Personal accountability in software development is actually hard, and less useful than it seems. What good is it if you discover a 12 y.o. vulnerability and can blame a specific person? (Like the recent one in PHP?) You may learn the name of that programmer who made the mistake, but it does not really tell you anything about that person, unless their overall record is terrible, and you are not entitled to any damages anyway.
In closed source, this is even worse. So a certain Windows function sucks. Now what. You never learn any other provenience than "Microsoft did it". Tough luck even getting support, and you certainly don't get your license fee back. This sort of accountability is worse than useless.
The only exception I see is knowledge within a specific development team. We usually know what sort of errors our colleagues are prone to, and we may be able to work around their personal weak spots.
Agentic coding done well has a dimension of trust that human coding cannot achieve. I can tell my agents to aim for 100 per cent test coverage without overburdening them or running out of budget and time. I can ask other agents and other models to review code written by the first agents, and then again. Every layer of such review adds some credibility to the result, and its mechanistic character ensures certain level of repeatability. Sure, it can happen that all involved mechanisms share the same weakness and let a particular bug through. But it is similar to several sieves over one another lining up perfectly to let something through, and that is quite rare.
If I had to choose, I would trust code designed, implemented and reviewed by three different SOTA models over code designed, implemented and reviewed by three median programmers today. Three elite programmers would have been a different story, but that would also be expensive like hell, and much like you don't eat most of your dinners in a Michelin restaurant, you have to economize with your code development in a similar way.
[–] customguy 10 hours ago
LLM are great at creating code, obviously, but what is your (or anyone's) experience with them making code "better" in that way? Finding patterns in messy things and making it more "tidy", renaming things to be less verbose yet still descriptive, or even more descriptive, that sort of thing? Where you can "apply" agents to a codebase and have it come out more accessible to LLM and humans, because it's better code and less of it, if you will. I know rewriting things is more enticing and often more practically useful, but are there efforts/experiment in that direction? With actual code I can look at, comparing before and after?
[–] datadrivenangel 13 hours ago