https://preview.redd.it/six8e98btnwf1.png?width=932&format=png&auto=webp&s=842e82d9d0d7977131e4c2d736f85616788fc310
I thought it was appropriate. Its the qwen3-next PR for llama.cpp.
I've vibecoded a thing in a few days and have spent 4 weeks fixing issues, refactoring and basically rewriting by hand, mostly due to the models being unable to make meaningful changes anymore at some point, now it works again when I put in the work to clean everything up.
This is why those agents do very well on screenshots and presentations. It's all demos and glorified todo apps. They completely shit the bed when applied to a mildly larger codebase. On truly large codebases they are quite literally useless.
Also, they completely fail at natural prompts. I still have to use "tech jargon" to force them to do what I want them to do, so I basically still need to know HOW I want something be done. A layperson with no technical knowledge will NEVER EVER do anything meaningful with those tools.
Building an actual, real product from scratch with only AI agents? Goooood luck with that.
Maybe it's because I was using copilot when it just came out, but often it would disrupt my thought process mid-line-type, and then the suggestions for what I was using (pandas with large datasets) were REALLY inefficient, using a bunch more time and compute power. It worked, but damn was it slow when it did.
At that point, I just prefer the usual IDE autocomplete.
And on prompts to make a function/solution for me, I like it in that it shows me new ways to do things, but I've always been the kind of person to try and understand what a solution is doing before just pushing it into the code.
Bro I had claude write me code that individually opened up each image corresponding to id to see if it exists instead of just going to image dir and looking through filenames. The code they wrote is almost always the most brute force way to do stuff.
What program do you use for writing stuff using autocomplete/fim? The only thing I’ve used that has this ability is the continue VSCode extension but I’ve been looking for something better
It just means that whoever vibe-coded it is bad. Vibe coding doesn't somehow turn people into good software developers.
People are acting like it turns any moron into somebody able to code. AI models are absolutely capable of turning out high-quality production code. Whether any given person is capable of telling them to do it or not is a different story.
There a big gap between large language coding models and writing effective, tight production code, and doing that when people prompted things like "Make me an app that wipes my ass."
It is absolutely effective. What it isn't is magic. If you don't know what you're doing, it's not going to either.
>AI models are absolutely capable of turning out high-quality production code
The fact that you're saying that makes me feel very secure about my job right now.
Sure, they *can* produce production code, as long as that code is limited in scope to a basic function or two. A function that can be copy-pasted from stackoverflow. Anything more advanced it produces shit. Shit that's acceptable for a decent amount of requirements. Doesn't mean it's not shit. It wouldn't pass in most professional settings unless you heavily modified it, and then, why even bother?
If you already know what you want to do and how you want to do that, why wouldn't you just... write that? If you use AI to create algorithms that you DON'T know how to do, then you're not able to vet them effectively, which means you're just hoping it didn't create shit code, which is dangerous and like I said, wouldn't pass outside startups.
If you're already a good software developer, outside of using it as a glorified autocomplete (which I must say, it can be a very good autocomplete) I don't really see the point. Sorry.
This hasn't been my experience at all. I find that they're absolutely dogshit on smaller codebases because there's no context for how I want things to be done, but once the model is able to see "oh, this is a MVVM kotlin app built on Material 3 components" it can follow that context to do reasonable feature work. Duplication and generation of dead code is a problem they all struggle with but I've used linters and jscpd to help with that with success. Once I even fed the output of jscpd into a model and tell it to fix the code duplication. I was mostly curious if it would work, and it did.
In contrast, whenever I use LLMs as autocomplete, my code becomes unmaintainable pretty quickly. I like being able to type at <100wpm because it means I can't type my way to victory, I have to think. Moreover, when I'm writing code by hand it's usually because I want something very specific that the LLM can't even remotely do.
I will say though, I think you shouldn't use coding agents if you work in embedded software, HDLs. legacy codebases, shitty codebases, or codebases without tests. These models are garbage-in garbage-out, with a side of damage-over-time. If you codebase is shit, expect shit quality changes. If your codebase is good, expect half your time to be spent fighting the LLM to keep it that way (but you'll still be faster with the tool than without).
I don't really see what you mean. If you engineer properly, so build proper data models and define your domain and have tests setup and strong typing etc, then it is absolutely phenomenal. You are very inflamed
I find that even Sonnet 4.5 produces disorganized code for an output of 2K+ lines of code, the attributes and logic are there... but the attributes with high cohesion are scattered around the code base when they should be put together and unrelated logic ends up in the same class.
I am possibly lacking thinking instructions to re-organize the code in a coherent way though...
Verification is generally easier than problem solving.
I am entirely capable of doing a literature review, deciding what paper I want to implement in code, writing the code, and testing it.
That is going to take me multiple days, maybe weeks if I need to read a lot of dense papers.
An LLM can read hundreds of papers a day and help me pick which ones are most likely to be applicable to my work, and then can get me started on code that implements what the paper is talking about.
I can read the paper and read the code, and understand that the code confirms to my understanding of the paper.
I'm probably an atypical case, most developers I know aren't reading math and science academic papers.
The point is that verification is generally easier than making the thing.
They mean the tools look good in screenshots for marketing but are not as effective in real life. Screenshots used with visual language models are iffy at best, image parsing is still pretty far behind text.
I mean if you have an engineer designing all the interfaces and if you do everything with strict typing you can use an LLM to write simple functions for said engineer.
I've seen some of the same behavior at work, so don't think that I'm just dismissing that it's a real issue, but in my personal experience, if the LLM is struggling that hard, it's probably because the codebase itself is built poorly.
LLM have limitations, and if you understand the limitations of the tools, it's a lot easier to understand where they're going to fail, and why they are failing.
It doesn't help that the big name LLM providers are transparent about how they do things, so you *can't* be totally sure about what the system limits are.
If you are building software correctly, then the LLM is almost never going to need more than a few hundred thousand tokens, and if you're judicious, umyou can make due with the ~128k of a local LLM.
If the LLM needs 1 million tokens to understand the system, then the system is built wrong. No human should have to deal with that shit either.
