A differential oracle: making agentic code prove its own correctness
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Six months ago I was running a kitchen. I taught myself to build agentic systems, and the thing I'm proudest of is the part nobody demos: the evaluation layer. Coding agents are easy to demo and hard to trust . The moment an agent touches a real codebase — deploys, commits, user-facing changes — you need what any production system needs: review, tests, and a way to catch your own regressions. The problem: correctness you can't hand-check I build a match-3 game. Match-3 resolution has thousands…
1Key Takeaways
- Six months ago I was running a kitchen.
- I taught myself to build agentic systems, and the thing I'm proudest of is the part nobody demos: the evaluation layer.
- Coding agents are easy to demo and hard to trust .
- The moment an agent touches a real codebase — deploys, commits, user-facing changes — you need what any production system needs: review, tests, and a way to catch your own regressions.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that six months ago I was running a kitchen.
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