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
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
There’s a classical intuition in computer science that verifying a solution is easier than finding one. For NP-complete problems, this
Continue readingWhy Verification Is Harder Than Generation for AI Coding Agents
June 24, 2026 turned out to be one of the most packed days in recent AI news. Three major announcements
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
OpenTelemetry has had a remarkable 2026. What started as a project to unify traces, metrics, and logs has grown into
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