On the road to fully automated luxury gay space communism.
Spreading Linux propaganda since 2020
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my clue that the trumpets of the AI collapse are tuning up is that, last week, the least tech savvy person I know in my cohort was telling me, the person everyone they know goes to for random technical assistance/context, about how powerful "AI" (LLM) is and how it's about to take over everything.
it's like that bit about how, when the shoeshine kid and your gardener have stock tips, it's time to get out of the market because now literally everyone is regurgitating the "New Paradigm!" cliches.
I imagine the flop of ChatGPT 5 along with it becoming clear that current gen models aren't living up to the expectations might be starting to cool investor expectations.
I've been having an ongoing argument the past month with with a 70-something step relative I see often who has always come to me for computer advice about this exact thing. I've tried to let it go many times, but she keeps hammering at it, even bringing it up out of the blue. Since you mentioned something similar, forgive me for popping in with my own rant here, but it has been really getting on my nerves.
She absolutely will not hear it that "AI" does not mean it is actually intelligent but rather a marketing scam and that it has zero chance of developing general intelligence. It's been disappointing because like I said, she used to trust me about computer stuff, but now angrily asks me "do you even know about <some pop sci "expert"> and the projects they're working on?!," namedropping all these supposed "respected" scientists she's been reading about the impending AI apocalypse and thinking I'm uninformed for not knowing them. It's like shit lady, I used to argue with Ray Kurtzweil's singularity nuts 12 years ago about this same sort of garbage. She's actually fairly youthful in her views for a boomer, she's a sci fi fan and prides herself on being socially progressive, and frequently talks about how much she loves science, but has always had a real "woowoo" new-agey bent to that
The conversation first came up because she told me about how she's been literally losing sleep with actual insomnia, thinking about AI with respect to what it will mean for her grandkids, what will happen to them in a world where machines become "ever more" intelligent, repeating talking points she heard somewhere about how "once machines become intelligent, they'll have no use for us and only see us as a threat." So many brainworms to sift through, from colonialist thinking to buying into "AI" hype. I did my best to disabuse her of this belief to begin with partly just to help her sleep better at night, though I admit I did remind her there are many many other real things to worry about regarding the world her grandkids will be inheriting. But she's sticking to it vehemently. She's been going off on me about how "all the top thinkers on the subject" agree with her, going on about how even Stephen Hawking thought AI would be a disaster (she thought that was an ace in the hole because I used to like discussing theoretical physics with her and never had the heart to differentiate for her the real but niche contributions Hawking made from his celebrity). Rather than think about what I said as someone who tends to know more about this kind of thing, she has decided I don't actually know anything.
It really has been a trip watching how the propaganda/hype mill and the shoehorning of "AI" into everything has broken so many brains who only 5 years ago would have laughed at anyone else for thinking the plot of Terminator was really happening.
Honestly, I can't imagine these LLMs are actually contributing any sort of benefit when you consider the amount of trash you have to wade through and fix once they've done what they've done. For every quickly typed up professional e-mail or procedure they do they're wasting multiple hours of programmer time by introducing bs into codebases and trampling over coding conventions which then has to be reviewed and fixed. I imagine it will get to the point where AI can do things on its own without the hallucinations and the flat out errors and whatnot, but it ain't now and I don't think it's anytime soon.
I find they have practical uses once you spend the time to figure out what they can do well. For example, for coding, they can do a pretty good job of making a UI from a json payload, crafting SQL queries, making endpoints, and so on. Any fairly common task that involves boilerplate code, you'll likely get something decent to work with. I also find that sketching out the structure of the code you want by writing the signatures for the functions and then having LLM fill them in works pretty reliably. Where things go off the rails is when you give them too broad a task, or ask them to do something domain specific. And as a rule, if they don't get the task done in one shot, then there's very little chance they can fix the problem by iterating.
They're also great for working with languages you're not terrible familiar with. For example, I had to work on a Js project using React, and I haven't touched either in years. I know exactly what I want to do, and how I want the code structured, but I don't know the nitty gritty of the language. LLMs are a perfect bridge here because they'll give you idiomatic code without you having to constantly looks stuff up.
