Perhaps you're doing some amazing low-level work, but it feels like you're way overestimating how much of our industry does that. A massive amount of developers show up to work every day and just stitch together frameworks and libraries.
In many ways, it feels similar to EVs. Just because EVs aren't yet, and may never be, effective to moving massive amounts of cargo in a day with minimal refueling, doesn't mean that they aren't an effective solution for the bulk of drivers who have an average commute of 40 miles a day.
Three months ago I would have agreed with you, but anecdotal evidence says Codex 5.2 and Opus 4.5 are finally there.
People say a lot of things, and there is a lot of context behind what they're saying that is missing, so then we end up with conversations that basically boil down to one person arguing "I don't understand how anyone cannot see the value in this" with another person thinking "I don't understand how anyone can get any sort of value out of this", both missing the other's perspective.
6-12+ months ago the results I was getting with these tools were highly questionable but in the last six months the changes have been pretty astounding
There's other more advanced coding AI tools but this has accomplished most all of my needs so far
>They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
>LLMs pretend to do this with big confidence and fail miserably.
This is true for most developers as well. The mean software developer, especially if you outsource, has failure modes worse than any LLM and round-trip time is not seconds but days.
The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
But that's exactly the *promise* of LLMs by the hypepeople behind it.
I do not know and do not care what the "hypepeople" say. I can tell you that, by pure logic alone, LLMs will be superior at simple and routine tasks sooner, which means they will compete with outsourced labor first.
LLMs need to be measured against their competition and their competition right now is outsourced labor. If an LLM can outperform an offshore team at a fraction of the cost, why would any company choose the offshore team? Especially when the LLM eliminates some of the biggest problems with offshore teams (communication barriers, round trip times).
If LLMs take any programmer jobs they will at the very beginning make those outsourced jobs obsolete, so the only relevant question is whether they have done that or are in the process of doing so. If they don't, then their impact will be minimal, if they do, then their impact will be massive. I think that this line of thinking is a far better benchmark then asking whether an LLM gets X or Y question wrong Z% of the time.
And "a few moments later" happens the same as with those "cost effective" clouds.
[1] https://www.heise.de/en/news/IDC-Many-companies-want-partly-...
[2] https://www.idc.com/resource-center/blog/storm-clouds-ahead-... (original)
then again, i have some suspicion that alot of consumer-focused end products using llms in the backend (hello chatbots) expecting big returns for all those tokens spent may have some bad news coming... if the bubble starts popping i'm guessing it starts there...
The bottleneck will soon be ideas for the things to build.
But they do not compete. They have totally different jobs.
No, it won't. The utility of LLMs is already growing asymptotically now...
Why would you trust the hype when you can verify this stuff yourself pretty easily.
"completely useless" and "real programming" are load bearing here. Without a definition to agree on for those terms, it's really hard not to read that as you're trying to troll us by making a controversial unprovable claim that you know will get people that disagree with you riled up. What's especially fun is that you then get to sneer at the abilities of anybody making concrete claims by saying "that's not real programming".
How tiresome.
Ultimately it all boils down to the money - show me the money. OAI have to show money and so do its customers from using this tool.
But nope, the only thing out there where it matters is hype. Nobody is on an earnings call clearly showing how they had a numerical jump in operating efficiency.
Until I see that, this technology has a dated shelf life and only those who already generate immense cash flows will fund its continued existence given the unfavourable economics of continued reinvestment where competition is never-ending.
Yes, there are things LLMs can't do at all, some where they are actively dangerous.
But also there are decently sized parts of "software development" where any above average LLM can speed up the process as long as whoever is using it knows hot to do so and doesn't fight the tool.
the product market fit for LLMs is already clearly found, there's just no moat to it. tokens are a commodity
I can confirm that they are completely useless for real programming
Absolutely incredible tools that have saved hours and hours helping me understand large codebases, brainstorm features, and point out gaps in my implementation or understanding.
I think the main disconnect in the discourse is that there are those pretending they can reliably just write all the software, when anyone using them regularly can clearly see they cannot.
But that doesn't mean they aren't extremely valuable tools in an engineer's arsenal.
If you know the problem space well, you can let LLMs(I use Claude and ChatGPT) flesh it out.
Both for code? For me, it's Claude only for code. ChatGPT is for general questions.
I also use ChatGPT to summarise my project. I ask it to generate mark down and PDFs, explaining the core functionality.
I’m in enterprise ERP.
Can you elaborate slightly on what you pay for personally and what your employer pays for with regards to using LLMs for Enterprise ERP?
Why so narrow minded?
The GP didn't mention anything about freelancing so unless you know them or stalked them you are perhaps being narrow minded here.
