I’m Alex, an engineering manager in Bangkok. I write about how browsers work, how systems fail, and what it takes to ship reliable AI features.
User feedback can improve an AI product, but biased signals and self-reinforcing exposure loops can optimize for familiarity, agreement, or clicks instead of accuracy, fairness, and useful outcomes.
The best AI feedback mechanisms let users complete their work, correct the system in context, and understand how their data will be used instead of forcing them through detached ratings and ambiguous comparisons.
Useful AI observability connects user-visible failures to the exact prompt, evidence, model, tool call, policy, and release that produced them while respecting the privacy of the people using the product.
An AI application becomes more capable by adding context, guardrails, routing, caching, and actions for named problems, not by adopting every component before the first request succeeds.
