AI Agents for Beginners, Part 1

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AI agents can now write more code than any of us can read. That changed how I think about AI.

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Mapping structural variance against execution outputs to estimate coding agent reliability without ground truth.

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Towards Spec-Driven Test Automation: Part 3

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A controlled test of Gemini, DeepSeek, ChatGPT, and Claude on leakage, reporting delays, promotion effects, and structural breaks.

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From guarding one agent to steering a fleet

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Trying to answer the question of "Can LLM agents discover?" through the lens of creativity

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Coordinating agent capability development with the application it powers

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Giving an LLM permission to act, in 9 steps

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I traced 2,500 listing checks with Weights & Biases Weave, removed avoidable model work one change at a time, and scored every version against the same answers.

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One run, and what it actually proves

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Agent design patterns every data engineer must know

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Every boundary you draw removes a signal your tooling was relying on. That is a structure problem, not a search problem.

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RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.

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I tested TypeSafe AI’s Jev on 3,080 classification tasks to see how its accuracy, latency, calibration, and confidence compare with LLMs — and whether it works as a practical decision layer for AI systems.

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Get more out of your coding agent subscriptions

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Retrieval is not evidence. How to build AI that proves its own claims.

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Why a green test suite can mean nothing

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Learn how to effectively code up an internal tool using Claude code or Codex

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Why this matters less for what Astra can do and more for what every other model hasn't been tested for

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