Code that does not work is just text.
I’ve never thought of it that way. I’m going to add copy writer to my resume.
This made me laugh so hard one of the dogs came to check in on me.
Oh my goodness, that’s adorable and sweet of your dog! Also, I’m so glad you had such a big laugh. I love when that happens.
He’s a sweet guy. … Mostly. Very much in need of a lot of attention. Sometimes he just sits next to you on the couch and puts his paw on you if you’re not giving him enough attention.
Here he is posing with his sister as a prop:
Oh my goodness, he sounds precious! I’ve had a sweet and needy dog like that in the past, too. It can be a lot, but I loved it (and miss it,) haha.
Both your dogs are very cute! You and your pups gave me a much-needed smile. Thank you for that. :) Please give them some pets from me!
Conversely, code that works is also text
But working code can be made into numbers.
But text is also numbers
But not just text
Also that’s not converse to what the parent comment said
Did you want to converse about conversing?
The image is taken from Zhihu, a Chinese Quora-like site.
The prompt is talking about give a design of a certain app, and the response seems to talk about some suggested pages. So it doesn’t seem to reflect the text.
But this in general aligns with my experience coding with llm. I was trying to upgrade my eslint from 8 to 9, and ask chatgpt to convert my eslint file, and it proceed to spit out complete garbage.
I thought this would be a good task for llm because eslint config is very common and well-documented, and the transformation is very mechanical, but it just cannot do it. So I proceed to read the documents and finished the migration in a couple hour…
I asked ChatGPT with help about bare metal 32-bit ARM (For the Pi Zero W) C/ASM, emulated in QEMU for testing, and after the third iteration of “use printf for output” -> “there’s no printf with bare metal as target” -> “use solution X” -> “doesn’t work” -> “ude printf for output” … I had enough.
Sounds like it’s perfectly replicated the help forums it was trained on.
I used ChatGPT to help me make a package with SUSE’s Open Build Service. It was actually quite good. Was pulling my hair out for a while until I noticed that the project I wanted to build had changes URLs and I was using an outdated one.
In the end I just had to get one last detail right. And then my ChatGPT 4 allowance dried up and they dropped me back down to 3 and it couldn’t do anything. So I had to use my own brain, ugh.
chatgpt is worse among biggest chatbots with writing codes. From my experience Deepseek > Perplexity > Gemini > Claude.
Yeah you can tell it just ratholes on trying to force one concept to work rather than realizing it’s not the correct concept to begin with
That’s exactly what most junior devs do when stuck. They rehash the same solution over and over and it almost seems like that llms trained on code bases infer that behavior from commit histories etc.
It almost feels like on of those “we taught him these tasks incorrectly as a joke” scenarios
Did it at least try puts?
It’s pretty random in terms of what is or isn’t doable.
For me it’s a big performance booster because I genuinely suck at coding and don’t do too much complex stuff. As a “clean up my syntax” and a “what am I missing here” tool it helps, or at least helps in figuring out what I’m doing wrong so I can look in the right place for the correct answer on something that seemed inscrutable at a glance. I certainly can do some things with a local LLM I couldn’t do without one (or at least without getting berated by some online dick who doesn’t think he has time to give you an answer but sure has time to set you on a path towards self-discovery).
How much of a benefit it’s for a professional I couldn’t tell. I mean, definitely not a replacement. Maybe helping read something old or poorly commented fast? Redundant tasks on very commonplace mainstream languages and tasks?
I don’t think it’s useless, but if you ask it to do something by itself you can’t trust that it’ll work without singificant additional effort.
A lot of words to just say vibe coding
Sorta kinda. It depends on where you put that line. I think because online drama is fun when we got to the “vibe coding” name we moved to the assumption that all AI assistance is vibe coding, but in practice there’s the percentage of what you do that you know how to do, the percentage you vibe code because you can’t figure it out yourself off the top of your head and the percentage you just can’t do without researching because the LLM can’t do it effectively or the stuff it can do is too crappy to use as part of something else.
I think if the assumption is you should just “git gud” and not take advantage of that grey zone where you can sooort of figure it out by asking an AI instead of going down a Google rabbit hole then the performative AI hate is setting itself up for defeat, because there’s a whole bunch of skill ranges where that is actually helpful for some stuff.
If you want to deny that there’s a difference between that and just making code soup by asking a language model to build you entire pieces of software… well, then you’re going to be obviously wrong and a bunch of AI bros are going to point at the obvious way you’re wrong and use that to pretend you’re wrong about the whole thing.
