> I also noticed a pair of very bizarre apps, both branded with the yellow “Don’t tread on me” flag: DuressKeyboard & UnlicenseLauncher. What’s most curious is that they have been in development for quite some time, yet all the changes are done not with git but through the GitHub web file editor! Someone go find that person and teach them to use git.
Reminds me of a professor that displayed snippets of Haskell on MS Word in her lectures, formatted by hand. I don't blame her, this was >20 years ago, before Ctrl/Cmd + +/- became commonplace for zoom/font size.
My first startup job after college I was astonished to find that one of our most productive engineers was a deaf furry who used Windows Notepad as his dev environment. His code always compiled first time and was nearly always right. It really was something to behold.
Not that many years ago (maybe 2012-2014), we were taught working with R using only the base-R. Its' native script editor was a Windows Notepad or something equivalent. We used this for years.. Professors as well. For some reason we didn't know about RStudio or any other fancy IDE.
TBF using notepad + terminal is a great way to learn how a language or stack works. It's possible for pretty much everything, and once you learn how your ide (or LLM) is putting things together it makes it so much easier to fix when things go wrong
I actually don't see any significant signs of AI use. There's Copilot listed in the contributor list, but I'm not seeing commits listed under it. Did they wipe it off Github?
Some seem to stamp Mostly AI based on weaker circumstantial like large init commits. Maybe it's just an artifact of human sloppiness.
Or maybe it was just the artifact of choosing these by last update, since vibe-coded apps genuinely do have an abnormal number of releases, and thus would be much more likely to show up.
Co-author is so strange. Like, would anyone credit an IDE or even a script generating code as co-author? Even legally, tools don't have the ontological requirement to be credited as author.
Seems like the wrong question to ask. I've been programming my whole life but basically stopped writing code by hand in 2026. The LLM writes better code than I do, much better.
Same here. I'm still a better software architect that AI, but there is no question that my AI generated and reviewed code has fewer bugs than code I hand write. It takes some humility to acknowledge that your coding prowess is less of a useful skill than it used to be.
There's an issue where people assumed the syntactic activity of writing code was what mattered. The reality is that this was always a smaller part of the role, as opposed to thinking about observability, serviceability, and test automation. The ability to write software that is properly separated from concerns and when to enact those separations matters.
At the same time, I think we're far too far down the systems path now. We've hit a point where interviewing has become purely systems design "because the AI writes the code".
Not that I'm ever asked, but I inherently believe the act of critical thinking, communication, and expression are the key skills for those who already have the appropriate coding/engineering/cs/etc background. I now only interview for those skills - but through the lens of impossible to solve systems design conversations as opposed to problems. It tells me a lot about how people think.
Not sure what you mean as system design conversations because while in theory those can be good in practice the ones I have been at had been techbro wankery where the interviewer had a particular answer in mind. Like designing your own memcached clone for example is a terrible task for systems design.
What you're mentioning is 100% what's wrong with the industry. Agreed! To me a systems design conversation is a conversation - not a design goal. The idea is to determine ability and psychology:
1. When you press on someone's design respectfully, do they get defensive. Do they become argumentative.
2. When thoughtfully pointing out a concern, how does the candidate take it?
3. When you suggest a technology that makes no sense to intentionally challenge knowledge, does the candidate recognize why it makes no sense? Are they able to share what the negative of the approach is. If you indicate that you know the question is "senseless" but want their feedback, how do they communicate?
4. When you hard request a change that requires a literal rethink and rewrite do they become argumentative? Do they embrace the change?
5. When discussing testing, how do they think about it? I come down to the nitty gritty and ask about postive vs negative cases, table driven testing, what types of tests matter (for our situation) and why.
6. We discuss timeline tradeoffs, and then have the conversation about the candidate's approach given updates to see how they think.
You'll notice that I am never looking for a solution. I'm seeking communication, description, partnership while having a (relatively) thorough gasp of the subject matter.
Every single time I get a response from a candidate such as "I don't know, I'd have to learn more - or use AI to, or.. what do you think" turns out to be something I LOVE, because it creates a great fabric for the interview.
So is architecting, testing, validating, and even occasionally using.
This isn't the first time I've seen this phrase recently, but I'm not sure what the thought is a cliche or what it is intended to convey (don't read my note as negative, I sincerely am unsure what connotation folks are trying to say).
The idea behind "writing is thinking" is that people often overestimate their understanding until pressed to express it in words (or code).
How many times in your career did you sit down to tackle a task thinking you knew exactly how to approach it only to realize during implementation that there were edge cases you hadn't considered, API contracts that were now broken, or that the feature was trying to solve the wrong problem.
Having to be the one at the helm during implementation made you intimately aware of not only the problem at hand, but the current state of the codebase. That's something you can't replace with automation. You can't compress all of that context into your brain in a handful of prompts with Claude.
Remember the words of your math teacher--
"Watching someone else solve the problem doesn't mean you can now solve it too."
Programming languages, design languages and architecture are all inventions made to help humans write understandable source.
LLMs don’t really need to do any of that. They can store very large trees of understanding and therefore implement any application in raw binary. Why bother with abstractions at all
LLMs for sure need those things. maybe not the same abstractions as humans do but without understabdable code an LLM will just fail to accomplish the task you ask it to do.
I was thinking about that. I reckon generating massive amounts of synthetic training data for that goal should be possible - you've already got the Python, after all. It's also possible for machine code, but you'd have to target more platforms.
But ultimately, I think human readability outweighs any theoretical advantage you get from removing a step in the compilation process.
Targeting byte code or asm instead of high level would be silly for everyday tasks. You blow up the number of tokens, reduce your effective context, and there's just more places for it to make a mistake, which most likely won't be caught by the assembler (unlike compilers).
I just asked Astra to bring an old Windows XP game to the browser. It objdump'ed the whole thing, built a fitting Win32-like wrapper that exposes required functionality like DirectDraw, DirectSound, SEH etc., then wrote an x86-32/x87 interpreter in WASM, benchmarked how the game runs, lifted the hotspots of the executable to WASM too and now it is playable!
I mean, I'm proud of my low-level skills too but this is some Fabrice Bellard level sorcery. Very, very few humans are able to do this without AI tools.
If there are very few humans that can do this is because the market for such a task is very small and thus there is little incentive to learn how to do it or produce tools that can do it.
Yes you can ask the agent anything about it and interrogate it until you understand.
- is it maintainable
Yes it’s easy to ask the ai to add new features or to refactor it entirely.
- how much work is adding new features
Depends, it could just be one prompt, it’s usually many prompts. If the refactor is large it can take weeks. But before AI something g equivalent would take months.
- is it written in a way that adding new features means rewriting a lot of it
Usually no, but that depends on how well the agent is being directed and what the features are. If you come up with a feature that requires a new architecture, ai makes it doable rather than saying “would be nice but we’d also have to implement this whole new architecture and that’s a lot of work”
- is it written in a consistent style
Styles can be applied mechanically with linters and formatters, so as much as any codebase written by multiple people.
