I started out very sceptical. When Claude Code landed, I got completely seduced — borderline addicted, slot machine-style — by what initially felt like a superpower. Then I actually read the code. It was shockingly bad. I swung back hard to my earlier scepticism, probably even more entrenched than before.
Then something shifted. I started experimenting. I stopped giving it orders and began using it more like a virtual rubber duck. That made a huge difference.
It’s still absolute rubbish if you just let it run wild, which is why I think “vibe coding” is basically just “vibe debt” — because it just doesn’t do what most (possibly uninformed) people think it does.
But if you treat it as a collaborator — more like an idiot savant with a massive brain but no instinct or nous — or better yet, as a mech suit [0] that needs firm control — then something interesting happens.
I’m now at a point where working with Claude Code is not just productive, it actually produces pretty good code, with the right guidance. I’ve got tests, lots of them. I’ve also developed a way of getting Claude to document intent as we go, which helps me, any future human reader, and, crucially, the model itself when revisiting old code.
What fascinates me is how negative these comments are — how many people seem closed off to the possibility that this could be a net positive for software engineers rather than some kind of doomsday.
Did Photoshop kill graphic artists? Did film kill theatre? Not really. Things changed, sure. Was it “better”? There’s no counterfactual, so who knows? But change was inevitable.
What’s clear is this tech is here now, and complaining about it feels a bit like mourning the loss of punch cards when terminals showed up.
[0]: https://matthewsinclair.com/blog/0178-why-llm-powered-progra...
Unless you can run the LLM locally, on a computer you own, you are now completely dependent on a remote centralized system to do your work. Whoever controls that system can arbitrarily raise the prices, subtly manipulate the outputs, store and do anything they want with the inputs, or even suddenly cease to operate. And since, according to this article, only the latest and greatest LLM is acceptable (and I've seen that exact same argument six months ago), running locally is not viable (I've seen, in a recent discussion, someone mention a home server with something like 384G of RAM just to run one LLM locally).
To those of us who like Free Software because of the freedom it gives us, this is a severe regression.
This is a thoughtful article. Thanks `tptacek
My LLM use is: 1 - tedious stuff; web pages interacting with domain back end. 2 - domain discovery.
In a recent adventure, I used Claude 4 to tease out parameters in a large graph schema. This is a combination of tedium and domain discovery (it's not my graph and I'm not a domain expert). In the first day, Claude uncovered attributes and relations no other LLM or Google search uncovered. And it worked!! The next day, I allowed it to continue. After a bit, results didn't pass the sniff test.
I checked into details of Claude's thinking: it decided to start making up schema attributes and inventing fallback queries on error with more made up attributes. It was "conscious" of its decision to do so. By the time I caught this, Claude had polluted quite a bit of code. Sure, plenty of well placed git commits helped in rolling back code...but it's not quite that simple..over the many git commits were sprinkled plenty of learnings I don't want to toss. It took another two days of carefully going through the code to pull out the good stuff and then roll things back. So now I'm at day five of this adventure with cleaned up code and notes on what we learned.
I suspect continual improvements on tooling will help. Until then, it's a short leash.
I used it about 15 minutes ago, to help me diagnose a UI issue I was having. It gave me an answer that I would have figured out, in about 30 minutes, in about 30 seconds. My coding style (large files, with multiple classes, well-documented) works well for AI. I can literally dump the entire file into the prompt, and it can scan it in milliseconds.
I also use it to help me learn about new stuff, and the "proper" way to do things.
Basically, what I used to use StackOverflow for, but without the sneering, and much faster turnaround. I'm not afraid to ask "stupid" questions -That is critical.
Like SO, I have to take what it gives me, with a grain of salt. It's usually too verbose, and doesn't always match my style, so I end up doing a lot of refactoring. It can also give rather "naive" answers, that I can refine. The important thing, is that I usually get something that works, so I can walk it back, and figure out a better way.
I also won't add code to my project, that I don't understand, and the refactoring helps me, there.
I have found the best help comes from ChatGPT. I heard that Claude was supposed to be better, but I haven't seen that.
I don't use agents. I've not really ever found automated pipelines to be useful, in my case, and that's sort of what agents would do for me. I may change my mind on that, as I learn more.
For me, a highly productive but boring use of LLMs for code is that they excel at providing midwit “best practice” solutions to common problems. They are better documentation than the documentation and can do a lot of leg work e.g. Linux syscall implementation details. My application domains tend to require more sophisticated solutions than an LLM can provide but they still save a lot of rote effort. A lot of software development exists almost entirely in the midwit zone.
Much more interesting, they are decent at reducing concepts in literature to code practice for which there are no code examples. Google and StackOverflow turn up nothing. For example, I’ve found them useful for generating specialized implementations of non-Euclidean computational geometry algorithms that don’t really exist in the wild that I’ve ever seen. This is a big win, it literally turns months of effort into hours of effort.
On the other hand, I do a lot of work with algorithms that don’t exist in literature, never mind public code, with extremely performance-engineered implementations. There is an important take away from this too: LLMs are hilariously bad at helping with this but so are human software developers if required to do the same thing with no context.
Knowledge for which there is little or no training data is currently a formidable moat, both for LLMs and humans.
Why haven't we seen an explosion of new start-ups, products or features? Why do we still see hundreds of bug tickets on every issue tracking page? Have you noticed anything different on any changelog?
I invite tptacek, or any other chatbot enthusiast around, to publish project metrics and show some actual numbers.
Most completely reasonable objections to LLMs were totally dismissed.
I've used LLMs to crank out code for tedious things (like generating C-APIs and calling into poorly documented libraries) but I'm not letting them touch my code until I can run it 100% locally offline. Would love to use the agentic stuff but from what I've heard it's still too slow to run on a high end workstation with a single 4080.
Or have things got better lately, and crucially is there good VisualStudio integration for running local agents / LLMs?
If you let it run in the "write my code for me" mode, and ask it to fix some mistake it made, it will always add more code, never remove any. In my experience, in the end the code just ends up so brittle that the LLM will soon get stuck at a point that it never manages to overcome some mistake, no matter how many times it tries.
Has anyone managed to solve this?
> Part of being a senior developer is making less-able coders productive, be they fleshly or algebraic. Using agents well is both a both a skill and an engineering project all its own, of prompts, indices, and (especially) tooling. LLMs only produce shitty code if you let them.
A junior developer often has negative value to a team, because they're sapping the time of more senior developers who have to help train them, review code, fix mistakes, etc. It can take a long while to break even.
The raw cost of Cursor's subscription is surely dwarfed by your own efforts, given that description. The actual calculous here should be the cost to corral Cursor, against the value of the code it generated.
Videogame speed running has this problem solved. Livestream your 10x engineer LLM usage, a git commit annotated with it's prompt per change. Then everyone will see the result.
This doesn't seem like an area of debate. No complicated diagrams required. Just run the experiment and show the result.
The dialog around it is so adversarial it's been hard figuring out how to proceed until dedicating a lot of effort to diving into the field myself, alone, on my personal time and learned what's comfortable to use it on.
When people are sick of AI, this is what they are talking about. AI hype people ignore this perspective each and every time.
It doesn't matter how great your paper Mill's paper is, if you're dumping PCBs in the river, people are going to quite rightly get pissed off.
I take serious issue with the "but you have no idea what the code is" rebuttal, since it - to me - skims over the single largest issue with applying LLMs anywhere where important decisions will be made based on their outputs.
To quote from the article:
People complain about LLM-generated code being
“probabilistic”. No it isn’t.
It’s code. It’s not Yacc output. It’s knowable. The LLM
might be stochastic. But the LLM doesn’t matter. What
matters is whether you can make sense of the result, and
whether your guardrails hold.
Reading other people’s code is part of the job. If you can’t metabolize the
boring, repetitive code an LLM generates: skills issue! How are you handling the
chaos human developers turn out on a deadline?
EDIT: formatting
https://fly.io/blog/vscode-ssh-wtf/
Where he dunks on how SSH access works in VSCode. I don't know. The code and architecture behind this feature may well be bananas, but gets the work done. Sounds like a clear case of mediocority. I wonder how does he reconcile those two articles together.
For me this is more of a clickbait. Both of the articles. With that in mind, if I am nuts for being sceptical of LLMs, I think it is fair to call the author a clickbaiter.
The LLM has one job, to make code that looks plausible. That's it. There's no logic gone into writing that bit of code. So the bugs often won't be like those a programmer makes. Instead, they can introduce a whole new class of bug that's way harder to debug.
Question: If everyone uses AI to code, how does someone become an expert capable of carefully reading and understanding code and acting as an editor to an AI?
The expert skills needed to be an editor -- reading code, understanding its implications, knowing what approaches are likely to cause problems, recognizing patterns that can be refactored, knowing where likely problems lie and how to test them, holding a complex codebase in memory and knowing where to find things -- currently come from long experience writing code.
But a novice who outsources their thinking to an LLM or an agent (or both) will never develop those skills on their own. So where will the experts come from?
I think of this because of my job as a professor; many of the homework assignments we use to develop thinking skills are now obsolete because LLMs can do them, permitting the students to pass without thinking. Perhaps there is another way to develop the skills, but I don't know what it is, and in the mean time I'm not sure how novices will learn to become experts.
I think in code.
To me, having to translate the into natural language for the LLM to translate it back into code makes very little sense.
Am I alone in this camp? What am I missing?
> If you build something with an LLM that people will depend on, read the code. In fact, you’ll probably do more than that. You’ll spend 5-10 minutes knocking it back into your own style.
with Joel Spolsky's fundamental maxim:
> It’s harder to read code than to write it.
https://www.joelonsoftware.com/2000/04/06/things-you-should-...
> The AI has suggested a solution, but the added code is arguably useless or wrong. There is a huge decision space to consider, but the AI tool has picked one set of decisions, without any rationale for this decision.
> [...]
> Programming is about lots of decisions, large and small. Architecture decisions. Data validation decisions. Button color decisions.
> Some decisions are inconsequential and can be safely outsourced. There is indeed a ton of boilerplate involved in software development, and writing boilerplate-heavy code involves near zero decisions.
> But other decisions do matter.
(from https://lukasatkinson.de/2025/net-negative-cursor/)
Proponents of AI coding often talk about boilerplate as if that's what we spend most of our time on, but boilerplate is a cinch. You copy/paste, change a few fields, and maybe run a macro on it. Or you abstract it away entirely. As for the "agent" thing, typing git fetch, git commit, git rebase takes up even less of my time than boilerplate.
Most of what we write is not highly creative, but it is load-bearing, and it's full of choices. Most of our time is spent making those choices, not typing out the words. The problem isn't hallucination, it's the plain bad code that I'm going to have to rewrite. Why not just write it right myself the first time? People say "it's like a junior developer," but do they have any idea how much time I've spent trying to coax junior developers into doing things the right way rather than just doing them myself? I don't want to waste time mentoring my tools.
Sure, there might be a way to change your workflow and never ever open a secret file with those editors, but my point is that a software that sends your data without your consent, and without giving you the tools to audit it, is a no go for many companies, including mine.
On the other hand, if you slip up and give it too much to chew on or just roll bad RNG, it will spin itself into a loop attempting many variations of crap, erasing and trying over, but never actually coming closer to a correct solution, eventually repeating obviously incorrect solutions over and over again that should have been precluded based on feedback from the previous failed solutions. If you're using a SOTA model, you can easily rack up $5 or more on a single task if you give it more than 30 minutes of leeway to work it out. Sure, you could use a cheaper model, but all that does is make the fundamental problem worse - i.e. you're spending money but not actually getting any closer to completed work.
Yes, the models are getting smarter and more efficient, but we're still at least a decade away from being able to run useful models at practical speeds locally. Aggressively quantized 70b models simply can't cut it, and even then, you need something like 10k tps to start building LLM tools that can overcome the LLM's lack of reasoning skills through brute force guess and check techniques.
Perhaps some of the AI skeptics are a bit too harsh, but they're certainly not crazy in the context of breathless hype.
Claude, Gemini, Copilot and and ChatGPT are non-starters for privacy-minded folks.
So far, local experiements with agents have left me underwhelmed. Tried everything on ollama that can run on my dedicated Ryzen 8700G with 96GB DDR5. I'm ready to blow ~10-15k USD on a better rig if I see value in it but if I extrapolate current results I believe it'll be another CPU generation before I can expect positive productivity output from properly securely running local models when factoring in the setup and meta.
I haven't found a way to use it that makes me develop faster.
The articles talks about "tedious code." If you need to generate a large static value table or something, then OK an LLM might give you a really fast result and cut through the tedium. Most of us were already writing short scripts to do that. I'm open to the possibility that an LLM can do it faster. But it's such a rare requirement that the productivity gains are truly negligible here even if they can. And in those cases, it's obvious what the repetitive task needs to be. I often find myself writing the code by hand to be quicker than coming up with a prompt to get it to write the code that I then need to review for correctness.
The article then mentions scaffolding. Things like "bookkeeping" when it comes to creating and setting up a new repo (whatever he means by that). This is why I have, historically, been a big fan of frameworks and generators. Point being, this is already a solved problem and I haven't found a way to further improve the state of this world with LLMs. LLMs might be an alternate tool that work just as well. But they haven't made my existing daily workflow any faster. Setting up new repos is also something that is done so rarely that even if an LLM netted a 100% increase in efficiency, it wouldn't really impact much.
I am an AI "skeptic" but I'm not a naysayer. I do use LLMs regularly. I just don't use them for developing code because I have yet to find a problem that they solve for me. Don't get me wrong, there are problems that they can solve... I just haven't come across any solutions to previously-unsolved problems. Meaning I can swap an existing solution for an LLM-based one... and it is a valid solution... but I don't observe any increase in productivity from doing so. The existing solution was already working fine.
I am genuinely looking forward to the day when this changes. When I identify a single existing problem without an existing solution that LLMs solve for me when developing software. I just have yet to come across one.
It was obviously huge. You could see it taking off. Yet a lot of people proudly displayed ignorance and backed each other up on it to the point that discussion around the topic was often drowned out by the opposition to change. Now today it takes minutes of playing with ai coding agents to realise that it’s extremely useful and going to be similarly huge.
Resistance to change is not a virtue!
In no way can AI be used for anything larger than generating singular functions or anything that would require writing to or modifying multiple files.
Technically you might be able to pull off having AI change multiple files for you in one go, but you'll quickly run into sort of "Adobe Dreamviewer" type of issue where your codebase is dominated by generated code which only the AI that generated it is able to properly extend and modify.
I remember when Dreamviewer was a thing, but you essentialyl had to make a choice between sticking with it forever for the project or not using it at all, because it would basically convert your source code into it's own proprietary format due to it becoming so horribly messy and unreadable.
Regardless, AI is absolutely incredible and speeds up development by a great deal, (even) if you only use it to generate small snippets at the time.
AI is also an absolute godsend for formatting and converting stuff from anything and to anything - you could e.g. dump your whole database structure to Gemini and ask it to generate an API against it; big task, but since it is basically just a conversion task, it will work very well.
1. Input is an 256x256 pixel elevation map stored as a greyscale .png file, and a minimum and maximum elevation. A pixel value of 0 corresponds to the minimum elevation, and a pixel value of 255 corresponds to the maximum elevation. Read in the .png file and the elevation limits. Then construct a 256x256 floating point array of heights. From that array, construct a triangle mesh with X and Y dimensions 0..255. Perform a mesh reduction operation on the triangle mesh to reduce the number of triangles. Mesh reduction must not generate holes in the mesh. From the reduced mesh, generate a glTF file where the UV parameters run from 0.0 to 1.0 along the X and Y axes.
2. Given four glTF files constructed as above, corresponding to four quadrants of a larger square, construct a single 511x511 mesh which combines all four input meshes to cover a larger area. Because the input meshes are 0..255, not 0..256, there will be gaps where the four quadrants meet. Fill those gaps with reasonable triangles. Perform a mesh reduction as above. From the reduced mesh, generate a glTF file where the UV parameters run from 0.0 to 1.0 along the X and Y axes.
Rust code is preferred; Python code is acceptable.
So, what service should I sign up for?
Also the emphasis on greenfield projects? Starting is by FAR the easiest part. That's not impressive to me. When do we get to code greenfield for important systems? Reminds me of the equally absurd example of language choice. You think you get to choose? What?
