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Moozonian News
twitter.com• Jul 18, 2024• 1 min read
Show HN: How we leapfrogged traditional vector based RAG with a 'language map'TL;DR: Vector-based RAG performs poorly for many real-world applications like codebase chats, and you should consider 'language maps'.Part of our mission at Mutable.ai is to make it much easier for developers to build and understand software. One of the natural ways to do this is to create a codebase chat, that answer questions about your repo and help you build features.It might seem simple to plug in your codebase into a state-of-the-art LLM, but LLMs have two limitations that make human-level assistance with code difficult:1. They currently have context windows that are too small to accommodate most codebases, let alone your entire organization's codebases.2. They need to reason immediately to answer any questions without thinking through the answer "step-by-step."We built a chat sometime a year ago based on keyword retrieval and vector embeddings. No matter how hard we tried, including training our own dedicated embedding model, we could not get the chat to get us good performance.
Moozonian News
news.google.com• Jul 15, 2024• 1 min read
AnimateSVG: Autonomous Creation and Aesthetics Evaluation of Scalable Vector Graphics Animations for the Case of Brand Logos | Proceedings of the AAAI Conference on Artificial Intelligence - The Association for the Advancement of Artificial IntelligeAnimateSVG: Autonomous Creation and Aesthetics Evaluation of Scalable Vector Graphics Animations for the Case of Brand Logos | Proceedings of the AAAI Conference on Artificial Intelligence The Association for the Advancement of Artificial Intelligence
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Moozonian News
tv.algora.io• May 2, 2024• 1 min read
Show HN: I built vector search for COSS podcasts & livestreamsHey HN! I built COSSgpt using videos from the Open Source Founder Podcast [1] and livestreams from COSS Office Hours [2][3]I transcribed the VODs using Whisper and vectorized fixed-size segments from the transcripts with MPNet on Replicate GPUs. I made these segments overlap a little to prevent semantic meaning being lost inbetween segmentsThen I indexed the vectors using HNSWLib in-memory vectorstore [4] and persisted the entire vectorstore into Tigris object storage [5] to cache multimedia and vectors across all Fly.io regionsI built the app in Elixir, almost entirely server-side rendered with minimal diffs sent to the client over WebSockets using Phoenix LiveView. I also used Livebook [6] a ton when I was building the multimedia processing & ML pipeline. I'm super bullish on Elixir for building webapps and/or MLops!Let me know what you think :) If you're curious you can find the code at https://github.com/algora-io/tv[1]: https://algora.io/podcast [2]: https://tv.algora.io/peerrich
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