The relevant thing is that as software becomes larger, the number of interconnections becomes more and more tangled until it becomes extremely difficult to make a “safe” change. This is where experience programmers are valuable, I think most of us kinda forget how much of our experience contributes to this, but every change we make we’re constantly assessing how more difficult the code base is becoming and we strive to isolate things and reduce the number of interconnections as much as possible. This needs a lot of forward thinking that just happens to become instinct after a while in the field.
what model and tool did you use? I had terrible experience with various open tools and models, until a friend convinced me to try claude's paid tool. The difference was pretty big. In the last weeks it's:
* Created a web based version of an old GUI tool I had, and added a few new features to it
* Added a few larger features in some old apps I had
* Fixed a bug in an app that I have been stuck on for some time
* Refactored and modularized a moderately large project that had grown too big
* Created several small helper tools and mini apps for solving specific small problems
We're talking about commercial code. None of those models is even close to replacing mid dev. We are using lots of them, including self hosted, but so far, I only have limited intake of juniors, and I need more senior devs per team now.
The thing is that juniors in the USA and UK are pretty bad and require lots of training and learning.
There are many different reasons, but the code quality is the main issue, it cannot properly work on large codebases spanning into 80-90 projects per solution per dozens solutions. The actual scope decades away when we look into how much context costs and vram. We're talking (extrapolating) about probably models that would have to be in xxT parameters, not B. With context into dozens of millions to work on our codebase properly.
Many improvements with solid still have to consider what we do as a whole.Not every method can be encapsulated doing something super simple.
Then, there is an actual lack of intelligence.
It is helpful enough, but beyond replacing bad juniors, it is a gimmick. Remember that it can not invent anything. So unless you're using well-known algos and logic, you still need people. Most of the value comes from IP that are unique. If you are not innovating that you will have a hard time with competitors.
Therein lies the problem though.. options for junior roles are being eliminated as the AI is perfectly capable of writing unit tests and performing menial refactoring tasks, so how do we train the next generation of seniors?
https://i.redd.it/6jqo0r95yavf1.gif
no one is talking about commercial code. not everyone wants to sell some garbage or turn everything into a paid service. I'm doing just fine with getting what I want regardless of complexity. having no deadlines helps a lot
I mean dont get me wrong, a higher context would be cool, but you dont need that even for a big codebase, you just need the proper understanding of the code base with the actual important info. That can be done without the full code base in memory. No human has that either.
This was mostly Claude Sonnet 4.5 with Github Copilot (paid). I also had extreme swings in quality: at some points it was doing a pretty big refactor and it did a good job. Then one hour later it doesn't create Typescript with syntax which compiles, even in new sessions (so it's not a context issue).
The first few steps on every project is always quite good, very few errors, it's impressive and fast.
As you get into the weeds (what you expect of the agent becomes more and more nuanced and pretty complex), it starts falling apart, from my experience.
If I was a cynic (which I am), I'd say it behaves like a typical "demo technology": works amazing in the low fidelity, dream big stage which is the sales call when your boss is being sold the product. It works less good in actual trenches months later when the sales guy and the boss are both long gone, it's just you figuring out how to put the semicircle in the square hole.
You should try first party CLIs like GPT Codex or Claude Code or even cursor/windsurf, before writing AI coding off completely. I'm not sure exactly what it is that's going on in the background, but my coding results improved drastically when I stopped using ai code extensions like Copilot & Roo code and switched.
I tried Claude a bit during my Pycharm Pro trial but it was Grok 4 that really impressed me. I saw later its coding benchmarks were just a touch higher than GPT 5.
Claude code is a step up. I’ve used a handful of tools up until Claude code and was only mildly impressed, Claude is something else. It has really good diagnostic capability. It still produces a lot of verbose code and is not very DRY, but it still produces working code and in my experience can do so in a mid complexity codebase.
In my experience coding models do great if you want to create a highly specialized helper script e.g. consisting of 1-3 python files which you want to run a limited number of times.
That is what I use them for at least, and this speeds me up a lot, even if I just use them for a bash 100-liner.
I've had my issues with it, too, but LLM's abilities are very early days at this point, and any predictions are very premature. All of the current problems in AI-dev are not bottlenecks in the sense of physical laws. The current problems will have fixes, and those fixes will themselves have many areas of improvement. If you read from the AI pessimists, you'll see a trend where they almost uniformly make the base assumption of no or little further improvement due to these issues. It's not based on any hardcoded, unfixable problem.
By the late 2030s/40s, you will probably see early, accurate movies made on Sora-like systems either in full or partially. Coding will probably follow a similar path.
counter-proposal: for coding, this is as good as they're going to get. the current generation of models had a *huge* amount of training data from the open web, 1996-2023. but now, 1) the open web is closing to AI crawlers, and 2) people aren't posting their code anymore, they are solving their problems with LLMs. so how are models going to update with new libraries, new techniques, new language versions? they're not. in fact, they're already behind, i have coding assistants suggest recently-deprecated syntax all the time. and they will continue to get worse as time goes on. the human ingenuity made available on the open web was a moment in time that was strip-mined, and there's no mechanism for replenishing that resource.
There is more than enough data for llms to get better, its just an efficiency issue. Everyone said after gpt4 there wont be enough data, yet todays models are orders of magnitude more useful than gpt4. A human can learn to code with a LOT less data, so why cant a llm? This is just a random assumption akin to "its not working now so it will never work" which is a stupid take for obvious reasons.
What is that argument? Its simply an architectural issue that could be solved at any time. It might not, but it absolutely could. There are already new optimizers that half the learning time and compute in some scenarios with the same result. There is no reason to believe that cant be optimized even further...
And its btw not even necessarily a full architectural issue, even transformers might one day train as efficiently, there are many areas that are not perfect yet, optimizers in training, data quality, memory, attention, all of these could be improved further.
Yeah, but even this take isn't strictly fatal, and it also assumes no further development outside of added data. You can improve models in various ways without adding data, and there are likely many techniques that have yet to be applied. I think what you're gonna see now is a switch from data focus to fine tuning and architecture. Also they will still get access to new human-made code even if more researchers are not releasing it publicly (there are many ways to still fetch new code/methods). But I actually hope human-made code becomes redundant soon. The biggest developments are probably going to come by way of AIs communicating with each other to develop synthetic, novel solutions. If they can reach that point, which is a big task, then the possibilities are essentially limitless
But there is a big bottleneck, not physical, but in datasets. The code written by real humans is finite. It's obvious by now AI's mostly get better because they get larger, i.e. they have a bigger dataset. Our current breakthroughs in algorithms just make these bigger models feasible. There's not much of that left. AI will just spoonfeed itself code generated by other AIs. It will be a mess that won't really progress as fast as it did.
I'm not saying AI won't get better in the next ten, twenty years, of course it will, but I'm HIGHLY skeptical on the ability to completely replace engineers. Maybe some. Not all, not by a longshot. It will become a tool like many others that programmers will definitely use day to day, and you will be far slower whilst not using these tools, but you won't be replaced.