Imagine all the code these agents are going to pump out that can never be reviewed in a reasonable time frame. The noise generated at the whim of bike-shedding vibe coders is going to drown all the senior reviewers soon enough. I'll call that Cowboy Coders on Steroids. Anyone with skills will be buried in reviews, won't have time for anything else, and I predict stricter code gen policies to compensate.
Well, except that in order to fix that 1% you'd need to read and understand whatever the LLM did and then look for that 1%. I get the shills just thinking about this, whether the original programmer was human or not. I'd rather just write everything myself to begin with.
Best use of evidence is deductive: Lots of code is tedious and uninteresting -> LLMs are fast at generating lots of tedious code -> LLMs help productivity.
Weakest part of the argument: The list of rebuttals doesn't have an obvious organization to it. What exactly is the main argument they're arguing against?
It's not stated outright but because the post is bookended by references to 'those smarter than me', I think this is an argument against the shaming of developers using (and loving) LLM tools.
Which I think is fair.
Overall, the post did not add anything to the general discussion. But the popularity of the author (and fly.io posts) may make it a beacon for some.
On 'just read the code' - all well and good. Of course this implies insisting on team members who can read the code, will read the code and are empowered to read the code.
Otherwise orgs will try to hire pure vibe coders who aren't interested in that and only allow time for literally just instructing agents to generate code because it sounds cheaper and execs don't understand the nuance so long as it looks like product is shipping - until it all blows up and the one standing senior developer on hand is supposed to fix a prod issue buried in millions of lines of vibe reviewed code ASAP.
On 'but it's cheaper than a junior': cloud hosted LLM systems are currently massively subsidised to an absurd degree. The cost side of things is all smoke and mirrors geared towards accelerated market adoption at all costs. It's not a profitable enterprise at the model development level.
At some point that AI economy is going to expect to make that money back, and future (especially near-future) hardware advancements don't explain where all of that is going to come from.
I do not have unlimited funds to plug in some token and burn a bunch of money when writing code.
I am gpu poor. I'm lucky that 8gb vram can run the smallest models. But the output is so poor that I lose out to anyone using a hosted service.
If anything this article shows that building great programs is less democratized than it once was.
It's the complete opposite of OP's main language GO, as he says:
> Go has just enough type safety, an extensive standard library, and a culture that prizes (often repetitive) idiom. LLMs kick ass generating it.
Python is an interpreted dynamically typed language and the static type checkers are not there yet (most popular 3rd parties libraries have no type hints for example). Also it allows for many different programming styles that the LLMs struggle to choose from.
1. Every extra line of code is much more risky. 2. It's much harder to verify the LLM's code.
On the other hand I think rust will be in a good place in regards to LLMs in the next few years thanks to the robustness of the language and the quality of its diagnostic messages. Those 2 attributes should compound very well.
You still need to read this tedious code to verify that it actually does what you want it to do. Given this, I'd much rather prefer to write the tedious code myself than having to make sense of someone else's tedious code.
I doubt that. First, human attention and speed is very limited. Second, when I see something, I am already predisposed to assume that it is right (or at the very least, my subsequent inquiries are extremely narrow and anchored around the solution I have seen presented to me.)
This 16-minute, expletive-filled, edgy-old-man-trying-too-hard-to-be-cool article could easily be dismissed as yet another AI creed that somehow found its way to the top of the HN front page.
But I don't want to make the claim lightly, so I did an experiment.
I signed up for copilot pro, and have been using their 'edit' feature. This is more than just their auto complete.
I set myself a goal to create a playable web game of classic frogger.
It took 4 hours with copilot "edit" and my full attention.
I didn't write a single line of code, but I did ask it to refactor and gave it a project description.
I suspect this would have taken me 4 days full time to get to this level.
Try it out:
> But it is an inherently plagiarist technology
Was
> Developers frequently engage in copyright infringement, and so will I, so unless if you're a lawyer, shove it up your ass
"I am a bad person so I get to continue being bad" is not the gotcha you think it is, Patrick.
I would jump off a bridge before I accepted that as my full-time job.
I've been programming for 20+ years and I've never wanted to move into management. I got into programming because I like programming, not because I like asking others to write code on my behalf and review what they come up with. I've been in a lead role, and I certainly do lots of code review and enjoy helping teammates grow. But the last fucking thing I want to do is delegate all the code writing to someone or something else.
I like writing code. Yes, sometimes writing code is tedious, or frustrating. Sometimes it's yak-shaving. Sometimes it's Googling. Very often, it's debugging. I'm happy to have AI help me with some of that drudgery, but if I ever get to the point that I feel like I spend my entire day in virtual meetings with AI agents, then I'm changing careers.
I get up in the morning to make things, not to watch others make things.
Maybe the kind of software engineering role I love is going to disappear, like stevedores and lamplighters. I will miss it dearly, but at least I guess I got a couple of good decades out of it. If this is what the job turns into, I'll have to find something else to do with my remaining years.
- all creatives are flat against it because it’s destroying their income streams and outright stealing their intellectual property
- some technical leaders are skeptical because early returns were very bad and they have not updated their investigations to the latest tools and models, which are already significantly ahead of even six months ago
- a tech concern is how do we mentor new developers if they don’t know how to code or develop logic. LLMs are great IF you already know what you’re doing
- talent is deeply concerned that they will be reduced and replaced, going from high paying careers to fast food salaries
We have a lot of work to balance productivity with the benefits to society. “Let them eat cake,” is not going to work this time either.
The problems I have with the stuff relating to MCP is that the tech around it is developing so fast that it's hard for outsiders to catch up with what the best working setup is, for example.
What would you do, for example, if you want to selfhost this?
- which models (qwen ai coder?)
- which api (with ollama? Bolt? Aider? Etc)
- how to integrate PRs with a local gitlab/gogs/forgejo instance? Do you need another MCP agent for git that does that?
- which hardware dependencies to run it?
I am currently trying to figure out how to implement a practical workflow for this. So far I'm using still a synchronous MCP agent setup where it basically runs on another machine in the network because I have a too unperformant laptop to work with.
But how would I get to the point of async MCP agents that can work on multiple things in my Go codebases in parallel? With the mentioned PR workflows so that I can modify/edit/rework before the merges?
The author makes a lot of claims and talks always about that their opponents in the argument are not talking about the same thing. But what exactly is the same thing, which is reproducible locally for everyone?
Am I the only one who remembers when that was the stuff of science fiction? It was not so long ago an open question if machines would ever be able to transcribe speech in a useful way. How quickly we become numb to the magic.
Above pulled from A Brief History of the Woodworking Plane [0]. A woodworking tool that has evolved over 2,000 years. Now there are electric planers, handheld electric planers and lots of heavy machinery that do the same thing in a very automated way. If a company is mass producing kitchen cabinets, they aren't hand planing edges on boards, a machine is doing all that work.
I feel like with AI we are on the cusp of moving beyond a "Golden age" and into an "industrial age" for coding, where it will become more important to have code that AI understands vs. something that is carefully crafted. Simple business pressure will demand it (whether we like it or not).
^ A comment I made just yesterday on a different thread.
For software developers AI is like the cabinet maker that gets a machine to properly mill and produce cabinet panels, sure you can use a hand plane to do that but you're producing a very different product and likely one that not many people will care about, possibly not even your employer when they see all the other wood shops pumping out cabinetry and taking their market share.
[0] https://www.handplane.com/879/a-brief-history-of-the-woodwor...
My mind doesn't develop a mental model of that code, I don't know where the relevant parts are, I can't quickly navigate through it and I have to reach the LLM for every small change.
Which is why I like Copilot style editing more than agents as a working model but agents are just so much more powerful and smarter thanks to everything available to them.
Point being: there's some real power in these tools, but if the ground is not already well-trodden, they risk creating a huge time sink that could be better served just learning to do the thing yourself.
It really does feel like I've gone from being 1 senior engineer to a team that has a 0.8 Sr. Eng, 5 Jrs. and one dude that spends all his time on digging through poorly documented open source projects and documenting them for the team.
Sure I can't spend quite as much time working on hard problems as I used to, but no one knows that I haven't talked to a PM in months, no one knows I haven't written a commit summary in months, it's just been my AI doppelgangers. Compared to myself a year ago I think I now PERSONALLY write 150% more HARD code than I did before. So maybe, my first statement about being 0.8 is false.
I think of it like electric bikes, there seems to be indication that people with electric assist bikes actually burn more calories/spend more time/go farther on an electric bike than those who have manual bikes https://www.sciencedirect.com/science/article/abs/pii/S22141....
Maybe I'm doing it wrong, but I seem to have settled on the following general algorithm:
* ask the agent to green-field a new major feature.
* watch the agent spin until it is satisfied with its work.
* run the feature. Find that it does not work, or at least has major deficiencies [1]
* cycle through multiple independent iterations with the agent, doing something resembling "code review", fixing deficiencies one at a time [2]
* eventually get to a point where I have to re-write major pieces of the code to extract the agent from some major ditch it has driven into, leading to a failure to make forward progress.
Repeat.
It's not that the things are useless or "a fad" -- they're clearly very useful. But the people who are claiming that programmers are going to be put out of business by bots are either a) talking their book, or b) extrapolating wildly into the unknown future. And while I am open to the argument that (b) might be true, what I am observing in practice is that the rate of improvement is slowing rapidly, and/or the remaining problems are getting much harder to solve.
[1] I will freely grant that at least some of these major deficiencies typically result from my inability / unwillingness to write a detailed enough spec for the robot to follow, or anticipate every possible problem with the spec I did bother to write. T'was ever thus...
[2] This problem is fractal. However, it's at least fun, in that I get to yell at the robot in a way that I never could with a real junior engineer. One Weird Fact about working with today's agents is that if you threaten them, they seem to do better work.
I do use the AI tools to some extent, if for no other reason than that they are currently the path of least resistance, and google+friends have lately played themselves out of the game.
He is probably right we should get acquainted with using agents before dismissing it :-).
You forgot the screeds against the screeds (like this one)
I'm not saying that AI can't make you productive, it's just that these claims are really hard to verify. Even the recently posted Cloudflare OAuth worker codebase took ~3 months to release (8 Mar - 20 May), producing a single file with >2k lines. Is that going to be harder to maintain than a codebase with a proper project structure that's easily parseable by a human?
Side note of interest, from Wikipedia: "Flying Machines Which Do Not Fly" is an editorial published in the New York Times on October 9, 1903. The article incorrectly predicted it would take one to ten million years for humanity to develop an operating flying machine.
VCs are already doubting if the billions invested into data centers are going to generate a profit [1 and 2].
AI companies will need to generate profits at some point. Would people still be optimistic about Claude etc if they had to pay say $500 per month to use it given its current capabilities? Probably not.
So far the only company generating real profits out of AI is Nvidia.
[1] https://www.goldmansachs.com/insights/articles/will-the-1-tr...
[2] https://www.nytimes.com/2025/06/02/business/ai-data-centers-...
This entire section reads like, oddly, the reverse of the "special pleading" argument that I usually see from artists. Instead of "Oh, it's fine for other fields, but for my field it's a horrible plagiarism machine", it's the reverse: "Oh, it's a problem for those other fields, but for my field get over it, you shouldn't care about copyright anyway".
I'm all for eliminating copyright. The day I can ignore the license on every single piece of proprietary software as I see fit, I'll be all for saying that AIs should be able to do the same. What I will continue to complain about is the asymmetry: individual developers don't get to violate individual licenses, but oh, if we have an AI slurp up millions of codebases and ignore their licenses, that's fine.
No. No, it isn't. If you want to ignore copyright, abolish it for everyone. If it still applies to everyone else, it should still apply to AIs. No special exceptions for mass-scale Open Source license violations.
How does this section fit in with the agent section just after? In an agentic model, isn't the merge getting done by either the model or a tool, and the retry-loops on failures would be mostly invisible?
E.g. when using Aider + Gemini Flash 2.5, probably 90% of the changes apply cleanly from my perspective (maybe half actually apply cleanly, the other half after a couple of roundtrips of Aider telling the model that the patch didn't apply). The 10% that only apply partially I usually throw away and redo the prompt, it's really rare that I start merging the code manually.
Neither of these is accurate, but I guess nuanced thinking or considering anything below surface vibes is out these days.
So far after playing with them I'm using them as:
1. A junior intern that can google really really fast and has memorized a large chunk of the Internet and the library, and can do rough first-pass research and dig for things.
2. Autocomplete 2.0 that can now generate things like boilerplate or fairly pedestrian unit tests.
3. Rubber duck debugging where the rubber duck talks back.
4. A helper to explain code, at least for a first pass. I can highlight a huge piece of code and ask it to summarize and then explain and walk me through it and it does a passable job. It doesn't get everything right but as long as you know that, it's a good way to break things down and get into it.
For those things it's pretty good, and it's definitely a lot of fun to play with.
I expect that it will get better. I don't expect it to replace programmers for anything but the most boring mindless tasks (the ones I hate doing), but I expect it to continue to become more and more useful as super-autocomplete and all the other things I listed.
> But AI is also incredibly — a word I use advisedly — important. It’s getting the same kind of attention that smart phones got in 2008, and not as much as the Internet got. That seems about right.
However, I just don't think the AI coding part is that interesting or future-thinking. We're seeing so much more progress in semantic search, tool calling, general purpose uses, robotics, I mean, DeepMind just won a Nobel for goodness' sake.
Don't get me wrong, I use ChatGPT to write all kinds of annoying boilerplate, and it's not too bad at recalling weird quirks I don't remember (yes, even for Rust). But hard problems? Real problems? Zero shot. Novel problems? No way.
> But I’ve been first responder on an incident and fed 4o — not o4-mini, 4o — log transcripts, and watched it in seconds spot LVM metadata corruption issues on a host we’ve been complaining about for months.
I'm going to go ahead and press (X) to doubt on this anecdote. You've had an issue for months and the logs were somehow so arcane, so dense, so unparseable, no one spotted these "metadata corruption issues?" I'm not going to accuse anyone of blatant fabrication, but this is very hard to swallow.
Listen, I also think we're on the precipice of re-inventing how we talk to our machines; how we automate tasks; how we find and distribute small nuggets of data. But, imo, coding just ain't it. Donald Knuth calls computer programming an art, and to rob humanity of effecting not just coding—but any art, I'd argue—would be the most cardinal of sins.
I have to say, my ability to learn Rust was massively accelerated via LLMs. I highly recommend them for learning a new skill. I feel I'm roughly at the point (largely sans LLMs) now where I can be nearly as productive in Rust as Python. +1 to RustRover as well, which I strongly prefer to any other IDE.
Had NFTs simply been deployed as some kind of protocol that could be leveraged for utility rather than speculation, I think the story would be a complete 180. No clue personally how to achieve that, but it feels like it could be done.. except that, too, would have been completely perverted and abused by centralized behemoths, leading to a different but terrible outcome. Can you imagine if all data became non-fungible? Convince all the big identity vendors (Google, Apple, etc) to issue key pairs to users that then get used by media companies to deliver audio and video keyed only to you that's embedded with maybe some kind of temporal steganographic signature that's hard to strip and can be traced back to your key? It's not just cracking AACS once and copying the bytes. It becomes this giant mess of you literally can't access anything without going through centralized authorities anymore. Then build more anti-patterns on top of that lol. Prolly better that it was mostly just monkey JPEGs and rug pulls.
Anyways, I'm so far off topic from what's actually being discussed -- just couldn't help myself from veering into left field.
> Fly.io builds on the work of a tremendous open source infrastructure community. We want open source authors to benefit from their work because we think a healthy, thriving open source ecosystem will help us build better products.
To the fine article:
> Meanwhile, software developers spot code fragments seemingly lifted from public repositories on Github and lose their shit. What about the licensing? If you’re a lawyer, I defer. But if you’re a software developer playing this card? Cut me a little slack as I ask you to shove this concern up your ass.
My CEO friends that overstate the efficacy of LLMs are all nuts.
(I say this as someone who has been working in "AI" as part of Digital transformation roadmaps for SEVEN years and as someone who has used LLM's daily in my workflow for 3 years).
Most of the time I can go faster than these tools if I have confidence in myself and allow the momentum to build up over the course of 20-30 minutes. Every time I tab out to an LLM is like a 5 minute penalty over what I could have done unaided on a good day.
