I Don't Want to Code with LLM's

I can’t take this article seriously, and neither should you. Being anti AI/anti LLM is solidly in the Luddite camp; there’s really no more debates to be had. Every serious inquiry shows productivity gains by using ai.

It’s anyone’s prerogative to continue to advocate for the horse and buggy over the automobile, but most people won’t bother to take the discussion seriously.

The data showed llms are better. This put debate to rest. Now we are post-debate.

What data are you talking about? Why do you value it above the data showing the opposite?

If you're working on something where the cost of bugs is high and they're tricky to detect, LLM generated code may not be a winning strategy if you're already a skilled programmer. However, LLMs are great for code review in these circumstances - there is a class of bugs that are hard to spot if you're the author.

As a simple example, accidentally inverting feature flag logic will not cause tests to fail if the new behavior you're guarding does not actually break existing tests. I and very senior developers I know have occasionally made this mistake and the "thinking" models are very good at catching issues like this, especially when prompted with a list of error categories to look for. Writing an LLM prompt for an issue class is much easier than a compiler plugin or static analysis pass, and in many cases works better because it can infer intent from comments and symbol names. False positives on issues can be annoying but aren't risky, and also can be a useful signal that the code is not written in a clear way.

>Reviewing is worse than writing

I think the reason this discussion keeps coming up is that the people who are getting a lot out of these tools are people who are, at best, the software-equivalent of assembly-line workers. If something can be easily understood by passively reading it then it probably isn't complicated or novel and therefore it's not surprising a pseudorandom bullshit generator can do it for you; all it lacks is a unit testing system which can verify that its interpretation of the problem-statement matches the interpretation which would be most obvious to a human and that is evidently not a solved problem thus far.

If the hardest part of your job is understanding code written by other people and even code written by yourself in the distant past, then LLMs are of literal use because the problem they solve was never a significant bottleneck and in fact their "solution" only serves to pump a higher volume of fluid through the neck of the proverbial bottle.

It's the difference between reading somebody's paper in a mathematical journal to understand how they came to the conclusion they are presenting, and merely using the identity they have proven on faith. If all that mattered was to perform some calculation based on their work then its clear which approach will get more work done in less time but if you don't take it for granted that everything in the journal is correct or if you want to be able to further develop ideas based upon their proof then you have to spend a few days or even weeks trying to understand how each step leads to its successor.

It's also why i hate the old adage about not reinventing wheels, it promotes ignorance by asserting that education itself is ignorance.