This is basic online disinformation playbook stuff and I may suck at coding, but I know a thing or two about that. People with progressive ideas should get good at beating those one of these days, because that’s a bad outcome.
People seem to disagree but I like this. This is AI code used responsibly. You’re using it to do more, without outsourcing all your work to it and you’re actively still trying to learn as you go. You may not be “good at coding” right now but with that mindset you’ll progress fast.
I think the effects of it are… a bit more nuanced than that, perhaps?
I can definitely tell there are places where I’m plugging knowledge gaps fast. I just didn’t know how to do a thing, I did it AI-assisted once or twice and I don’t need to be AI assisted anymore because I understood how it works now. Cool, that. And I wouldn’t have learned it from traditional sources because asking in public support areas would have led to being told I suck and should read the documentation and/or to a 10 video series on Youtube where you can watch some guy type for seven hours.
But there are also places where AI assistance is never going to fill the blanks for me, you know? Larger trends, good habits, technical details or best practices that just aren’t going to come up from keeping a smart autocorrect that can explain why something was wrong.
Honestly, in those spaces the biggest barrier is still what it was: I don’t necessarily want to “progress” on those areas because I don’t need it and it’s not my job. I can automate a couple things I didn’t know how to automate before, and that’s alright. For the rest, I will probably live with the software someone else has made when it exists.
The problem is hubris, right? I know what I don’t know and which parts I care to learn. That’s fine. Coding assistant LLMs are a valid tool for someone like that to slightly expand their reach and I presume there’s a lot of people like that. It’s the random entrepeneurs who have been sold by big corpos that they don’t need a real programmer to build their billion-dollar app anymore that are going to crash and burn and may take some of the software industry down with them.
It catches things like spelling errors in variable names, does good autocomplete, and it’s useful to have it look through a file before committing it and creating a pull request.
It’s very useful for throwaway work like writing scripts and automations.
It’s useful not but a 10x multiplier like all the CEOs claim it is.
Fully agreed. Everybody is betting it’ll get there eventually and trying to jockey for position being ahead of the pack, but at the moment there isn’t any guarantee that it’ll get to where the corpos are assuming it already is.
Which is not the same as not having better autocomplete/spellcheck/“hey, how do I format this specific thing” tools.
Yeah, it’s still super useful.
I think the execs want to see dev salaries go to zero, but these tools make more sense as an accelerator, like giving an accountant excel.
I get a bit more done faster, that’s a solid value proposition.
I think the main barriers are context length (useful context. GPT-4o has “128k context” but it’s mostly sensitive to the beginning and end of the context and blurry in the middle. This is consistent with other LLMs), and just data not really existing. How many large scale, well written, well maintained projects are really out there? Orders of magnitude less than there are examples of “how to split a string in bash” or “how to set up validation in spring boot”. We might “get there”, but it’ll take a whole lot of well written projects first, written by real humans, maybe with the help of AI here and there. Unless, that is, we build it with the ability to somehow learn and understand faster than humans.
I don’t know, some of these guys have acccess to a LOT of code, and even more debate about what those good codebases entail.
I think the other issue is more relevant. Even 128K tokens is not enough for something really big, and the memory and processing costs for that do skyrocket. People are trying to work around it with draft models and summarization models, so they try to pick out the relevant parts of a codebase in one pass and then base their code generation just on that, and… I don’t think that’s going to work reliably at scale. The more chances you give a language model to lose their goddamn mind and start making crap up unsupervised the more work it’s going to be to take what they spit out and shape it into something reasonable.
It’s not much use with a professional codebase as of now, and I say this as a big proponent of learning FOSS AI to stay ahead of the corpocunts
Yeah, the AI corpos are putting a lot of effort into parsing big contexts right now. I suspect because they think (probably correctly) that coding is one of the few areas where they could get paid if their AIs didn’t have the memory of a goldfish.
And absolutely agreed that making sure the FOSS alternatives keep pace is going to be important. I’m less concerned about hating the entire concept than I am about making sure they don’t figure out a way to keep every marginally useful application behind a corporate ecosystem walled garden exclusively.
We’ve been relatively lucky in that the combination of PR brownie points and general crappiness of the commercial products has kept an incentive to provide a degree of access, but I have zero question that the moment one of these things actually makes money they’ll enshittify the freely available alternatives they control and clamp down as much as possible.
I use it sometimes, usually just to create boilerplate. Actual functionality it’s hit or miss, and often it ends up taking more time to fix than to write myself.