Getting the model prepared to help you in a realistic domain often takes a few minutes of arranging code & comments so that it is forced toward something remotely sane. I'll scaffold out entire BS type hierarchies just so I can throw a //TODO: .... line in the middle somewhere. Without this kind of structure, I would be handling unfiltered garbage most of the time.
It's not that these tools are bad, it's that we need to recognize the true cost of engaging with them. ChatGPT is like a jackhammer. It will absolutely get you through that concrete slab. However, it tends to be quite obnoxious & distracting in terms of its operational principles.
https://www.kaggle.com/competitions/konwinski-prize/
Currently, the #1 spot sits at a score of 0.09, not 0.9. A far cry from being useful. I know that open source models are not as good as closed source, but still, we're a long way from LLMs being good for code on their own.
And that supports OP's point - these tools aren't AGI, they produce trash that needs evaluation, but they're still useful.
It’s like half of the arguments are designed as engagement bait with logical consistency being a distant concern:
> If hallucination matters to you, your programming language has let you down.
This doesn’t even make sense. LLMs hallucinate things beyond simple programming language constructs. I commonly deal with allusions to functions or library methods that would be great if they existed, but the LLM made it up on the spot.
The thing is, the author clearly must know this. Anyone who uses LLMs knows this. So why put such a bizarre claim in the article other than as engagement bait to make readers angry?
There are numerous other bizarre claims throughout the article, like waving away the IP rights argument because some programmers pirate TV shows? It’s all so bizarre.
I guess I shouldn’t be surprised to scroll to the bottom and see that the author is a HN comment section veteran, because this entire article feels like it started as a reasonable discussion point and then got twisted into Hacker News engagement bait for the company blog. And it’s working well, judging by the engagement counts.
Only if you are familiar with the project/code. If not, you were throw into a foreign codebase and have no idea how to tweak it.
The fact that LLMs can generate this kind of output is not incompatible with the OP's argument about how useful they can be when combined with agents and code review. But search is the lowest-friction way for anyone to interact with LLMs currently, and right now it's a pretty poor advertisement.
This really doesn't accord with my own experience. Using claude-code (esp. with opus 4) and codex (with o3) I've written lots of good Rust code. I've actually found Rust helps the AI-pair-programming experience because the agent gets such good, detailed feedback from the compiler that it can iterate very quickly and effectively.
Can it set up great architecture for a large, complex project from scratch? No, not yet. It can't do that in Ruby or Typescript either (though it might trick you by quickly getting something that kinda works in those languages). It think that will be a higher bar because of how Rust front-loads a lot of hard work, but I expect continuing improvement.
Anyway in a couple of months I'll try again. Until that time, I guess the search result summaries are nice?
I guess this presupposes that it is ok for 3rd parties to slurp up your codebase? And possibly (I guess it ostensibly depends on what plan you are on?) using that source code for further training (and generating that same code for others)?
I imagine in some domains this would not be ok, but in others is not an issue.
He implies that if I am talking about some other flavor of AI than he personally uses than I am just not serious— but that’s what they said six months ago and two years ago. These fucking boosters have already blown their credibility.
If you want to prove that AI agents writing code for you is safe then perform a large scale test and show your results. We’re not crazy for doubting you. You are crazy for not doubting yourself.
Personally I find AI coding tools situationally useful. I certainly wouldn't use them to write all my code, but I also think I'd be a fool not to leverage them at all.
They are constantly talking about AI doing all the tedious boilerplate bullshit. Don't get me wrong, some of my code is that too and its not fun. However the pro-AI people talk as if 80% of your day is dealing with that. For me its simply a rare enough occurence that the value proposition isn't that big. If that is the killer app of AI, it just doesn't sound that exciting to me.
If the kind of work this person is doing requires no thought, then of course an LLM will be perfect for it.
This kind of guilt-by-association play might be the most common fallacy in internet discourse. None of us are allowed to express outrage at the bulk export of GitHub repos with zero regard for their copyleft status because some members of the software engineering community are large-scale pirates? How is that a reasonable argument to make?
The most obvious problem with this is it's a faulty generalization. Many of us aren't building large-scale piracy sites of any sort. Many of us aren't bulk downloading media of any kind. The author has no clue whether the individual humans making the IP argument against AI are engaged in piracy, so this is an extremely weak way to reject that line of argument.
The second huge problem with this argument is that it assumes that support for IP rights is a blanket yes/no question, which it's obviously not. I can believe fervently that SciHub is a public good and Elsevier is evil and at the same time believe that copyleft licenses placed by a collective of developers on their work should be respected and GitHub was evil to steal their code. Indeed, these two ideas will probably occur together more often than not because they're both founded in the idea that IP law should be used to protect individuals from corporations rather than the other way around.
The author has some valid points, but dismissing this entire class of arguments so flippantly is intellectually lazy.
My issue with it is that it gates software development behind paid services with various levels of context supported. Absolutely not the dream I have of how more software should be open source and everyone should be empowered to make the changes they need.
Speeding is quite common too, yet if you get caught — especially overdoing it — you'll have a problem.
Also, in this case, presumably everything produced with AI is fair game too? The argument being made here isn't even "it's not plagiarism", rather "it's plagiarism but I don't care" — why would anyone else respect such an author's copyrights?
> I can feel my blood pressure rising thinking of all the bookkeeping and Googling and dependency drama > of a new project. An LLM can be instructed to just figure all that shit out. > Often, it will drop you precisely at that golden moment where shit almost works, and development means > tweaking code and immediately seeing things work better. That dopamine hit is why I code.
This is me . Just yesterday I finished something I have been putting off for 2 years. I maintain 2 mobile phones one because of work and i needed a way to see sms messages from the other phone. I wanted to write a simple andoird app that reads incoming messages and saves them somewhere for me to use for OTP's and other stuff. For the longest time I wanted to build something like this for myself but I have been putting it off just because of all the weird issues i had been getting with mobile development. A couple of days ago since I had some time and I fired up gemini and asked it to write me a simple flutter app for and to save the messages to supabase and voila it was amazing . With some help and some tweaking I have a fully functioning mobile app on andoird , a small tauri application on desktop to read the messages nicely formatted in a data table without much coding on my part at all. Truly remarkable.
Loved this style of writing in 2005 from Maddox on the best site in the universe or whatever.
Sorry if I don't want google and openAI stealing my or anyone else's work.
That should have been the start of the essay.
New title: LLMs make Go programming feel good
My experience has been decent. I don't know that it has truly saved me much time but I can understand how it FEELS like it has. Because it's writing so much code (sometimes), it's hard to vet all of it and it can introduce subtle bugs based on faulty assumptions it made about different things. So, it will dump a lot of code at once, which will get me 90% of the way there, but I could spend an hour or two trying to nudge it to fix it to get it to 100%. And then I will probably still need to go back and reorganize it, or have it go back and reorganize it. And then sometimes it will make small adjustments to existing, committed code that will subtly break other things.
Something that has surprised me (in hindsight, it isn't surprising) is that sometimes when I feel like it misunderstood something or made a faulty assumption, it was actually me that had the misunderstanding or ignorance which is humbling at times and a good learning experience. It is also pretty good at bug hunting and DEFINITELY very good at writing unit tests.
I count myself as pretty lucky that this domain seems to be very well covered in training. Given the law of averages, most people's domains will probably be covered. I'm not sure how it would fare with a niche domain.
You keep using that tool, to your advantage. I’d you’re really altruistic you post some videos of how productive you can be like DHH did with his blog in 15 minute videos.
If you’re really that much more productive, the skeptics won’t be able to keep up and it should only take 6 months or some for that to become self evident.
But every time I try this,this happens. I've tried the same thing on multiple agents and they all make up some magic api that doesn't exist, even when I've used the right thing elsewhere in the code to do the same job with that function commented as such. Asking them to use that always fails.
This seems a massive flaw to me that is just hand waved away here.
Yes, there are people who think LLMs are just a fad, just like NFTs, and I agree these people are not really serious and that they are wrong. I think anyone who has used an AI coding agent recently knows that they are highly capable and can enhance productivity in the right hands.
But, as someone who gets a lot of value in AI coding agents, my issue is not with gen AI as a productivity enhancing tool - it's with the absolute torrent of BS about how AI is soon going to make coders obsolete, and the way AI has been shoved onto many engineering teams is like yet another incarnation of the latest management fad. My specific arguments:
1. As the author pretty much acknowledges, AI agents still basically suck at large, system-wide "thinking" and changes. And the way they work with their general "guess and check" method means they can churn out code that is kinda sorta right, but often leaves huge holes or outright laughable bugs.
2. Hallucinations are the worst possible failure modes - they look correct, which makes it all the more difficult to determine they're actually bullshit. I shudder to think about who will need to maintain the mountains of "vibe code" that is now being generated. Certainly not fucking me; I had a good career but I think now is definitely the right time to peace out.
3. Even if I could totally agree that there is a strong business case for AI, I can still, as an individual, think it makes my job generally shittier, and there is nothing wrong with having that opinion.
I don't think I'd be so anti-AI if I saw a rational, cautious debate about how it can enhance productivity. But all I see are folks with a vested interest overselling its capabilities and minimizing its downsides, and it just feels really tiresome.
I barely write any scaffolding code, because I use tools that setup the scaffolding for me.
I'll be damned if I give up control of my machine to a tool that hallucinates actions to take using hastily put together and likely AI-generated "agents". I still want to be the primary user of my machine, and if that means not using cutting edge tools invented in the last 6 months, so be it. I don't trust the vast majority of tools in this space anyway.
> I’m sure there are still environments where hallucination matters.
Still? The output being correct matters in _most_ environments, except maybe art and entertainment. It especially matters in programming, where a 99% correct program probably won't compile.
> But “hallucination” is the first thing developers bring up when someone suggests using LLMs, despite it being (more or less) a solved problem.
No, it's not. It's _the_ problem that's yet to be solved. And yet every AI company prefers chasing benchmarks, agents, or whatever the trend du jour is.
> I work mostly in Go. [...] LLMs kick ass generating it.
I also work mostly in Go. LLMs do an awful job generating it, just as with any other language. I've had the same shitty experience generating Go, as I've had generating JavaScript or HTML. I've heard this excuse that the language matters, and IME it's just not the case.
Sure, if you're working with an obscure and niche language for which there is less training data, I suppose that could be the case. But you're telling me that there is no good training data for Rust, the trendiest systems language of the past ~decade? C'mon. Comparing Rust to Brainfuck is comical.
I won't bother responding to all points in this article. I will say this: just as AI doomsayers and detractors deserve criticism, so does this over-the-top praising. Yes, LLMs are a great technology. But it is also part of a wildly overhyped market that will inevitably crash as we approach the trough of disillusionment. Their real value is somewhere in the middle.
ChatGPT is also pretty usefull - especially for naming things.
However, on a global scale, I would compare the shift towards 'vibe coding' and massive LLMs use to the one we got when people doing the coding don't even know what Assembly even is. Current state of software is that it's incredibly bloated and slow (and I do love python). Doesn't matter that much since we've got massive improvements in hardware, but still. In 5 years, instead of not really realizing that we're writing slow and bloated code, we will not really realize we're writing incorrect code.
I don't know anything about web developement, but in the environements I've seen during my career - where most code is quite critical and a stupid mistake, one that an LLM could easily hallucinate, can cost a lot - I don't see it replacing humans anytime soon. It's a pretty good and usefull tool though.
However I've tried coding agents, and well they suck... for me. Nearly all of the code I write these days is performance sensitive, as well as design sensitive (making abstractions for others to use), and not close to what could be considered boilerplate. The boilerplate I do write usually takes a trivial amount of time to write by hand. I'll keep trying whatever is new in AI every few months, I certainly don't want to be left behind, but so far that strategy hasn't left me optimistic.
> but the code is shitty, like that of a junior developer
It's worse than a junior. Juniors learn and grow, if they're a good junior it's often fairly rapid and self directed. Juniors can also ask for guidance or help, this is something maybe agents will be capable of in the future, but I could see it going pretty poorly for reasons tied to the first point about not learning. While LLMs contain a ton of information, more than a junior could, the junior is a better reasoning machine; I don't consider LLM "reasoning" to be anything close to the meatbag version.
> but the craft
Craft matters, it's important to enjoy and take pride in your work. If you enjoy managing AI agents that's fine, lots of programmers end up on the manager career path, but it's not for everyone.
With the amount of supervision you need to give this stuff, I can't see it being useful for much more than auto-completing the next few lines of code as you type. You've got to read and go over the whole output carefully anyway, so you might as well have finer grained control over it.
Me? I like programming.
Except that's not the argument people are making. They are arguing it will replace humans. They are arguing it will do research level mathematics. They are arguing this is the start of AGI. So if you want to put your head in the sand and ignore the greater message that is plastered everywhere then perhaps some self reflection is warranted.
Right now (for me) it's very frequent, depending on the type of project, but in the future it could be less frequent - but at some you've gotta test what you're rolling out. I guess you can use another AI to do that but I don't know...
Anyway, my current workflow is:
1. write detailed specs/prompt,
2. let agent loose,
3. pull down and test... usually something goes wrong.
3.1 converse with and ask agent to fix,
3.2 let agent loose again,
3.3 test again... if something goes wrong again:
3.3.1 ...
Sometimes the Agent gets lost in the fixes but now have a better idea what can go wrong and you can start over with a better initial prompt.
I haven't had a lot of success with pre-discussing (planning, PRDing) implementations, as in it worked, but not much better than directly trying to prompt what I want and takes a lot longer. But I'm not usually doing "normal" stuff as this is purely fun/exploratory side-project stuff and my asks are usually complicated but not complex if that makes sense.
I guess development is always a lot of testing, but this feels different. I click around but don't gain a lot of insight. It feels more shallow. I can write a new prompt and explain what's different but I haven't furthered my understanding much.
Also, not knowing the codebase, you might need a couple attempts at phrasing your ask just the right way. I probably had to ask my agent 5+ times, trying to explain in different ways how translate phone IMU yaw/pitch/roll into translations of the screen projection. Sometimes it's surprisingly hard to explain what you want to happen when you don't know the how it's implemented.
And yet I'm overwhelmingly demoralized and depressed about AI's encroachment on software engineering, so much so that I am finding it difficult to marshal cognitive resources. I'm finding it harder and harder to engage with coding problems, even though I love programming.
The last thing I expected was that AI, even kept at a distance, would rob me of the joy I find in one of the things I love most.
It so devalues what I do and my basic capabilities as a human being that I kind of want to die. I'll stick around for my daughter, my partner, and any future children, but I'm not optimistic at all about the future.
So then where do the junior developers come from? And then where do the senior developers come from?
I gave chatgpt (the free desktop web version) my Ruby solution to one of my favorite adventofcode challenges and asked it to write in in Rust. I've no experience in Rust except reading the few first chapters of the book but with the help of the compiler I managed to fix it and it gave the right responses 100x faster than Ruby did. So chatgpt did a great job keeping the logic intact.
I would have done the same thing if LLMs didn't exist, write it in Ruby first and then in Rust. The flexibility and expressiveness of Ruby allows to actually find a working solution. Experienced Rustaceans would do it directly in Rust, but writing in Ruby should be easier and an LLM can transform it.
I am a believer that these tools will be (if they aren’t already) extremely useful. Game changing in many ways, and I worry about the future of knowledge fields. Definitely not a skeptic in the trajectory. I do wonder if that makes those of us who are on the cutting edge more valuable as AI can quickly reproduce things that are well understood.
For those that think only procedurally, I can see how it helps them. Because procedural first development has a lot of boilerplate logic.
For those who think model first, the AI may help them rubber duck, but ultimately the physical writing of the characters is minimal.
Most of my time is thinking about the data model. The AI writes almost all of my procedures against said data model. But that is about 20% speedup.
I use Claude for specific questions, it works great (but apparently I’m doing it wrong according to the author), code generation doesn’t, I don’t want my boilerplate code to have subtle differences in every file, that’s the worst kind of boilerplate and that’s what I get if I automate it.