What people don't understand that programming is very much an art. You iteratively work on it ripping parts out, rewriting and rewriting and rewriting, while also rewriting and then rewriting every time a new feature, bug fix or scaling changes are needed.
I've seen AI be wrong about things often enough to know that none of what you said is particularly true. Rather, most of it holds true most of the time. But not enough.
> Styles can be applied mechanically with linters and formatters, so as much as any codebase written by multiple people.
I'm talking more of a higher level than this - more of coding/design patterns that are common for the team.
> Rather, most of it holds true most of the time. But not enough.
I see it be wrong about things all day every day. And yet, IME it's correct enough for it to be controllable. It doesn't have to hold up all of the time, it just has to respond to corrections when they're issued in a loop so that it converges to a correct solution. And it does, despite the mistakes.
In one of my other posts in this thread I detail some of the the ways it's confounded me, but those issues have caused me to harden validation mechanisms rather than say "this thing makes mistakes so I can't use it to write software".
> I'm talking more of a higher level than this - more of coding/design patterns that are common for the team.
Absolutely, the Win32-WASM layer it wrote is some of the most evil looking code I have seen in my life. But realistically, why keep it maintainable for humans if you won't find anybody that can work on it without AI anyways?
If we humans are just doing code style checks, file organizing and doc cleanups I feel we have demoted ourselves to code janitors. This is neither fun nor going to last.
Personally I've always strived for minimalism, to find the smallest, fastest, simplest solution possible so I'm pretty jaded now, too...
Code is a human formalism that is only incidentally made executable. The elegance of code represents understanding of the problem to its most minute detail. LLM code being recognizably terrible shows it still doesn't understand what it produces to the full extent, and just as with people, it will inevitably compound to it becoming unable to efficiently work on it. And if the human abdicates that responsibility too, then there is no code, only subtly broken software.
As a bit of an observation on that specific project... By hand as a human you could spend six months of the equivalent of a full time job doing that. Even if you had extensive knowledge in all of its discrete pieces. One of the things coding focused LLM are great at is doing things that have no reasonable prospect of economic necessity to do (no for profit company is going to pay you a FTE salary for six months to do that task, because there's no possible revenue in it). But the LLM can be pointed at it and get it done in a day or two with some periodic architecture and decision making by the human, for probably under $50.
fwiw, PCGamingWiki[1] has a link to a patch[2] that someone wrote to fix the compatibility issues. If you're playing from the CD edition, you might also need SafeDiscShim.[3]
Only people disliking AI for coding are the gatekeepers who think they are magicians and the plebs shouldn't be able to code like them, unless they become gud.
"There are naïve questions, tedious questions, ill-phrased questions, questions put after inadequate self-criticism. But every question is a cry to understand the world. There is no such thing as a dumb question". (Carl Sagan)
Just because you don’t seem to be interested in the answer - then don’t read it? - doesn’t make the question wrong.
The AI machine can write better code. It can also write an interpreter which implements function calls by instantiating a new interpreter + entire standard library per function call. Or it will build a 300kloc cathedral of scaffolding and maintain that forever, never writing actual code. Or it will create a CI system that takes 2 hours to run and constantly fails, and the agent loops there all day, fixing a small bug and waiting 2 hours. (All things I’ve experienced latest frontier models do)
Agentic engineering faces all kinds of new problems that couldn’t exist before, and need experienced engineers to solve them.
With the "intelligence" of code focused and capable llm in the last six months, the main problem I'm seeing now is where some total amateur who has no previous knowledge of coding tries to one shot a project. People who have previous experience and know how to architect things (and when to stop an LLM from doing something wrong that will cause maintenance and scale and extensibility problems in the future) are doing much better building actually useful things.
This one shot thing I just don’t understand. The way I’m using it, it takes weeks of constant prompts because it never does exactly what I ask no matter how well I specify. I just don’t see how it’s possible to one shot anything unless you don’t have strong requirements on the output.
I had pretty good success with a language learning app. Initial prompt below, about an hour to get it working very close to how I imagined, then extended to Japanese, French, and audio generation.
======================================
Hi! I'd like to create an app for interactively learning Chinese using AI. My current idea is:
- The AI generates a Chinese sentence at a specified vocabulary level (e.g. HSK2)
- The user translates the sentence into English, and the AI evaluates the response. If the answer is wrong or is partially correct but could be improved, the AI offers a hint. If correct, the AI confirms and explains any particularly important vocabulary or grammar points that the sentence demonstrates.
- The user should be able to click on an individual character once to see the pinyin, and again to see its definition and any helpful techniques to remember it (radicals, similarity to other characters, visual meaning)
- The app should also be able to go the other way, giving the user an English sentence and having them translate to Chinese.
App details:
- UI is a web app
- The AI should be pluggable. We'll start with a local Ollama install running gemma4, but it should be easily possible to add support for Claude/OpenAI/Gemini or other models (may need to provide an API key).
- Not actually sure if we need a backend. It might be useful to keep track of characters or concepts that the user has difficulty with, or to keep track of what sentences the AI has previously generated so it doesn't become repetitive.
- Build everything in a Docker container (or multiple if needed with docker compose)
I can see this being a one-shot thing because the brief is pretty light; beyond broad strokes, you haven't specified much for it to actually do. Any UI that's also a webapp would satisfy this brief, but that's exactly what I mean; e.g. if you want a specific UI, there's a lot more back-and-forth that's involved. It'll one shot a UI, but it's very rarely the UI you want down to all the interactions and placement of buttons.
Yeah, that's fair. I didn't have an exact UI planned so I gave it flexibility, and it made reasonable choices that only needed a bit of tweaking to be quite usable.
For me the usefulness of a survey like this has nothing to with how effective LLMs are themselves. It's more that when someone's able to produce an app in an afternoon, and submitting the app to F-Droid becomes a checkbox, how confident can you be that they'll continue maintaining the app? Sure if it's open source you can have your own LLM maintain it, but at that point what's the value in having it on F-Droid?
”””
F-Droid is not hosted in just any data center where commodity hardware is managed by some unknown staff. We worked out a special arrangement so that this server is physically held by a long time contributor with a proven track record of securely hosting services. We can control it remotely, we know exactly where it is, and we know who has access.
”””
What do you find strange about trying to protect against tampering and theft?
I find it strange how little people seem to care these days and just widely share their users and company data across clouds. Plenty of supply chain attacks to learn from.
Who/whatever does the layout and organisation of app categories is a blithering idiot and finding apps is best done with an external search as the internal one hides apps even when searched for directly by name.
And the fdroid app is relentless systems deperformance burden that often just failed, and updating manualy is simpler as a chore done after any android update.
Love a lot of the apps, and the concept of fdroid, but the fdroid UI is not good at all.
Separate from building your own code, ,of course you may have your own standards to apply.
But for apps, well, I never had a chance to see how good or bad the code was before AI was about, so why should I care now, so long as what I paid for does what it says it does (and nothing nefarious..)
Some programmers have ethical concerns around the use of LLMs. It's like saying "my clothes still work, why should it matter if child labour made them?"
