A Real Lesson in Debugging with Codex
paul_cal · x · 2026-07-11
The core of this post isn't about a model failure, but a genuine experience of agent-assisted debugging.
The author noticed abnormal output distributions and degraded feedback from a deployed model. Their first instinct was to retrain the model and adjust the sampling distribution, so they instructed Codex to make the changes. An hour later, the issue persisted. Further investigation revealed that the actual failure wasn't in the ML model itself, but in a rule-based scoring system where a contaminated input was corrupting the overall results.
The author reflects that if they had done everything manually, they might have spent more time eliminating simpler causes first. The agent helped shift focus back to the more likely root cause faster. However, they also caution that such misjudgments are easy to make, fundamentally stemming from a lack of "skin in the game" leading to skewed priorities.
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