There are real world implications of all the infrastructure being built for this too, not to mention the extremely short e-waste cycles during the boom we’re in right now. To me it also seems like LLMs are the wrong thing to solve for, it’s like a gasoline car, make the engine larger and more fuel consuming to go faster and no matter how much you perfect it, it will still be fossil fuel being burnt at a high rate. What’s the alternative, I don’t think we know yet, but if we were a smarter species we would see that this is not it.
I haven't used coding agents. Can someone who has (or knows about these things) elaborate, or point me to a good resource? Thanks!
> Professional software developers are in the business of solving practical problems for people with code.
Are we? Is that our job? You might think it is, and it might even feel like it. But it is not. Our job, in 99% of cases, is to make stock price go up. That means making software that people will buy, making people want to keep buying it, and doing whatever makes your investors happy.
So what happens as you use these tools? Your job changes from coder to "LLM manager", but let's assume you're happy with that. You're more "productive", which is an artificial metric and nothing to do with your real job as described above. That's fine for now, but what happens down the road? Now your company is paying more and more money to these LLM services, and paying you less and less.
> "but they take-rr jerbs" So does open source.
This is a gross misunderstanding of open source. Most open source developers are paid to be, full time, by companies. Why? Warm fuzzy feeling? Goodness of their heart? No. It's because it's good for business. These open source techs are a baseline, there' not worth being competitive on. Build the open base in the direction you want, and build the "business value" ontop for profit.
> We used to pay good money for databases.
We still do. Have you looked at managed PostgreSQL prices lately? Also if you haven't noticed, Oracle hasn't gone anywhere.
> LLMs really might displace many software developers. That’s not a high horse we get to ride. Our jobs are just as much in tech’s line of fire as everybody else’s have been for the last 3 decades.
This isn't the same thing as replacing punch cards, or moving away from Java 6, or replacing artisans with factory machines. This is centralisation and rent-seeking. As companies become more and more dependant on a handful of companies with the upfront capital to build massive server farms... what do you think is going to happen? Once they've convinced enough people that LLMs are essential to their work it's going to cost a lot more than $20/month. Once these companies are entrenched they will wring every last dollar out of every company that's grown to depend on them, many will simply fold.
Maybe we are in a seismic shift in our industry. But the beneficiaries are not going to be whole-hearted LLM adopters, no amount of saying "you need to change how you work" is going to save any of us. The only people that will benefit are going to be the people that own the data centres.
Translation: Drop your work and current tools every few months to pick up the shiny new thing or you aren't cool or even sane.
Reality: Your favorite cool new tool will be integrated into development work as it is proven, documented, and time allows. This idea that development must leap onto the latest shiny new thing every few months is extremely corrosive to reasonable business practice. The needs of the many outweigh the preferences of the lead developer.
I can maybe even see that point in some niches, like outsourcing or contracting where you really can't be bothered to care about what you leave behind after the contract is done but holy shit, this is how we end up with slow and buggy crap that no one can maintain.
My feeling is that AI will keep the software industry in the same trend: produce more software of lower quality while being more profitable.
People sometimes say "perfect is the enemy of good". But this implies that putting less effort is "good enough". I believe it goes the other way: why would a company put efforts into selling you something good, if it can make you pay the same for something worse? It's all about maximising profit, and AI is no exception there.
The author describes a world where most code is boring grunt work and most collaborators are mediocre. This world is not familiar to me. I can see LLMs being useful in such a world, but it’s not where I work.
The comment about buying a table instead of building it really stuck with me. We’re the ones who design the table! Saying we should buy one from the store is like saying farmers should buy their food from the store instead of putting in all that work to grow it.
Making fun of people who care about copyright (implying that fly abuses GPL for instance) is just icing on the cake.
This is in fact what I am doing (though I am not trying to convince anyone not to use LLMs). How can I get into this other mode? Any pointers? Do I need to pay up for a service? I do not want to carry out vibe coding, I want to use LLMs to solve physics, math and logic problems/puzzles. I'd appreciate any recommendations.
It sounds like someone taking a condescending tone to say that "Fast fashion is great! The clothes are really fine, and being able to wear a t-shirt once and then throwing it away instead of washing it is more convenient. This is most definitely progress. Sustainability? Hmm I can't say, that's not my field of expertise, therefore it's probably not important".
I think leveling things out at the beginning is important. For instance, I recently talked to a senior engineer who said "using AI to write programming is so useless", but then said they'd never heard of Cursor. Which is fine - but I so often see strong vocal stances against using AI tools but then referring to early Copilot days or just ChatGPT as their experience, and the game has changed so much since then.
But I'm not thrilled about centralized, paid tools. I came into software during a huge FOSS boom. Like a huge do it yourself, host it yourself, Publish Own Site, Syndicate Elsewhere, all the power to all the people, borderline anarchist communist boom.
I don't want it to be like other industries where you have to buy a dog shit EMR and buy a dog shit CAD license and buy a dog shit tax prep license.
Maybe I lived through the whale fall and Moloch is catching us. I just don't like it. I rage against dying lights as a hobby.
My part-time obsession over the last few months has been trying to demonstrate this and come up with a method for finding these magic keys (I even tried to get the LLMs to search for me, lol). What I really want is to give the latest thinking models (200k input, 100k output) a 5-6 page design doc (4k words, 5k tokens) and have them produce a complete 5kloc (50k tokens) microservice, which would show a 10x compression. It's hard, but I haven't seen any reason to think it wouldn't work.
For better or worse, I think this will be close to what IC jobs will be like in few years. Fundamentally, our jobs are to try work with other functions to agree to some system that needs to exist, then we talk to the computers to actually implement this. If we switch kotlin+compiler for design doc+llm, it still going to be somewhat the same, but far more productive. Agents and such are somewhat of a stop-gap measure, you don't want people giving tasks to machines, you want to accurately describe some idea and then let the computers make it work. You can change your description and they can also figure out their own tasks to evolve the implementation.
It's improved to the point where the summary that I would email a junior developer is a pretty good prompt that produces code pretty close to want I want.
I had a weird project recently where I needed a stand alone event handler that would send an email receipt to the customer, another email to customer service, log the event in an accounting system via an API. I wrote up a proposal for the rest of the management team and, once accepted, pasted the proposal into ChatGPT (and a few others, but ChatGPT gave the best results).
I basically got a full recipe for setting up AWS API Gateway, Lambda, and SES, the Python Lambda function with api calls to SES, suggestions on using AWS SAM, etc. etc. Tweaked the prompt a couple of times and then took over from there. I had this into the hands of the testing team in less than a day.
I'm pretty sure this would reduce my team size in ANY role I've had in my career. Maybe not halve the teams, but it would eliminate a lot of the junior or journeymen developers.
You know what doesn't constantly nag for my attention? Dead text files on disk. They just sit there, waiting for me to grace them with my presence. That's the way it should be. Computers serve me, not the other way around.
In 2025 I am about 800-900 hours deep into using Cursor, blowing through 500 credits each 2 days (500 credits = 20$). I also organize a local "vibe code" workshop and meetup, with people from all sorts of experience levels, from no coding experience, to twice my senior. And what I am seeing is that what separates those from those who are good at using AI and those who are not, generally is a level of rigid stubbornness.
I can't wait for time to do its course here and those people who now are "all nuts" to slowly see programming of the past become an old relic.
And who the fuck will train those interns, now they've been replaced by machines?
It kinda invalidates the entire premise because it builds on this false foundation of “look how useful it is to a mere plebeian” and the skills actually required to sift through LLM garbage are what puts you in the top 5% of programmers.
Most senior developers cannot conduct useful code review and spot errors. In every company I’ve been at, there were only 1 in 20 people that would give the useful in-depth reviews required to pick up LLM bullshit.
This style of engagement bait is brutal and it severely overshadows other nuggets of wisdom of how LLMs can be useful.
Using AI for over a year now on a daily basis, most AI tools do not struggle with Rust, they struggle to provide value. If I do not instruct them to keep the output to the bare minimum, I usually end up with 10x the output what would be a simple solution to my problem.
Generally though I find LLMs have a pretty rapidly diminishing return on what you can expect out of them. They’re like a 3-5 year senior programmer that has really learned their domain well, but doesn’t have the judgement of a principal engineer. You get to a point where you need to reach in and right things and really pay attention, and at that point the diminishing returns set it rapidly and you’re better off just doing the rest yourself. Refactors and stuff can be delegated but that’s about it.
I find this true regardless of the language. None the less, I’ve been able to improve my overall velocity dramatically completing several projects in the last few months in the span of one typically. If tooling improves I hope to continue that but I’m already getting close to the limit of how fast I can conceive of useful creative things.
While I don’t believe the “AI will take all jobs” VC tales, I also think not using LLMs in software development workflows is similar to not wanting to use Excel as an accountant in the 1990s.
AI won’t replace all jobs, but companies that cleverly integrate it into their processes will be faster and more efficient compared to those that don’t.
But who knows, maybe AI will accelerate so rapidly that it will fix the economy. Maybe we'll have robots everywhere doing all the work. But I worry about the lack of market incentives for people to adapt AI to real world use cases.
For example, I'm an open source developer who likes to tinker but I've been booted out of the opportunity economy. I can't afford to program robots. People like me are too busy using AI to parse spreadsheets and send targeted ads to even think about automating stuff. We work for companies and have no autonomy in the markets.
If things had worked out differently for me, I'd probably own a farm now and I'd be programming robots to do my harvest and selling the robots or licensing the schematics (or maybe I'd have made them open source, if open source had worked out so well for me). I don't have access to such opportunity unfortunately. The developers who worked for big tech are good at politics but often disconnected from value-creation. Few of them have the skills or interest to do the work that needs to be done now... They will just continue leveraging system flaws to make money, so long as those flaws exist.
To be sure, you definitely should not blindly trust the people who have a stake in selling AI products. However, you probably should trust the people who have no stake in selling AI, but are using it to accelerate themselves. The problem is, these people are probably too busy building things to spare any time convincing you. And in fact, it is a competitive advantage for them if others don't figure it out.
Here's my take: Everyone needs to figure out how AI works for them. The thing with AI is that it is a technology that behaves like people. It has the speed, efficiency and scale of computers but the fallibility and quirks of humans. This is why it's so confusing to discuss, and why folks have such varying experiences. The right approach to working with AI is like working with people: understand what they're good at and where they are weak, and then work with them to achieve your goals.
This will require using it "in anger" on many non-trivial tasks over a significant period of time, which will take persistence and patience. Yes, the hype is so over-the-top that you might not want to invest your valuable time on it. But you owe it to yourself to ensure you can fully capitalize on the sea change that is happening.
If it helps, I (like the OP) have no stake in selling AI and I posted this comment about my experiences on the other thread about the AI coding -- https://news.ycombinator.com/item?id=44163543 -- The upshot is, AI has enabled me to produce innovative (probably cutting edge) work in domains I had little prior background in. And I've not even used agents yet!
I've never been a completionist. I'm distracted. I like novelty. I make half-form experiments, but mostly provide deeply immersive conversations for the benefit of a small group of people around me, some of whom go on to make/do things that I proudly feel my influence on. The final 10% of work often defeated me.
Now, I just make it happen. The pain is gone. Something else can carry the last 10%, and my ideas get realized by me (and AI).
I can't even conceive how this will change the world en masse. Knowledge domains (not just tech, but science, art, etc) have been built of the things that the type-A completionists knew how to see. The others didn't finish, and what they saw of the world has largely been marginalized.
The mind of ADHD and creative types is about to get much more represented in the output products of civilisation. This is going to be a big recalibration, and I think a re-balancing toward something more healthy tbh
That is not my objection, though some seem to think it is.
I got into coding because I found it interesting and fun. I've avoided managing others because I hate that sort of thing. Working with an LLM, prompting it to do work then assessing that work and adjusting the prompts or cleaning/completing the output myself, is far too much like outsourcing work to a 3rd party junior, and I don't want to do that whether said junior is a human in a far off land (or just down the street, for all I know!) or some LLM run by MegaCorp-ᴙ-Us.
I was already seriously disliking much of the modern programming industry (remote teams, the shit-show that client-side web work continues to devolve into, and much else) anyway. If it gets to the point where I can't do my job (or can't do it competitively enough) without using LLMs on top of dealing with the other things I don't care for, I'll be retraining for something else entirely. Heck, I've got the mortgage paid, I don't have or plan to have a family beyond the cats, I could just about survive on shelf-stacking…
I know it isn't a passing fad, though I'm convinced the current and near-future iterations aren't going to be quite as significant in the longer term as many believe. I don't object to others using it (aside from the matter of “MegaCorp-ᴙ-Us”s of the world suddenly deciding that piracy is just fine because they stand to benefit from it, in fact are already benefitting from it, in this case). But I don't plan to be nagged into using it myself.
> Do it on your own time.
If/when that career shift happens, or I survive as I am long enough to retire, I will probably do exactly that. As it stands I spend more than enough of my life on tech in DayJob, my own time is increasingly filled with unrelated hobbies and people.
Unless there's a significant sense of what people are working on, and how LLMs are helping -- there's no point engaging -- there's no detail here.
Sure, if your job is to turn out tweaks to a wordpress theme, presumably that's now 10x faster. If its to work on a new in-house electric motor in C for some machine, presumably that's almost entirely unaffected.
No doubt junior web programmers working on a task backlog, specifically designed for being easy for juniors, are loving LLMs.
I use LLMs all the time, but each non-trivial programming project that has to move out of draft-stage needs rewriting. In several cases, to such a degree that the LLM was a net impediment.
Yes we are, because the kind of work you need to do in C++ or Rust is probably entirely different from the work this person manages to get the LLM to do in Go.
This is kind of a first though for any kind of technology. The speed of development and change here is unreal. Never before has a couple months of not being on top of things led to you being considered "out of date" on a tool. The problem is that this kind of speed requires not just context, but a cultural shift on the speed of updating that context. Humanity just isn't equipped to handle this rate of change.
Historically in tech, we'd often scoff at the lifecycle of other industries - Airlines haven't changed their software in 20 years?? Preposterous! For the vast majority of us though, we're the other industry now.
....for the vast majority of my career, anyone who suggested doing this - much less letting code that no one in the world (much less the company) truly understands the logic flow of do this - would be fired.
One does not need to be a skeptic about machine learning and its potential as technology to refuse to engage with its practical applications when they are clearly ethically corrupt. The ends do not justify the means. Just like refusing to buy products of forced labour or disproportionate environmental impact, or to invest in war and bloodshed, everyone makes this decision for themselves.
Seriously? Is this argument in all earnestly "No profession has been more contemptuous therefore we should keep on keeping on"? Should we as an industry not bother to try and improve our ethics? Why don't we all just make munitions for a living and wash our hands of guilt because "the industry was always like this".
Seems a bit ironic against the backdrop of <https://news.ycombinator.com/user?id=tptacek>:
> All comments Copyright © 2010, 2011, 2012, 2013, 2015, 2018, 2023, 2031 Thomas H. Ptacek, All Rights Reserved.
(although perhaps this is tongue-in-cheek given the last year)
I've never merged anything with an unclear copyright to main, let alone if I positively suspected that it had been plagiarized.
> Cut me a little slack as I ask you to shove this concern up your ass. No profession has demonstrated more contempt for intellectual property.
Every place I've been employed was nearly paranoid about copyright, with rules in place about never copying and pasting anything copyrighted into the company code base.
All the major open source distros and projects, likewise.
There are some bad apples out there, like, oh, Sam Altman, who wants massive plagiarism to be fair use.
So, au contraire, you can shove your contemptuous blog up your ass, buddy.
I'm glad I got out when I did. :) This simply isn't something I want to do for a living. I'm glad there are people who do, though.
(Especially if that code was spit out by an alien copypasta that is really good at sounding plausible with zero actual intelligence or intent?)
Like, if all I care about is: does it have enough unit tests and do they pass, then yeah I can audit that.
But if I was trying to solve truly novel problems like modeling proteins, optimizing travel routes, or new computer rendering techniques, I wouldn’t even know where to begin, it would take tons of arduous study to understand how the new project full of novel algorithms is going behave?
> An agent can occupy itself for hours putzing with your tests in a VM and come back later with a PR.
> Kids today don’t just use agents; they use asynchronous agents. They wake up, free-associate 13 different things for their LLMs to work on, make coffee, fill out a TPS report, drive to the Mars Cheese Castle, and then check their notifications. They’ve got 13 PRs to review. Three get tossed and re-prompted. Five of them get the same feedback a junior dev gets. And five get merged.