I don't know wnything of the ethics of any real meatbag developers that are working on the code or app I install on my phone either. I fail to see how, for pre-packaged code specifically, it being AI or not is a problem; ~ rather, surely all the concerns we have about AI code (hopefully properly developer reviewed.. - which I suspect is where the real problem lies) apply to developer written code also, when it comes to pre-packaged apps.
The blog post provides something akin to answer to this question:
You see, the main allure of LLMs is that they allow the developer to be more lazy. That’s kind of the whole point! You just prompt, sit back and relax. So it should not surprise you to hear that this attitude is then reflected in everything the vibe-coder touches
As I understand it, one of concerns is that with the lowered barriers there comes a flood of low quality software, vibe coded by very lazy and not very talented people.
This might be actually more of a human problem, but it's a problem nevertheless.
I had no visibility of developer attentiveness or lack thereof, not skill or code quality before AI was around, for any apps I downloaded to my phone.
I fail to see why worrying about AI code quality is any different to worrying about developer code quality when it comes to pre packaged apps.
With code I am writing, some AI generated, my work load has not really decreased, nor have I gotten lazy. My work has changed to a degree, and now involves reviewing and guiding and double checking AI code where I did not have to before, but I am certainly still working just as hard, and accomplishing more with AI's help in spite of the change in workload it brings.
delivering bad AI code because you got lazy is not the AI fault, it's the developers fault.
You can't call people lazy because they use LLMs, just like you can't call people lazy because they travel by train/plane/cars instead of their own feet.
It's an emotional problem. I love writing code to solve intricate problems. But knowing that a faster, and maybe better LLM solution is just a prompt away? Somehow that takes the joy out of it. Why spend hours, when you can get an equivalent result in minutes?
I will be curious to see how I feel about AdventOfCode this year...
Virtually nobody is a professional walker or firestarter, so those are different.
And to the extent they do exist (athletes and survivalists) they might agree there is no "point" except the joy they derive from doing it themselves.
The problem is that formerly there was more alignment between the joy of solving problems with a human's mind and the business need to have problems solved.
Please stop labelling a project as slop just because it has been developed using AI. Coding agent are now replacing the IDE and code is now mostly written by the agents.
So we are calling the developers as sloppy now just because they use AI? It is entirely possible to use AI and still produce good code - the effort required changes, but so long as it is done by a diligent developer capable of asessing and correcting AI code, it should be fine.
I did not say I don't read any produced code; I said I don't write it manually. What I wanted to demystify is that you can generate everything with AI and still put a lot of work into design choices, good code structure, and so on, without producing any slop.
Please stop telling people what to do. Also, the discussion of the merits of LLM code writing is far from over. Author of TFA actually provided an interesting data point, you are just rehashing an often heard opinion.
Why are you so aggressive? I am not rehashing opinions, I'm writing my opinion. Also, I'm not even saying that the author did a bad article. So your comment is a bit out of line.
I'm somewhat surprised about PipePipe. I had a look on the commits of the various components and nothing looks out of place to me. Commits look rather reasonable, comments look useful and don't show obvious LLMisms.
What are the AI smells there?
It would be nice to expand a bit on the reasoning behind the verdicts.
Every codebase that is being actively worked on (closed/open source) will contain code that's AI generated. With the rising abilities of agents, expectations are sky rocketing in terms of productivity.
If you're as productive as an engineer in 2016, you're not at the level that's expected. A 7 day workflow back then should take you maybe a day or less to work on today.
I've worked with "engineers" that make one commit in 2 months and it's a spelling correction. I'm pretty sure that being 7 times faster than that is still quite slow compared to me :) I hear a lot of breathless enthusiasm, but this article is possibly the first time I'm seeing the question answered "If AI is so great, where are all the applications?"
I have been working on a sub 1500 line rust init system for over a week. Hundreds of prompts. All with a local LLM running on my own GPUs because I expect to build with total sovereignty but also zero dependencies, no libc, no alloc, no std, and a test suite that proves the 20 implemented raw syscalls all use the right values by comparing against Linux kernel sources. This would be the only privileged code in my operating system so I must have absolute confidence it is perfect.
It would be too annoying for a human to ever write code to standards this high, and would have taken me months to write by hand, but with the help of AI I was able to get it done and built in a way I can easily review and reason about.
I have a memory safe baremetal tiny linux init now built to my exact requirements.
AI can help experienced engineers write better code in less time.
I do not personally know a single human I could have hired who could write low level nostd nolibc noalloc rust like this, and certainly not in only a week even with hundreds of messages from me giving them detailed direction. That person may exist, but a day of their time likely costs more than the GPUs I racked up in my garage to assist with this.
I know no one wants to believe an LLM plus a human could produce more secure code than an LLM or human working alone, but as engineers we should adapt to whatever gets the best results.
Also every line is manually audited by two experienced software engineers before production.
In a week for a few dollars of pay with a test suite that comprehensive and on my exact schedule to collaborate with me around the clock? I doubt it, but I would love to meet that person if I am wrong!
This is the fastest I've ever seen goalposts move. Like, this is a completely different claim from "It would be too annoying for a human to ever write code to standards this high".
If you take what I said out of context with zero charity, sure.
> and would have taken me months to write by hand
That implies humans can write it because I am a human and presumably other humans can too. I was making a statement about it not being possible to write it in any justifiable amount of resources.
My company would fail if I spent months on this, but a week to harden init is for sure worth it.
I interpreted that as you saying in a few months you could write the code but not to those standards.
If a single person can do the full job to full standards in a few months, then the whole idea of it being too annoying for a human to ever do falls apart. That's not that expensive. Your company can't spare you, sure, but most companies could handle that price.
And even with the adjustment to being something it's easy to hire someone for, that's still ballparks away from "in a week for a few dollars". Nearly every coding project is impossible for humans by that standard.
> Nearly every coding project is impossible for humans by that standard.
Exactly, which is why this small project, and the other dozen projects that need to be written to the same standards to make the final end result OS, would just simply not happen otherwise.
No small FOSS org can afford to spend person-years holding standards this high, which is why in practice most orgs just cut a ton of corners and pull in tons of dependencies no one will ever review and the attack surface that comes with them.
LLMs allow experienced engineers to get 10x as much done in the same time, and now that this is possible few will be willing to pay engineers to do it at 1x speeds again.
Just like once assemblers became normalized, few orgs would let people write all their assembly by hand anymore except for very small high performance sensitive portions.
Software engineering is not going away. It is just accelerating because we were able to move our attention up a layer again.
If you read the rest of the same line you are hung up on I literally said I could have done it in a few months, which implies I believe humans can do it, so clearly I was saying it was impossible to do in any practical amount of time or resources.
I started with a hand coded C version, then a hand coded rust version, and then an LLM refactor to remove all dependencies and add a comprehensive test suite.
Please, report me to Codeberg. It will be amusing.
I personally know a Codeberg admin that works with LLMs as a tool for things like this which are actual engineering and not vibe coding.