Overall, they can definitely save you time, but they're not a replacement for a human developer, and the time saving is mostly a quality of life improvement for the developer as opposed to some transformational benefit in how you work. And here's the rub in terms of a business model. Having what's effectively a really fancy autocomplete isn't really the transformative technology companies like OpenAI were promising.
With React I would be surprised if it was really idiomatic. The idioms change every couple years and have state management quirks.
It uses hooks and functional components which are the way most people are doing it from what I know. I also find the code DeepSeek and Qwen produce is generally pretty clear and to the point. At the end of the day what really matters is that you have clean code that you're going to be able to maintain.
I also find that you can treat components as black boxes. As long as it's behaving the way that's intended it doesn't really matter how it's implemented internally. And now with LLMs it matters even less because the cost of creating a new component from scratch is pretty low.
Does it memoize with the right selection of stateful variables by default? I can't imagine it does without a very specific prompt or unless it is very simple boilerplate TODO app stuff. How about nested state using contexts? I'm sure it can do this but will it know how best to do so and use it by default?
In my experience, LLMs produce a less repeatable and correct version of what codegen tools do, more or less. You get a lot of repetition and inappropriate abstractions.
Also just for context, hooks and functional components are about 6-7 years old.
I tend to use it to generate general boilerplate. Like say I have to talk to some endpoint and I get a JSON payload back. It can figure out how to call the endpoint, look at the payload, and then generate a component that will render the data in a sensible way. From there, I can pick it up and add whatever specific features I need. I generally find letting these things do design isn't terribly productive, so you are better off deciding on how to manage state, what to memoize, etc. on your own.
I also find the quality of the tools is improving very quickly. If you haven't used them in half a year or so, your experience is already dated. You get by far the biggest bang for your buck with editor integrated tools that can run MCP, where they can run code and look at output.
Finally, I personally don't see anything wrong with hooks/functional components even if there's already a new fad in Js land. The churn is absolutely insane to me, and I frankly don't understand how people keep up with this. You can start a project in Js, and by the time you finish it the Js world has already moved on to some new bullshit.
I used to work with ClojureScript when I needed frontend functionality before. There's a React wrapper called Reagent. It's basically a better version of hooks/functional components, it worked this way for over a decade. In that time, React itself went through a dozen different ways of doing things. The value gained has been rather unclear to me.
Yes I'm sure it can do a lot of boilerplate. I'm just saying I doubt it is very idiomatic. It is essentially a souped-up regurgitation machine drawing from a collection of a bunch of open source code over a long period of time and quality as well as documentation.
This can be fine for many purposes but if it is for a substantial project that other people will need to maintain I would suspect it is a technical debt generator. As the saying goes, code is read more than it is written. Writing the code is usually the easy part. Producing a maintainable design and structure is the hard part. That and naming things.
I mean all code is technical debt in the end, and given how quickly things move in Js land, it doesn't matter whether you're using LLMs or writing code by hand. By the time you finish your substantial project, it's pretty much guaranteed that it's legacy code. In fact, you'll be lucky if the libraries you used are still maintained. So, I don't really see this as a serious argument against using LLMs.
Meanwhile, as you note, what makes code maintainable isn't chasing latest fads. There's nothing that makes code written using hooks and functional components inherently less maintainable than whatever latest React trend happens to be.
And as I pointed out earlier, LLMs change the dynamic here somewhat because they significantly lower the time needed to produce certain types of codes. As such, you don't have to be attached to the code since you can simply generate a new version to fit new requirements.
Where having good design and structure really matters is at the high level of the project. I find the key part is structuring things in a way where you can reason about individual parts in isolation, which means avoiding coupling as much as possible.
I mean all code is technical debt in the end, and given how quickly things move in Js land, it doesn't matter whether you're using LLMs or writing code by hand.
I just explained why design and maintainability are the hard part and something LLMs don't do. LLMs lead to the bad habit of skipping these things, which junior devs do all the time, wasting a lot of resources. Just like a junior dev writing spaghetti can make a middle manager very happy because it's "delivered on time", they'll eventually have to pay in the form of maintenance far more than if better practices had been used.
Writing boilerplate React components that fetch JSON from APIs is the easy stuff that takes very little time. If you throw in intermediate things (like basic security) you will likely need to spend more time reviewing its slop than just doing it yourself. And it will likely be incapable of finding reasonable domain abstractions.
