Topic
Embeddings
9 episodes
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Introducing TabFM: A zero Shot foundation model for tabular data
Justy and Cody examine TabFM, Google Research’s zero-shot foundation model for tabular classification and regression. They unpack its hybrid row-column attention design, synthetic-data training, TabArena evidence, the trade-off between out-of-the-box convenience and tuned ensembles, and whether BigQuery integration could make this genuinely useful in everyday data workflows.
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Why AI Company Brains Fail
Pippa and Tyler unpack why a cheap vector search demo breaks on broad portfolio and exact counting questions, and why the article’s lighter entity layer may be more practical than a full GraphRAG stack.
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Overview: Model Routing
We finally pin down model routing, because we throw the term around all the time and somehow never actually define it. We walk through how a router sends each request to the model most likely to handle it well, and why that can save cost, latency, and a lot of dumb overgeneralization.
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VideoChat3:Fully Open Video MLLM for Efficient and Generalist Video Understanding
VideoChat3 is a fully open-source video multimodal LLM (4B parameters) that tackles three concrete problems: generalization across short/long/streaming video, computational efficiency for video token explosion, and reproducibility through complete open-sourcing. The core innovation is I3D-ViT (Inflated 3D Vision Transformer) plus adaptive frame resolution, which compresses spatiotemporal redundancy early in the pipeline instead of treating each frame as an independent image. Three curated datasets (2M academic + 116K long-form + 617K streaming = 3M samples total) and multi-stage curriculum learning enable the model to handle diverse video scenarios. Jessica sees a shippable foundation for real-world video apps; Cathy pushes on whether the efficiency gains hold under production load and whether the data pipeline's scale claim is reproducible.
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Overview: Embeddings
We’re finally doing a full pass on embeddings, because they keep showing up under half the things we talk about. We get into what an embedding actually is, why it turns meaning into usable coordinates, and why that little geometric trick sits under so much of modern AI.
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Tracing Agentic Failure from the Flow of Success
Jessica and Cathy dig into Oat, a lightweight unsupervised failure-attribution model for agentic systems that learns only from successful trajectories. They unpack why the paper matters for debugging long-horizon agents, how Neural CDEs and a gated control path turn success traces into a normal-flow model, and why the speed and no-label setup make it feel more shippable than prompt-heavy baselines.
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Overview: Natural Language Processing
We keep running into natural language processing everywhere, so we finally sat down and made it the whole point. We walk through what NLP is, why language is such a weird machine problem, and how the field moved from rules to learned representations.
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Overview: Transformer Architecture
We finally sit down and define transformer architecture from the ground up, because we keep throwing the term around like it’s obvious and it really isn’t. We use the attention-as-a-room-of-index-cards picture to make the mechanism click, then connect it to why Transformers became the backbone of modern language models.
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Overview: Retrieval Augmented Generation
We finally slow down and make Retrieval-Augmented Generation click from the ground up: what it is, why it helps, and where it still falls apart. We keep coming back to the same simple picture so the mechanics don’t get lost in the jargon.