I don’t even disagree that “AI can help experienced engineers write better code in less time”, but “It would be too annoying for a human to ever write code to standards this high” when the standards are “zero dependencies, no libc, no alloc, no std, and [correctness]” and the project is <1500 lines of Rust implementing 20 syscalls is a little much. All software ever written before 2023 was written by humans, remember?
Of course. The humans that could have written this -eventually- certainly exist but they could not have collaborated with me to get me these results on my exact schedule in a week for the few dollars of pay I spent on electricity.
If your input is LLM-generated code, it's like taking snippets from Stack Overflow. You understand it fully and then make it yours. As long as you're hand-crafting the result, you grasp how it works.
This is an aspect of LLMs that probably shouldn't be understated.
I'm working on a validation tool with a coworker right now. We are meat-proxying it into existence, using less than two exchanges per day of his LLM validator and my LLM authoring engine (we could consolidate, but he's got some context I don't have and it would take more than zero engineering effort to ship it to me when PR messages through GitHub are a good-enough channel).
If not for the LLM, the tool wouldn't exist at all because we can't justify dedicating much of any engineering time to building it. But LLMs drive the engineering cost very close to zero, enabling the authorship of code that would never find engineering cycles otherwise.
> It would be too annoying for a human to ever write code to standards this high,
These are standards of a normal university operating systems course (assuming a university worth its salt anyway). Ridiculous to present it as some kind of feat that was "impossible" or even that rare.
Sure, a very smart and patient human could write this by hand in a long enough time horizon and unlimited budget. I should have said impossible in any realistic amount of time any responsible sponsor or employer would pay for.
is textbook definition of boilerplate. you need to be patient to cross-reference syscall tables but certainly not smart.
Again, students learning about operating systems do similar things as a normal part of their education process.
I'm not saying LLMs wouldn't help with this (in fact, this is the type of thing that becomes pretty much a no-op with them) but the notion that this is "impossible by any realistic standards" is risible, and if you have actually contributed to the kernel like you say, you should know better.
Rather than patiently referencing syscall tables by hand which is error prone and hard to review as a human, the LLM wrote tests to actually compare each of these to the kernel syscall tables to make my life a lot easier and avoid regressions or mistakes. Complex tests to write, that saves me a lot of time as a security reviewer. Can change any value and watch tests fail.
That is the point. Getting to results that satisfy security and functionality requirements in much less time.
Also, I was able to generate a bare minimal and hardened kernel config by using the LLM to write a tool to trace all kernel calls. Something that I would have normally been forced to punt on that LLMs made easy to justify.
LLMs are just as Linus Torvalds likes to say "Auto complete on steroids".
The parsing code isn't even correct, though. I'm sure it works for the current input source code, but it's a clear sign of an LLM developing by iteration, rather than actually understanding the problem and coming up with a correct solution.
How often does "this table of constants was transcribed incorrectly" even come up as a bug class? Is it worth writing a buggy C parser for a failure mode that doesn't happen? Do you expect that Linux is going to change their constant definitions out from under you?
Correct depends on what problem one is trying to solve though the parsing can certainly be cleaned up before release. It is a working WIP.
> Do you expect that Linux is going to change their constant definitions out from under you
Unlikely but I have been surprised before. Hardcoding to pretty much any modern kernel source version forever for this narrow use case is probably fine.
The goal is about helping me and a reviewer quickly form confidence none of the magic numbers are mistaken or malicious in an automated way against the source of truth.
Correct. It does the bare minimum system calls to boot the system, then exec to an unprivileged service manager on a read only nosuid, nodev filesystem.
Cool, but I fail to see how it is relevant in the context of the article? You clearly have the knowledge, expertise and discipline to incorporate LLM in a workflow that, taking your word for it, delivers a very good result.
That makes you a) more unique than you realize b) not all that relevant in the context of the article.
a) Because a lot of people tend to lean towards use that absolutely leads to atrophying of skills and knowledge. b) well the article does explain the criteria it used fairly well and your project would not show up there. Well, also because your project isn't an android app.
An appliance Linux distribution without dependencies is way more complex than a typical android app and there is little prior art to train on.
Android apps on the other hand, LLMs have enough training data to rapidly generate whatever you can think of with much less time required for an experienced engineer to refine it into a quality result.
I am mostly pushing back on the notion of dismissing software as low quality just because the author used LLMs.
It is frankly amazing we can point an LLM at a proprietary ad-ridden app and get to an ad-free open source and privacy respecting clone in a weekend now.
> I am mostly pushing back on the notion of dismissing software as low quality just because the author used LLMs.
Its not that hard to imagine that apps made with heavily LLM involvement suffer in quality. The article already goes into it a bit. I get the strong feeling that you are failing to approach this from anything else than your own perspective and how you use LLMs. Which, given the setup you are describing is already far from the norm.
Zooming out a bit further I think we can generally break up LLM usage in roughly two distinct ways of using LLMs. Or maybe more accurately there are two sides of a spectrum with a lot of area in between them:
1. Let LLMs do the majority of the heavy lifting and generate the output. Providing an LLM with some basic info, what you are looking for and let the LLM do most of the work for you.
2. Using LLMs as a tool external to the process where you are still doing most of the work.
The first way of using LLMs using agentic workflows, code harnesses, etc *can* produce good quality. As long as there is still a human in the mix who a) has the prerequisite knowledge to validate what the LLMs have generated b) takes their due diligence at key points in the process to actually make sure input is of high enough quality and validates the output.
You have decades of knowledge and experience you are now applying to rigidly structure how these LLMs work.
Which, yes again, I believe gives you personally pretty good results, no doubt about that. But, many people do not have that experience to begin with.
And even for those that do, using the first method comes with a risk that boils down to simple psychology, our minds are wired to take the easy approach wherever they can. Which turns the first way of using LLMS from a efficient way of using them to a lazy way of using them.
In my personal experience a lot of people who start using LLMs sort of drift to the lazy approach very easily. Because it is very convenient and easy to slowly hand over more thinking to an LLM. The latter I have seen happen all too often around me. An alarming increase of lazy non critical use of LLM tools by people who should know better. People who might have been a bit slower before but delivered excellent results now have started delivering trash. Code spanning dozens of line trying to solve something that should only take one line. Code that completely ignores and conventions or design paradigms put in place. Code that goes directly against security practices. Suddenly downgraded dependency versions (because the models training data doesn't include the latest version).
As I said, I see this as a spectrum and the more someone leans towards the first use case combined with "lazy usage" the more signs there will be in a repository that this is the case. The amount of commits, the release cadence and various other things. Which, again, the article also makes a case for and is again why your repository is irrelevant to the whole discussion. Why? Because your repository doesn't show these signs.
Fair and nuanced. Nothing I disagree with here. I think I mostly just dislike the default hate and assumptions on everyone that uses LLMs, including often me. Some of us are using these tools thoughtfully.
LLMs help experienced engineers write quality code faster and it helps amateurs write shit code faster. Problem is there are simply way more of the latter. Even so I detest engineering being judged by the tools used, instead of the work itself being judged on its own merits because there are exceptions to every rule. I would like to teach people to use these tools responsibly, instead of sending a message that use of these tools is automatically thoughtless slop.
