The Best AI for Coding in 2026
Our coding guide is live, and this thread is about a rule that matters regardless of which assistant you use: generated code needs the same security and review discipline as human-written code.
Full guide: /best/ai/for/coding
WhatAI did not spend six weeks generating code on production codebases for this guide, and we do not have a first-party defect-rate benchmark to publish. The safer way to discuss the risk is by failure mode.
Review generated code carefully for input handling and injection risks, stale or inappropriate dependencies, secrets and configuration mistakes, authentication and authorisation errors, weak validation, misleading error handling, and assumptions that do not match the deployment environment. The exact pattern and frequency depend on the model, prompt, repository context, language, framework, and review pipeline.
Generated code should go through normal tests, static analysis, dependency and secret scanning, and human review. Anything touching authentication, payments, personal data, permissions, infrastructure, or other high-impact boundaries deserves stricter review. A second AI pass can suggest tests or possible issues, but it should not be treated as an independent security sign-off.
For the thread: if AI-generated code has caused a real bug or security issue in your work, share the failure mode, the tool, what caught it, and what you changed in your pipeline. Real practitioner evidence is more useful than a universal percentage without a reproducible benchmark.