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These notes are my working reference - the decision rules, gotchas and defaults I want to find again - written so others can use them too. The posts carry the stories and worked examples; a note keeps the short version and links back. For code and worked projects, see Projects.

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[[ Evaluating an agent ]] A fluent answer proves nothing, so evaluate the loop rather than the vibe. Compare relevance, retrieval recall, groundedness and latency against a baseline, and read examples next to the scores: grounded, useful and found-the-right-document are three different failures. Offline golden... [[ A/B testing ]] Random assignment creates comparable groups; a shared metric defines what is compared. A p-value describes how surprising the data would be under a specified null hypothesis. It is not the probability that the hypothesis is true. Decide the measurement and... [[ Serving SDK ]] A trained model is not yet a service. The shared path should own the repeated work - authentication, tracing, deployment conventions - and stay narrow: a small ordinary path, inspectable behaviour, errors that suggest the next action. Defaults save configuration;... [[ Kubernetes ]] Kubernetes stores desired state while controllers keep trying to make the running system match it. A Deployment manages replicas, a Service gives traffic a stable destination, and Helm packages configuration. That mental model is more useful than memorising object names... [[ FM synthesis ]] One oscillator wobbles another's pitch fast enough that you hear new partials instead of vibrato. The carrier sets the base pitch, the modulator sets the brightness, and the index sets how much: low index is a round tone, high index... [[ Exposure triangle ]] Three knobs, one budget: aperture buys light with depth of field, shutter buys it with motion blur, ISO buys it with noise, and stops are interchangeable - one more stop of ISO pays back one faster shutter. Set aperture for...