A curated survey of 94 prominent engineers describing exactly how they use coding agents. Each entry names the agents and model families they favor, summarizes how code review is handled in practice, and gives one concrete practice or ritual the engineer recommends adopting. The dataset was assembled from public repos, talks and posts and links each claim to its original evidence; the stated aim is to show what actually works so readers can pick one technique to try in their own workflow.
Clear patterns emerge: common tooling includes Claude Code, Codex/OpenCode, Amp, Cursor and provider-specific CLIs, with models ranging from Claude Opus/Fable to GPT-6/Astra and niche models like Grok. Review strategies split between automated cross-model or adversarial reviews, spot-checking high-risk areas, and full human line-by-line checks for core paths; many teams limit agent output to what they can realistically review. Practical rituals crop up repeatedly - autogenerated pre-commit reviews, subagent pipelines, QA passes per release, fresh-eyes reviewer bots, daily code limits, sensors/sidecars pointing to risky diffs, and agent-written tests or screenshots on PRs. The bottom line: agents are broadly embraced but tightly scaffolded with review patterns, CI loops and minimal-risk rules so automation scales without giving up human control.
Summary generated by AI from the linked article. hn.today is not affiliated with Hacker News or Y Combinator.