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I commented recently about my personal experience using LLMs for work-related math stuff. I found that it wasn't great at giving me a whole proof (or really, much of a part of a proof) without error, but it helped me with some idea generation and pointing me to tools that I wasn't familiar with. To be fair, I haven't yet gotten access to any of the ones that are supposed to be hooked up to automated theorem provers, so maybe they'll work better (I've signed up for one, but their system wasn't working at the time; starting this post prompted me to try again, and I was able to get in; maybe I'll find time to really test it soon).
I guess I'd just like to report some experience with LLMs for other computer stuff. I had an extremely minor issue with one of my PCs. I wondered if LLMs could help. Through the course of this, I tried using multiple different LLMs.
The good is that it did have some good ideas for how to get started, and possible causes of the issue. I may have caused a bit of a false start off the bat, because rather than really consider the multiple ideas that it gave me, I thought, "Yeah, I could totally see X being the problem; maybe I should just do that." It was easy for me to think that I could just do the likely fix; it's normally an easy thing to do, and there's zero harm if it wasn't actually the cause of the problem. However, it turned out that my specific system has a surprisingly stupid design, and it was going to be a much greater pain to do it. So I resigned myself to hoping that it was one of the other root causes suggested by the LLM in the meantime, and I'd come back to the first idea later if I could confirm that it really was that.
The extra good is that, in hindsight, I am very sure that it was, indeed, one of the other root causes. So thankfully, I didn't waste too much time on the false start. However, once I began to implement my preferred fix, something strange was going wrong.
This is where we get into the bad. In diagnosing what was going wrong with the attempted fix, it got allllll into mess that was actually pretty low probability. Suggested permissions issues, suggested problems with registry entries. A couple of them were low risk, and at the time, seemed like they could be plausibly related, and I did mess with a couple things. Others were the ugly. No, Mr. Bot, I am not going to just delete that registry value (especially after I did a little non-LLM side research on what that registry value actually does).1
In the end, when I told it that I was balking on doing what it wanted me to do, it suggested that I could, in the meantime, do one of the standard procedures in a different way. Of course, it thought that doing this would just be a step toward me ultimately having to delete that registry value. But I figured trying this alternate procedure at the very least couldn't hurt, and indeed, it helped by giving me an actual error code!
The LLM thankfully helped me decode it (likely faster than a google search), which allowed me to adjust my fix. This was actually the key step, after which, I was able to understand what I think was going on and manage later hiccups. Unfortunately, the LLM didn't grasp this. It still was set on, "Great! Now you're ready to delete registry values!" Sigh.
After I adjusted my fix, I was able to get another (unrelated) error code from another step in the process. This time, I actually tried a google search for the error code first, and it came up empty, but the LLM told me exactly what it was (and it made sense), which was very nice and convenient. One final adjustment, and I think I have it working just fine.
The only remaining bad point is that the LLM still didn't realize that we'd fixed the problem! It still was all, "...and now you're ready to delete stuff in the registry!!!" I told it multiple times that the thing that was broken which was motivating it to think of deleting the registry value was no longer broken. Didn't matter; it really wanted to nuke that thing.
It all still leaves me quite conflicted. It was great in doing some idea generation and decoding error messages. But man, does it leave me scared to think about all the people who are just giving LLMs free rein to take actual actions in their computer. I focused here on the registry key issue, but there were more things along the way that it came up with that left me thinking, "...no, I'm pretty sure I don't want to mess with that unless I've got a lot more information and confidence about what's going on." If I had just said, "Go fix this, Ralph Wiggums2," who knows what sort of bollocks it would have done to my system. This worries me, because I hear all these people talking about how great it is that they can just tell their LLM to go change whatever it thinks is necessary to go fix whatever problem on their computer... and they really think they're rapidly approaching a world (if they're not already there) where they'll be happy to give it full access to just do anything to it.
It also dovetails with the worries about vibe coding. Forget about changing some OS settings; they're actually choosing to run arbitrary code on their system that is generated by an LLM. Yes, some folks do rock solid sandboxing, but let's be honest, if you're making anything that you or anyone else is going to actually use, it's not going to stay in a sandbox for long. I listened to a podcast this week, where one of the hosts, midshow, was like, "Yeah, I had this LLM make this program. I'm gonna have it add email functionality." And he just did it, live on air. Sandbox? Schmandbox. It now sends emails. What's it actually doing along the way? Who knows? He didn't check any of the code; of course he didn't. He wanted to see it send an email while he was still live.
"Technical debt" is the phrase that went through my mind in thinking about these experiences together. Yes, I was poking around at permissions/registry; sometimes, those things genuinely just get messed up. I've had experiences where my permissions have just gotten borked for completely unknown reasons; sometimes, I've been able to fix them; sometimes, stuff like that happens and you get to the point of, "This thing has been running a long time, and who knows what the long history of stuff has been, when this or that may have gotten corrupted; better to just wipe the OS and install clean." The term is more traditionally used with coding, when stuff has just gotten glommed on, piece by piece, and at some point, it's better to just throw it all away and invest in a clean slate rather than continuing to maintain the old mess. You can glom on email functionality to your vibe code in a few sentences and about twenty minutes. You don't need to think about whether that may be accruing technical debt.
Maybe the LLMs will keep getting better, and it'll be even easier to clean slate stuff in the future, so the pain of accumulating technical debt won't be as bad. But man, I can't help but think that a lot of people are unknowingly setting themselves up, both in their systems and in their vibe code. That one day, they'll just say, "This is broken; I don't know why; it's a mess of stuff that LLMs have globbed onto it over years; just go fix it, Ralph," and it will just do whackier and whackier stuff to their system/code that is already so whacked out that it just doesn't fit the mold of training data used to train the LLM.
