Insights
Technical clarity for consequential AI decisions.
Field notes for leaders and engineering teams deciding what to build, where it should run, how to evaluate it, and what production readiness actually requires.
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RAG or fine-tuning? Start with the system boundary.
The useful question is not which technique is more advanced. It is where knowledge, behavior, evidence, and change should live.
Read insight ↗Ideas you can use in architecture reviews.
Original thinking grounded in the constraints that appear after the prototype: permissions, uncertainty, integration, cost, latency, and human ownership.
Edge AI vs cloud inference: a decision framework.
Latency gets the attention, but bandwidth, privacy, resilience, hardware lifecycle, and operational ownership decide the architecture.
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Why computer vision prototypes fail in production.
The model is only one part of a visual inspection system. Lighting, optics, process variation, review workflows, and drift determine the result.
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Evaluate the workflow, not only the model.
A strong benchmark score does not guarantee a useful enterprise system. Evaluation has to follow the user, evidence, actions, and failure path.
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From visual data to an operational intelligence system.
Detection is only the opening layer. Useful systems structure evidence, expose it through the right interface, and close the loop with monitoring and action.
Read insight ↗An AI agent needs permissions, recovery, and evidence.
Tool access turns a language model into an operator. That makes authorization, idempotency, auditability, and safe failure part of the core architecture.
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