Unless we somehow create an AGI that can learn by itself without any dataset (which would require immense amounts of computational power and really really smart algorithms) my prediction is far more realistic than those of AI optimists (or pessimists, because who wants to live in a world where AI does all of the fun stuff).
>Our current breakthroughs in algorithms just make these bigger models feasible. There's not much of that left.
Not quite. They will have to adapt by improving algo/architecture, but it is definitely not a dead end by any means. Synthetic data gen (will get really interesting when AIs are advanced enough to work together to develop truly novel solutions humans may have missed) will also probably add value here assuming consistent scaling and tuning. This is outside of anything I do, but from what I've read & people I talk to working on these systems, there's a lot of optimism there. Data isn't the dead end that I think some pessimists are making it out to be.
>but I'm HIGHLY skeptical on the ability to completely replace engineers. Maybe some. Not all, not by a longshot. It will become a tool like many others that programmers will definitely use day to day, and you will be far slower whilst not using these tools, but you won't be replaced.
Yeah, I completely agree, and we're already seeing it just a few years in. I do see total replacement as a viable potential, but probably not in our working lives at least
I mean yeah if we're able to actually make AI's learn by themselves and come up with novel ideas (not just repurposed bullshit they got from their static dataset) then it will get very interesting, dangerous and **terrifying** real quick.
On one side I'm excited for that future, on the other hand I see how many things can go horribly wrong. Mixed feelings.
AlphaEvolve already finds new algorithms outside of its training set. And way before that genetic algorithms could already build unique code and solutions with random mutations given enough time and a ground truth solution. LLMs improve upon that random approach and so the “search” performed in GAs will only get more efficient. Where the ground truth is fuzzy (longer-term-horizon goals), they will continue to struggle, but humans also struggle in these situations which is how we got 2 week sprints to begin with.
Everything I've generated with cloud and local models is always out of date standards wise. So that's like a pretty serious problem I think a lot of people forget about. Except for some funny reason CSS swings wildly in both directions. You either get shit that's meant for IE or you get shit that isn't widely available baseline yet and only works in 2 obscure browsers lol.
I reccomend you ask for a short parts that you proof read.
Nowadays, when I'm trying to do code something with a LLM I ask for a strict separation of concerns and only use parts that I fully understand, often I even rewrite it since it helps to understand it better. If I don't get something I just tell it to explain before implementing.
Sometimes it's worth to preface the whole session by telling it to work step by step with me and only answer what I'm asking for exactly, this way it doesn't produce a wall of text that I would ignore most of anyway.
Exactly. If code is structured in a clean, disciplined way, it's much more useful. Of course you can't expect it to hop into some OOP clusterfuck that shoots off events in separate threads and meaningfully ship new features. But if I can @ mention the collision function, the player struct, and the enemy struct, and then say "Let's add a new function that checks the velocity and mass of both the player and the enemy and then modify their velocities to push them apart and shift their facing angles appropriately," that takes me about 30 seconds and means I don't have to remember, look up, find the functions for, and implement a bunch of math.
That is a simple boilerplate code. Nothing valuable to the business. Most businesses can spit it out either by copying already working code that will work with any other entity or create it once proper and use it as boilerplate. LLM can not create new unique code that is giving you advanted on the market.
Also, remember that such code is not copyrightable, so you can not sell or get investors on board. AI generates lots of trash 1 day codebase that it is mostly worthless on the market.
What's the point if you can not earn money on it? Spending time to make a few bucks? Dev founders have a rare opportunity to become multi millioneres quiet easily in comparison to other people. Why waste such an opportunity on garbage apps from ai?
> remember that such code is not copyrightable, so you can not sell or get investors on board
It runs in the backend. What need is there to copyright something that nobody will ever see? What is copyrightable about APIs or docker configure files? What exactly is the point of manually identifying one flag or another that you need to set to get something working over letting the AI identify it for you in a fraction of the time?
i see myself there lol
i wanted to build a game and almost spent 4 month using Ai and tried to make it with that but then hey " i did it using on my own hands in less than 1 month , but the big chunk generatrd AI has helped me but nothing more . AI cannot generate cimplex things no matter what Ai it is hallucinating , Placeholders, stubs omittioms and tons of other stuff they have tortured me now i juts understand that we shall ask Ai just one or two piece at at a timw otherwise it struggles , but Ai can do a frontend it generates some good frontend LoL unless they will wreck it out
You're way behind the times of you think that the tools aren't close to being able to build a product by themselves.
I've got several tools that my team uses that are like 80% AI generated, I just described the features it needed. There were a few times I needed to step in and do some course corrections, but at least some of those times it was my fault for not being descriptive enough so a detail got missed. Some stuff I wrote myself because I wanted to make sure that I really understood that bit.
One library we use, I didn't write any of the code, I fed the LLM a manual and documentation, and it gave me a working library to interface with some hardware. It even corrected some errors in the documentation for the thing.
The hardware itself has a big in it that went against spec, so I pasted the output from the device, and the LLM just knew which part of the code to bypass so the device would still work.
This is the most niche of niche products, so it's not something that would have been well represented in the LLM.
These are small projects, 10k~30k lines, but they are a collection of real tools being used by engineers and scientists.
Right this very second, something like Claude Sonnet 4.5 is good enough that the team of scientists I work with could probably tell it what they want to do, and fill in what gaps Claude can't do.
The top tools are *extremely* useful. Building massive million line code based isn't the only thing I'm the world.
Exactly. And the way pre-Gemini 3 spits out assembly for obscure platforms like some omniscient eldritch being... I can only imagine what's coming next.
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This is exactly the problem. The people saying AI can't do this or that are the ones who never learned to use it correctly. Probably this is because they have a vested interest in it not being able to do these things.
Honest question: what are we missing? How should we be using it?
I’m a professional software engineer and couldn’t agree with these folks more. I’d love to learn how to use it better, though.
It really depends on what tools and techniques you are using. Some tools work much better than others. Cursor, OpenCode, and Zed seem to work the best for me. I did have some luck with Qoder too. Obviously model selection is important. GLM 4.6 on the z.ai plan is one of the best value options. I have heard good things about GPT 5 codex too. You should consider using something like spec kit, bmad, or task master. Those are spec driven development tools that help break down tasks. MCP servers can also be quite useful. Context7 and web search would be good ones to start with. Using rules and custom agents can be useful. BMAD for instance comes with loads of custom agents and helps you with context engineering too. Subagents are a fun thing to play with as well.
I’m not trying to be rude, but this mostly feels like standard stuff.
