Panel Discussion on AI for Code Generation
summary
Panelists describe concrete workflows: Cursor for code and Copilot handle completions, Gemini and Claude handle research and large outputs, ChatGPT handles conversational edits; teams run CodeRabbit L1 reviews for PR triage and use cursor rules to auto-generate API decisions and component checklists. A Figma→code pipeline uses evals that score output 0–100 (one run returns 30% with justification). Agents save days debugging from stack traces, sometimes overreach (one agent converts TypeScript to JavaScript), so strict tests, canary deploys and prompt/approval guardrails are mandatory.
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chapters (9)
- 0:00Introduction to AI code generation
- 3:14How AI changed coding workflows
- 10:50AI tools and preferences
- 17:35AI in debugging and reviews
- 25:03AI hallucinations and limitations
- 33:47Guardrails for AI in development
- 35:53Will AI replace developers?
- 42:41Future of AI in development
- 43:04Final advice on AI usage
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