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Practical write-ups on AI engineering, computer vision, and shipping production software — drawn from real projects.
Practical patterns for agent memory, tool calling, execution tracing, and failure handling drawn from shipping a production LLM operating system.
Moving a defect-detection model from a notebook to a reliable inspection pipeline with distributed acquisition, traceability, and dataset capture.
Why CSV breaks down, how to design a normalized schema, and using the repository pattern to keep storage swappable and round trips minimal.
Whether it’s a web app, an AI feature, or an automation that saves your team hours — let’s talk about what to build and how.