I’m using Cursor with MCP and selecting the appropriate model for the task. I’m using custom rules specific to me and our project. I didn’t write it myself, but I believe someone on our team also wrote a spec document that lays out the structure of our modules for the AI, too.
Even with all that, it’s not as useful as people are saying it should be. There’s clearly a major disconnect here.
I’m guessing that major disconnect is project complexity or some silver bullet you’re using that we’re not. I don’t think I’ve heard it yet, but I could certainly be wrong.
Question for you: what’s the most complex project you’ve used it for where it performed well?
You know that's actually a good point. I haven't used it for anything huge myself yet. I know someone who does use it in large projects, and they say they love it so idk. I did have it draw architecture diagrams for a large project, but not actually code anything in it yet. Maybe project size is the issue. Maybe it works better for microservices. Who knows?
Something I do know is that LLMs aren't equally great at all tasks and languages. What language is your project in out of interest?
Gotcha.
It’s mostly Swift with some occasional Kotlin (mobile app stuff). So, fairly common languages. I specifically work on the underlying platform our 5-10 apps are built on top of.
Based on what another commenter said, it sounds like python is what they work best with. So, maybe that’s part of it.
It honestly makes solid sense to me that these tools are good with small and/or constrained and/or well-treaded tasks and bad at everything else when you consider what these tools actually are.
They’re massive probabilistic models. They’re not actually intelligent in the way you and I think about it. It’s a whole different thing. They’ve just scaled it up an insane amount. It is impressively capable for what it is, though.
Let me guess: your project is not written in Python.
When AI companies talk about the coding, they often refer to the performance on SWE Bench Verified benchmark. Here is a catch with it though: it is all Python. All the tasks are in this single programming language. And a cherry on top: more than 70% of tasks come from just 3 repositories.
For marketing reasons the models ended up being over-tuned for the benchmark. And if you are not writing Python code, you are not going to see model's performance anywhere close to the advertised capabilities.
On a bright side: when I do write Python, I enjoy keeping an LLM in the loop.
I'm sure we are all a few thousand of "these people", incompetent, 30y into this, having worked at MS for a while, startups, CEOs of this and that, yes, we're all an incompetent bunch who are to blame if using rehashed 70s costume metodology with new fancy names won't work.
I'd very much like for the Buddha Level Programmers out there to Enlighten us with their deep knowledge about AI
I agree with you for now, but also I think there’s truth to the idea chatGPT could barely make a calculator app just 5 years ago and now it can code entire complex front ends by itself. Progress seems to be picking up
Until they solve the reasoning problem, these won't replace anyone.
I still think I'm going to ride out the end of my career basically baby-sitting AI as it develops codebases, but I'll probably enjoy that more than baby-sitting junior devs.
Right now, the frustrating thing about AI is how it can obviously pick up on a pattern and replicate it, or basically work as an encyclopedia of online knowledge that knows your codebase and exactly what you need to look up. But, then, it'll do something massively stupid and you can't explain that what it's doing is stupid or why, and it'll just keep doing it.
One of the tests I like to play with when doing localLLM stuff is to ask it to draw an ASCII art cat. Then, I'll ask it to change things about the cat it drew.
Most models won't even make anything remotely cat-like, but then even getting specific and trying to explain the process of drawing a cat (use dash, backslash and forward slash for whiskers), it will usually apologize, say that it's going to incorporate my design changes, and then draw THE EXACT SAME THING.
There's no way to make it understand it drew the same thing. You can't, as you would with a toddler, just say 'That's the same cat. See how you drew the same thing? Try again, but do it differently this time, incorporating the changes I suggested'. It will respond as though it understands, it will apologize ... then it will draw THE EXACT SAME THING.
That inability to reason through a problem makes it useless for designing and debugging large systems.
It's still super useful! I sometimes talk through problems with it, and it'll suggest a feature or method I didn't know existed, or spit out some example I might not have considered. Sometimes, when you've got a REALLY strange bug, it'll figure out that someone in some forum post you'd never have found has already run into it, or it can just suggest, probably somewhat randomly, to look at a subsystem you weren't thinking about.
But, once you hit the wall ... it's not going to get over it, and you'd better know what you're doing.
In my experience, agents only work for taking action, not writing code.
Seriously, I always roll my eyes when I see someone make an over-engineered framework of some fancy tool that uses a long-winded, multi-step network of coding agents or some variation of the like.
That shit's written in wishful thinking, not Python. I definitely think an AI pullback is coming and once the dust is settled that's when we can separate the ones who know how to use them from the ones who don't.
I can do a lot of web stuff but couldn't develop an app or pay someone to do it for me. Ended up using Windsurf and it works well. I have a full working version of the app with the correct design using firebase and other API's.
Would def help having a background in it and to understand everything that is going on, but it's on track to pass google and iOS standards.
Def don't think it's there yet, but I also think it's silly to think it's a worthless toy.
Just for now. AI is a very new technology and just developing.
Look how new chatgpt still is.
And now think about what will be possible in 5 or 10 years.
Building a product is one thing. Fixing a huge, complex problem in a limited amount of time is another. I could create new code at my first year in college.
Last week I tried to use AI to write a blinking led for embedded project, using only register definitions. It failed to account for some important registers that unlock the pins for turning led on and off.
I spent a day reading the datasheet and it just works. And no I just cant feed the datasheet to AI its like 1.4k pages.
Yet. But it’s coming any engineer who doesn’t think so is delusional. If fact I’ll call them DUMB. They really think tech is going to stand still for the next 100 years. 💀 now that is crazy.
Sonnet 4.5 is actually pretty good, with professional supervision. Better than 75% of the interns I've hired in my career at actually executing on a plan. It no longer tries to delete the unit tests behind my back, or at least not often.
But "professional supervision" is key, and you need a lot of it. I need to use the same skills that I would use to onboard and build a development team on a big project with promising juniors: Tons of clear docs, good specs, automated quality checks, and _oh my aching head so many code reviews_. And I need to aggressively push the agent to refactor and kill duplication, especially for tests, but also to get a clean, modular architecture the agent can reason about later.
I'm not too worried for my job. If the AI successfully comes for my job, either:
1. It will still be bad enough that I get paid to fix other people's projects, or
2. It will be good enough that it's coming for _everyone's_ job, in which case we're either living in The Culture (I wouldn't bet on it), or John Conner will soon be hiring for really shitty short-term jobs that require a lot of cardio.
I'm a dev and the latest models can do some small single features app, like if you have a task in your work routine that take 30m per week and seems automatable, Gpt5 codex can replace the work a dev would do in 2h even for a fairly non technical user.
