Azmat
TTT-Discover Explained: Why Test-Time RL Outruns Best-of-N Sampling Play Introduction You have seen this movie. A model tackles a hard problem, fails, tries again, fails differently, then repeats the same mistake with fresh confidence. You can sample more. You can crank temperature. You can run best of n sampling until the GPU fans sound like … Read more
Qwen3-TTS Local Voice Lab Guide Play Introduction Text-to-speech used to be the “nice-to-have” layer you glued onto a product at the end. Now it’s the product. A good voice turns a demo into something people keep open. A bad voice turns your app into a tab people close and forget. That shift is why the … Read more
Weekly AI News January 24 2026: The Pulse And The Pattern Play Introduction AI News January 24 2026 is one of those weeks where the headlines feel like they’re arguing with each other in the group chat. Vision models learn time. Safety teams publish their rulebooks. Tiny “thinking” models squeeze onto phones. Meanwhile, the business … Read more
D4RT By DeepMind, Real-Time 4D Scene Reconstruction From One Video Play Introduction There’s a small magic trick your brain pulls off every time you watch a video. You do not just see pixels. You infer a world. You know what’s behind the mug when a hand passes in front of it. You keep track of … Read more
X Algorithm GitHub Explained: How X Builds Your “For You” Feed End To End Play Introduction X finally did the thing every social platform says is “too sensitive” to share, it published real, runnable code for its For You feed stack. Not a glossy blog post. Not a diagram with inspirational arrows. A repo you … Read more
Claude’s Constitution & Consciousness: What Changed and What Users Will Notice Play Claude coverage index Introduction You can learn a lot about a company’s AI by watching it fail. Not in the dramatic, “the demo exploded on stage” way, but in the quiet way your prompt hits a policy wall, your harmless question gets a … Read more
Mechanistic Interpretability (2026): Reverse-Engineering LLMs Play Introduction Mechanistic interpretability is the “take it apart and see how it works” branch of AI interpretability: instead of treating a model as a black box and correlating inputs to outputs, you try to recover the internal computations that produce behavior, down at the level of activations, learned features, … Read more