I'm working better and harder with Roocode in Code mode (and somtimes Architect). I've only been doing it for a few weeks, and it reads to me like I'm already using an 'old approach'.
> But if you’re a software developer playing this card? Cut me a little slack as I ask you to shove this concern up your ass. No profession has demonstrated more contempt for intellectual property.
The OP isn’t wrong. Some of the most brilliant minds in computer science have built tools to shatter DRM, enable widespread piracy, and repeatedly lift code wholesale from public repos if it helps them move a product forward. The modern crop of technologists (myself included) have long had a hypocritical view on IPR: “rights for me, not for thee” to oversimplify things. The entirety of the modern public cloud is built by trodding on the backs of open source projects while wearing lawn-aerating spikes. We steal shit from others so our Founders and Masters can reap billions in profit; to decry plagiarism now feels incredibly dishonest and insincere, at the very least.
Look, I’m an AI skeptic myself. On the whole, these tools are bad. They’re taking jobs, polluting the planet, and dumbing down a society that still can’t try rebooting something when it fails, let alone identify where their WiFi ends and the internet begins. They’re toxic walled gardens that can reauthor reality to the whims of the VCs and Billionaires funding them, locking you into subscriptions forever.
But even I will admit that modern LLMs, when it comes to writing code, are actually really good for the majority of C-tier devs and below. Are they as helpful to bleeding-edge engineers on the cusp of the future? Nah, not really, but if you’re just writing a checklist app for iOS to help you stay on task, or a Packer manifest to lay out some VM templates, they’re kinda awesome.
But agents like Claude Code and Cursor run on a user’s machine right? So how can they work in the background like that? are there agents that can work async?
That's what a lot of the highest paid programmers work on. I work in such a firm and the agent they have for us is incapable. And it's the latest and greatest from whoever coders love to rave about online.
I love that AI written tests and other generally greenfield things work well and can be vetted quicker than writing and getting the code oneself. But for most more complex tasks it's not there. I'm ready when it is though
I've found LLMs can't really write a whole program there isn't already a template for. They're good enough at writing individual functions, and as the author mentioned, their breadth of knowledge is better than any individual.
Honestly, I see them as a supercharged version of googling, without all the out of context answers you get from Google since they also place ads and whatnot. I find an LLM can get me an answer far quicker than a regular internet search, even if it's imperfect and I'm probably not going to put it straight into my code.
Whoever says otherwise should read their own comments from 2 years ago and see how wrong they were about where AI is today.
Not saying singularity will happen for sure, but is it a possibility? Hell yeah.
Are you kidding me? Mosaic was released in 1993. In 1998 Apple released the iMac on a theory, based on some marketing research, that people might be really interested in "getting online." Five years. And that's about a decade before we realized we might want a touch phone. More than a decade before an App Store.
This is very much still the top the first. AI will make even the internet seem small. Which makes sense — the entire whole internet is merely one input into this technology.
When that doesn't work, they start inventing hyper-specific scenarios and graphs with grounding that exists only in their imagination to push the topic further. If the AI can't solve a problem in a domain for you, well, it's that domain that's the problem, not AI. You simply need to re-invent the paradigm around your entire problem - easy stuff.
Finally, when they've failed to convince anyone with a modicum of rationality, they resort to some version of "well, fuck you, it's the future and I'll just be leaving you behind!"
Interestingly, this article has all three.
(1) Ask to write an implementation plan for a specific change or a feature. It will go through the source code, look up references, make notes and produce a plan
(2) Review the plan. Point out missing things, or stuff that needs improvement.
(3) Once I'm satisfied with the plan - ask to draft PR. Launch a few attempts in parallel and pick the one that I like the most.
(4) While drafting PR, Codex will run unit tests (even can run E2E tests in its container), linting and type checkers at every single step. This helps a lot with the stability.
(5) I review the code and merge the PR if I like it. Ask to cleanup - if not.
This feels like working with a remote team - very patient and diligent at that.
Ultimately, I get to get more features done per day. But I also feel more tired by the end of the day due to a higher level of cognitive load. There are more decisions to make and less idle time (e.g. no more hours spent tidying up the code or doing relaxing and pretty refactoring).
TLDR; this AI thing works really well at least for me. But it comes with trade-offs that might slow down its adoption by companies en masse.
We're in the middle of a major shift - there will benefits to those that adapt first. People outside the field have no idea what's coming, even those of us in the field are underestimating the shift.
There were a few outliers in the 60s who understood what the computing revolution meant and would mean, but most did not. This is likely an even bigger change than that.
Thus sucking up all the time you "saved" by having the LLM write the code you saved. The machine made the easy parts of coding easier while making the hard parts harder.
"You’ll spend 5-10 minutes knocking it back into your own style."
As if style is the only thing that matters?
"LLMs are showing signs of adapting to local idiom, but we’re not there yet."
First step fallacy. Ladders are showing signs of being tall enough to reach the moon, but we're not there yet.
I am actually doing this the whole day long. For example i have setup today a fresh new debian vps for some interns. U had to provide them with a docker system, support for go, nginx stuff and i made a quick hello world app in angular with a go backend. I could have done it myself. But i asked chatgpt to provide me with all the commands and code. No idea how an agent could do this for me. I got everything running in like 30 minutes.
I did, and learned a ton, and likely not going back to how I was before, or how I used it a week ago.
The comments in the article about not reading the agent is good but it’s more than that…
Vibe coding is for non-coders. Yet, you get a feel for the vibe of the AI. With windsurf, you have two or three files open, and working in one. It starts smashing out the multi, interspersed, line edits and you know with a flutter of your eyes, it’s got your vibe and correctly predicted your next ten lines. And for a moment you forgive it for leading you astray when you read what it said.
I've seen Antirez use it for hard to find bugs in the Redis codebase (videos are on YouTube, albeit Italian), I've seen Cursor refactor in a matter of minutes code that would've taken hours, correctly, I've seen security researchers leverage it to find complex edge cases, etc
My 2 cents is that developers that won't learn to use the tools will be left behind (albeit not for long imho), but also, that sales people promoting the idea that devs can be replaced equally dangerous.
For most software projections, they cannot even remotely do that - at least not today and as far as I can tell.
And that is to say, it's not that they can write a not-so-large fraction. They cannot write any significant fraction of it. They can often write you a draft that helps you get there faster, though. They can also facilitate web search result consolidation etc. which can also help. But - they can also lead you down wrong paths and dead-ends, on occasion.
> Kids today don’t just use agents; they use asynchronous agents. They wake up, free-associate 13 different things for their LLMs to work on, make coffee, fill out a TPS report, drive to the Mars Cheese Castle, and then check their notifications. They’ve got 13 PRs to review. Three get tossed and re-prompted. Five of them get the same feedback a junior dev gets. And five get merged.
I think this is my key point of disagreement. Professional software developers often don't see themselves as artisans, because Steve Jobs's unseen feet are implemented through our implicit standards of ourselves. I don't go out of my way to be a perfectionist on the job, but neither will I try to push 1000 lines of spaghetti to solve a minor problem that seems like it ought to be a simple fix.
I've worked with people in the past who don't have this instinct, and it's my experience that their productivity is quite low if not negative, even though they often perceive themselves to be 10x developers who can handle all kinds of edge cases super fast. I suspect that most AI-generated code today ends up in this niche.
If you do that, I'll swallow my AI skepticism.
I would love to have an LLM that I can turn loose on an unfamiliar codebase that I can ask questions of. I would love to have an LLM that will fill in my Vulkan boilerplate. etc.
I use emacs and Mercurial. You can demonstrate magic to me and I can be convinced even if it's not mainstream.
Rewriting Javascript slop to StackOverflow standards is not convincing me.
Get to it.
(The OAuth stuff posted earlier certainly moved my needle, but the fact that they needed a gaggle of reviewers as well as hand holding when the LLM got stuck mutes the impact significantly.)
But, you still have to read it:
> Reading other people’s code is part of the job...I have to read the code line-by-line anyways.
So instead of writing the tedious code, I only have to read it. Oh but don't worry, I don't have to read it too carefully because:
> Agents lint. They compile and run tests. If their LLM invents a new function signature, the agent sees the error
But remember...
> You’ve always been responsible for what you merge to main.
So now I have to oversee this web of agents and AI ontop of coding? Am I doing more now for the same pay? Am I just speedrunning myself toward lower pay? Is AI adoption a prisoner's dilemma toward lowing my wages hardest?
Because is good at coding compared to many other disciplines (e.g. math), it makes the internal AI politics among programmers more of an issue. Add fuel to that fire baby!
I really want to like AI assisted coding, but I guess i am just stuck as to what to do.
I have tried co-pilot, both 2 years ago, and recently. I have tried some other AI plugin on VScode (i thought that was cursor, but I don't see it in the extensions when i now search, so it was probably something else). If anything, I have found with these solutions that they are great for the first few tasks - generate these models, scaffold this, add oauth, etc - but after that they lack all consistency with the code they themselves generated (like if i add a new model, the output is wildly different to what it already created) and it takes more time to rewrite that output than to copy-paste and change whats already there.
@op - could you explain exactly what tooling you are using, maybe give an example of these async agents, etc? Would be most appreciated for us luddites who would love to be more productive.
Conversely, if I say, "Put the button in the upper right corner exactly like the existing button in the upper left," I'll get a button in the upper right, but not exactly like the upper left button.
Tell me you won't use it and explain properly, without appeal to "legacy" or "human values" or other abstracts which don't self validate.
Now note that this is POV for every child on earth.
The new discipline is provider of LLM/AI guard rails, and absolutely requires knowing what is/not correct.
Computer science was an immensely fun subject to learn. I moved to one of the big cities and was bewildered with how much there was to learn, and loved every second of it. I gradually became good enough to help anyone with almost anything, and spent lots of my free time digging deeper and learning.
I liked CS and programming - but I did not like products built by the companies where I was good enough to be employed. These were just unfortunate annoyances that allowed me to work close enough to what I actually enjoyed, which was just code, and the computer.
Before LLMs, those like me could find a place within most companies - the person you don't go to for fast features, but for weird bugs or other things that the more product-minded people weren't interested in. There was still, however, an uncomfortable tension. And now that tension is even greater. I do not use an LLM to write all my code, because I enjoy doing things myself. If I do not have that joy, then it will be immensely difficult for me to continue the career I have already invested so much time in. If I could go back in time and choose another field I would - but since that's not possible, I don't understand why it's so hard for people to have empathy for people like me. I would never have gone down this path if I knew that one day, my hard-earned-knowledge would become so much less valuable, and I'd be forced to delegate the only part of the job I enjoyed to the computer itself.
So Thomas, maybe your AI skeptic friends aren't nuts, they just have different priorities. I realize that my priorities are at odds for the companies I work for. I am just tightly gripping the last days that I can get by doing this job the way that I enjoy doing it.
So what, people should just stop doing any tasks that LLMs do subjectively better?
… most of the code you write is tedious boilerplate? What do you do all day? I can honestly say the most tedious code I write is migrations, because I have to edit three files. I do this like once a week. It would take way longer to explain to an ai what to do than just do it myself.
… you run the ai on your procrastination and as a result do heads down work? Seriously? 1, programming with AI is like playing a slot machine. It keeps you hooked just on the verge of winning the jackpot for far longer than the tedious task would have distracted you. And let’s say a good agentic case - it runs for 30 minutes before it’s done. That’s not even enough time to “boot up” a serious problem, and your already having to put it aside to go check the work you yourself are saying is mediocre? And you’re advocating to run multiple of these? Even the most ardent vibe covers I know talk about babysitting the agents. How is this unlocking deep work time in any way shape or form?
… craft is relagated to hobbies? I guarantee you, this person loves Linear. It was probably written on a MacBook. This whole “we solve real problems” BS is just a cop out because being great is hard.
… mediocre code is good code? All code is a liability. If you generate 10 times the code, and are admitting it’s the lowest bar of quality possible, it is at a minimum 10x the liability on your shoulders.
… letting LLMs refactor your tests? Serious question, who actually does this? Because I would LOVE to have LLMs write tests for me. I have tried every. single. time. I need to write something that needs a lot of tests to ask LLMs for tests for TWO YEARS, and not once have I ever used a single test it generates. It generates worse tests than it does code, and the last thing I want is 42 asserts in 8 tests across 600 LoC that nobody understands that poorly tests what should have been 2 asserts in 2 tests in 12 lines of code.
Is it really? I scraped all HN comments with the words "Rust" and "LLM" from the last 6 months, put it into Gemini and asked it to surface quotes of people saying they had a good time, versus quotes of the opposite. It was about 60% positive and 40% negative.
Does anyone have recent personal experience with this they can talk about?
When a LLM has been tamed to the point that I can define and request a PowerCLI programme, I can be fairly sure that AI is a solved thing.
we could choose to be
of course if you're a temporarily embarrassed billionaire like ptacek, you certainly don't want the workers doing this
Oh, GH Copilot was adding citing capabilities which was going to be in production around this time? Any progress on that front?
Thanks.
But I’m at least grateful for LLMs making me more aware of how important it is for me to focus on my own skills building. I will never use it because I value having those skills sharp
But how do you quality check this part? I am proofreading all these transformations and I am not sure if this is really faster than actually recording a VIM macro, which will do the steps I need.
What a strange disclaimer. Image generation was a (career)killer feature long before vibe coding was halfway useful.
> People coding with LLMs today use agents. Agents get to poke around your codebase on their own. They author files directly. They run tools. They compile code, run tests, and iterate on the results. ...
Is this what people are really doing? Who is just turning AI loose to modify things as it sees fit? If I'm not directing the work, how does it even know what to do?
I've been subjected to forced LLM integration from management, and there are no "Agents" anywhere that I've seen.
Is anyone here doing this that can explain it?
I've been using Zed and Claude Sonnet 4 (and sometimes trying Opus) heavily over the past weeks. For small edits where I have lots of unit tests, the results were great. So great that they worry me with regards to job security. For exploring a new programming domain it was also somewhat useful. I work a lot with the Typescript compiler API right now, and it has almost no documentation. Since the AI can see into every GitHub repository out there, it's much better, and more efficient, at learning APIs based on code from other folks. On the other hand it means I don't do that job, and I am forced to rely 100% on how the AI presents the Typescript compiler API to me. Are there better methods I could use? Who knows.
Where it's abysmal is code architecture. Sometimes it's almost comical: it adds an if statement to handle one highly specific edge case in a program that only makes sense if it solves the general case. This didn't happen often thought.
The hardest part was to force it to reuse existing code from the same file. My use case is transforming a Typescript AST into a GraphQL AST. The code is one big switch statement with lots of recursive calls. The AI would often add 300 lines of code that duplicate some logic which already exists somewhere else.
In the end I rewrote the whole thing from scratch. At around 900 lines of code the AI was starting to really struggle. When I wanted to take over, I realized that I didn't have the in-depth knowledge to do so. And trying to understand the code the AI had written proved futile.
Ultimately that's on me, I should have been more diligent reviewing the dozens of 300 line of code changes the AI throws at me over the course of a day. But I wasn't, because reviewing is really, really hard. For many reasons. And AI makes it even harder.
Am I therefore nuts? I find this whole article extremely one sided. Surely, based on the sheer amount of both positive and negative press, the answer is somewhere in the middle.
As somebody who comes from a politically left family, and was also around in the early days of the Web, let me tentatively note that this issue has a particular political slant, too. The left has strong roots in being able to effectively critique new developments, economic and social, that don't come from its own engines of innovation which have revolved around those critiques, plus solidarity, organization, and sociopolitical action.
The movement's theorists work far more slowly on how to integrate the effect of those changes into its vision. That means when something like this comes along, the left's cultural norms err on the side of critique. Which is fine, but it makes any other expression both hard to convey, and instantly suspect in those communities. I saw this in the early Web, where from a small group of early adopters of all political slants, it was the independents, heterodox leftists, and the right, -- and most vocally, the libertarians -- who were able to most quickly adapt to and adopt the new technology. Academic leftists, and those who were inspired by them took a lot longer to accomodate the Net into their theses (beyond disregarding or rejecting it) and even longer to devise practical uses for it.