If it's for a throwaway project none of this really matters, of course.
By the time you finish your substantial project, it's pretty much guaranteed that it's legacy code. In fact, you'll be lucky if the libraries you used are still maintained.
If it is a production system with any prioritization of security it will need to be regularly maintained, including with library updates. If a library becomes unmaintained then one either needs to use a different one or start maintaining it themselves.
So, I don't really see this as a serious argument against using LLMs.
There are different ways to make code unmaintainable. It seems like you're saying writing code in JavaScript means you always do a rewrite when it comes time to do maintenance work (it moves fast!). This is just not true and is something easily mitigated by good design practices. And in terms of any org structure, you are much less likely to get a green light on a rewrite than on maintaining the production system that "works".
Meanwhile, as you note, what makes code maintainable isn't chasing latest fads. There's nothing that makes code written using hooks and functional components inherently less maintainable than whatever latest React trend happens to be.
I'm not sure what you mean by this. When I said hooks and functional components were 6 years old it was in the context of doubting whether LLMs are up on modern idioms. You said it wrote idiomatic code, citing 6-7 year old idioms. That's not great evidence because they are long-established over several major version releases and would be a major input to these LLMs. I mentioned a few newer ones and asked whether they were generated for your code.
React written with hooks and functional components is more maintainable than legacy implementations because it will match the official documentation and is a better semantic match to what devs want to do.
And as I pointed out earlier, LLMs change the dynamic here somewhat because they significantly lower the time needed to produce certain types of codes. As such, you don't have to be attached to the code since you can simply generate a new version to fit new requirements.
I don't get attached to code...
LLMs do the easy part and then immediately require you to do the harder parts (review and maintenance) or scrap what they generate for the hardest parts (proper design and abstractions). Being satisfied with this kind of output really just means having no maintenance plans.
Where having good design and structure really matters is at the high level of the project. I find the key part is structuring things in a way where you can reason about individual parts in isolation, which means avoiding coupling as much as possible.
It matters at all levels, right down to the nouns and adjectives used to describe variables, objects, database tables, etc. Avoiding coupling will mean knowing when to use something like dependency injection, which I guarantee LLMs will not do reliably, maybe even not at all unless it is the default pattern for an existing framework. Knowing to use dependency injection will depend on things like your knowledge of what will need to be variable going forward and whether it is easier to reason about behavior using that pattern in your specific context. If using domain model classes, are implementing an abstract method or are they passed the implementation and just know how to call it? Etc etc.
I just explained why design and maintainability are the hard part and something LLMs don’t do.
Ok, but I've repeatedly stated in this very thread that design is something the developer should do. Are you even reading what I'm writing here?
Writing boilerplate React components that fetch JSON from APIs is the easy stuff that takes very little time. If you throw in intermediate things (like basic security) you will likely need to spend more time reviewing its slop than just doing it yourself. And it will likely be incapable of finding reasonable domain abstractions.
I have to ask whether you actually worked with these tools seriously for any period of time, because I have and what you're claiming is directly at odds with my experience.
If it is a production system with any prioritization of security it will need to be regularly maintained, including with library updates. If a library becomes unmaintained then one either needs to use a different one or start maintaining it themselves.
Not sure what this has to do with code written by LLMs. If I have a React component it has fuck all to do with me updating libraries in the project. Furthermore, LLMs are actually quite decent at doing mechanical tasks like updating code to match API changes in libraries.
There are different ways to make code unmaintainable. It seems like you’re saying writing code in JavaScript means you always do a rewrite when it comes time to do maintenance work (it moves fast!).
No, I'm saying the exact opposite which is that you shouldn't try to chase fads.
I’m not sure what you mean by this. When I said hooks and functional components were 6 years old it was in the context of doubting whether LLMs are up on modern idioms. You said it wrote idiomatic code, citing 6-7 year old idioms. That’s not great evidence because they are long-established over several major version releases and would be a major input to these LLMs. I mentioned a few newer ones and asked whether they were generated for your code.
You might have to clarify which particular fad you're on currently, because I haven't been keeping up. However, I do see hooks and functional components used commonly today.
I don’t get attached to code…
Everybody gets attached to code, it's inevitable. If you have a bunch of code to solve a particular problem and it takes a lot of effort to rewrite it, then you're not going to throw it away easily. When your requirements start changing, it makes sense to try to adapt existing code to them rather than write code from scratch.
