Comment by SilverSlash
3 months ago
Someone needs to make a compilation of all these classic OpenAI moments. Including hits like GPT-2 too dangerous, the 64x64 image model DALL-E too scary, "push the veil of ignorance back", AGI achieved internally, Q*/strawberry is able to solve math and is making OpenAI researchers panic, etc. etc.
I use Codex btw, and I really love it. But some of these companies have been so overhyping the capabilities of these models for years now that it's both funny to look back and tiresome to still keep hearing it.
Meanwhile I am at wits end after NONE OF Codex GPT-5.4 on Extra High, Claude Opus 4.6-1M on Max, Opus 4.6 on Max, and Gemini 3.1 Pro on High have been able to solve a very straightforward and basic UI bug I'm facing. To the point where, after wasting a day on this, I am now just going to go through the (single file) of code and just fix it myself.
Update: some 20 minutes later, I have fixed the bug. Despite not knowing this particular programming language or framework.
> I am now just going to go through the (single file) of code and just fix it myself.
That's front page news, in this era.
I understand how laughable that sounds when I say it out loud. But the reality is, when I'm in a state of 'Tell LLM what to do, verify, repeat', it's really hard to sometimes break out of that loop and do manual fixes.
Maybe the brain has some advanced optimization where once you're in a loop, roughly staying inside that loop has a lower impedance than starting one. Maybe that's why the flow state feels so magical, it's when resistance is at its lowest. Maybe I need sleep.
> it's really hard to sometimes break out of that loop and do manual fixes
You're aware of the MIT Media Lab study[0] from last summer regarding LLM usage and eroding critical thinking skills...?
[0] Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task June 2025 DOI:10.48550/arXiv.2506.08872
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> But the reality is, when I'm in a state of 'Tell LLM what to do, verify, repeat', it's really hard to sometimes break out of that loop and do manual fixes.
My experience is rather that I am annoyed by bullshit really fast, so if the model does not get me something that is really good, or it can at least easily be told what needs to be done to make it exceptional, I tend to use my temper really fast, and get annoyed by the LLM.
With this in mind, I rather have the feeling that you are simply too tolerant with respect to shitty code.
I have the same problem. I had lines directly in front of me where I needed to change some trivial thing and I still prompted the AI to do it. Also for some tasks AI are just less error prone and vice versa. But it seems the context switch from prompting to coding isn't trivial.
I think it’s called "sunk cost fallacy".
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> Maybe the brain
…is already damaged by reliance on AI.
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Thank you for the belly laugh.
Are you sure they are not just refusing to solve your UI bug due to safety concerns? They may be worried you'll take over the world once your UX becomes too good.
I don't want the world, sounds like people would start bothering me all the time. I'd never get a moment to relax. But if I had the world, I'd do better with it than what we got.
> a very straightforward and basic UI bug
Show us the code, or an obfuscated snippet. A common challenge with coding-agent related posts is that the described experiences have no associated context, and readers have no way of knowing whether it's the model, the task, the company or even the developer.
Nobody learns anything without context, including the poster.
A pretty easy way to construct a bug that is easy for a human to solve but difficult for an AI is to have it to do something with z-indexes. For instance, if your element isn't rendering because something else is on top of it, Claude will struggle, because it's not running a browser, so the only way it could possibly know there was a bug would be to read every single CSS and HTML file in your entire repo. On the other hand, a human can trivially observe the failure in a browser and then fix it.
This is a pretty simple thing, but you can imagine how CSS issues get progressively more difficult for AIs to solve. A CSS bug can be made to require reading arbitrarily much code if you solve by only reading code, but by looking at relatively few elements, if you look at the page with your eyes.
This can be somewhat solved by hooking up a harness to screenshot the page and feed it into the AI, but it isn't perfect even then.
That's hard to believe in my case. I tried a variety of prompts, 3 different frontier models, provided manual screenshot(s), the agent itself also took its own screenshots from tests during the course of debugging. Nothing worked. I have now fixed the bug manually after 15-20 minutes of playing around with a codebase where I don't know the language and didn't write a single line of code until now.
What's hard to believe? OP just asked what the bug was.
> after wasting a day on this, I am now just going to go through the (single file) of code and just fix it myself.
Seriously, you wasted a whole day just so you wouldn't have to look at a single file of code?
> Update: some 20 minutes later, I have fixed the bug. Despite not knowing this particular programming language or framework.
Be really careful there, you might have accidentally learned something.
It is entirely plausible they were just experimenting with AI tooling to better understand how to use it and what it is capable of. Their saying, 'Despite not knowing this particular programming language or framework.' indicates to me this is probably the case.
Nope. I've been working on this project for a couple of days now and things were mostly going well. A significant portion of the mvp backend and frontend was built and working. Then this one seemingly simple bug appeared and just totally stumped both Codex and Claude Code.
There was even another UI component (in the same file) which was almost the same but slightly different and that one was correct. That's what I copy pasted and tweaked when I fixed the problem. But for some reason the models were utterly incapable of making that connection.
With Codex and Claude Code I thought maybe because these agentic coding tools are trained to be conservative with tokens and aggressively use grep that they weren't looking at the full file in one go.
But with Gemini I used the web version and literally pasted that entire file + screenshots detailing what was wrong (including the other component which was rendering correctly) and it still couldn't solve it. It was bewildering.
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I told my manager I wrote my code line by line (most of it) in a check-in. I showed him @author my name, and we laughed for a bit.
But I think that is the best way to have a clear mental model. Otherwise, no matter how careful, you always have tech debt building and churning.
Also they really suck at UI bugs and CSS. Unit test that stuff.
I had a problem that required a recursive solution and Opus4.6 nearly used all my credits trying to solve it to no avail. In the AI apocalypse I hope I'm not judged too harshly for my words near the end of all those sessions lol.
> I am now just going to go through the (single file) of code and just fix it myself.
You can't it's all vibed, you'll face the art vs build internal struggle and end up re-coding the entire thing by hand.
Do you have examples of Anthropic doing so? I’d love to see it. To me it seems they have a proclivity to being hyper-accurate.
yeah they all suck at ui. have you given it a feedback loop? update code, screenshot, read image repeat etc. that's the best i've found as long as tokens aren't a concern
Haven't you heard? "Coding is solved."