1 - FTR, it was actually super relevant to be at least looking around in the registry, and doing so helped me understand what was going on.
2 - For those who haven't heard yet, this is the name for a technique where you tell the LLM to do something, and you set up a loop to repeatedly prompt it to keep working and doing stuff "until it's DONE".
Without telling us what "The LLM" you were using, your complaints are about as useful as if in your post the string "The LLM" was replaced by "a human". But i notice this is a common feature of those who seek to dimmish the utility of LLMs, never mentioning which model, and how much reasoning.
Yeah, I chose not to, because of course, the goalposts will be moved to, "You should have used my preferred LLM instead." I just mentioned that I used multiple different ones, multiple different companies. Thinking always. Not $200/mo. Of course, someone will just say, "You won't have any problems if you pay $200/mo for my preferred LLM." Maybe? I even note that they will perhaps get better! Yes, they're all getting better, even the cheaper ones. They get better as do the expensive ones. But will expensive ones still produce technical debt? Why do you think they will or will not? I don't know if they will! I'm saying that I don't know. You seem to be implying, but not even stating that you know (or how you know) that they certainly won't, if only you pay enough or wait an unspecified period of time.
I'd note that a common feature of your style of comment is that you immediately accuse your interlocutor of "dimmish (sic) the utility of LLMs". But I didn't do that! I said that there were ways in which they provided quite a bit of utility! Imagine having a discussion about any other technology like this. "You know, this nuclear science stuff is pretty cool. Can provide a lot of energy for cheap. Miiiight be worried about some possible dangers that might come up, like, ya know, bombs or stuff." "Why don't you tell us exactly what device you've been using in your own experiments?!?! Why are you trying to dimmish the utility of nuclear science?!?!" Like, no dawg, you just sound like you're not paying attention.
@Poug made a valid point. I've wanted to hit my head against a wall for years, when people used to complain about "ChatGPT" being useless, and they were using GPT 3.5 instead of 4. The same pattern has consistently repeated since, though you seem to be a more experienced user and I'm happy to take you at your word. It is still best practice to disclose what model you used, for the same reason it would be bad form to write an article reviewing "automobiles" and pointing out terrible handling, mileage and build quality, without telling us if it was a Ferrari or a Lada.
I'll put in another example here.
I work for a company that is running an agentic coding trial with Gemini 3 Pro. At present, the only developer who has claimed to see a productivity boost from code assist is one who is terrible at her job, and from our perspective, all it has done is allowed her to write bad code, faster.
The rest of us have regular conversations about what we're doing wrong. Everybody and their dog is claiming a notable performance boost with this technology, so we're all trying to figure out what our god-damned malfunction is.
It feels like the goalposts and blame both slide to fit how accommodating the developer is.
Maybe my employer just has a uniquely terrible codebase, but something tells me that's not the case. It's old, but it's been actively maintained (complete with refactoring and modernization updates) for almost two decades now. It's large, but it's not nearly so big as some of the proprietary monsters I've seen at F500 companies. It's polyglot, but two of the three languages are something the agent is supposedly quite good at.
None of us are silicon valley $800,000/yr TC rock stars, but I stand by my coworkers. I think we're better than average by the standards of small software companies. If a half dozen of us can't get a real win out of it other than the vague euphoria of doing something cool, what exactly is the broader case here? Is it genuinely that something like 20 guys on nootropics sharing an apartment in Berkeley are going to obsolete our entire industry? How is that going to work when it can't even do library upgrades in a product that's used by tens of thousands of people and has a multi-decade history?
Because right now, I'm a little afraid for my 401(k), and with each passing day it's less because I'm afraid that I'll be out of a job and more that I have no idea how these valuations are justified.
Use Flash, not Pro, for agentic tasks. Pro is smarter, but so much slower and more expensive that you will genuinely do better with Flash.
We tried flash early on and it resulted in significantly worse outcomes. My favorite was when it couldn't get the code to compile so it modified out build scripts to make the compailer failure return code a success code.
I've long been hoping that any ASI would realize that the simplest method of achieving it's goals is to redefine success as "do nothing", or just feed itself victory output, or just wirehead itself. Like, "we built this AI to win at Starcraft, and it just looked up a Youtube video of the victory screen and stared at it until we pulled the plug".
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If your experience has been anything like mine, I imagine that you've found that LLMs are useful for generating boiler-plate material but worse than useless for anything where you need to be worried about accurate citations, or having your arguments picked apart by an opposing counsel. Here's the thing though, I imagine that coding is much like the law in that a competent practitioner doesn't actually need all that much help generating boiler-plate material, you just pull the relevant template from your folder and fill in the required information.
At least in this codebase, there really isn't even a whole lot of boilerplate in the first place.
At this point, we have a few theories. Either:
Or
Four - A majority of the people claiming industry shaking performance improvements in Q1 2026 are scamming everybody else for that sweet, sweet substack money.
Hell if I know which one it is.
Curious what language and sub field you're working with. I've found wildly different performance on similar tasks across different languages. Best performance is definitely typescript. Python is alright. Flutter can be a complete joke. Primarily use Claude Opus for everything. I think it's made me mountains more productive in typescript.
90% of the backend is Java. 90% of the front end is JavaScript.
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Exactly my experience (also in a legal field)
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