It matters at all levels, right down to the nouns and adjectives used to describe variables, objects, database tables, etc.
It really doesn't if you have good interfaces between components. You don't inspect all the code in libraries you include, you focus on the API of the library instead. The same logic applies here. If you structure your project into isolated components with clear boundaries, then your focus is on how the component behaves at the API level.
Avoiding coupling will mean knowing when to use something like dependency injection, which I guarantee LLMs will not do reliably, maybe even not at all unless it is the default pattern for an existing framework.
Again, I'm not suggesting using LLMs to do design. My whole point was that you do the design, and you use LLM to fill in the blanks. In this context, you've already figured out what the component will be and what scope it has, the LLM can help create features within that scope.
Knowing to use dependency injection will depend on things like your knowledge of what will need to be variable going forward and whether it is easier to reason about behavior using that pattern in your specific context.
Also, things like dependency injection are an artifact of OO programming style which I find to be an anti pattern to begin with. With functional style, you naturally pass context as parameters, and you structure your code using pure functions that take some arguments and produce some result. You can snap these functions together like Lego pieces and you can test them individually. This meshes quite well with using LLMs to generate code and evaluate whether it's doing what you want.
If using domain model classes, are implementing an abstract method or are they passed the implementation and just know how to call it? Etc etc.
Have you written code in styles other than OO?
Ok, but I've repeatedly stated in this very thread that design is something the developer should do. Are you even reading what I'm writing here?
Yes, and I'm replying in-context:
I say that code is read more than it is written and that a boilerplate generator is going to be a technical debt machine that had no thought for maintenance.
You say that all code is technical debt and don't engage further with that point.
I reiterate that design and maintainability are what LLMs are bad at and using them for the easy stuff isn't exactly a boon.
I'll repeat again: LLMs are technical debt machines. There are ways to have more or less debt, it is not all the same regardless, and LLMs will just repeat patterns they've seen before, and these may be bad designs, bad patterns, that are difficult to maintain for any project used or maintained by real people. You will then need to, of course, check the result, which is the thing that is less easy and takes longer, so very little has been optimized.
Or this is a throwaway project and all of this is moot.
I have to ask whether you actually worked with these tools seriously for any period of time, because I have and what you're claiming is directly at odds with my experience.
Which part of what I said is at odds with what you're saying? I've used and rejected these tools as a waste of time and something to help others avoid.
Not sure what this has to do with code written by LLMs.
It's a direct response to what I quoted where you dismiss maintainability concerns by saying all substantial projects are legacy code and you'll be lucky if libraries are maintained. Is... is that not obvious?
If I have a React component it has fuck all to do with me updating libraries in the project.
I have no idea what this means. React is a library and it also needs to be updated, not to mention the request hook library(ies) your boilerplate would presumably use. If you are maintaining this project and it has any security aspects you'll need to update these things over time.
Furthermore, LLMs are actually quite decent at doing mechanical tasks like updating code to match API changes in libraries.
If you're updating libraries to ensure security I would hope you're not just trusting the outputs from such edits. You should review them as if an incompetent junior dev submitted a PR. I'm sure it can do a 90% okay job 70% of the time.
No, I'm saying the exact opposite which is that you shouldn't try to chase fads.
But we haven't discussed fads at all?
You might have to clarify which particular fad you're on currently, because I haven't been keeping up. However, I do see hooks and functional components used commonly today.
So basic modern idioms for an extremely popular UI library are just fads now? I've already asked about memoization (using currently documented recommendations) and contexts. You are being inconsistent, here: originally it was doing great writing idiomatic code. Once I question its ability to correctly recognize the need for and implement idioms from library improvements in the last 5 years I'm just chasing fads. Seems like it's not really thay good at idiomatic code, eh?
Everybody gets attached to code, it's inevitable.
Nope. I don't. The exact opposite is more common with more experience: you get the itch to rewrite. Throw the whole thing in the trash and start over. But this is not a practical use of time 9 times out of 10.
If you have a bunch of code to solve a particular problem and it takes a lot of effort to rewrite it, then you're not going to throw it away easily.
Oh I definitely am because I can now see how to remove the complexity and make it more versatile.