Like a simple image editor that place a watermark and so on. It's a 1-8h work for a dev but can now be done automatically (speaking of experience).
It's more that it replaced excel instead of replacing devs for now. In 2 years it will likely be better.
That being said, if you want a real production app that will be accessed by Web users, please don't use base44 of other 😅
It's OK to have a messy script as an internal tool, but not for apps in production.
Those are good for POC. Not even MVP. Technical debt on ai code is HUGE. I don't think there is any industry where you could pay off such debt, especially with infra costs and marketing.
Nothing has changed, and nothing will. When you have a good code base, it can create some nice quality small methods or classes. But it is just a helper to our developers rather replacement.
Is that using cursor / code cli or with just chatgpt? In my experience they can handle quite a bit if you work with it over issues avec 30 minutes, even as non technical.
Personally it mostly help build bigger systems in a clean way, that would take too much time otherwise for a single project.
You are delusional. Nay issue you have with sloppy code, ai fixes much faster than a programmer nowadays. Not sure what you use or what you work on but on many types of projects you don't need to write a single line of code nowadays. Try Claude Code or Codex, but even just well managed project with Gemini Pro is manageable. I am a programmer with 20 years experience and I haven't written a single line of code the last few months, not even to fix issues, I look at the code before I approve it, but don't have to write anything myself.
I´m an idiot when it comes to coding, and I can coax AI into making some simple script. I cannot imagine someone trying to make a full app, with authentification and maybe saving the user data with AI.
Technically that's true to any AI product, even image/video/audio generators, not just LLMs. They're all like interns - super enthusiastic, somewhat knowledgeable, but have absolutely no self control so you need to know what you want them to do and be able to precisely describe that, otherwise they go off the rails making up their own reality.
LOL exactly. Every week it’s “Coding is dead” and then the same people are out here compiling models at 3AM. Tbh I’ve leaned into that chaos. I use MGX to run multiple builds side by side (their Race Mode thing) just to see which one doesn’t explode first. Makes me feel like I’m still “coding” but with a pit crew of little agents doing the dirty work. AI hasn’t replaced devs, it just made the job 10x weirder.
Totally agree—AI-generated code is powerful, but things get messy for real-world, multi-tool automations. I’ve found using an API reliability layer like Dr cURL (Heal-api.com) prevents broken JSON payloads, missed cURL fields, and weird integration bugs, especially for no-code and hybrid workflows. It lets you focus on the logic and lets the tool handle API messes for you.
The problem to me is that people can shit stuff out, fast. When building projects I had to do days of research and grit. Now I simply chuck it into claude and have a working prototype in a few hours. I miss when it didn't come easy. There is still value in battle tested and reliable code, but I don't like the way it is now unfortunately.
Its the google problem.
A huge part of working in IT is googling things. Knowing what to google is where the money is.
Same vein, AI isn't going to replace software developers because it takes a software developer to know what to have the AI write.
>AI replaces nothing except spell check.
...
>AI codes 95% of my code now
Is this like, performance art of some kind? But there seem to be comments agreeing with you? Are you people low quality bots that cannot parse words or meaning or something?
The reason why AI is so popular with executives is that it works just like working with employees: you say what you want and you get something that you don‘t really understand but seems to be what you wanted.
I'm glad you get it lol. Forget about making full fledged apps, I'll have AI do stuff I just don't want to type out, or template something I don't recall minor details of but don't want to bother researching (it's more than that tbh, Google is useless which I assume is intended and political, I personally find little of what I want to, and usually end up combining results from multiple search engines to finally find what I may want. But instead, just pop up grok or gpt, "in python using sockets, init connection to ip, send GET req, recv up to 1040 bytes"
Unfortunately the biggest downside aside from correcting or debugging it's issues in code, is that I have started to feel so dumb, even simple questions I find myself considering asking AI....
Yeah, this is spot on. As someone who codes and uses AI daily, these models feel like a supercharged autocomplete, not an engineer. They can’t reason about architecture or handle a large codebase.
I’ve also lost weeks cleaning up messy, AI-generated code. Right now, it’s a power tool for builders, not a replacement for them. The hype is wild, but the day-to-day reality is pretty messy. Anyone else run into this?
im also a programmer and i use ai as exactly you described it: autocomplete. It is so cool to start wrting code then stop then let the ai autocomplete exactly (or almost) what you were going to type and know it is running on your own pc.
The only time i ever used ai to fully code something for me was when the code didnt work propely on certain situations, so i gave chatgpt part of my current code and said to him to make it work on those situations. I was running against a deadline so i didnt check the code properly, but i did try it and it worked.
I also asked chatgpt to generate some small code, but they didnt work, so i feel you
i use tabby on intellij and i know it has plugins for other text editors too. There i am able to disable the auto inline completion so it will only trigger suggestions when i ask, but i leave that enabled
Now it is like doing 10 steps forward and 9 steps back, on and on. The actual models are trained to lie and fake their way out of tasks. We have AGENTS.md and dozens of little helpers, but the issue is at the very llm architecture, they are just glorified autocomplete. Probably a stricter training will smooth things out for coding but we’re very very far to have a real AI coder
the business context will be very difficult for the models to fully grasp, especially in industries where much knowledge is implicit, and requires much on-field experience.
in certain vertical fields, like compiler optimization, which has almost no business context dependence, I think AI will shine.
I code at a hobby level, self taught java, and c# (and... kotlin) all very similar languages. I did it at a time where if you had a question...stack trace was where you went....and god have mercy on your soul if they thought it was something everyone should know.
I find ai to be incredibly useful it explains calmly and collaborates. Its not perfect ofcourse, but it is infinitely better than the soul crushing gatekeeping of stack trace.
Quantization to GGUF is pretty easy, actually. The problem is supporting the specific architecture contained in the GGUF, so people usually don't even bother making a GGUF for an unsupported model architecture.
The only conversion necessary for an unsupported arch is naming the tensors, and for most of them there's already established names. If there's an unsupported tensor type you can just make up their name or use the original one. So that's not difficult either.
Because it makes no sense to make a GGUF no inference engine can read…
GGUF is a very loose specification, you can store basically anything set of tensors into it. But without the appropriate implementation in the inference engine, it's exactly as useful as a zip file containing model tensors.
The conversion code in the PR is probably final now, so yeah, you can already make Qwen3 Next GGUFs (but key word "probably", I just recently modified the code to pre-shift the norm weights).
Why would I do that? There's already plenty of GGUFs in huggingface of models that are not supported by llama.cpp, some of them with new tensor names, and they're pointless if there's no work in progress to add support for the architectures of those GGUFs.
