Long-form technical overviews that explain important AI research topics, such as LLM evaluation, post-training and reinforcement learning. Readers get first-principles explanations plus practical implementation details drawn from papers and tech reports.

Research posts from Redwood Research on catastrophic AI risk, focused on AI control, monitoring of agentic models, and red-teaming of safety systems. Readers get technical write-ups of papers, experiments and engagements with AI developers, including methods, results and open questions.

A personal technical newsletter from an engineer working through applied machine learning, covering model interpretability, feature engineering and cost-based decision analysis for manufacturing problems. Issues include study notes with glossaries and a worked technical memo with concrete figures and recommendations.

Weekly lessons that explain one AI engineering concept at a time, such as transformer internals, LLM inference serving and agent-based bug hunting. Each issue pairs concrete numbers and worked examples with a paid script readers can run themselves.