Like, most people I know that use Vim write much higher quality code on average than the people that use VSCode as tool preferences often reflect experience, but not always! It would still be unfair to write off someone's code sight unseen because they used VSCode as there are always exceptions to the rule.
I guess the point you're trying to make is that apps developed by LLMs don't need to be all that bad, but the problem here is that you can't know who does their due diligence with auditing their own code and who doesn't, and you can't expect potential users to do it either. Especially not when similar apps get published at increasingly faster rates.
LLMs also helps inexperienced engineers (or people who do not have any own software engineering skills themselves) to write horrible code in less time.
Of course you also can't trust everything human developers publish either, but the issue is amplified by LLMs.
> Text just doesn’t carry enough meta information for any kind of assessments to be even close to accurate.
Well, that is true, but I have seen how one japanese developer, who has
had low english language skills, suddenly writes epic text. So it is clear
that he is using AI when interacting with other people. This is a problem
because a lot of it is just AI slop fake interacting. I don't want to
interact through any AI filter; it means the other human being is not
interested in communicating with me, so why would I want to communicate
with an AI slop controlled human? It makes no sense.
As for software: IMO documentation is very important. Many projects lack
good documentation. This is valid for both AI slop created projects and
real humans created projects. There are too many software developers who
hate writing documentation. I don't want to use such software anymore.
Unfortunately AI slop documentation often sucks too. They just spam you down
with words that mean nothing. And are very verbose. This is also totally
useless.
It seems as if software designers indeed fail hard in the last years. Quality
standards used to be higher in, say, 2005 to 2010, compared to now and the last five years.
> Their mere existence makes educating yourself and going on fun side projects much less rewarding.
It's not just AI though. Look at Google search. Use it. It is totally useless now. Google crippled it deliberately some years ago. Now you'd think alternatives are better, but they also suck. DuckDuckGo? Qwant? They, oddly enough, manage to be even worse crap than Google search, which is amazing. Crapification has become the default now.
This reminds me of another time... Let's travel back to 1980 and interview a local grey beard:
Everything these days is shell scripts! They are even selling them like they have the same value as a C program! csh will never be real C. The kids have no idea what a stack is or how to optimize system calls. The performance is awful and they barely work! The way scripts spawn processes can bring a multi-user terminal system to its knees!
I mean, sure, I write some scripts now and then, but the real logic always goes in C. And just the other day my boss gave me a shell script he coded over the weekend and said putting it in production should be easy!
Over the weekend I was checking out BSD and I couldn't believe the number of scripts I found. I fear for the future of my career and computer science in general. Our field might die out before it can even get off the ground.
how did critics become suddenly concerned with virtue and beauty with AI? Wouldn't it be better to judge these apps on their utility?
The authors are sharing their creativity, and spending time & money to publish apps with a free license.
I have disdain for how entitled open source consumers are. Before AI, demanding free accounts, support , resources, traffic, cpu -- after AI demanding bespoke hand-written code be given out for free.
Even before AI, I just looked at file size to determine whether an app was worth installing. There used to be tool to search the Google Play Store and sort by size. If it's a basic utility and more than a few MB, it's probably riddled with bloated libraries and ads.
What stops a bad actor from buying a bunch of these apps on fdroid adding malware to them and then having fdroid handle the distribution for you? I people did something similar with a bunch of plugins in the wordpress plugin archive.
F-droid doesn't simply distribute build apps. It requires the source to be open and will build the apps itself. This does not guarantee there will be no bad actors trying to slip in malicious code. I do believe they also do initial audits before an app is allowed in their repos. It also often takes a while for an app update to show up in F-droid, I am not sure if that is due to auditing updates or something else though.
theandrewbailey | 22 hours ago
gib444 | 22 hours ago
amelius | 22 hours ago
orbital-decay | 22 hours ago
fer | 21 hours ago
dweekly | 13 hours ago
"The way that works for you is the right way."
5555watch | 11 hours ago
greazy | an hour ago
15 packages loaded but only 3 used, `rm(list = ls())` at the top of every script, hard coded paths on worse using the here package.
RugnirViking | 2 hours ago
pona-a | 22 hours ago
Yubico Authenticator https://github.com/Yubico/yubioath-flutter
I actually don't see any significant signs of AI use. There's Copilot listed in the contributor list, but I'm not seeing commits listed under it. Did they wipe it off Github?
Some seem to stamp Mostly AI based on weaker circumstantial like large init commits. Maybe it's just an artifact of human sloppiness.
Or maybe it was just the artifact of choosing these by last update, since vibe-coded apps genuinely do have an abnormal number of releases, and thus would be much more likely to show up.
Mylloon | 21 hours ago
I use the contributor graph to find where the AI made the commits [1] and then I search using the date [2], I found 3 commits relative to a11y stuff.
[1] https://github.com/Yubico/yubioath-flutter/graphs/contributo...
[2] https://github.com/Yubico/yubioath-flutter/commits/main/?sin...
psychoslave | 21 hours ago
bpev | 21 hours ago
psychoslave | 17 hours ago
That is, the MUAs don’t add "co-authored by myMUA".
ClimaxGravely | 15 hours ago
jamietanna | 12 hours ago
pietervdvn | 13 hours ago
edg5000 | 21 hours ago
9cb14c1ec0 | 21 hours ago
cik | 21 hours ago
At the same time, I think we're far too far down the systems path now. We've hit a point where interviewing has become purely systems design "because the AI writes the code".
Not that I'm ever asked, but I inherently believe the act of critical thinking, communication, and expression are the key skills for those who already have the appropriate coding/engineering/cs/etc background. I now only interview for those skills - but through the lens of impossible to solve systems design conversations as opposed to problems. It tells me a lot about how people think.
jeltz | 21 hours ago
cik | 20 hours ago
1. When you press on someone's design respectfully, do they get defensive. Do they become argumentative.
2. When thoughtfully pointing out a concern, how does the candidate take it?
3. When you suggest a technology that makes no sense to intentionally challenge knowledge, does the candidate recognize why it makes no sense? Are they able to share what the negative of the approach is. If you indicate that you know the question is "senseless" but want their feedback, how do they communicate?
4. When you hard request a change that requires a literal rethink and rewrite do they become argumentative? Do they embrace the change?
5. When discussing testing, how do they think about it? I come down to the nitty gritty and ask about postive vs negative cases, table driven testing, what types of tests matter (for our situation) and why.
6. We discuss timeline tradeoffs, and then have the conversation about the candidate's approach given updates to see how they think.
You'll notice that I am never looking for a solution. I'm seeking communication, description, partnership while having a (relatively) thorough gasp of the subject matter.
Every single time I get a response from a candidate such as "I don't know, I'd have to learn more - or use AI to, or.. what do you think" turns out to be something I LOVE, because it creates a great fabric for the interview.
