Clear, practical breakdowns of the AI papers and ideas that matter: agents, reasoning, safety, multi-agent systems. Written for practitioners, not academics.
OpenAI's o1 jumped from the 11th to the 83rd percentile on competitive programming. The difference wasn't better data or more parameters; it was reasoning tokens, invisible chains of thought that let models think before they answer.
Stanford deployed 25 agents that planned a party autonomously. But most production agents today can't remember what you told them ten minutes ago. The memory problem isn't a model limitation; it's an architectural one, and new solutions are emerging.
Agents have moved from academic benchmarks to production systems processing millions of conversations. The gap between hype and reality comes down to architecture. This guide walks through model selection, tool design, and instruction engineering with production examples.
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