I used Claude 3.7 to upgrade my eslint configs to flat and upgrade from v7 to v9 with Roo Code and it did it perfectly
Ai code is specifically annoying because it looks like it would work, but its just plausible bullshit.
And that’s what happens when you spend a trillion dollars on an autocomplete: amazing at making things look like whatever it’s imitating, but with zero understanding of why the original looked that way.
I mean, there’s about a billion ways it’s been shown to have actual coherent originality at this point, and so it must have understanding of some kind. That’s how I know I and other humans have understanding, after all.
What it’s not is aligned to care about anything other than making plausible-looking text.
It needs good feedback. Agentic systems like Roo Code and Claude Code run compilers and tests until it works (just gotta make sure to tell it to leave the tests alone)
All programs can be written with on less line of code. All programs have at least one bug.
By the logical consequences of these axioms every program can be reduced to one line of code - that doesn’t work.
One day AI will get there.
Welp. Its actually very in line with the late stage capitalist system. All polish, no innovation.
Awwwww snap look at this limp dick future we got going on here.
Its like having a junior developer with a world of confidence just change shit and spend hours breaking things and trying to fix them, while we pay big tech for the privilege of watching the chaos.
I asked chat gpt to give me a simple squid proxy config today that blocks everything except https. It confidently gave me one but of course it didnt work. It let through http and despite many attempts to get a working config that did that, it just failed.
So yeah in the end i have to learn squid syntax anyway, which i guess is fine, but I spent hours trying to get a working config because we pay for chat gpt to do exactly that…
Try to get one of these LLMs to update a package.json.
ones that can run cli tools do great, they just use npm
I’m pretty sure that is how we got CORBA
now just make it construct UML models and then abandon this and move onto version 2
Hello, fellow old person 🤝
Write tests and run them, reiterate until all tests pass.
Bogosort with extra steps
That doesn’t sound viby to me, though. You expect people to actually code? /s
You can vibe code the tests too y’know
Return “works”;
Am I doikg this correctly?
You know, I’d be interested to know what the critical size you can get to with that approach is before it becomes useless.
It can become pretty bad quickly, with just a small project with only 15-20 files. I’ve been using cursor IDE, building out flow charts & tests manually, and just seeing where it goes.
And while incredibly impressive how it’s creating all the steps, it then goes into chaos mode where it will start ignoring all the rules. It’ll start changing tests, start pulling in random libraries, not at all thinking holistically about how everything fits together.
Then you try to reel it in, and it continues to go rampant. And for me, that’s when I either take the wheel or roll back.
I highly recommend every programmer watch it in action.
Is there a chance that’s right around the time the code no longer fits into the LLMs input window of tokens? The basic technology doesn’t actually have a long term memory of any kind (at least outside of the training phase).
Was my first thought as well. These things really need to find a way to store a larger context without ballooning past the vram limit
The thing being, it’s kind of an inflexible blackbox technology, and that’s easier said than done. In one fell swoop we’ve gotten all that soft, fuzzy common sense stuff that people were chasing for decades inside a computer, but it’s ironically still beyond our reach to fully use.
From here, I either expect that steady progress will be made in finding more clever and constrained ways of using the raw neural net output, or we’re back to an AI winter. I suppose it’s possible a new architecture and/or training scheme will come along, but it doesn’t seem imminent.
I’d rather recommend every CEO see it in action…
They’re the ones who would be cock-a-hoop to replace us and our expensive wages with kids and bots.
When they’re sitting around rocking back and forth and everything is on fire like that Community GIF, they’ll find my consultancy fees to be quite a bit higher than my wages used to be.
well, it only took 2 years to go from the cursed will smith eating spaghetti video to veo3 which can make completely lifelike videos with audio. so who knows what the future holds
The cursed Will Smith eating spaghetti wasn’t the best video AI model available at the time, just what was available for consumers to run on their own hardware at the time. So while the rate of improvement in AI image/video generation is incredible, it’s not quite as incredible as that viral video would suggest
But wouldn’t you point still be true today that the best AI video models today would be the onces that are not available for consumers?
Probably is still true, but I’ve not been paying close attention to the AI market in the last couple of years. But the point I was trying to make was that it’s an apples to oranges comparison
There actually isn’t really any doubt that AI (especially AGI) will surpass humans on all thinking tasks unless we have a mass extinction event first. But current LLMs are nowhere close to actual human intelligence.