It wasn't that long, I should say -- a matter of months or years, and any latent objections were quickly swamped by younger voices who were familiar with the power of the Net; but from my point of view it seriously set back that movement in practicality and popularity during the 80s and 90s.
I see the same with AI: the left has attracted a large generational of support across the world from providing an emotionally resonant and practical alternative to the status quo many people face. But you quickly lose the mandate of heaven if you fail to do more than just simplistically critique or reject a thing that the average person in the world feels they know better, or feels differently toward, than you do. This is something to consider, even if you still strongly believe yourselves to be correct in the critiques.
i don't get it. It keeps making stuff up or gives me wrong stuff that merely compiles when i tell it signature doesn't exist.
So genuine question for AI Boosters, what should I be using?
I would like the 10x productivity gains we were promised :)
I think it could do with a little bit more friendly, but… it’s got its own charm.
It also convinced me I should be using agents rather than raw dogging Gemini 2.5 pro
> Does an intern cost $20/month? Because that’s what Cursor.ai costs.
> Part of being a senior developer is making less-able coders productive, be they fleshly or algebraic.
But do you know what another part of being a senior developer is? Not just making them more productive, but also guiding the junior developers into becoming better, independent, self-tasking, senior coders. And that feedback loop doesn't exist here.
We're robbing ourselves of good future developers, because we aren't even thinking about the fact that the junior devs are actively learning from the small tasks we give them.
Will AI completely replace devs before we all retire? Maybe. Maybe not.
But long before that, the future coders who aren't being hired and trained because a senior dev doesn't understand that the junior devs become senior devs (and that's an important pipeline) and would rather pay $20/month for an LLM, are going to become a major loss/ brain drain domestically.
I think we've passed the threshold beyond which not using AI is just doing things the hard way for no appreciable gain.
okay wtf is this weird jab at yacc supposed to be? I generally prefer to write my own LR parsers because it's really not that difficult but I never felt like yacc code was difficult to read or not "probabilistic".
AFAIK most serious compilers don't use yacc either because they need to be able to parse context-dependent grammars, and also reinventing wheels is often worth the effort when the wheel is 50 years old and forces you to make compromises because it also has to support use-cases you are not interested in. But again, it's not bad, it does the thing it is designed to do and it does save a lot of effort if you're fine dealing with its limitations.
Only serious complaint I have about Yacc is that the basic posix form has frustrating limitations such as not being able to have two separate yacc instances because they'll declare the same symbols, and I'm fairly certain it's not thread-safe either. Bison fixes some of these while still remaining "probabilistic".
Anyways I'd rather use Yacc+Lex (preferably Bison+flex) to generate my parser than have to go read everything your "agent" wrote with a fine-toothed comb to make sure it didn't introduce any crippling memory corruptions or dangerous ROPportunities.
> Meanwhile, software developers spot code fragments seemingly lifted from public repositories on Github and lose their shit. What about the licensing? If you’re a lawyer, I defer. But if you’re a software developer playing this card? Cut me a little slack as I ask you to shove this concern up your ass. No profession has demonstrated more contempt for intellectual property.
> The median dev thinks Star Wars and Daft Punk are a public commons. The great cultural project of developers has been opposing any protection that might inconvenience a monetizable media-sharing site. When they fail at policy, they route around it with coercion. They stand up global-scale piracy networks and sneer at anybody who so much as tries to preserve a new-release window for a TV show.
So it starts with a humble strawman, and then the author illustrates how dumb they think their fellow developers really are if they have the make different decisions based on different values.
Most developers I interact with who are what you might call 'AI skeptic', are most concerned with how this tech will be used, especially since its creation is rooted in mass scale disregard for ownership of anything.
> "The great cultural project of developers has been opposing any protection that might inconvenience a monetizable media-sharing site.... They stand up global-scale piracy networks",
IMO the author is here projecting or something, cause I literally never met someone with this opinion, and I've also been in tech/developer circles for over 20 years. Personal use of pirated content is very different from commercial distribution or making money of share sites. Not everyone's values are so rooted in making money with complete disregard to the impact of their actions in doing so.
I get it, the author wants to keep using LLMs are for people to stop trying to make them feel bad but trying to make a case for how their arguments are just dumb. But the author completely missed the 'why' behind the arguments. In the end, LLMs are a tool. Use them or not is up to the individual. But that doesn't give a blanket social license to use them in any way people, or more importantly, companies want.
You lost me here. I have often found it to be far more than a 10-minute style issue, but fundamental misunderstanding of the code purposes that I need to fix.
Tells you what you need to know about the AI culture.
What I tried is indeed "set up Cursor" or "copy-paste ChatGPT", and it was all bad, I have no idea what is this "agent" and "MCP" stupp.
This is a come back in 5 years when it works sort of thing.
The LLM coding agents might help you fire some swe to save a penny or two, but soon enough, it will also help your customers or suppliers to replace your business model.
But code is something I like to write myself, it's not about speed of producing code. It's keeping it such you still fully understand it.
As a software engineer, I need to call out this rhetoric. We are not all morally bankrupt. Many of the people with the most ethical backbone I know are also software engineers.
I don't use "AI" for the same reason I don't use amphetamine. I'm sure I could get stuff more stuff done faster if I used it, but I don't, because the manufacturing and the distribution of it is completely unethical.
The blatant stealing of intellectual property is only the tip of the shitberg. As bad are the giant ecological footprint[1], the colonialism of the grossly underpaid RLHF, and the conceding of even more of our digital lives to trillion dollar corporations.
We can and must ethically evaluate our work, and that includes our tools. Nobody's perfect, but doing one immoral thing does not mean you get a free pass to skip morality altogether.
1. https://www.greenpeace.de/publikationen/environmental-impact...
Q: but the code that AI generates is incoherent gibberish
Guess what, life’s tough. Fixing incoherent gibberish is part of the job you wuss. Honestly, I bet you write incoherent gibberish. Would you rather be fixing incoherent gibberish that an AI wrote or incoherent gibberish that you wrote?
Q: but I have to re-do everything it touches. Everything takes me ten times as long!
What are you, some kind of vibe-coding YouTuber? Everything taking ten times as long is part of the job, cupcakes. What’s important is that now it takes ten times as long automatically. I like the way Zed does it, where it tells you to leave while it’s changing stuff.
> People coding with LLMs today use agents. Agents get to poke around your codebase on their own. They author files directly. They run tools. They compile code, run tests, and iterate on the results. They also:
Every once in a while I see someone on X posting how they have 10 agents running at once building their code base, and I wonder if in 3 years most private industry coders will just be attending meetings to discuss what their agents have been working on, while people working on DoD contracts will be typing things into vim like a fool
If you've had a dog you know that "dog training" classes are actually owner training.
Same with AI tools. I see big gains for people who spend time to train themselves to work within the limitations. When the next generation of tools come out they can adapt quickly.
If this sounds tedious, thats becuase it is tedious. I spent many long weekends wrestling with tools silently wrecking my entire codebase, etc. And that's what I had to do to get the productivity improvements I have now.
I am curious about this part though
It's not just Rust. It's everything that has been created post the last vacuum. You need a large corpus for LLM to get decent-ish.
I would like to point out: LLM's look really intelligent until you converse with them in a less popular language, it will make weird mistakes and interpret your questions all wrong. I am Dutch and they really suck at that.
Seriously, this is the most based defense of LLM that I’ve read over the years. All the yapping in the thread only proves your points.
I want to see the hype but without paying up front. My focus area is c#, .net, js.
RIP Junior devs
Otherwise, well written and persuasive.
Yes, yes, and yes. SOOOO much yes. We are going to be in for a wild ride once everyone gets on board with the reality of coding agents.
The cost is a pretty obvious indication that it's nowhere near the capability of an engineer. A robotic worker that can produce code 24/7 without tiring would be worth even more than a person of equivalent skill. I don't know what kinds of goofy hiring ya'll are doing, but all of the juniors I've been involved in hiring actually worked out great.
I suspect C# Asp.Net Core not so much. Any thoughts?
OP therefore loses the argument.
So sick of hearing comparisons between LLMs and Juniors, first of all are we really are seniors? After almost 20 years sometimes I still think I know nothing. Second, when I was a Junior just me being younger, fresher and with an eager learner attidute brought a net positive impact on the team I joined back then, that I still hang out with those people to this day and we still have fun joining new projects together. I'm so sick of these snobbish "seniors" that keep throwing rocks at younger and well prepared people
I expected better from Thomas.
Things have changed.
I was willing to read until I got to this part. He can shove this drivel up his own ass for all I care if that's how he thinks. What a waste of time.
People like to think that intelligence is some intrinsic force but it's far better thought of as situational. We are contextually smarter or dumber based on the emotions we're trying to process from a situation and how our motivation reasoning allows us to avoid certain negative emotions.
The subset of people the OP is trying to argue against are people who are otherwise normally intelligent but have an emotional need for AI to be unimportant that they're unable to process so they build a social community of terrible arguments.
The nature of the internet is that visibility is terribly correlated (and often anti-correlated) with popularity so OP thinks this is some large contingent of people when it's instead algorithmically served conflict generators.
Writing against that is also an algorithmic conflict generator, as evidenced by the number of upvotes here. The correct response is to realize that people putting up terrible arguments about AI don't actually care about AI, they're using this issue to process unresolved trauma from other, unrelated areas of their life and taking their ideas literally instead of seriously is a waste and diversion. There are plenty of smart people with far more nuanced views that still disagree with you where they're interested in the content of the argument.
Let me know when I can use one of these without violating ITAR/EAR.
Yesterday I “wrote” 18k lines of code with Claude & Cursor. Mostly UI, some backend. This would have taken me a week without LLMs.
I’m sold.
Maybe you are, but I didn't get a Master's degree to produce quick shit for shareholders
1. Thomas is obviously very smart.
2. To be what we think of as "smart" is to be in touch with reality, which includes testing AI systems for yourself and recognizing their incredible power.
I use a prompt that convinces the LLM they are a Staff Engineer at Google and care deeply about best practices and writing clean, quality code.
While not perfect, it does improve the code quality.
you get a link to a figma design and you have to use your eyes and common sense to cobble together tailwind classes, ensure responsiveness, accessibility, try out your components to make sure they're not janky, test out on a physical mobile device, align margins, padding, truncation, wrapping, async loading states, blah blah you get it
LLMs still suck at all that stuff that requires a lot of visual feedback, after all, you're making an interface for humans to use, and you're a human
in contrast, when i'm working on a backend ticket ai feels so much more straightforward and useful
But if you're actively avoiding everything related to it, you might find yourself in a position where you're suddenly being left in the dust. Maybe not now, not next month, not next year, but who some time in the future. The models really are improving fast!
I've talked with devs that (claim they) haven't touched a model since ChatGPT was released - because it didn't live up to their expectations, and they just concluded it was a big nothingburger.
Even though I don't follow the development religiously anymore, I do try to get acquainted with new releases every 3 months or so.
I hate the term "vibe coding", but I personally know non-tech people that have vibe coded products / apps, shipped them, and make more money in sales than what most "legit" coders are making. These would be the same "idea people" that previously were looking for a coder to do all the heavy lifting. Something is changing, that's for sure.
So, yeah, don't sleepwalk through it.
Me: So what is your assessment of the discussion at Hacker News?
Claude: The Hacker News discussion strikes me as much more grounded than the blog post itself. What I find compelling is that many commenters are sharing specific technical experiences rather than making broad ideological arguments. When someone describes spending hours fighting with Claude agents that keep refactoring code into spaghetti, that's concrete data about the current state of these tools. ...
Me: Well, what about you, Claude? You are made by a company whose president has recently made some public statements saying that, you know, 50% of white collar jobs will be replaced soon. And the company, Anthropic, also is very aggressively developing and selling tools to help with AI coding. What are your thoughts about that?
Claude: That's a really fair question to ask me directly. I think there's an obvious tension in my position here since I'm built by a company that has clear financial incentives around AI adoption, including coding tools. When I look at my own capabilities honestly, I can see both sides of this. I'm genuinely useful for certain coding tasks like explaining unfamiliar code, generating boilerplate, or helping debug specific problems. I've helped people learn new programming languages faster than they might have otherwise. But I'm also clearly not at the level where I could replace an experienced software engineer working on complex system design or novel problem solving. ...
[1] https://claude.ai/share/c934e67c-f132-43e8-8c31-ab238b5be25a
> Professional software developers are in the business of solving practical problems for people with code. We are not, in our day jobs, artisans.
Well that's just wrong - we have failed to capture tremendous value through knowledge of our craft and to reduce it down to min-maxing effort/profit is not a pursuit that I can get behind. Nobody is talking about building Ivory towers, but we can do a lot better than most of the common denominator technologies in use today. We're failing our users most of the time (and of course people like the author say it doesn't matter because it doesn't show up in the quarterly results).
> Nobody cares if the logic board traces are pleasingly routed.
Speak for yourself.
I think it comes all down to that, do you have pride in what you do or you don’t ?
I make a wall with bricks, even if it will be covered with coating i will do my best to have regular joints and pacing.
Could make it faster, no one would notice the difference but me… i hate that feeling when you done something and you know it’s barely enough, just barely, it’s kind of shit and you really don’t want others to see it.
On the opposite side, some people will take pride in building wall twice as fast as me and won’t care it’s horrendous.
Both cases are valid, but me i know i can’t do a work I’m not proud of.
I get it. You're being so productive. (Or so you claim. I don't believe you.)
But if I don't affirm your work choices, or embrace them myself, you lose your mind like a little caffeinated squirrel.
> If you can’t metabolize the boring, repetitive code an LLM generates: skills issue!
Case in point. Touch grass.
> First, we need to get on the same page. If you're [6 months out of date with the technology you think of as overhyped after having observed it for the last 4 years or so], you're [not fairly evaluating it].
Perhaps, but this is an extremely unconvincing approach to the argument right out of the gate - it's basically on the same level as "communism has never been tried".
Especially if you're going to start out by describing your interlocutors as "crazy", rather than positing that an effectively completely new thing now exists that ought to be evaluated differently from Copilot et. al.
"You're nuts!" says the guy with his head intentionally buried in the sand. Also way to tell me your business model is a joke without telling me your business model is a joke. Enjoy it while it lasts.
I mean a tool is a tool, nothing wrong with that - but most of the resistence stems from AI being shoved down our throats at warp speed. Its already everywhere and I can't opt out, that stinks.
As for the skepticism in terms of adoption and usefulness, its mainly a question of whether or not it will continue improving - there's no way to no what lies ahead, but if it came to a grinding halt today well then the high water mark just isn't all that impressive.
> Yeah, we get it. You don’t believe in IPR. Then shut the fuck up about IPR. Reap the whirlwind.
This is the point that matters, and I don't think everyone is on the same page that LLMs are essentially over glorified data laundering.
The industry would get just as much "value" if we declared a jubilee and wiped out all licenses and allowed unlimited plagiarism (Looking at Zuckerburg and his 10 TB of pirated data). In fact, if AI owners published their training data sets with a capable search engine, I would bet money of it out performing LLMs in most cases. Why waste all that man power reinventing Netflix again? Just copy paste the code and give everyone their time back, sheesh.
> Kids today don’t just use agents; they use asynchronous agents. They wake up, free-associate 13 different things for their LLMs to work on, make coffee, fill out a TPS report, drive to the Mars Cheese Castle, and then check their notifications. They’ve got 13 PRs to review. Three get tossed and re-prompted. Five of them get the same feedback a junior dev gets. And five get merged.
I'm in a role that is behind the times, using a bespoke in-house framework that is immune to the benefits of LLMs, so I don't get to see what you see - so as a skeptic, I'm not convinced this isn't just the illusion of speed. I have not seen convincing results, show me the amazing things being made by AI (AI tooling itself does not count) - but yes, maybe that's because its all siloed into walled gardens.
> But something real is happening. My smartest friends are blowing it off. Maybe I persuade you. Probably I don’t. But we need to be done making space for bad arguments.
Yeah all the arguments have been made, good and bad, we're all waiting to see how it plays out. But I'd rather take the side of being a skeptic - if I'm right then I'm in the right place. If I'm wrong, that's cool too, I don't mind playing catch-up. But fully embracing the hype is, IMO, tantamount to putting all your eggs in one basket, seems like a needless risk but if that's worth it to you to get ahead then by all means, slurp up the hype.