When your requirements start changing, it makes sense to try to adapt existing code to them rather than write code from scratch.
Changing requirements is a design process challenge. It indicates prototyping or gross incompetence if it's happening frequently. Sometimes that incompetence is out of one's hands, like a client that refuses to communicate effectively, but it is less about the code and more about social skills and planning.
If requirements change it can imply modifying an existing codebase ir throwing it all away to start over. It really depends on what the new requirements are. If suddenly there is a need for a very flexible event-driven strategy, you probably need to start over.
It really doesn't if you have good interfaces between components.
It actually really does because it enables capturing and therefore correctly addressing domain problems. Junior devs call meaningful variables "x" and "y" because they can't see this yet, then take forever during a review ir maintenance cycle weeks later because they are tryinh to figure out the meaning and function if meaninglessly named and organized things. In React these would be the same people putting every component module in a module called "components" even for large projects.
"Good interfaces" doesn't address the points I'm raising at all. The best interfaces cannot fix the wrong abstraction being chosen. If your domain needs to represent accounts but you never represent actions taken directly (e.g. "deduct $34.56") you will end up with a confusing mess.
You don't inspect all the code in libraries you include, you focus on the API of the library instead. The same logic applies here. If you structure your project into isolated components with clear boundaries, then your focus is on how the component behaves at the API level.
Until it comes time to maintain your systems and you find that your domain elements are not represented and you don't understand the variables because you let a markov process write your components.
There is another inconsistency here. What you're repeatedly getting at is that you want to treat the things generated by LLMs and things you can wall off and regenerate, machine-edit, etc as needed. Implicit here is that you don't need to actually maintain that code, but actually you do if you have any seriousness about the project. Certainty in function. Security. Ever needing to refactor to add new features. And then this will fall apart because you now have to wade through whatever the LLM generated with all the warts I've described.
Or this is a throwaway project and none of this matters. No real design elements, no security, no user testing, etc. Which is fine, just not a great endorsement for LLMs.
Again, I'm not suggesting using LLMs to do design.
Using dependency injection is a basic pattern. While it is part of design, it is also part of implementation and it changes how the rest of a codebase may function. React uses it a lot in its implementation. It is in no way separate from the task of having LLMs write components and they will produce wrong results without taking this into account. And this is just one example.
Of course, people can make all of these mistakes. But if they are following an intentional design, they will make fewer of them and therefore need less review.
My whole point was that you do the design, and you use LLM to fill in the blanks.
These things are not separable a la a black box. The problems I have noted remain.
In this context, you've already figured out what the component will be and what scope it has, the LLM can help create features within that scope.
I'm sure it can with 90% efficacy for 70% of simple features. And then you need to rename half of the things. And write a bunch of tests. And do a security audit. Accessibility audit?
Anyways the point was that LLMs don't actually understand these things, just patterns, so they fall on their face for choosing something like dependency injection. There are many things like dependency injection and it won't recognize when to use them. You say this is left up to design, but it will be interwoven with implementation, even of components - the thing you're having the LLM do.
Also, things like dependency injection are an artifact of OO programming style which I find to be an anti pattern to begin with.
This is incorrect. Dependency injection is a ubiquitous pattern. It's even used in functional programming. And you'd better get stoked about OOP if you are using JS because just about everything is an object. Including the functions you're calling to generate components. And if you dive into the libraries you're using they will be chock full of semi-complex classes.
With functional style, you naturally pass context as parameters, and you structure your code using pure functions that take some arguments and produce some result.
Can you give me an example of when you have passed a function as an argument to a function? Think about what that pattern entails, how the function is used, how and when it is called, and the name of the method (if applicable) doing the calling.
You can snap these functions together like Lego pieces and you can test them individually. This meshes quite well with using LLMs to generate code and evaluate whether it's doing what you want.
Modularity and testability is basically just as easy and powerful between OOP and FP. Though I do want to emphasize that you are actually using a lot of classes and objects. Every time you call a method, for example. "await fetchedData.resolve()" etc etc.
Have you written code in styles other than OO?
lmao
I feel like we're just going in circles here. I really don't have anything new to add over what I've already said above.
Can you give me an example of when you have passed a function as an argument to a function?
Literally any higher order function lmfao.
