Because there’s no clear owner, I’m currently responsible for a few repositories. Most of them are data-pipeline related—projects that had been drifting without proper history, or ones I inherited after the original maintainer left and there was no obvious handoff target. Lately, as I’ve been studying Claude Code and SDD, I realized these repos couldn’t be a better testbed, so I started putting them to work in earnest last week.
The direction for improvements was fairly clear, and at this point it’s no longer surprising to see it blaze through the kind of repetitive, manual tasks I’d been postponing simply because there were too many scripts to touch. I already know it’s good at that.
There was one piece of code I’d been eyeing for optimization, but since the total runtime was already around 20 seconds, I hadn’t felt a strong need to tune it further. As an experiment, I asked:
“From a performance standpoint, and also from a maintainability standpoint, are there parts of the current logic you would recommend refactoring?”
On the performance side, it suggested changing three sections that I’d also been uneasy about. For convenience, I’ll call them P1, P2, and P3. On the maintainability side, it proposed two improvements—one of which I genuinely hadn’t known. To be fair, I haven’t been keeping up with pandas updates.
I started with P1. I suspected that with better use of pandas I could remove a for-loop, but I’d left that code untouched partly because my pandas knowledge wasn’t strong enough. As expected, it handled the task using a pandas function I wasn’t familiar with. I compared the outputs before and after, saw nothing unusual, and committed immediately.
Next came P2. It was similar in nature to P1, except this one had a triple nested for-loop. Seeing it written out like that was a bit embarrassing. Still, as with P1, it solved the problem by leveraging pandas in a way I hadn’t considered. This time, however, the before/after outputs didn’t match. That was an issue.
“There are subtle differences in the output—can you analyze the cause? You can compare these two files: before.tsv and after.tsv.”
It analyzed the file structure and generated a temporary Python script to begin comparing the files. What happened next was the part that truly stood out. There were two major bugs: one was a bug in the code it had written, and the other was a bug in the original implementation—meaning its result was actually the correct one. Out of roughly 40 differences, it fixed the ones caused by its own bug, and then claimed that the remaining four were due to a defect in the original code.
Skeptical but curious, I opened the original output and inspected it closely. By now you can probably guess the conclusion: it really was a bug in the original. It wasn’t something likely to surface often, which is probably why it went unnoticed—but that code had been sitting there for at least a few years.
What impressed me most wasn’t just that it “optimized” existing logic, but that it correctly understood the intended behavior of that code segment and formed an opinion about what the output should be. Of course, it’s also possible that the bug could have been reproduced mechanically during the process. And yes, my comments may have helped (to some extent). Still, it was an impressive outcome.
I committed the changes, pushed them, and deployed them to the service. After confirming things behave correctly in production, I plan to move on and finish P3 as well.
Kirin's Technology Essay 