Under the heading: "but you have no idea what the [LLM's] code is."
> Are you a vibe coding Youtuber? Can you not read code? If so: astute point. Otherwise: what the fuck is wrong with you? You’ve always been responsible for what you merge to main. You were five years go. And you are tomorrow, whether or not you use an LLM. If you build something with an LLM that people will depend on, read the code.
> People complain about LLM-generated code being “probabilistic”. No it isn’t. It’s code. [...] The LLM might be stochastic. But the LLM doesn’t matter. What matters is whether you can make sense of the result
> Reading other people’s code is part of the job. If you can’t metabolize the boring, repetitive code an LLM generates [...] how are you handling the chaos human developers turn out on a deadline?
The prior look upon Claude Code/Cursor/Windsurf much more favorably, as they are able to ship their ideas much faster.
This is a bit of a hot take, so I would love any replies to bring me back down to earth.
Interns don’t cost 20 bucks a month but training users in the specifics of your org is important.
Knowing what is important or pointless comes with understanding the skill set.
I used to believe in the stereotypical copyleft anti IP ethos. In the modern day of corporate LLMs and disrespectful scraping, the lay of the ground is different and people understandably should be less inclined to open source their code.
One point the author might be missing is:
> If you were trying and failing to use an LLM for code 6 months ago †, you’re not doing what most serious LLM-assisted coders are doing
The tech giants and AI bros alike have been bombarding us with "but look what it can do NOW", "the new shiny model is revolutionary, now buy tokens", "look what's around the corner" and whatever else for over 2 years now. Can you really blame a typical "skeptic" for not being interested in putting their time to veryfing these claims themselves after seeing mediocre results so many times? You start to develop a filter for these things.
> Are you a vibe coding Youtuber? Can you not read code? If so: astute point. Otherwise: what the fuck is wrong with you? You’ve always been responsible for what you merge to main. You were five years go. And you are tomorrow, whether or not you use an LLM.
This identifies a critical lever: it doesn't matter that AI sometimes makes mistakes, hallucinates, makes poor architecture decisions. All that matters is your PR to master, because that's what you always have been and will continue to be judged on. Merging trash? That's on you! Failing to utilise new tools and taking 10x longer to make the feature? Also on you!
If it becomes so easy to write apps, then everyone can build their own. They won't need your app.
The entire point of most SaaS business revolves around the simple concept/question: build vs. buy.
So if there's no point in buying since building is so easy, then AI isn't helping you like your software product/SaaS.
I think programmers may just become the "IT person" or "webmaster" or "AI guy" at a company that needs software. Not at a separate company building it for others.
This may greatly reduce the need for programmers. This will in turn reduce the need for fancy computers like that expensive MacBook Pro. It will reduce the need for web hosting. Don't get me wrong, these things will still exist...but the demand will decrease.
Essentially it will become the biggest bubble since the dot com bust. It'll be much larger.
The dust will eventually settle and we'll all adapt to a new world with AI...but I think the years and years of over specialization could come to a screeching halt.
The industry will likely implode and suck in all of the orbiting industries along with it. I hope I'm wrong, but I don't think people quite understand here. I greatly suspect there will be far fewer sales of high end computers. Compute power will continue to move to data centers.
Think about it this way. Code aside, because it's not just a coding thing. If everyone can create amazing videos and full on films with AI, then they don't need all those high end machines. They don't need tons of hard drive space that takes all the raw footage and different cuts to make a video. You prompt and the get the exact result you need. Assuming AI gets that good.
So the more we push into AI the greater than "gravity" here so to speak and the stronger the implosion. I just see an entire industry collapsing in on itself personally. If I'm believing all the FOMO logic out there.
If "we'll see our first one person billion dollar company." Whoever said that is a moron. You understand what that means, right? If anyone can do all that, then how could that even exist? Are we all going to become billionaires now? No, to say something as stupid as that is to say software is trivial and worthless. So how could anyone start a software business worth so much?
It's not that it's a fad. It's that the hype has gotten way ahead of the capability. CEOs laying off double digit percentages of their workforce because they believe that in 6 months AI will actually be able to do all those jobs and they want to get the message out to Wall St to juice the stock price today.
come at me.
okay then (profanity begets profanity)
> Reading other people’s code is part of the job. If you can’t metabolize the boring, repetitive code an LLM generates: skills issue!
Do you /intend/ to spend /most/ of your work life reading AI puke? If so: sanity issue! You are advocating for replacing creative action that gives you Flow with reaction that gives you hemorrhoids, as the /bulk of the work/. Fuck right off.
> How are you handling the chaos human developers turn out on a deadline?
By correcting the deadline setting methodology for the next round of development.
> Does an intern cost $20/month? Because that’s what Cursor.ai costs
then rely on Cursor.ai to raise your next-gen seniors who are going to be supposed to review AI puke in the future
> but the craft [...] Do it on your own time
Go choke on a bag of dicks. You are the problem. This is why quality no longer exists in the world, only lowest common denominator consumerist shit. This is why cars, home appliances, and tools have been getting both worse and more user-hostile. Because nobody values the quality of their own creative output anymore. Quality should not be an exception (which makes it very expensive), but the standard. Quality should be a near-commodity, and therefore at least occasionally affordable for anyone.
> I buy a fucking table
A ridiculously low quality, but hey, cheap!, table, that will fail you at the most inopportune moment. IKEA does have its place, but it's probably not in your office where you receive clients. Do you want them to see the table top fly off?
> They almost certainly have a bigger bag of algorithmic tricks than you do: radix tries, topological sorts, graph reductions, and LDPC codes
and you'll have to review that bag of algorithmic tricks embedded in AI puke, with potential hallucinations introducing obscure, critical mathematical bugs. Good luck verifying all that as a senior, based on proofs and first principles. A human coder will either use a well-reviewed library, or cite a textbook, and implement the textbook very closely, so you can follow it, and have some trust in the intent at least.
> No profession has demonstrated more contempt for intellectual property
how disingenuous. you are showing contempt for copyright. You are totally missing the point of copyleft. Copyleft doesn't advocate for anarchy, it advocates for user freedom via the software copyright framework. When AI barfs GPL'd code snippets into a proprietary code base, that harms user freedom.
> and watched it in seconds spot LVM metadata corruption issues
you are drifting; this is not code generation. Fuzzy pattern matching has always been fair game.
> Are you a vibe coding Youtuber? Can you not read code? If so: astute point. Otherwise: what the fuck is wrong with you?
> You’ve always been responsible for what you merge to main. You were five years go. And you are tomorrow, whether or not you use an LLM.
Loved this bit. In a professional setting, any code — AI-written or not — should be read by at least two people, the submitter and the reviewer.
Something fascinating about this HN crowd in particular is its lack of foresight. Companies producing medicine have to take into account longterm effects, yet people working in these “tech” companies push out AI after AI with not a single care in how it’s going to impact people.
I'm a skeptic because the megacorps have decide ai slop is the future and its going to push us deeper into hypercapitalist dystopia.
Programming as a career is going to end up as a ai slop janny. You aren't going to be paid more if your job is easier or whatever, you're gonna get paid less (and then end up doing more atomised work) because "an ai can do it". That's the direction that everything seems to be heading.
Very true and humbling.
> The median dev thinks Star Wars and Daft Punk are a public commons. The great cultural project of developers has been opposing any protection that might inconvenience a monetizable media-sharing site. When they fail at policy, they route around it with coercion. They stand up global-scale piracy networks and sneer at anybody who so much as tries to preserve a new-release window for a TV show.
This is pretty disingenuous and mean-spirited. He's using SV startup types to smear all developers, including the typical, humble, un- or undercompensated FOSS dev who absolutely respects intellectual property, and quite reasonably, expects their own IP to be respected as well. But because Sam Altman types follow the "It's Better to Beg for Forgiveness than to Ask for Permission" rule, altruistic, non-sociopathic developers have no right to complain?
This is dead right; my AI skeptic friend are ideologically opposed to LLMs and GenAI.
Their arguments have nothing to do with how good or bad LLMs are:
- IP issues (for code in a jupyter notebook which will never face IP scrutiny)
- because it exploits slaves and poor people (but so does the computer they use and the clothes they wear).
I also have a few programmer friends who make more legitimate complaints about code quality but because they are friends there is also an obvious subtext of ideological opposition.
My opinion is you should use LLMs anyway; you can hate "capitalism" and still take advantage of the system.
During my time as a programmer (gaming + astrophysics), the following things have happened:
1. The rise of GPGPU programming, which has enabled 1000x increases in performance
2. A revolution in CPU architecture, from single core, to massively multicore
3. C++98 - C++26
4. Transformational increases in CPU memory
5. (In the graphics space): dedicated raytracing hardware, the fully general purpose GPU + OpenCL/CUDA, deferred rendering, FSR/DLSS
6. Mobile phones were invented
7. 64-bit
8. The internet went from being for MSN and facebook to what it is today
9. The rise of Valve/Steam and the incredible transformational existence of the indie games market, which enables individuals to self publish their code and products for virtually nothing
10. Clang was born, and GCC came back to life. MSVC became standards compliant, finally
11. Rust was invented, and is taking off. People actually take security seriously now (!)
12. Unified CPU/GPU memory spaces, GPU architectures becoming scalar etc
All of these have had vastly more impact on my programming than LLM's ever had. I've always a bit wondered who people are who find it transformational, because I can vaguely gesture at any part of my programming tooling and find that its vastly different to 10 years ago
>interact with Git, run existing tooling, like linters, formatters, and model checkers, and make essentially arbitrary tool calls (that you set up) through MCP.
I mean.. you can just do all these things anyway. Its literally more work for me to use an LLM to run a linter than to run a linter (its built into my IDE). So's git. And formatters as well. You can also make arbitrary tool calls.. through your ide/scripts/precommit/postcommit/etc. I have no idea why you'd even want an LLM to do this!
>People complain about LLM-generated code being “probabilistic”. No it isn’t
>The LLM might be stochastic
????????? That's literally the entire point. I want deterministic answers, like clang based autocomplete instead of the nightmare that is prob- i mean stochastic autocomplete
>If hallucination matters to you, your programming language has let you down.
They matter beacuse they waste your time with bullshit that you then have to fix. No programming language can truly enforce correct logic constraints which are one of the primary difficulties with writing code. I literally have no idea what the OP is even doing, actually writing code has always been a vastly minimal amount of time - the main bottleneck is the train of thought to make sure that everything's correct. The literal typing is an afterthought. No programming language can bypass that step, they can (at best) handhold you through certain kinds of problems
>Does an intern cost $20/month? Because that’s what Cursor.ai costs.
For the moment. They'll jack up the prices while enshittifying, and then good luck. I do not want to subscribe to a service to be able to code. I use free IDEs and free tools for exactly this reason. If you rely on a paid-for 3rd party you are doing it wrong and will regret it. This is one of the silliest things in the whole article
>But people select languages in part based on how well LLMs work with them, so Rust people should get on that
They really don't. People select languages based on:
1. What they're taught at school/university
2. What language meets their domain requirements
3. What language their job requires
>Meanwhile, software developers spot code fragments seemingly lifted from public repositories on Github and lose their shit. What about the licensing? If you’re a lawyer, I defer. But if you’re a software developer playing this card? Cut me a little slack as I ask you to shove this concern up your ass. No profession has demonstrated more contempt for intellectual property.
Are you joking. Is this article a troll? Programmers give a lot of a crap about copyright law. GPL/MIT/etc are hugely important, and people respect the ever loving crap about it in general - even most major companies won't just blatantly pirate GPL works
There's a tonne more in this article, but it smells like someone who has literally no idea what they're talking about talking out of their arse, and it shows profoundly
How is it the responsibility of the Rust community that there weren't enough metric tons of free code for the machine to slurp up? And the phrasing makes it sound like it's the community's fault for not feeding OpenAI enough code to be stripped of its license and authorship and get blended into a fine latent soup. It's a lot like people coming to a one-man FOSS project with a laundry list of demands, expecting to be treated with the religious reverence of a major enterprise contract.
The whole tone, the pervasive "use it or you'll be left behind"—where users saying they don't want or need it only proves further evidence of its imminent apotheosis—superficially reminds me of previous FUDs.
And how is it not concerning that the thing described as intelligent needs billions of lines to generalize a language a human can learn from a single manual? Will it need hundreds of kLOC to internalize a new library, or even its new version, beyond in-context learning? The answer is yes; you are choosing to freeze the entire tech stack, when fixing its abstractions could actually save you from boilerplate, just so the machine can write it for you at $200 a month with a significant error rate.
...Right?
> If you’re making requests on a ChatGPT page and then pasting the resulting (broken) code into your editor, you’re not doing what the AI boosters are doing. No wonder you’re talking past each other.
They’re playing 3D chess while you’re stuck at checkers.
I do things suboptimally while learning the ropes or just doing things casually. That doesn’t mean that I judge the approach itself by my sloppy workflow. I’m able to make inferences about what a serious/experienced person would do. And it wouldn’t involve pasting things through three windows like I would do.
So of course I don’t judge AI by “ask chatty and paste the response”.
Yes indeed: “deploying agents” is what I would imagine the Ask Chatty And Paste workflow taken to Perfection to look like.
> LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google. They look things up themselves. Most importantly, they don’t get tired; they’re immune to inertia.
Most Rube Goldberg machines are very tedious and consist of fifty-too-many parts. But we can automate most of that for you—
I could not have ever imagined a more Flintstones meets Science Fiction clash than AI According To Software Engineers. You’re using AI to generate code. And no one cares how much. It’s just so tedious in any case.
A wortwhile approach would have been to aspire to make or generate technology artifacts that could be hidden behind a black box surface with a legible interface in front. Is the code tedious? Then make the AI come up with something that is well-designed, where the obvious things you want is given freely, where minor customizations are just minor tweaks, and larger deviations require only proportionally larger changes. Uh, how about no? How about generating 20KLOC line “starter” some-framework project with all the 20KLOC “tedious” bits hanging out, then we can iterate from there. The AI made a Git log and everything so it’s ya know audited.
But maybe I’m being unfair. Maybe we are moving towards something not quite as stupid as Deploy ChatGPT 50X? Or maybe it’s effectively going to behind a black box. Because ya know the AI will deal with it all by itself?
> Are you a vibe coding Youtuber? Can you not read code? If so: astute point. Otherwise: what the fuck is wrong with you?
> You’ve always been responsible for what you merge to main. You were five years go. And you are tomorrow, whether or not you use an LLM.
No!, and what the fuck is wrong with you? We are Flintstone technologists and I’ll be damned if I can’t get my AI brain chip-injected, genetically enhanced for speed horsey cyborg for my modern horse-drawn carriage patent.
What about that "superpower" we had to make something out of _nothing_ but our own brains? Sure, you may have looked up some code (do some research and learn), but that mental exercise.. just knowing your code inside-out, can never be compared to the "oh look at that, it actually works" with LLM stuff.
Well guess what, now, you traded that superpower for a subscription! (yes, you are now a middle man of software peddling - paying some company that stole other peoples code and ideas and did not pay _them anything_ in return), and eventually you will get to a point when the internet is down, you won't write any software that day cause you cant get the N model to work.
Did we go to the next step in software evolution? If it has a cost per month (and you pay thieves) (yes that what they are), its going to be a hard "no" for me, you just became a cow. Sure, now this is whats expected of you? OK. This has some strong neoliberalism drift, I assume I can be wrong so please comments welcome I am quite interested in this part.
yes, augmentations seem like amputations, is it McLuhan? I'm not sure, Will we retain our power to produce software? No! Humans dumb down very quickly, a generation is all it took to reduce reading to kids below expected educational levels [2]. There will be less professionals and lots of peddlers that have no skills, and software will be so junk it will be seriously scary to catch a plane in 10 years time.
Interesting times.
[1] https://news.ycombinator.com/item?id=44116724 [2] https://www.theguardian.com/lifeandstyle/2025/jun/02/gen-z-p...
Fast forward 10 years, the skeptics will be right on a few things, and the fanboys will be right on other things.
I for one know this isn’t just a “fad” like NFTs were, but I cannot help but feel skeptical about the sweeping claims of software engineering’s doomsday.
Programmers who don't work to deepen their understanding? Nah, sorry, not programmers I want anywhere near the code I'm working on.
