How to learn in the modern landscape of AI
AI changed both the supply and the demand of knowledge. The skill that matters now is not collection — it is evaluation, synthesis, and productive disagreement.
Short technical notes — on AI systems, the research itself, and how we keep it all running. Written for people who like the working shown.
AI changed both the supply and the demand of knowledge. The skill that matters now is not collection — it is evaluation, synthesis, and productive disagreement.
Both approaches improve LLM output quality. They solve different problems. Choosing the wrong one wastes months.
Single-agent or multi-agent? The answer depends on failure tolerance, observability, and whether subtasks are actually independent.
Token spend is the visible line item. It is rarely the largest cost. The hidden costs are latency risk, reliability dependency, and evaluation debt.
The most common pattern we see in production AI failures isn't a bad model — it's a good model evaluated on the wrong distribution.
MMLU measures something real. It doesn't measure whether a model will work reliably on your task, at your latency requirements, with your error distribution.
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