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Byte-Latent Transformers: Training AI Without a Tokenizer
Every LLM you've ever used runs on a tokenizer that chops text into chunks before the model sees a single character. Meta's Byte-Latent Transformer throws that away entirely — and matches LLaMA 3 performance. Here's how.
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Hopfield Networks → Modern Associative Memory: The 1982 Idea Hidden Inside Every Transformer
A forgotten idea from 1982 about how brains store memories turned out to be the exact math behind transformer attention. Here's the full journey — from energy landscapes to softmax.
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KV Cache Compression: How LLMs Handle Million-Token Contexts Without Running Out of Memory
Every token you generate costs memory — permanently. At 1M token contexts, the KV cache alone can consume 100GB+ of VRAM. Here's how modern LLMs compress it without losing what matters.
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MCP Server Starter: Build Tools AI Agents Can Actually Use
Most tutorials show you how to call an LLM. This one shows you how to build the server on the other side — the tool layer that AI agents call into. MCP in 30 lines of Python.
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TRIBE v2 and the Rise of In-Silico Neuroscience
What if you could simulate a neuroscience experiment before running it on a single human subject? TRIBE v2 — a tri-modal foundation model trained on 1,000+ hours of fMRI data — is making that real.