Deep dives into AI engineering, ML systems, and production deployment. Sharing lessons learned from building and scaling AI applications.
A research engine for the most expensive question in software: build it, or buy it? It does the homework, cites every source, argues with itself — and hands the call back to you.
LLM latency isn't one number — it's prefill and decode, pulling against each other. A mental model for finding the milliseconds before you reach for a quantization library.
Most vector databases run the same core algorithm, so 'which is fastest' rarely decides anything. The differences that bite are operational: who runs it, how it filters, what it costs at scale.
Fine-tuning is the most over-reached-for tool in the LLM toolbox. A grounded guide to deciding between prompting, retrieval, and training — before you spend the weeks.