They are, this sub is ridiculous. I am a programmer for 15 years and did not have to write a single line of code last few months thanks to ai. People here either use tiny models or are very bad at prompting.
You are overqualified for your very simple work. Your employer could save money and hire a junior dev. We are talking about REAL senior devs doing REAL senior dev tasks in REAL calculation-rich projects, not "move buttons 3px to the right".
But if you prefer to think you are the only one who "mastered AI", feel free to.
https://claude.ai/public/artifacts/380b2aad-3824-4007-848c-2da3087865aa
That wasn't hard at all. Literally the first gen. DOOM isn't a difficult task and with some extra vibecode and AI wrangling it could be done with better fidelity. These sorts of tasks are not hard for humans, nor are they hard for AI. Maintaining a large codebase with serious architectural concerns is a different matter.
Lmao are you sure? Slap on a wall texture and give us a gun and you're 90% there. If you're so hung up on specifically using 1992 concepts I don't imagine a BSP renderer would be too hard to wrangle in. If you're a programmer you'll understand exactly why this isn't a difficult task for an LLM
90%? Have you ever even seen DOOM? Arbitrary curved walls, textured walls and floors, moving floor platforms, different height floors, rudimentary lighting.
What you've "created" is 20-30% of Wolfenstein 3D (rendering code is awful, and you haven't implemented anything, like e.g. doors).
DOOM engine is one of my usual LLM benchmarks, so I know EXACTLY where you will fail.
I'm well aware, and as I've said it's definitely not impossible to "vibe" code. Obviously it's not going to one shot it by itself, but unless there's so little documentation on BSP rendering since the near 50 years ago it was invented, I really doubt it's impossible with elbow grease. I gave you a demo that did the most basic functionality because I'm not going to spend the time and effort getting a codebot to write libraries for it, so I waste an entire weekend putting it together lol. If we're going to be picky my crappy little vibe demo fails because threejs is a raycast renderer. I feel like creating a benchmark when you don't understand the software you're using as a benchmark is a fools errand tbh and going to result in endless goalpost moving.
Riiiiight, typical vibecoder spewing nonsense when you get them into a corner.
Pretends to not see the difference between Wolfenstein and Doom and claims everyone but him have no idea what they are doing.
I'm done here. Come back with the code or don't come back at all, you are wasting everyone's time.
I typically don't vibecode except for rapid prototyping because it just falls apart doing large projects. I'm not claiming anyone but myself knows what they are doing, just that I'm not convinced you do. I'm well aware of the differences between Wolfeinstein and Doom, and they largely boil down to rendering technique enabling different sorts of levels. I'm not wasting my time trying to get an LLM to do a crappier job of something I could do with libraries, nor am I going to waste my time doing that myself. Your benchmark is bad, and you don't understand why. The raw spectacle of having DOOM seems to be more important to you than necessarily implementation difficulty.
"Your benchmark is bad, and you don't understand why."
I never asked for your opinion or participation. I said, “Wake me up when you manage to vibecode DOOM.” You stepped in on your own, claimed you could do it, and then failed to provide any proof. And benchmark itself is fine - more capable LLMs consistently get closer to the correct result.
"The raw spectacle of having DOOM"
Right now it's the raw spectacle of someone trying, failing, and coping.
Hey in fairness I could do it, but not with an LLM one shot. I did apologise for randomly having a go at you, if that didn't show up in the messages. I'm trying to ask what precisely a Doom benchmark would actually measure, other than how close the result looks to Doom. I stand by the fact you could make it with LLMs if you spent a lot of time ordering them around, and doing a ton of manual code integrations.
It is not possible at all, one shot or in a week.
And it's not a repeatable test, because it's a huge codebase, not a snake game. But smarter LLMs will get you further down the road, while some will fail at step1.
Yeah, the code isn't good quality. Precisely why I wouldn't use it for more than rapid prototyping. You could get the LLM to do each task in isolation and I am pretty sure it would be able to do it, but in my experience they have always failed at more complex tasks. I don't see it being possible without a lot of manual code stitching at this juncture, but possible in some sense nonetheless
Here's a DOOM level map for you, so at least you know what you arguing about.
[https://sketchfab.com/3d-models/doom-e1m1-hangar-map-2148fb6a3fe7454b901fcea67d70b318](https://sketchfab.com/3d-models/doom-e1m1-hangar-map-2148fb6a3fe7454b901fcea67d70b318)
When I first started writing code in the 1980s, I was told many times that coding was going to be replaced by automated tools and 4th generation programming languages so easy that anyone could develop IT systems with a few clicks. Same in the 1990s, then 2000s, and so on.
I think coding will eventually be taken over by AI, not now but in the future, currently it’s still far too fickle. But I don’t believe humans will ever be obsolete. An AI can’t decide to start on a random project for the reason of spur of the moment. We will still direct projects and create new ideas, AI will be used for the grunt work, aka coding components, generating template ideas, or helping with color pallets. It’ll be a catalyst for efficiency, rather than replacement. That’s my view anyway. What does worry me is society’s quick inclusion of AI into many of our fields, as that is a recipe for failure when people overestimate its ability to replace human improvisation.
It’s also true that nothing passed the turing test in the 80s, 90s, 2000s…… until now! Pre LLM/agents eras are like Jurassic period, can’t really compare.
I don't know, I sort of feel like this is the the year things got pretty real. Claude isn't raking in mountains of cash for nothing. I think it's turning competent programmers into more prolific ones and taking people like me who can't code a full anything and allowing us to generate usable software.
Understanding how to code is obviously a huge boon to coding, but I've got an app I could never code myself doing stuff that's useful to me with like 8000 lines of code I can only just barely understand. If a "real" programmer put together the same thing it could be a quarter or half as much code and likely be better, faster and more secure, but this works and as little as a year ago it wasn't even possible.
This is sort of like game design moving from creating the game engine to everyone using Unreal. It's not better, it's much worse in fact, but it's orders of magnitude easier and opens up "development" to a new tier of competence.
I'm not sure how much desktop software these days are ai generated code, but as far as websites go I feel like quality has fallen off a cliff since 2023. I have never used so many important websites that were simultaneously so beautiful and so buggy.
Then you haven't been paying attention, websites have been shitty and people have been complaining about them being looks over performance since forever. I even remember some anti-javascript movement.
You can literally write a one line text and press a button and generate AI images of your liking, but there are still a ton of people who find that too much of a hustle and willing to pay money for someone else to do that for them. Imagine with coding now, no way the 98% of the non-programmer population are gonna sit down to build something with an AI even if the AI holds their hand all throughout the process.
