Topic
AI Agents & Reasoning
Getting LLMs to plan, use tools, remember, and reason — what the agent papers actually demonstrate and how the patterns hold up in production.
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Toolformer: What the Paper Actually Says
A 6.7B model beats GPT-3 175B on math by learning to use a calculator. Toolformer's self-supervised training pipeline is the interesting part — and it's more constrained than the demos imply.
Generative Agents: What the Paper Actually Says
The Generative Agents paper isn't just a simulation demo. It's a formal architecture for agent memory: stream, retrieval, reflection, and planning. Every agent framework you're using borrowed from it.
The AI agent observability stack — what to measure, what to use
Most teams reach for one observability tool and call it solved. Different tools measure different things — traces, hallucinations, cost, drift. Here's a map of the space and where to start.