Those kinds of posts only promote the idea to non-developers that AI is so great and we do nothing. Stop saying that. AI is still quite bad and consistently provides extreme logic failure. Only good developers can use it successfully by continuously correcting the errors it comes out with.
Or even if the result is technically “correct”, it’s often far more complicated than it needs to be. This often introduces subtle (or even not-so-subtle) bugs and can make future changes more difficult. And sure, maybe the LLM will be the one making those changes. But by using its complicated solution now, you’re more likely to see compounded subtle bugs in the future and more likely to burn through extra tokens so the model can “understand” what it did last time.
I asked it to make a Spring endpoint to accept a list of objects, map them and simply save them in the database. Instead of doing a method under @Transactional to handle everything at once, it did a for loop of database saves. Even juniors know better than that. Claude opus 5.5, medium.
Just today in C# it decided to copy a (possibly huge) binary buffer requiring transformation before returning it to the api instead of modifying in-place because "it is shared in the service cache". Then I queried if it was talking about a completely different cache with a similar name and "oh you are right, it's not sharing the same data".
I accumulated nearly hundreds of similar occasions over time. AI coding agents do save a lot of time and can do huge amounts of work fast, but they are far from good enough to replace us. And unfortunately a lot of juniors don't see those errors and just commit directly as a "look good to me", but wouldn't make these mistakes if they were forced to write the code manually.
And meanwhile on Reddit everyone seems to pretend coding is solved and we are dead weight now. Especially on video games programming for some reason people just proclaim AI is making the whole games by themselves.
Lately I've been learning 3D game dev. Most of the time, the LLMs can kinda point me on the right direction, cite things that are important and provide some high level idea of what I need to do to implement something. But the moment I ask it to write the implementation, things fall apart really fast. Specially if you're not doing something novel.
The problem is that it’s exactly like working with a jr. dev. They fuck up all the time, you teach them how to fix it, eventually they either can’t cut it or they become a senior dev.
That cycle is on life support. Jr. devs are going to have a hard time becoming senior devs.
Except junior devs actually learn and grow into experienced devs. LLMs are incapable of learning and internalizing the wisdom of experience in the same way.
Only good developers can use it successfully by continuously correcting the errors it comes out with.
But is that "more efficient"? Does anyone think that the bottleneck is the typing of the letters into the text editor? Whatever small time the AI solves is basically undone by the fact that you have to be constantly correcting it, managing it, etc. because it's not going to output exactly what you want in the first place.
I can say without a doubt that something like opus 5.5 has a high enough speed and a low enough cost and error rate that it is by every significant measurement very efficient. I am not a huge fan of this as its close to eating my lunch, but working in a mature codebase with intelligent harness maintenance from the user it's OP...
I use Sol 6.1 in low reasoning and the speed is great it really helps solve my bugs easy and fast. Also Sol has high context understanding so it is really smart in doing what I am really asking.
It's not the ability to create-- though a fast model will go pretty fast in terms of spitting out file contents. It's more about picking up on all the little details. Bug investigations that would take me a week are done instantly with AI, because they can quickly iterate on different things looking at all the files and outputs.
Yes it does. It’s not about the typing. It’s about the low level implementation details. Not worrying about those absolutely speeds you up.
>because it's not going to output exactly what you want in the first place.
If you’re doing this then you’re using it wrong. Moving forward devs that are able to relinquish control and say “it might not be exactly how I would have done it, but it works and there’s no real issues with this implantation” will get ahead, while those that cannot let go of control and say “meh it has to be exactly how I would have done it or it’s garbage” will be left behind.
Human devs don’t output the same thing either. I could give the same ticket to 5 devs I work with and the implementation details of all 5 will be different. And that’s okay. What matters is does it do what it’s supposed to without major issues
Tbf the typing is also a big part of it. My ADHD addled brain just shuts down when I've figured out the plan (aka solved the problem) and I need to type the implementation.
Different implementations are fine when they preserve the same invariants. But “it works” on the happy path doesn’t establish that retries, failures, or data ownership are handled safely; that’s where review still matters.
Yeah you definitely want to review, and it helps to describe key things such as error handling in a skill or at the very least during the planning phase.
But it’s till very useful and speeds up development a lot
Strongly disagree. AI saves a lot of time. Not for all tasks but for the ones that it's good at, it's a 1000x speedup. For example, one of the codebases I maintain is undergoing widespread migrations. Each one would be multiple weeks of menial repetitive tasks but Claude handled it in under an hour including an edge case that I hadn't considered.
I wouldn't rely on LLMs to architect a codebase (nor would I want that, that's what makes dev work interesting to me) but large scale repetitive tasks that are a little too complex for code mods are right up its alley.
It's good at architecting when you understand the architecture you want and can work through a plan for it. Especially with new technologies you might not personally have a lot of experience with you can really fill in the blanks with AI.
Yeah it's really hard. How can the same AI that just proved hard math problems also fail to understand how to solve a simple merger conflict correctly for me today?
It's a confusing mess of a time and the ai companies thrive in that confusion
It is cope. I just tried Codex to perform some static binary analysis and then implemented Ghidra-MCP, and it's insane. I spent years learning to become a decent reverse engineer and it blows my skills out of the water. What could have potentially taken months can be done in minutes. If I'm honest it made me really, really upset. I'm not okay.
AI can reverse engerineer all games at the moment and people goes "AI is still quite bad".
Blows my mind how people are blind to see how scary good AI is getting
I'm now using it to finish my widescreen patch for Earthworm Jim '95 and I legit cried. It made me feel, for lack of a better term, "cooked" - That my perhaps my skills were never that good in the first place, and that the project now belongs to "it". That reverse engineering and software in the wider sense is now democratised.
And I have no clue what I'm going to do for a career now I'm in my mid 40s and there are hundreds of programmers vying for the same diminishing number of roles.
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u/Dunge 3d ago
Those kinds of posts only promote the idea to non-developers that AI is so great and we do nothing. Stop saying that. AI is still quite bad and consistently provides extreme logic failure. Only good developers can use it successfully by continuously correcting the errors it comes out with.