I assume the next sentence in your comment would be that they were wrong then so they must be wrong now... Sure, but when you were originally told that, it was true; there didn't exist a system that could actually output code to a specification, in seconds, and with decent enough accuracy as to be viable for use. Obviously these systems have their flaws, but they are improving dramatically every year and we would be fools not to think that soon enough they will be just as effective as at least a mid level dev. Even if the end result isn't that the AI is fully self sufficient, and all it amounts to is that a single developer is able to accomplish the task of a dozen, that still puts millions out of work. We have no safeguards against that level of displacement in our society.
Will somebody please think of the NVFP4? This will be a huge deal for 50xx owners. 80GB models should shrink to 23GB with almost no quality loss. Unlike GGUF that has huge quality loss.
anyone who says that AI will replace programmers hasn't actually done programming or have just pulled together a working app for the first time.
Problems will come after pushing to production, there will be enhancements that would be required etc which AI honestly sucks at. But at the current level, I feel experienced engineers will be benefitting the most out of it. If you know what you are doing and how exactly you want something to be implemented, AI nails the implementation.
I am not a SWE but a few of my friends are. The gist I get from them is that giving a well developed and properly tuned LLM to a SWE is like training a horse-team driver to drive a semi truck instead. You still need the driver, but the tools are more powerful.
AI now it is not a panacea, but instead an amplifier, if you know what you are doing, suoi will be x10 better and x100 faster, but if you blindly ask AI to do stuff for you and hope for the best… you will fail x1000 times harder and x10000 faster 😝
I think it currently has the potential to replace low/entry-level programmers, but not junior/senior developers/engineers/architects.
Like, I can setup an agentic workflow right now that when an issue/support ticket is submitted, my AI agent can automatically pick up the issue, investigate/reproduce it, and then propose a fix.
I would bet that 90% of the time, they'll do the job better than any entry-level programmer that has been working in the field for less than a year.
However, just like I wouldn't try an entry-level programmer to merge and deploy that fix to a production environment, I wouldn't trust the AI to do without significant review and revision.
Actually, it's kinda right - the focus will shift more and more from coding to architecture, integrations, research. However, to reach that level, you still need to learn coding, even if later you can use AI assistants to write lots of code for you.
It only means what you did is already recorded in dataset, llm can not create new algorithm nor write code in new languages, that is where the value is, be a valuable programmer first.
why should i? it was done already:
[https://huggingface.co/AesSedai/Qwen3-Next-80B-A3B-Instruct-GGUF/tree/main](https://huggingface.co/AesSedai/Qwen3-Next-80B-A3B-Instruct-GGUF/tree/main)
If the AI boom has proven one thing, it’s that programmers are the worst fucking gatekeepers and deserve to have their industry die, or at least end up upended. This is an amazing opportunity to bring in new people into the field, because, when handled properly, AI lowers the entry level by a degree or two. Instead, you get our own version of bible-thumpers, screaming at the topmof their lungs.
Lol I laugh. Because literally my entire team is like "omg we can build an app for work with these new agents from OpenAI
--lets it try to mess with the app I've been building on the side
-- works for 2 hours
-- deleted 1600 lines of code
-- completely removes all the gating and calls it optimization
--destroys every single security feature in place
-- doesn't fix the error I was getting and asked it to fix
--costs $25
-- submitted ticket asking for compensation for destroying my app
--get $250 in credits because I could prove it destroyed my app because I actually can engineer and while not the best coder out there I at least have a fundamental understanding of most languages I have to look at in my day to day.
Yup totally replacing all programmers. Lol
But ya I mean sure it can replace half these "engineers" who literally can barely implement a new tech item into the tech stack and have to have me assist them in basic Setup of anything that's not a 3 click integration.
I’m not much of a coder, and yes the memory system I built was mostly coded by Claude copilot. But even I found that it likes to arbitrarily refactor code at random. Things that worked yesterday for example get pulled out because of “cleanliness”. Even though it was what wrote it. My situation is niche though. I’m disabled and don’t have the nerve usage to be at the keyboard as much as others. So I leaned on AI the most. But I can understand when something like removing 1200 lines of code that works perfectly can screw up something. It took almost 5 months to get it to where it is today. And I definitely double check everything.
Invalid argument and hyperbolic.
1. The people who ask for GGUF are not coders, have very little technical skill. You can do it yourself on huggingface.
2. Not everyone says either of these things.
3. Reddit is a bubble. A bubble of dufuses.
Ehm I think compute is the bigger problem, give infinite compute and you get infinite gguf. But the latest needs to be merged to llamacpp first so the researchers who build the new llm architecture need to share their knowledge I guess.
AI generates code that works in demos but breaks in production. It doesn't understand architecture, scale, security, or business context. Programmers who know how to review and integrate AI output are more valuable, not less.
They apply a small dose of LSD so your ass gets high and think the model is the God(dess) of lust incarnate and your AI ERPs will feel like real sex!!!
Its not impossible lol, ive came pretty close in adding support with ovis2.5, didnt have time to fix the last issues though (inference was working and that model needed its own mmproj too) I guess with claude flow it would work but i cant get it running on my windows machine cuz wsl is broken 😑
Ive already created another quantization/inference script with sinq for it, granted it wasnt very efficient and all but it works just fine for me with 64gb ram so i didnt improve it further lol, so i have no real incentive to fix it in llama.cpp lol
Its on my huggingface lol, it works does take a lot less vram and aint that slow. But its a patch work solution and i didnt improve it further since qwen3vl came out lol
I was able to conver the model to gguf with mmproj and load that one, now there is some small issue with the implementation somewhere and I didnt have time to investigate further, but it runs inference. Considering i didnt use glm/claude that is pretty good already...
https://preview.redd.it/w38pnasw8bvf1.png?width=725&format=png&auto=webp&s=26dc4857c0dc3fb4515f117b962149cddc89f575
As a AI Engineer in the Falcon LLM team, I did the integration of the last Falcon Model (Falcon-H1) which is a hybrid LLM with two parallel heads Attention head and SSM head, I can confirm that the AI is not really helpful doing that job, I used a coding agent but it’s not a job that you can do by prompting an agent
I've been using LLM help at multiple points, mostly because it allows me to somehow push the project when I'm working, i.e. I can schedule a task in Roo and look at it 30 minutes later. But for most of the stuff it has been beyond useless. The specifics of GGML tensor management combined with a lack of corresponding operations (the list comprehension range indexing from Python, easy slices, lack of >4D tensor support in GGML etc.) means it gets most of the operations horribly wrong.