Cool then you have done a form of dependency injection with FP.
I don't think we are talking in circles, I feel very on top of this conversation. But we don't have to continue it.
I think we're going in circles because you've simply reiterated the points I've already addressed, and I don't see the point of me restating them so that you can restate yours again. But we can keep going if you feel this is productive.
I've done the equivalent of dependency injection with FP, which consists of passing a function to another. The point I was making there is that you can keep vast majority of your code pure with FP. This means you can test functions individually and you only have to consider their signature going forward. Any context the function needs is passed in explicitly. In this way you're able to push state to the edges of the application. This is far more difficult to do with OO because each object is effectively a state machine, and your application is a graph of these interdependent state machines.
The reason I brought up FP in the context of LLMs is due to the fact that this model fits very well with creating small components that can be reasoned about in isolation and composed to build bigger things. This way the developer can focus on the high level logic and composition, while letting the LLM fill in the details.
I've asked you earlier if you've recently used these tools seriously for any period of time, if you have not then I don't see much point having an argument regarding your assumptions of how these tools work.
I hadn't heard of Qwen. I have only used Deep Seek, and not much. What are Qwen's advantages over Deep Seek? And is there any other model from BRICS countries I should look for? Preferably open source.
And do you recommened a local solution? For which use-case? I have a mid-range gamer laptop. IIRC it has 6GiB VRAM (NVIDIA).
I've found Qwen is overall similar, their smaller model that you can run locally tends to produce somewhat better output in my experience. Another recent open source model that's good at coding is GLM https://z.ai/blog/glm-4.5
6gb vram is unfortunately somewhat low, you can run smaller models but the quality of output is not amazing.
How do you stay up to date on LLM? Do you recommend a web feed (RSS, Atom) for LLM news? My interests:
I find r/localLLaMA on reddit is a pretty good way to keep up. Reddit also has an easter egg where you can get rss feeds for communities, e.g.: https://reddit.com/r/localLLaMA.rss
Main models to look at are DeepSeek and Qwen, both have smaller versions of the model that run well locally and produce decent results. The two popular ways to run models are either using llama.cpp or ollama. Ollama is a bit easier to get started with and it has cli tools for downloading and managing models. My experience is that you want at least a 32gb model for decent results if you're doing something like coding tasks. For text generation, or making summaries, you can get away with smaller models.
In terms of privacy, if you run the model locally then there's no concern. There is also the whole MCP idea that's pretty popular now which allows models to use tools. You can use something like crush to run a model and have it use tools. The tool will show you the command LLM is trying to run and confirm whether you want to execute it. This kind of stuff can be useful for stuff like pulling information from the web and having the model figure out how to implement stuff like API endpoints based on that. It lets the LLM do stuff like run tests in a project, and fix errors, etc.
For accuracy, you basically have to know the subject yourself and be able to evaluate the output from the LLM. There's no way to guarantee that anything it produces is correct without a human reviewing it.
Thank you for the subreddit and the Easter Egg! I have added it to Emacs Elfeed.
Regarding model size: when you write "32gb", I hope you either mean 32 billion parameters, or (since you wrote lower case "b") 32 gigabits. Or do I actually need 32GiB VRAM?
For privacy: before I spend a large multiple of the Brazilian monthly minimum wage (electronics is expensive here), I would like to experiment with hosted solutions. I will try to remember to restrict hosted LLM to non-sensitive code. But I still don't want any leakage to US Big Tech. I fear they can correlate lots of individually non-sensitive stuff and make a profile of me. I will only use Chinese (hopefully we will have great Brazilian models in the future) models, so if they are secure (good encryption, quick patching of vulnerabilities, etc), I should be safe from US profiling. So out of the Chinese hosted coding agents, which have the best security?
Regarding accuracy, I am aware LLM cannot be trusted to be correct. But the more accurate it is, the less correction it will need. Less iterations too. And actually, sometimes I don't now the subject well, but if the LLM is reasonably reliable, I can just check the answer for obvious erros, then accept a moderate risk of error (if the impact is small).
Oh yeah, I was referring to billions of params there. And if you want to use a hosted model to play with it, I would recommend DeepSeek, their pricing is great and I've found it gets pretty decent results. The way I'd recommend using it would be through crush or a similar tool. It's a very different experience from using it in a web chat for example and asking it to come up with code.
