2 ms·

At some point I have to describe what I need with such a level of detail and precision, that if I had chosen to wrote it in my language of interest I'd have had

At some point I have to describe what I need with such a level of detail and precision, that if I had chosen to wrote it in my language of interest I'd have had the code I wanted long before i finish writing all the boundry conditions, safe guards, long term pitfalls etc. Thats if the system even respects all of those things, it might just lose track of some instruction at some point or make some bs placeholder and now we're back to bug hunting, only i'm now wading through code that is best described as slop.

onion2k 7mo ago
You're saying that projects need to be waterfall rather than Agile in order to avoid making architecture mistakes you'll regret later. The problem with that is that as you build you learn things, and that changes the plan. Consequently those mistakes are inevitable. What you should do is an approach where you have a high level plan up front, and detailed plans about the specific features as you need them. Mistakes will still happen but you'll be able to see them, and correct them, faster.
- Grimblewald 7mo ago
  No, I am saying that you are trading what you spend effort on when using LLM's. Do you give up the effort of writing for the effort of review? Weather you should and if you will benefit really depends on your expertise and skill set. If you're a novice, or even intermediate, use AI in a feedback kind of way, see if it can pinpoint and explain problems with your current code and offer feedback/recommendations. Not code, but dot point human language feedback like you'd get from a senior dev. If the AI is writing code that is beyond what you could produce yourself, you have no business trusting the code the AI produces. This is because, as anyone who can produce high level code will tell you, AI is wrong too frequently to blindly trust. You have to engage critically with output. Full auto is a disaster waiting to happen. The problem is, for many, good looking but fundamentally flawed code is indistinguishable from the real deal, and so they simply outsource their ability to critically think to a thing that simply cannot. This is a big problem and the evidence for this claim is littered throughout this comment section.

onion2k 7mo ago

You're saying that projects need to be waterfall rather than Agile in order to avoid making architecture mistakes you'll regret later. The problem with that is that as you build you learn things, and that changes the plan. Consequently those mistakes are inevitable. What you should do is an approach where you have a high level plan up front, and detailed plans about the specific features as you need them. Mistakes will still happen but you'll be able to see them, and correct them, faster.

Grimblewald 7mo ago
No, I am saying that you are trading what you spend effort on when using LLM's. Do you give up the effort of writing for the effort of review? Weather you should and if you will benefit really depends on your expertise and skill set. If you're a novice, or even intermediate, use AI in a feedback kind of way, see if it can pinpoint and explain problems with your current code and offer feedback/recommendations. Not code, but dot point human language feedback like you'd get from a senior dev. If the AI is writing code that is beyond what you could produce yourself, you have no business trusting the code the AI produces. This is because, as anyone who can produce high level code will tell you, AI is wrong too frequently to blindly trust. You have to engage critically with output. Full auto is a disaster waiting to happen. The problem is, for many, good looking but fundamentally flawed code is indistinguishable from the real deal, and so they simply outsource their ability to critically think to a thing that simply cannot. This is a big problem and the evidence for this claim is littered throughout this comment section.