Robotics has a data problem. While language and vision models have ridden scaling laws to ever-higher capabilities, robot learning has
Continue readingXiaomi-Robotics-1: When Scaling Laws Finally Arrive in Robotics
Moonshot AI has just dropped Kimi K3, and it’s a monster. At 2.8 trillion parameters, it’s the world’s first open-source
Continue readingKimi K3: Moonshot AI’s 2.8 Trillion Parameter Open-Source Behemoth
The open-weights LLM landscape just gained a significant new entrant. Inkling, released on July 15 by Thinking Machines Lab, is
Continue readingInkling: Thinking Machines Lab’s 975B Open-Weights Multimodal Model
Reinforcement learning has become the defining ingredient of modern LLM post-training. GRPO, PPO, and their variants drive the reasoning capabilities
The open-weight frontier has been moving fast. Over the past few weeks, two major releases have landed on HuggingFace that
Continue readingGLM-5.2 and Tencent Hy3: Two Different Bets on the Open-Weight Frontier
There’s a class of programming tasks that resists clean implementation: deciding whether a log line is “important,” repairing malformed JSON
Continue readingProgram-as-Weights: Compiling Natural Language Into Local Neural Programs
The vLLM v0.23.0 release landed last week with 408 commits from 200 contributors, and it packs several changes that directly
The dominant scaling narrative in large language models has been straightforward: more parameters, more data, more compute. But there’s a
Continue readingLoopCoder-v2: Why Two Loops Beat Four in Test-Time Compute Scaling
The open-source LLM landscape just got a new heavyweight contender. Z.ai (Zhipu AI) released GLM-5.2, a 753B-parameter mixture-of-experts model that
Continue readingGLM-5.2: The New #1 Open-Weight LLM and Why IndexShare Matters
The attention mechanism is the backbone of every transformer model, but it carries a brutal cost: quadratic complexity with respect
Continue readingHow MiniMax Sparse Attention Achieves 28x Compute Reduction at 1M Context Length