Review: AI Coding Assistants in Daily Work
After three months of weaving AI assistants into everyday engineering work — reviews, boilerplate, debugging — here is an honest scorecard.
Where they shine
Repetitive scaffolding, first-pass test ideas, and explaining unfamiliar code. Asking an assistant to summarize a legacy module before reading it saves real time, and generated boilerplate is usually fine after a quick pass.
Where humans stay required
Architecture decisions, security boundaries and anything touching production data. Assistants optimize for plausible answers; reviews must optimize for correct ones. The pattern that works: let the assistant draft, keep the human accountable for every merged line.
Small UX details matter
Latency shapes usage more than model quality. Tools that stream partial answers get used for exploration; tools that batch get used for well-defined asks.
Verdict
Adopt them like a fast junior pair: great throughput, zero authority. Keep reviews, credentials and deployments human-owned.
