Yes, but the bar for skepticism is higher than that, because LLMs also compile code and catch errors, and generate and run tests; compile errors and assertion failures are just more prompts to an LLM agent.
When used that way they also regularly get into loops where they lose track of what they were supposed to do.
The last time I set Cursor on something without watching it very very closely it spun for a while fixing tests and when it finally stopped and I looked what it had done it had coded special cases in to pass the specific failing tests in a way that didn't generalize at all to the actual problem. Another recent time I had to pull the plug on it installing a bunch of brand new dependencies that it decided would somehow fix the failing tests. It had some kind of complete rewrite planned.
Claude Code is even worse when it gets into this mode because it'll do something totally absurd like that and then at the end you have to `git reset` and you're also on the hook for the $5 of tokens that it managed to spend in 5 minutes.
I still find them useful, but it takes a lot of practice to figure out when they'll be useful and when they'll be a total waste of time.
It happens to me every once in awhile, but I'm not sure why I would care. I usually set it off on some question and go tab away to something else while it flails. When I come back, I have a better-than-average shot at a workable solution, which is a science fiction result.
When I first began programming as a teenager, one of the mental hurdles I had to get over was asking the computer to "too much"; like, I would feel bad writing a nested loop --- that can't possibly be the right answer! What a chore for the computer! It didn't take me too long to figure out that was the whole point of computers. To me, it's the same thing with LLMs spinning on something. Who gives a shit? It's not me wasting that time.
It can be a science fiction result and still not actually result in time saved for the human operator on the whole. For me the jury is definitely still out on whether it results in net time saved and it's not for lack of trying.
Whether it ends up getting good enough in the near future that it does become a net positive both isn't the question being discussed and still remains to be seen.
Have you tried an asynchronous agent coding flow? They were relatively rare until last week. But it took that for me to see the value. Now I happily queue up 4 or 5 tasks in the morning, come back at lunch to check in/feedback or merge at lunch, rinse and repeat.
It doesn’t replace the hard tasks (yet) and you do need to think about the tasks and the tooling but it’s a game changer.
I wasn’t kidding in a peer comment (except about the mars cheese castle). I started an agent task before leaving on a trip and gave it feedback from my iPad when I stopped. I have a real business problem solved now.
I feel like you might know where I'm coming from being confused at people's reaction to this stuff. This is science fiction. I think it's just not sinking in with people. If I could bet on this, I would bet everything I could on "skill with LLMs" being the high order bit of being an effective software developer 5 years from now.
I don't deny that, under the right circumstances, these tools can produce results that feel indistinguishable from magic, or like science fiction as you put it. But I don't think it's worth the costs. To me, the two most concerning costs are the unreliability, and the massive amounts of stolen training data and underpaid labor (the RLHF process) required for these models. I'm not comfortable relying on a tool built on such foundations.
My bet, and I realize this might just be wishful thinking, is that the high order bit for being an effective software developer in the near future will be skill at using more reliable and non-exploitative automation tools, such as programming languages with powerful macro systems and other high-level abstractions, to stay competitive with developers who sling LLM-generated code. So I'd better get started developing that skill myself.
It rewrote some comments, changed the test name and added extra assertions to the test. Baby sitting something like that seems like an absolute waste of time.
> It's a failure that it created and when told to fix it did this. It’s beyond bad.
No one's disputing this was bad. People are merely claiming it can also be good. I've dealt with plenty of humans this bad - it's not an argument that humans can't program.
It seems like the underlying issue is trust.competent programmer - even a junior - and trust them to finish the task correctly. It might take multiple tries, and they may ask for clarification, but since they’re human, we trust they are intelligent.
There are some people who fall into the bucket that we can’t trust them to finish the task correctly, or within a time frame or level of effort on our part to make the task offloading exercise have a positive benefit.
If we view LLMs in the same light, IMO currently they fall into “not trust” category to really give they a task and trust them to finish it correctly, with us being confident we don’t really need to understand their implementation.
If one day LLMs or some other solution reaches that point, then it definitely won’t look like a bubble, but a real revolution.
Very well put. The trick is to do either of the following:
1. Find simpler tasks for which the trust in LLMs is high.
2. Give tasks to the LLMs that have a very low cost to verify (even when the task is not simple) - particularly one off scripts.
I once had a colleague who was in the "not trust" bucket for the work we were doing. So we found something he was good at that was a pain for me to do, and re-assigned him to do those things and take that burden off of us.
In the last few months I've had the LLM solve (simple) problems via code that had been in my head for years. At any point I could have done them, but they were a chore. If the LLM failed for one of these tasks - it's not a big deal - not much time was lost. But they tend to succeed fairly often, because they are simple tasks.
I almost never let the LLM write production code, because of the extra burden that you and others allude to. But I do let it write code I rely on in my personal life, because frankly I tend to write pretty poor code for my personal use - I can't justify the time it would take to write things well - life is too busy. I welcome the code quality I get from Sonnet or Gemini 2.5 Pro.
That's my point in this thread. Writing code is a pretty diverse discipline, and many are dismissing it simply because it doesn't do one particular use case (high quality production code) well.
I didn't take LLM coding seriously until I found well respected, well known SW engineers speak positively about them. Then I tried it and ... oh wow. People dismissing them is dismissing not only a lot of average developers' reality, but also a lot of experts' daily reality.
He used an LLM to find a security vulnerability in the kernel. To quote him:
> Before I get into the technical details, the main takeaway from this post is this: with o3 LLMs have made a leap forward in their ability to reason about code, and if you work in vulnerability research you should start paying close attention. If you’re an expert-level vulnerability researcher or exploit developer the machines aren’t about to replace you. In fact, it is quite the opposite: they are now at a stage where they can make you significantly more efficient and effective. If you have a problem that can be represented in fewer than 10k lines of code there is a reasonable chance o3 can either solve it, or help you solve it.
> It rewrote some comments, changed the test name and added extra assertions to the test. Baby sitting something like that seems like an absolute waste of time.
I've worked with real flesh and blood developers who did the exactly same thing. At least with LLMs we don't have to jump into a 1h long call to discuss the changes.
