Woyce
As cars, drones, and software agents make decisions without a human in the loop, the legal system is still built around the assumption that a person caused the harm. This piece breaks down how liability actually gets assigned today and where the gaps are.
A plain explanation of test-time training — the technique that lets AI models update their own weights or memory while running, instead of staying frozen after pretraining.
RL environments are the simulated worlds where AI agents practice tasks and get scored, and they've become critical infrastructure for training today's agentic models.
A closer look at reinforcement learning with verifiable rewards (RLVR), the training method behind today's reasoning models, and how it differs from RLHF.
A practical explainer on knowledge distillation for large language models — how a smaller student model learns from a larger teacher model, and why it now lets compact models rival much bigger ones.
A look at why frontier labs are moving past pure transformer stacks toward linear attention, state space models, and hybrid designs that mix both.
A practical look at context engineering — the discipline of deciding what goes into an AI agent's context window, why it matters more than prompt wording, and how teams build it into production systems.
A plain-language look at Agent Skills and the SKILL.md format — what they are, how progressive disclosure works, and why they're becoming a shared standard across AI products.
A look at how AI agent evaluation is shifting from saturated pass/fail benchmarks like SWE-bench toward task-horizon metrics that measure how long an agent can work autonomously.
Background coding agents run in isolated cloud environments, work on tasks asynchronously, and return pull requests for review rather than requiring a developer to sit and watch.
A practical explainer on how AI agents are being given the ability to pay for things, and the three competing protocols — Google's AP2, Coinbase's x402, and OpenAI's ACP — trying to standardize it.
A look at why hyperscalers and AI labs are designing their own AI chips instead of relying solely on Nvidia GPUs, and what it means for cost, performance, and the broader AI supply chain.