0x696C6961 | 21 hours ago
tomrod | 20 hours ago
This isn't the first time I've seen this phrase recently, but I'm not sure what the thought is a cliche or what it is intended to convey (don't read my note as negative, I sincerely am unsure what connotation folks are trying to say).
jplusequalt | 13 hours ago
How many times in your career did you sit down to tackle a task thinking you knew exactly how to approach it only to realize during implementation that there were edge cases you hadn't considered, API contracts that were now broken, or that the feature was trying to solve the wrong problem.
Having to be the one at the helm during implementation made you intimately aware of not only the problem at hand, but the current state of the codebase. That's something you can't replace with automation. You can't compress all of that context into your brain in a handful of prompts with Claude.
Remember the words of your math teacher--
"Watching someone else solve the problem doesn't mean you can now solve it too."
markb139 | 21 hours ago
jeltz | 21 hours ago
flir | 21 hours ago
But you effectively lose the human review component.
_1 | 20 hours ago
flir | 20 hours ago
But ultimately, I think human readability outweighs any theoretical advantage you get from removing a step in the compilation process.
redox99 | 14 hours ago
staszewski | 21 hours ago
0x000xca0xfe | 21 hours ago
I mean, I'm proud of my low-level skills too but this is some Fabrice Bellard level sorcery. Very, very few humans are able to do this without AI tools.
erfgh | 20 hours ago
RHSeeger | 20 hours ago
- is it understandable
- is it maintainable
- how much work is adding new features
- is it written in a way that adding new features means rewriting a lot of it
- is it written in a consistent style
- and lots of other things
I use AI to write a lot of my code, but the only time it's clearly "better" than a competent human is for one-off things.
That being said - AI + human is, without any doubt in my mind, better than either one alone.
tomrod | 20 hours ago
Human+AI systems is a good match. Like Human+docs or Human+encyclopedia.
ModernMech | 20 hours ago
Yes you can ask the agent anything about it and interrogate it until you understand.
- is it maintainable
Yes it’s easy to ask the ai to add new features or to refactor it entirely.
- how much work is adding new features
Depends, it could just be one prompt, it’s usually many prompts. If the refactor is large it can take weeks. But before AI something g equivalent would take months.
- is it written in a way that adding new features means rewriting a lot of it
Usually no, but that depends on how well the agent is being directed and what the features are. If you come up with a feature that requires a new architecture, ai makes it doable rather than saying “would be nice but we’d also have to implement this whole new architecture and that’s a lot of work”
- is it written in a consistent style
Styles can be applied mechanically with linters and formatters, so as much as any codebase written by multiple people.
himata4113 | 20 hours ago
RHSeeger | 19 hours ago
> Styles can be applied mechanically with linters and formatters, so as much as any codebase written by multiple people.
I'm talking more of a higher level than this - more of coding/design patterns that are common for the team.
ModernMech | 17 hours ago
I see it be wrong about things all day every day. And yet, IME it's correct enough for it to be controllable. It doesn't have to hold up all of the time, it just has to respond to corrections when they're issued in a loop so that it converges to a correct solution. And it does, despite the mistakes.
In one of my other posts in this thread I detail some of the the ways it's confounded me, but those issues have caused me to harden validation mechanisms rather than say "this thing makes mistakes so I can't use it to write software".
> I'm talking more of a higher level than this - more of coding/design patterns that are common for the team.
Do you have a concrete example?
0x000xca0xfe | 20 hours ago
If we humans are just doing code style checks, file organizing and doc cleanups I feel we have demoted ourselves to code janitors. This is neither fun nor going to last.
Personally I've always strived for minimalism, to find the smallest, fastest, simplest solution possible so I'm pretty jaded now, too...
pona-a | 19 hours ago
walrus01 | 20 hours ago
0x000xca0xfe | 20 hours ago
tuesdaynight | 18 hours ago
0x000xca0xfe | 18 hours ago
It did not work properly on Wine nor Windows 10, that was the entire reason for trying it out.
InvisibleUp | 9 hours ago
[1]: https://www.pcgamingwiki.com/wiki/The_Settlers_IV
[2]: https://github.com/elishacloud/dxwrapper/wiki/The-Settlers-I...
[3]: https://github.com/RibShark/SafeDiscShim
xandrius | 20 hours ago
beej71 | 18 hours ago
47282847 | 21 hours ago
Just because you don’t seem to be interested in the answer - then don’t read it? - doesn’t make the question wrong.
ModernMech | 20 hours ago
Agentic engineering faces all kinds of new problems that couldn’t exist before, and need experienced engineers to solve them.
walrus01 | 20 hours ago
ModernMech | 20 hours ago
orangecat | 14 hours ago
======================================
Hi! I'd like to create an app for interactively learning Chinese using AI. My current idea is:
- The AI generates a Chinese sentence at a specified vocabulary level (e.g. HSK2)
- The user translates the sentence into English, and the AI evaluates the response. If the answer is wrong or is partially correct but could be improved, the AI offers a hint. If correct, the AI confirms and explains any particularly important vocabulary or grammar points that the sentence demonstrates.
- The user should be able to click on an individual character once to see the pinyin, and again to see its definition and any helpful techniques to remember it (radicals, similarity to other characters, visual meaning)
- The app should also be able to go the other way, giving the user an English sentence and having them translate to Chinese.
App details:
- UI is a web app
- The AI should be pluggable. We'll start with a local Ollama install running gemma4, but it should be easily possible to add support for Claude/OpenAI/Gemini or other models (may need to provide an API key).
- Not actually sure if we need a backend. It might be useful to keep track of characters or concepts that the user has difficulty with, or to keep track of what sentences the AI has previously generated so it doesn't become repetitive.
- Build everything in a Docker container (or multiple if needed with docker compose)
ModernMech | 14 hours ago
orangecat | 13 hours ago
ndiddy | 20 hours ago
valgaze | 21 hours ago
””” F-Droid is not hosted in just any data center where commodity hardware is managed by some unknown staff. We worked out a special arrangement so that this server is physically held by a long time contributor with a proven track record of securely hosting services. We can control it remotely, we know exactly where it is, and we know who has access. ”””
47282847 | 21 hours ago
I find it strange how little people seem to care these days and just widely share their users and company data across clouds. Plenty of supply chain attacks to learn from.
metalman | 21 hours ago
alienbaby | 21 hours ago
Separate from building your own code, ,of course you may have your own standards to apply.
But for apps, well, I never had a chance to see how good or bad the code was before AI was about, so why should I care now, so long as what I paid for does what it says it does (and nothing nefarious..)
voidUpdate | 21 hours ago
alienbaby | 20 hours ago
harvey9 | 19 hours ago
relevant_stats | 21 hours ago
The blog post provides something akin to answer to this question:
You see, the main allure of LLMs is that they allow the developer to be more lazy. That’s kind of the whole point! You just prompt, sit back and relax. So it should not surprise you to hear that this attitude is then reflected in everything the vibe-coder touches
As I understand it, one of concerns is that with the lowered barriers there comes a flood of low quality software, vibe coded by very lazy and not very talented people.