You are being narrow minded here.
You are being downright unpleasant and I don't think we should continue this conversation further until you open your mind.
How much would you be willing to pay to continue using Claude on a monthly basis before you stopped?
Do you currently maintain the new (as of two weeks ago) cash reserve to ensure it continues working when limits are reached and how much do you reserve for said reserve?
Finally, do you send your customer's code or data directly to Claude or do you use it indirectly on generic stuff and then manually specialize the outputs?
You and I must have completely different definitions of "real programming". In this very comment, you described a problem that the model solved. The solution may not have involved low-level programming, or discovering a tricky bug entrenched in years-worth of legacy code, but still a legitimate task that you, as a programmer, would've needed to solve otherwise. How is that not "real programming"?
If an LLM controlling a pair of robot hands was able to make a passable PB&J sandwich on my behalf, I _guess_ that could be useful to me (how much time am I really saving? is it worth the cost? etc.), but that's very different from those same robo-hands filling the role of a chef de cuisine at a fine dining restaurant, or even a cook at a diner.
The rest of us are just pumping out CRUD-burgers off the API assembly line. Not exactly groundbreaking stuff.
LLMs are really good with burgers, but not so much being a private chef.
Now if your CRUD app never gets any users sure it stays generic. But we’ve had low code solutions that solve this problem for decades.
LLMs are good at stuff that probably should have been low code in the first place, but couldn’t be for reasons. That’s useful, but it comes with a ton of trade offs. And these kind of solutions covet a lot less ground than you’d think.
Like LLMs they took away a _very_ specific segment of software, Zapier, n8n, NodeRED etc. do some things in a way that bespoke apps can't - but they also hit a massive hard wall where you either need to do some really janky shit or just break out Actual Code to get forward.
This was how I felt until about 18 months ago.
Can you give a single, precise example where modern day LLMs fail as woefully as you describe?
This is a bit of no-true-scottsman, no? For you "real programming" is "stuff LLMs are bad at," but a lot of us out in the real world are able to effectively extract code that meets the requirements of our day jobs from tossing natural language descriptions into LLMs.
I actually find the rise of LLM coding depressing and morally problematic (re copyright / ownership / license laundering), and on a personal level I feel a lot of nostalgia for the old ways, but I simply can't levy an "it's useless" argument against this stuff with any seriousness.
All those many, many languages, frameworks, libraries, APIs and there many many iterations, soooo much time lost on minute details. The natural language description, even highly detailed down to being directly algorithmic, is a much better level for me. I have gotten more and more tired of coding, but maybe part of it is too much Javascript and its quickly changing environment and tools, for too many years (not any more though). I have felt that I'm wasting way too much time chasing all those many, many details for quite some time.
I'm not pro-high-level-programming per se - I started a long time ago with 8 bit assembler and knowing every one of the special registers and RAM cells. I cherish the memories of complex software fitting on a 1.44 MB floppy. But it had gotten just a bit too extreme with all the little things I had to pay attention to that did not contribute to solving the actual (business) problem.
I feel it's a bit early even if it's already usable, but I hope they can get at least one more giant leap out of AI in the next decade or so. I am quite happy to be able to concentrate on the actual task, instead of the programming environment minutiae, which has exploded in size and complexity across platforms.
Can you elaborate on "real programming" ?
I am assuming you mean the simplest hard problem that is solved. The value of the work is measured in those terms. Easy problems have boilerplate solutions and have been solved numerous times in the past. LLMs excel here.
Hard problems require intricate woven layers of logic and abstraction, and LLMs still struggle since they do not have causal models. The value however is in the solution of these kinds of problems since the easy problems are assumed to be solved already.
so I see what you're saying. he comes up with the wrong answers a lot to a problem involving a group of classes in related files
however it's Continue, so it can read files in vs code which is really nice and that helps a lot with its comprehension so sometimes it does find the issue or at least the nature of the issue
I tend to give it bug n-1 to pre digest while I work on bug n
Some years? I don't remember any agents being good before just over 1 year ago with Cursor and stuff really didn't take off until Claude Code.
They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
LLMs pretend to do this with big confidence and fail miserably.
For every problem I need to turn my brain to ON MODE and wake up, the LLM doesn't wake up.
It surprised me how well it solved another task: I told it to set up a website with some SQL database and scripts behind it. When you click here, show some filtered list there. Worked like a charm. A very solved problem and very simple logic, done a zillion times before. But this saved me a day of writing boilerplate.
I agree that there is no indication that LLMs will ever cross the border from simple-boilerplate-land to understanding-complex-problems-land.