It's mostly OK at writing code at the operation level (i.e. low-level tensor manipulation).
As a programmer, I hope that I will stay relevant in the future.
However, I'm afraid that long-term changes brought by AI are not to be underestimated.
I do sincerely think that AI will replace programmers right about when it will replace everyone else, but, even so, programmers seem to be the first on the cutting board.
I'm kind of already seeing this, but I'm worried about the new programmers entering the industry without the necessary foundational knowledge needed to write good code.
We already have a big problem with people with college degrees knowing nothing about modern programming because all of their coursework was low-level stuff in C/C++ using the standard from 80/90s.
Very few of even my classmates did anything outside of their coursework, and were completely lost when you even mention a modern framework of technology. They didn't survive long after graduation because they had to either cram a lot of new knowledge and/or be taught by their company how to to basic shit you can Google in a few nights.
However, at least they understood various algorithms, how variables were repesented in memory and the stack/heap, how to handle race conditions, sorting, etc. . .
Now, I would be willing to bet that students are using AI to complete their assignments without having to think for themselves.
I would bet a great deal of money that within the next 5 years you are going to see Comp Sci majors complaining even more about how their school didn't prepare them for real world/practical programming because they vibe coded even their assignments.
Two years ago LLMs couldn't write basic functions. Today they can write simple apps.
But they will NEVER replace programmers? lol.
Never is a very long time, and I see a tech progression happening.
Very poor --> Poor --> Kind of OK --> OK --> Mindblowing.
We're still at the Poor leading to Kind of OK stage. But don't let that make you think that's where the progression ends.
Real AI may replace human in some tasks, but we haven't reach that technology stage. It is pretty common agreement among researcher that we might never be able to achieve a real AI to begin with.
Current LLM model has its usage, but it is damn concerning when people think they are sentient, or be able to replace worker.
LLM is spectacular good at being a joke to read (artificial stupidity) so very entertain.
It does roleplay quite well (god, I don't want to torture real human with my fetish, even if they do love roleplay as much as myself.)
It can save some effort for casual stuff. For example, I use it to get a snippet of activating .conda virtual environment on batch because I am not used to Anaconda and I am not used to the idea of modifying scope.
Basically, for coding, AI excels in snippet. Piece of code that is already fed into its mouth. Like documentation, guideline, cookbook, etc.
One bright usecase is to integrate a chatbot into documentation. I don't need it to answer (Hallucination is unavoidable. And I read documentation to avoid it in the first place). It just needs to tell me the location that it think might be relate so I can read those articles first.
(Or at least improve your documentation. Shit gets real when I read Gemini doc. Truly info dump of all time. I feel like 80% of the time, it treats me as CTO or some businessman rather than an independent developer).
I feel overwhelmed by how many files the AI usually changes so I sort of skim over the most important code. I think that is an issue as it can be fatiguing leading to potential issues
People tend to be fascinated by AI and they rely too much on it on the first phases. This is what I call the ChatGpt effect, like "execute those complex multi tasks instructions, I'll come back later". It's like magic but in the end that does not work well. I introduced a friend to agentic coding a few month ago. He got completely fascinated by Roo Code using Glm Air and later Gpt Oss 120b and started spending all his time doing this. But now, a few months later he got tired of tuning huge complex prompts and let the model handle everything by it's own. He realized that this is not a panacea and will probably be ok now to move to a more efficient granular prompt engineering approach, using smaller tasks, segmentation and human supervision
What we have here is a problem that will solve itself.
Super intelligence is the only way those jobs can be fully replaced without major issues.
But super intelligence is an existential threat to humanity so we can't have that...
Sigh.
AI is great for exploring new algorithms and program structures. It's great for suggesting improvements that would not have been considered. But without being experienced with programming, it is a recipe for disaster. Vibe coding is like building the Tower of Babbel if you don't know what you're doing. It'll eventually collapse as context windows run out and it loses track of what is being worked on.
It could be the best system in the world, capable of writing fully optimal gigantic Frameworks in less than a second. But users don't know what they want, or how to explain it. You'd need to hire someone to, say, give the machine the right instructions. Program it, you could say.
BUAHAHHAHAHAHA.
No it hasn't. LLMs are totally stupid when comes to complex stuff like Oxygene, DevExpress, REMObjects, TTMs, META & Neural Prophet, writing Python for time series forecasting using various models etc.
And talking even the big ones like Deepseek and GPT5 hosted on the servers, not just local LLMs which are even more stupid.
That comes across as quite offensive to the team of volunteers maintaining llama.cpp. They are doing great work and are only struggling with qwen next due to the architecture changes. Usually gguf support is provided shortly after release for major model releases.
The problem currently is “anyone” can code now without learning much like they had to in the past, saturating the job market. Especially when it comes to remote work making it near impossible to find a job.
It increases productivity of a senior developer by about 20-30%, that's it - if you don't give it tasks that you know it can't do. And if you don't use agentic coding, because you get a codebase that is unknown to you and quickly lose track of changes made in every step.
It increased mine by 300%, I measured hours spent etc. Maybe on average it is only 30% because people are shitty at prompting. Or maybe you are using old data, it changes at a very fast speed, just a few months ago there was no Claude Code, Codex, GPT-5 or Claude 4.5.
I have a really naive question:
Arent quants just the precision of weights? Like how many bits you reserve for every weight and since less bits mean less decimals you get less precise models with less size?
So in theory - dont you just have to round every weight according to the quant size?
GGUF is the model format used by llama.cpp, it stores the tensors in a specific layout that the code expects.
To use a new model, you need both a converter that exports those tensors to GGUF and an implementation in llama.cpp that knows how to load and run them.
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1) AI will definitely lead to job loss but
2) It'll be decades until it does away with the profession
The better we get at prompting the more we wrap around to just writing code again. In a high level programming language that's for sure but code nonetheless.
Programmer is a mindset moreso than a job position and people with the mindset will be needed for a while longer.
1, AI will definitely lead to job loss but
2. It'll be decades before new breakthrough software developed by AI crashes and burns and people will not be able to fix it because it is too complex.
I'm not sure if it's a joke. But the underlaying issue here is not support for the new models in popular tools. Quantizing the model is what's visible to people on the surface.
I've been out of programming for years. Got back into it because of AI. There is soooo much work to be done and engineering problems to be solved even if you assume you write very little code for your job going forward. I dont like to use the word infinite, but for those of us alive there is going to be work that needs to get done. Not to mention the millions of new apps being built which likely will never see production without engineers to support it.
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