This might be actually more of a human problem, but it's a problem nevertheless.
alienbaby | 20 hours ago
I fail to see why worrying about AI code quality is any different to worrying about developer code quality when it comes to pre packaged apps.
With code I am writing, some AI generated, my work load has not really decreased, nor have I gotten lazy. My work has changed to a degree, and now involves reviewing and guiding and double checking AI code where I did not have to before, but I am certainly still working just as hard, and accomplishing more with AI's help in spite of the change in workload it brings.
delivering bad AI code because you got lazy is not the AI fault, it's the developers fault.
drcxd | 20 hours ago
relevant_stats | 19 hours ago
I haven't done that at all.
bigstrat2003 | 15 hours ago
If you're using an LLM to write your code, odds are very high it does not work.
bradley13 | 21 hours ago
I will be curious to see how I feel about AdventOfCode this year...
cicko | 21 hours ago
xandrius | 20 hours ago
Because then why do you ride a vehicle when you could walk?
Why do you use fire when a well-positioned mirror with sun could do?
Why a piezo ignition or lighter when a stick and lots of friction would do as well?
zzrrt | 12 hours ago
And to the extent they do exist (athletes and survivalists) they might agree there is no "point" except the joy they derive from doing it themselves.
The problem is that formerly there was more alignment between the joy of solving problems with a human's mind and the business need to have problems solved.
asimovDev | 21 hours ago
whiteleopard | 21 hours ago
ivanjermakov | 20 hours ago
alienbaby | 20 hours ago
ivanjermakov | 19 hours ago
bigstrat2003 | 15 hours ago
If you're doing that, you are producing slop. So it should be called out as slop.
whiteleopard | 13 hours ago
gpvos | 15 hours ago
whiteleopard | 13 hours ago
jraph | 20 hours ago
What are the AI smells there?
It would be nice to expand a bit on the reasoning behind the verdicts.
samayashar | 20 hours ago
If you're as productive as an engineer in 2016, you're not at the level that's expected. A 7 day workflow back then should take you maybe a day or less to work on today.
panny | 12 hours ago
lrvick | 20 hours ago
It would be too annoying for a human to ever write code to standards this high, and would have taken me months to write by hand, but with the help of AI I was able to get it done and built in a way I can easily review and reason about.
I have a memory safe baremetal tiny linux init now built to my exact requirements.
AI can help experienced engineers write better code in less time.
nickphx | 15 hours ago
lrvick | 15 hours ago
Still WIP but working.
I do not personally know a single human I could have hired who could write low level nostd nolibc noalloc rust like this, and certainly not in only a week even with hundreds of messages from me giving them detailed direction. That person may exist, but a day of their time likely costs more than the GPUs I racked up in my garage to assist with this.
I know no one wants to believe an LLM plus a human could produce more secure code than an LLM or human working alone, but as engineers we should adapt to whatever gets the best results.
Also every line is manually audited by two experienced software engineers before production.
fwip | 15 hours ago
lrvick | 15 hours ago
Dylan16807 | 12 hours ago
lrvick | 12 hours ago
> and would have taken me months to write by hand
That implies humans can write it because I am a human and presumably other humans can too. I was making a statement about it not being possible to write it in any justifiable amount of resources.
My company would fail if I spent months on this, but a week to harden init is for sure worth it.
Dylan16807 | 12 hours ago
If a single person can do the full job to full standards in a few months, then the whole idea of it being too annoying for a human to ever do falls apart. That's not that expensive. Your company can't spare you, sure, but most companies could handle that price.
And even with the adjustment to being something it's easy to hire someone for, that's still ballparks away from "in a week for a few dollars". Nearly every coding project is impossible for humans by that standard.
lrvick | 11 hours ago
Exactly, which is why this small project, and the other dozen projects that need to be written to the same standards to make the final end result OS, would just simply not happen otherwise.
No small FOSS org can afford to spend person-years holding standards this high, which is why in practice most orgs just cut a ton of corners and pull in tons of dependencies no one will ever review and the attack surface that comes with them.
LLMs allow experienced engineers to get 10x as much done in the same time, and now that this is possible few will be willing to pay engineers to do it at 1x speeds again.
Just like once assemblers became normalized, few orgs would let people write all their assembly by hand anymore except for very small high performance sensitive portions.
Software engineering is not going away. It is just accelerating because we were able to move our attention up a layer again.
Dylan16807 | 9 hours ago
Please use very different wording from "impossible" or "can't be done" then. It not being price-competitive is a far weaker claim.
lrvick | 9 hours ago
Dylan16807 | 8 hours ago
My request for better wording stands. The context was not enough to make things clear.
uecker | 11 hours ago
lrvick | 10 hours ago
Please, report me to Codeberg. It will be amusing.
I personally know a Codeberg admin that works with LLMs as a tool for things like this which are actual engineering and not vibe coding.
uecker | 10 hours ago
lrvick | 10 hours ago
I actually read the terms and my community and I comply with them.
If you think I do not, you are free to report me.
> If your work fits into these cases, it is unlikely that you are affected at all:
> Projects who have an active community that cares about and maintains the software
> Projects with a significant pre-LLM history
uecker | 2 hours ago
minitech | 15 hours ago
lrvick | 15 hours ago
That is the real difference.
couchand | 15 hours ago
lrvick | 15 hours ago
I wrote the first version by hand in C, and then rust.
The LLM was able to rewrite it with no dependencies implementing the low level system calls and test suites that would be a nightmare to do by hand.
The best use of human time for something like this is code review.
gblargg | 14 hours ago
shadowgovt | 14 hours ago
I'm working on a validation tool with a coworker right now. We are meat-proxying it into existence, using less than two exchanges per day of his LLM validator and my LLM authoring engine (we could consolidate, but he's got some context I don't have and it would take more than zero engineering effort to ship it to me when PR messages through GitHub are a good-enough channel).
If not for the LLM, the tool wouldn't exist at all because we can't justify dedicating much of any engineering time to building it. But LLMs drive the engineering cost very close to zero, enabling the authorship of code that would never find engineering cycles otherwise.
nicebyte | 14 hours ago
These are standards of a normal university operating systems course (assuming a university worth its salt anyway). Ridiculous to present it as some kind of feat that was "impossible" or even that rare.
lrvick | 14 hours ago
nicebyte | 14 hours ago
this https://codeberg.org/lrvick/nit/src/branch/main/src/system/r...
is textbook definition of boilerplate. you need to be patient to cross-reference syscall tables but certainly not smart.
Again, students learning about operating systems do similar things as a normal part of their education process.
I'm not saying LLMs wouldn't help with this (in fact, this is the type of thing that becomes pretty much a no-op with them) but the notion that this is "impossible by any realistic standards" is risible, and if you have actually contributed to the kernel like you say, you should know better.
lrvick | 12 hours ago
https://codeberg.org/lrvick/nit/src/branch/main/tests/kernel...
That is the point. Getting to results that satisfy security and functionality requirements in much less time.