And yeah, the better the model is at getting stuff right on the first try the less hand holding you need to do. There are also some tricks I found that can help. One thing I get the model to do is to write a plan in markdown for the steps it's going to do. In particular, you can get it to generate a mermaidjs diagram, then inspect it visually, and then tell it change step x to do blah. Another thing you can do is write the scaffolding by hand, like making the file structure you want, put in function stubs, and then have the LLM start filling in the blanks. It's a really great way to focus it so it doesn't try to get creative. My general experience is that they're good at implementing tasks that are focused and well defined, but if you let them get creative then they can go off track really fast. Another thing I found is that if it doesn't get the solution mostly right on the first shot, it's unlikely to converge on a good solution. It will not rethink the problem, but will simply attempt to add kludges to address the specific issues you point out. So, you're better off to just start from scratch and try to reframe the problem statement.
It's important to keep in mind that it's a tool you have to spend some time learning the behaviors of, and how to get the most out of it.
In this comment thread you gave great tips! But very few people are likely to find them. Why don't you organize them and write a Lemmy post?
Yeah, I might try doing that if I get a bit of energy. Cause some of this stuff is obvious in retrospect, but it took me a while to realize to do these things.
A Reddit link was detected in your comment. Here are links to the same location on alternative frontends that protect your privacy.
I think that's going to change now though, as a result of LLMs. We're going to be stuck with whatever was the norm when the data was harvested, forever
Assuming the use of these tools is dominant over library developers. Which I don't think it will be. But they may write their libraries in a way that is meant to be LLM-friendly. Simple, repetitious, and with documentation and building blocks that are easily associated with semi-competent dev instructions.
I find Gemini really useful for coding, but as you say it's no replacement for a human coder, not least because of the way it fails silently e.g. it will always ime come up with the hackiest solution imaginable for any sort of race condition, so someone has to be there to say WTF GEMINI, ARE YOU DRUNK. I think there is something kind of transformative about it — it's like going from a bicycle to a car. But the thing is both need to be driven, and the latter has the potential to fail even harder
Exactly, it's a tool, and if you learn to use it then it can save you a lot of time, but it's not magic and it's not a substitute for understanding what you're doing.
The most useful application is in making garbo marketing images for products that used to be 100% photoshopped instead. Cool your fake product has an "AI" water splash instead of one from Getty. Nothing of value gained or lost except a recognition of how meaningless it is.
Also, the reason all the hype and 'culture' around these products focus on individual end users (write me a poem, be a chatbot, make me Pixar art etc) is because they're good at being flexible, at applying the algorithm to different shallow tasks. But when it comes to specific, repeated, reliable use cases for businesses they're much much worse. The error rates are high, it's actual ability for 'institutional memory' and reliable repetition is poor, and if you're replicating a known process previously done by people you still have to train or recruit new people to get the best out of the tech.
End the nightmare 2025
Yeah, the next nightmare is starting to get tired of waiting. 
They can make a bubble about Quantum Computing as a treat.
There's literally no reason to do that, so it will probably happen.
this isn't me fanboying LLM corporations. pop pop pop. this article is fucking stupid though.
On Tuesday, tech stocks suffered a shock sell-off after a report from Massachusetts Institute of Technology (MIT) researchers warned that the vast majority of AI investments were yielding “zero return” for businesses.
no they didn't. :// there was a small 1.5% "shock sell-off" (fucking lol) before rebounding. they're only down 0.5% over the past 5-days.
even softbank, who the article focuses on, is up 36.5% (god damn) over the past month. that's huge.
this week’s sell-off has yet to shift from a market correction to a market rout
omg stfuuuuuuuuuu. it's -10% for a correction we aren't even 0.5% of that.
come to think of it, "the market responded to an MIT study suggesting that the technology is worthless" is far too coherent for the stock market. The crash/bubble pop will come because a black cat crossed someone's path or a meteor is seen in the sky over the Bay Area.
It's always those "Comet sighted" events.
I wish I lived in more enlightened times.
Unfortunately it will probably be like the dotcom crash. Websites/services only became stronger afterwards, becoming inseparable from daily life. If a crash happens this year, the Facebook of AI is coming around 2030.
LET'S FUCKING GOOOOOOOO 