Intro topic:  Buying a CarNews/Links:<ul><li>Cognitive Load is what Matters<ul><li><a href="https://github.com/zakirullin/cognitive-load">https://github.com/zakirullin/cognitive-load</a></li></ul></li><li>Diffusion models are Real-Time Game Engines<ul><li><a href="https://gamengen.github.io/">https://gamengen.github.io/</a></li></ul></li><li>Your Company Needs Junior Devs<ul><li><a href="https://softwaredoug.com/blog/2024/09/07/your-team-needs-juniors">https://softwaredoug.com/blog/2024/09/07/your-team-needs-juniors</a></li></ul></li><li>Seamless Streaming / Fish Speech / LLaMA Omni<ul><li>Seamless: <a href="https://huggingface.co/facebook/seamless-streaming">https://huggingface.co/facebook/seamless-streaming</a></li><li>Fish: <a href="https://github.com/fishaudio/fish-speech">https://github.com/fishaudio/fish-speech</a> </li><li>LLaMA Omni: <a href="https://github.com/ictnlp/LLaMA-Omni">https://github.com/ictnlp/LLaMA-Omni</a> </li></ul></li></ul>Book of the Show<ul><li>Patrick: <ul><li>Thought Emporium Youtube<ul><li><a href="https://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQ">https://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQ</a></li></ul></li></ul></li><li>Jason: <ul><li>Novel Minds<ul><li><a href="https://www.novelminds.ai/">https://www.novelminds.ai/</a></li></ul></li></ul></li></ul>Patreon Plug <a href="https://www.patreon.com/programmingthrowdown?ty=h">https://www.patreon.com/programmingthrowdown?ty=h</a>Tool of the Show<ul><li>Patrick: <ul><li>Escape Simulator<ul><li><a href="https://pinestudio.com/games/escape-simulator/">https://pinestudio.com/games/escape-simulator/</a></li></ul></li></ul></li><li>Jason: <ul><li>Cursor IDE<ul><li><a href="https://www.cursor.com/">https://www.cursor.com/</a></li></ul></li></ul></li></ul>Topic: Vector Databases (~54 min)<ul><li>How computers represent data traditionally<ul><li>ASCII values</li><li>RGB values</li></ul></li><li>How traditional compression works<ul><li>Huffman encoding (tree structure)</li><li>Lossy example: Fourier Transform & store coefficients</li></ul></li><li>How embeddings are computed<ul><li>Pairwise (contrastive) methods</li><li>Forward models (self-supervised)</li></ul></li><li>Similarity metrics</li><li>Approximate Nearest Neighbors (ANN)</li><li>Sub-Linear ANN<ul><li>Clustering</li><li>Space Partitioning (e.g. K-D Trees)</li></ul></li><li>What a vector database does<ul><li>Perform nearest-neighbors with many different similarity metrics</li><li>Store the vectors and the data structures to support sub-linear ANN</li><li>Handle updates, deletes, rebalancing/reclustering, backups/restores</li></ul></li><li>Examples<ul><li>pgvector: a vector-database plugin for postgres</li><li>Weaviate, Pinecone </li><li>Milvus</li></ul></li></ul>

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