Author: Michael Lewis

Executives need to make data-driven decisions during live business reviews, where accuracy and speed matter. A conversational agentic AI assistant can meet this need by answering data questions instantly. But the stakes are high: a wrong number or a slow response in front of leadership carries immediate professional consequences, and a capable large language model […]

When you post-train a Mixture-of-Experts (MoE) model with Reinforcement Learning from Human Feedback (RLHF) or Group Relative Policy Optimization (GRPO) at scale, three simultaneous challenges emerge. The first requires coordinating heterogeneous compute for rollout generation and policy training. Second, sustaining high-throughput communication across hundreds of accelerators. And third, dynamically orchestrating every subsystem to keep them […]

What if a market forecast that looks precise is only describing noise? Machine learning market forecasting can turn vast amounts of historical data into signals about what may happen next, but a persuasive backtest isn’t proof that those signals will hold up in live markets. Prices reflect changing conditions, investor behavior, and information that models […]

Reinforcement learning (RL) post-training is becoming a standard step in building capable language model agents. Models learn to reason and act across sequences of steps by generating trajectories, receiving rewards, and updating their policy based on outcomes. Running this at scale, across multiple nodes with hundreds of GPU-hours of rollouts per training run, requires persistent […]

Anthropic shipped Claude Opus 5.5 on September 22, 2026, and buried inside the launch notes was a detail more interesting than the price cut: the model comes with a classifier system that quietly reroutes certain cybersecurity requests to an older, weaker model instead of answering them itself. The company also disclosed that its own containment […]