what you think and appreciate any feedback!

    2023 · siliconscripture.org

  24. 24IM

    AI search results are quickly becoming more important than SEO, but as businesses, we have no visibility over it! That's why I'm building "Ahrefs for AI search results". Track keyword performance on AI tools like ChatGPT, Claude, Perplexity & more

    2025 · linrush.com

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