Also, I was able to generate a bare minimal and hardened kernel config by using the LLM to write a tool to trace all kernel calls. Something that I would have normally been forced to punt on that LLMs made easy to justify.
LLMs are just as Linus Torvalds likes to say "Auto complete on steroids".
fwip | 11 hours ago
How often does "this table of constants was transcribed incorrectly" even come up as a bug class? Is it worth writing a buggy C parser for a failure mode that doesn't happen? Do you expect that Linux is going to change their constant definitions out from under you?
lrvick | 10 hours ago
> Do you expect that Linux is going to change their constant definitions out from under you
Unlikely but I have been surprised before. Hardcoding to pretty much any modern kernel source version forever for this narrow use case is probably fine.
The goal is about helping me and a reviewer quickly form confidence none of the magic numbers are mistaken or malicious in an automated way against the source of truth.
yjftsjthsd-h | 14 hours ago
What does that mean? Are you building a system without a root user or something?
lrvick | 12 hours ago
I design confidential compute systems.
creesch | 14 hours ago
That makes you a) more unique than you realize b) not all that relevant in the context of the article.
a) Because a lot of people tend to lean towards use that absolutely leads to atrophying of skills and knowledge. b) well the article does explain the criteria it used fairly well and your project would not show up there. Well, also because your project isn't an android app.
lrvick | 13 hours ago
Android apps on the other hand, LLMs have enough training data to rapidly generate whatever you can think of with much less time required for an experienced engineer to refine it into a quality result.
I am mostly pushing back on the notion of dismissing software as low quality just because the author used LLMs.
It is frankly amazing we can point an LLM at a proprietary ad-ridden app and get to an ad-free open source and privacy respecting clone in a weekend now.
creesch | 5 hours ago
Its not that hard to imagine that apps made with heavily LLM involvement suffer in quality. The article already goes into it a bit. I get the strong feeling that you are failing to approach this from anything else than your own perspective and how you use LLMs. Which, given the setup you are describing is already far from the norm.
Zooming out a bit further I think we can generally break up LLM usage in roughly two distinct ways of using LLMs. Or maybe more accurately there are two sides of a spectrum with a lot of area in between them:
1. Let LLMs do the majority of the heavy lifting and generate the output. Providing an LLM with some basic info, what you are looking for and let the LLM do most of the work for you. 2. Using LLMs as a tool external to the process where you are still doing most of the work.
The first way of using LLMs using agentic workflows, code harnesses, etc *can* produce good quality. As long as there is still a human in the mix who a) has the prerequisite knowledge to validate what the LLMs have generated b) takes their due diligence at key points in the process to actually make sure input is of high enough quality and validates the output.
You have decades of knowledge and experience you are now applying to rigidly structure how these LLMs work. Which, yes again, I believe gives you personally pretty good results, no doubt about that. But, many people do not have that experience to begin with. And even for those that do, using the first method comes with a risk that boils down to simple psychology, our minds are wired to take the easy approach wherever they can. Which turns the first way of using LLMS from a efficient way of using them to a lazy way of using them.
In my personal experience a lot of people who start using LLMs sort of drift to the lazy approach very easily. Because it is very convenient and easy to slowly hand over more thinking to an LLM. The latter I have seen happen all too often around me. An alarming increase of lazy non critical use of LLM tools by people who should know better. People who might have been a bit slower before but delivered excellent results now have started delivering trash. Code spanning dozens of line trying to solve something that should only take one line. Code that completely ignores and conventions or design paradigms put in place. Code that goes directly against security practices. Suddenly downgraded dependency versions (because the models training data doesn't include the latest version).
As I said, I see this as a spectrum and the more someone leans towards the first use case combined with "lazy usage" the more signs there will be in a repository that this is the case. The amount of commits, the release cadence and various other things. Which, again, the article also makes a case for and is again why your repository is irrelevant to the whole discussion. Why? Because your repository doesn't show these signs.
lrvick | 2 hours ago
LLMs help experienced engineers write quality code faster and it helps amateurs write shit code faster. Problem is there are simply way more of the latter. Even so I detest engineering being judged by the tools used, instead of the work itself being judged on its own merits because there are exceptions to every rule. I would like to teach people to use these tools responsibly, instead of sending a message that use of these tools is automatically thoughtless slop.
Like, most people I know that use Vim write much higher quality code on average than the people that use VSCode as tool preferences often reflect experience, but not always! It would still be unfair to write off someone's code sight unseen because they used VSCode as there are always exceptions to the rule.
autuni | an hour ago
LLMs also helps inexperienced engineers (or people who do not have any own software engineering skills themselves) to write horrible code in less time.
Of course you also can't trust everything human developers publish either, but the issue is amplified by LLMs.
Dwedit | 19 hours ago
shevy-java | 19 hours ago
Well, that is true, but I have seen how one japanese developer, who has had low english language skills, suddenly writes epic text. So it is clear that he is using AI when interacting with other people. This is a problem because a lot of it is just AI slop fake interacting. I don't want to interact through any AI filter; it means the other human being is not interested in communicating with me, so why would I want to communicate with an AI slop controlled human? It makes no sense.
As for software: IMO documentation is very important. Many projects lack good documentation. This is valid for both AI slop created projects and real humans created projects. There are too many software developers who hate writing documentation. I don't want to use such software anymore.
Unfortunately AI slop documentation often sucks too. They just spam you down with words that mean nothing. And are very verbose. This is also totally useless.
It seems as if software designers indeed fail hard in the last years. Quality standards used to be higher in, say, 2005 to 2010, compared to now and the last five years.
> Their mere existence makes educating yourself and going on fun side projects much less rewarding.
It's not just AI though. Look at Google search. Use it. It is totally useless now. Google crippled it deliberately some years ago. Now you'd think alternatives are better, but they also suck. DuckDuckGo? Qwant? They, oddly enough, manage to be even worse crap than Google search, which is amazing. Crapification has become the default now.
r0ze-at-hn | 17 hours ago
Everything these days is shell scripts! They are even selling them like they have the same value as a C program! csh will never be real C. The kids have no idea what a stack is or how to optimize system calls. The performance is awful and they barely work! The way scripts spawn processes can bring a multi-user terminal system to its knees!
I mean, sure, I write some scripts now and then, but the real logic always goes in C. And just the other day my boss gave me a shell script he coded over the weekend and said putting it in production should be easy!
Over the weekend I was checking out BSD and I couldn't believe the number of scripts I found. I fear for the future of my career and computer science in general. Our field might die out before it can even get off the ground.
tonymet | 15 hours ago
The authors are sharing their creativity, and spending time & money to publish apps with a free license.
I have disdain for how entitled open source consumers are. Before AI, demanding free accounts, support , resources, traffic, cpu -- after AI demanding bespoke hand-written code be given out for free.
gblargg | 14 hours ago
t1234s | 14 hours ago
creesch | 14 hours ago
More here:
- https://f-droid.org/en/docs/Inclusion_Policy/
- https://f-droid.org/en/docs/Security_Model/
mwkaufma | 7 hours ago
brandonp2412-re | 3 hours ago