Insights on AI, machine learning, and developer tools.
A practical guide to integrating LLMs into your applications — architecture patterns, prompt engineering, and common pitfalls.
Speculative decoding, smarter batching, and the un-glamorous work of fixing one slow tokenizer that dominated our P99.
Why the agent that confidently calls the wrong tool is worse than the one that says “I don’t know” — and how we evaluate for it.
Caching, fallbacks, cost controls, and the metric that matters more than tokens-per-second: tokens-per-dollar-per-useful-answer.