To the extent that an LLM can help deepen understanding, good! Use it! Use its code. But you use it according to your intentional, systematic understanding of what the you're doing and why. And that understanding is not a function of code generation or, worse, code churn for the sake of feeling like you're going fast and paying less.
> LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious.
I've been writing code for over 30 years and I can't recall ever writing "tedious code". I don't know what this even means. Code, by definition, should not be tedious. At any layer of the stack, code should solve non-tedious problems. The whole point of code is to avoid tedium.
Most code, most projects? I haven't seen this and doubt it's even close to true - if it is God help us. If you write tedious code, find another place to work or another career.
> Sometimes, gnarly stuff needs doing. But you don’t wanna do it. So you refactor unit tests, soothing yourself with the lie that you’re doing real work.
Seriously who kills time by refactoring unit tests? Is this projection by the author?
> But an LLM can be told to go refactor all your unit tests.
Wtaf?? The unit tests are what stands between the blind ambitions of a statistical model and correct code. Handing over tests to an LLM? One staggers.
Tests represent part of that problem definition and solution. They're integral to the code. They foster and deepen understanding - not to be subject to the musings of statistical process, unless those musings serve as a dialectic.
> If you can’t metabolize the boring, repetitive code an LLM generates: skills issue! How are you handling the chaos human developers turn out on a deadline?
Answer: Slow the F down! Chaos and programming don't belong. Managers who drive their teams like this are bad managers.
> If hallucination matters to you, your programming language has let you down.
The author seems to think that programming is about producing code. Producing code that compiles is not a problem. Producing correct and coherent solutions to problems that need to be solved is a problem. Hallucinations aside, how will you know that generated code is something to keep or reject unless you have some understanding of what the code should do before you see it?
> Does an intern cost $20/month? Because that’s what Cursor.ai costs.
Squee! Code for all my friends!
> but it’s bad at rust
No, it's bad at understanding. And so are humans, initially. But it's our job to build and deepen understanding and we use code to facilitate that.
> Professional software developers are in the business of solving practical problems for people with code.
Correct.
> [LLMs] devour schlep, and clear a path to the important stuff, where your judgement and values really matter.
This begs the question, who defines what's important? It sounds like the author agrees that it's us, not the model. I agree this is a useful method. It's like any code review - it sharpens the reader no matter the code quality.
> As a mid-late career coder, I’ve come to appreciate mediocrity.
How cool and nonchalant what a rebel he says the opposite of everyone what a baddie!
I think the author means "appreciate naivety", meaning, a first pass that's good enough is good enough - there's no need to go further. I'll concede this, but it's a concession and not something to boast about. Odds are decent you'll come back to this naive implementation and revise it. Yes, at a certain point there's diminishing returns but good programmers know when to invest in revision and when to move on.
> LLMs really might displace many software developers.
We've seen this cycle. Technology and globalization puts pressure on programmers. There are legions of programmers in parts of the world that will generate code for $20 an hour. You can pay $20 and hour - or $20 a month - and generate mountains of code that blows chunks - not because it's ugly but because it's incoherent or because it solves the wrong problem. We. have. seen. this. It's why Silicon Valley still pays massive salaries to good programmers.
> The cool kid haughtiness about “stochastic parrots” and “vibe coding” can’t survive much more contact with reality.
Time will tell. In the meantime I'll keep investing in understanding, not code, and see how things shake out.
It hammers 100% of the time, with no failure.
It requires the same amount of labour from my part but it delivers the same outcome every time.
That is what tools do, they act as an extension and allow you to do things not easily done otherwise.
If the hammer sometimes hammers, sometimes squeaks and sometimes screws then it requires extra labour from my part just to make it do what purpose specific tools do, and that is where frustrations arise.
Make it do one thing excellent and we talk then.
These kinds of articles that heavily support LLM usage in programming seem to FOMO you or at least suggest that "you are using it wrong" in a weak way just to invalidate contrary or conservative opinions out of the discussion. These are pure rhetorics with such an empty discourse.
I use these tools everyday and every hour in strange loops (between at least Cursor, ChatGPT and now Gemini) because I do see some value in them, even if only to simulate a peer or rubber duck to discuss ideas with. They are extremely useful to me due to my ADHD and because they actually support me through my executive disfunction and analysis paralysis even if they produce shitty code.
Yet I'm still an AI skeptic because I've seen enough failure modes in my daily usage. I do not know how to feel when faced with these ideas because I feel out of the false dichotomy (pay for them, use them every day, but won't think them as valuable as the average AI bro). What's funny is that I'm yet to see an article that actually shows LLMs strengths and weaknesses in a serious manner and with actual examples. If you are going to defend a position, do it seriously ffs.
- Large C codebase (new feature and bugfix)
- Small rust codebase (new feature)
- Brand new greenfield frontend for an in-spec and documented openAPI API
- Small fixes to an existing frontend
It failed _dramatically_ in all cases. Maybe I'm using this thing wrong but it is devin-level fail. Gets diffs wrong. Passes phantom arguments to tools. Screws up basic features. Pulls in hundreds of line changes on unrelated files to refactor. Refactors again and again, over itself, partially, so that the uncompleted boneyard of an old refactor sits in the codebase like a skeleton (those tokens are also sent up to the model).
It genuinely makes an insane, horrible, spaghetti MESS of the codebase. Any codebase. I expected it to be good at svelte and solidJS since those are popular javascript frameworks with lots of training data. Nope, it's bad. This was a few days ago, Claude 4. Seriously, seriously people what am I missing here with this agents thing. They are such gluttonous eaters of tokens that I'm beginning to think these agent posts are paid advertising.
Why? Because if I’m not right then I am convinced that AI is going to be a force for evil. It will power scams on an unimaginable scale. It will destabilize labor at a speed that will make the Industrial Revolution seem like a gentle breeze. It will concentrate immense power and wealth in the hands of people who I don’t trust. And it will do all of this while consuming truly shocking amounts of energy.
Not only do I think these things will happen, I think the Altmans of the world would eagerly agree that they will happen. They just think it will be interesting / profitable for them. It won’t be for us.
And we, the engineers, are in a unique position. Unlike people in any other industry, we can affect the trajectory of AI. My skepticism (and unwillingness to aid in the advancement of AI) might slow things down a billionth of a percent. Maybe if there are more of me, things will slow down enough that we can find some sort of effective safeguards on this stuff before it’s out of hand.
So I’ll keep being skeptical, until it’s over.
The basic structure is this: six months ago, I tried llms and they were trash, but holy cow they have improved so much, now I can use them to avoid tedious that I don't like! Don't be an idiot like my skeptical past self.
Then they accuse everyone who now disagrees with their take on the tech as being an incurious luddite who is blinded by their anti-progress bias.
Personally, as a non-user but close observer of the tech, I never doubted that the tech would improve but there are obvious problems with the tech beyond hallucinations that have to do with human meaning, understanding and power relations that cannot be solved by making the tech better.
My challenge to all of the booster is this: try to articulate your own personal vision of both ai utopia and ai dystopia. I personally find it borderline impossible to even imagine a utopia emerging from genai, but it is extremely easy for me to imagine dystopia, especially given the entities that are controlling the tech and competing to "win" the ai arms race.
For me, the representation of the Chinese state as filtered through western media is already a dystopia. Of course, having not been to China myself and being unable to speak any of their languages, I cannot personally verify the representation. But by competing with the Chinese on ai (and I mean Chinese in the way we define them in the west, which I recognize may be very different from both the actual lived experience in China and their self-conception), we become more like our own negative stereotypes of them. It is essentially a race to disempower ordinary people, remove agency from their lives, hand it over to agents who most certainly do not have the best interest of living humans in mind and call this victory. To "win" the ai war as presently defined would likely be disaster for us all.
There are these ridiculous handwaving things about solving climate change or even human mortality with this tech even though there is no evidence whatsoever that it will do this. Just because the people building it say it will do these things doesn't mean we should trust them.
Imagine if a primatologist tried to tell us that because they have trained chimpanzees to recognize colors and some words better than a three year old, we should now stop investing in education and direct all of out resources into training chimps to do all of our repetitive tasks for us to liberate us from the drudgery of doing anything for ourselves. With enough resources, you would see an explosion in chimp capabilities and this would come directly at the expense of humans, who now have no work to do and just sit on top of a pyramid built by chimp labor. Not only would the things we made be worse than what we could have if we focused on developing our own human capacities instead of chimp capacities, but we would live in fear that the chimps (who also are much stronger than us) will wake up to their plight and rise up against their rulers. Humans would also rapidly lose our own capabilities and become much more like chimps than the humans of today. Sound familiar?
I tend to believe that as it is currently being developed, this tech is far more likely to lead us in a direction like the chimp apocalypse than some post labor utopia.
That doesn't mean that the tech isn't getting better or can't do impressive things. I can hold both things in my head at once. But I am much more concerned with human flourishing and well being than some bored engineers who don't actually like programming (or at least don't like it under the current industrial conditions) feeling like they are being liberated from the tedium of their work. And instead of solving the real the underlying problems that make the work so tedious, we instead compound the problem by having ai generate even more of the exact kind of code that caused the problem i the first place.
So far in my experience watching small to medium sized companies try to use it for real work, it has been occasionally useful for exploring apis, odd bits of knowledge etc, but overall wasted more time than it has saved. I see very few signs of progress.
The time has come for llm users to put up or shut up - if it’s so great, stop telling us and show and use the code it generated on its own.
Here’s the thing from the skeptic perspective: This statement keeps getting made on a rolling basis. 6 months ago if I wasn’t using the life-changing, newest LLM at the time, I was also doing it wrong and being a luddite.
It creates a never ending treadmill of boy-who-cried-LLM. Why should I believe anything outlined in the article is transformative now when all the same vague claims about productivity increases were being made about the LLMs from 6 months ago which we now all agree are bad?
I don’t really know what would actually unseat this epistemic prior at this point for me.
In six months, I predict the author will again think the LLM products of 6 month ago (now) were actually not very useful and didn’t live up to the hype.
One of the more eye-opening aspects of this technology is finding out how many of my peers seemingly have no understanding or respect for the concept of art.
You've already lost me, because I view programming as an art form. I would no more use AI to generate code than I would use it to paint my canvas.
I think the rest of the article is informative. It made me want to try some things. But it's written from the perspective of a CEO thinking all his developers are just salt miners; miners go into the cave and code comes out.
I think that's actually what my hangup is. It's the old adage of programmers simply "copying and pasting from stack overflow" but taken to the extreme. It's the reduction of my art into mindless labor.
This sounds like the "No true Scotsman" fallacy.
> People coding with LLMs today use agents. Agents get to poke around your codebase on their own.
That's a nonstarter for closed source, unless everything is running on-device, which I don't think it is?
> Part of being a senior developer is making less-able coders productive
Speak for yourself. It's not my job.
I’ve seen lots of people:
* think that conflating LLMs and “AI” produces a lot of poorly reasoned arguments
* doubt the economic narratives being built around LLM technology
* think the current rate of progress in the technology is basically flat
* think most “AI companies” resemble most crypto companies
An addendum to the last point: very few crypto skeptics deny that BitCoin is a thing or think it’s going away, either. It’s just strawmanning.
The problem is that LLM never performs consistently. It works when it works. It doesn't when It doesn't. No one knows exactly why, and no one can tell when it's gonna fail. For example, even to this day, GPT sometimes gives me wrong calculations, even when it is instructed to use calculator for that. Who knows why it ignores the instruction, nor why it can't reliably perform the addition of two integers. That really screws up with the automation.
Anyways, I'm really tired of skeptic-skeptics. I hate some people believe "half-full" is genuinely better than "half-empty". I refuse that idea completely. It's more about which context you're in. If you need exactly a cup of water, it's half-empty. If you are lucky to have some water, it's half-full. If you have a clear direction you want to go, you discover a lot of deficiencies. If you're there just enjoying the moment, yeah, you can just keep enjoying the new toy.
If you really believe in the power of LLMs then it’s time to wake up. The writing is on the wall. Automation the workforce further into precarious jobs.
The idea that tech workers can’t stop so-called “progress” is at best a dumb self-fulfilling prophecy. Our workplaces depend on us. We have the power to put the brakes on whatever our employers are up to by organizing and striking.
Tech workers should be organizing to prepare for the profit-taking moves management has in store for us as the tech gets better and better. If LLMs really live up to their potential, It’s just going to get worse from here.
They keep bragging about how many people are going to lose their jobs and they mean us. They’re bragging about firing us! It’s foolish for us to sit idly by while we are the ones who make them their profits.
I, knowing far less than him, would've had a much more elaborate prompt, and o3 would've proved a lot more competent/capable. Yet with my friend, since he knows so much already, and has such a high bar, he thinks the AI should be able to do a lot more with just a few basic words in a prompt... yet, for that same reason, he (understandably) doubts the inevitable sub-par output.
That's what makes all these debates about "Why are smart people doubting LLMs??" so pointless. The smarter you are, the less help you need, so the less prompting you do, the less context the model has, the less impressive the output, and the more the smart person thinks LLMs suck. With this logic, of course the smartest people are also the biggest skeptics!
The arguments against genAI tend to point out things like: 1. Its output is unreliable at best 2. That output often looks correct to an untrained eye and requires expert intervention to catch serious mistakes 3. The process automates away a task that many people rely on for income
And the response from genAI advocates tends to be dismissive...and I suspect it is, in part, because that last point is a positive for many advocates of genAI. Nobody wants to say it out loud, but when someone on Reddit or similar claims that even a 10% success rate outweighs the 90% failure rate, what they mean is most likely "A machine that works 10% of the time is better than a programmer who works 60-80% of the time because the machine is more than 6-to-8-times cheaper than the programmer".
There's also the classic line about how automation tends to create more jobs in the future than it destroys now, which itself is a source of big disconnects between pro-genAI and anti-genAI crowds--because it ignores a glaring issue: Just because there's gonna be more jobs in the future, doesn't mean I can pay rent with no job tomorrow!
"You can write an effective coding agent in a week" doesn't reassure people because it doesn't address their concerns. You can't persuade someone that genAI isn't a problem by arguing that you can easily deploy it, because part of the concern is that you can easily deploy it. Also, "you’re not doing what the AI boosters are doing" is flat-out incorrect, at least if you're looking at the same AI boosters I am--most of the people I've seen who claim to be using generated code say they're doing it with Claude, which--to my knowledge--is just an LLM, albeit a particularly advanced one. I won't pretend this is anything but anecdata, but I do engage with people who aren't in the "genAI is evil" camp, and...they use Claude for their programming assistance.
"LLMs can write a large fraction of all the tedious code you’ll ever need to write" further reinforces this disconnect. This is exactly why people think this tech is a problem.
The entire section on "But you have no idea what the code is!" falls apart the moment you consider real-world cases, such as [CVE-2025-4143](https://nvd.nist.gov/vuln/detail/cve-2025-4143), where a programmer who is a self-described expert working with Claude--who emphasizes that he checked over the results with a fine-toothed comb, and that he did this to validate his own skepticism about genAI!--missed a fundamental mistake in implementing OAuth that has been common knowledge for a long while. The author is correct in that reading other people's code is part of the job...but this is difficult enough when the thing that wrote the code can be asked about its methods, and despite advances in giving LLMs a sort of train of thought, the fact remains that LLMs are designed to output things that "look truth-y", not things that are logically consistent. (Ah, but we're not talking about LLMs, even though kentonv tells us that he just used an LLM. We're talking about agentic systems. No true AI booster would "just" use an LLM...)
I actually agree with the point about how the language can catch and point out some of the errors caused by hallucination, but...I can generate bad function signatures just fine on my own, thank you! :P In all seriousness, this addresses basically nothing about the actual point. The problem with hallucination in a setting like this isn't "the AI comes up with a function that doesn't exist", that's what I'm doing when I write code. The problem with hallucination is that sometimes that function which doesn't exist is my RSA implementation, and the AI 'helpfully' writes an RSA implementation for me, a thing that you should never fucking do because cryptography is an incredibly complex thing that's easy to fuck up and hard to audit, and you really ought to just use a library...a thing you [also shouldn't leave up to your AI.](https://www.theregister.com/2025/04/12/ai_code_suggestions_s...) You can't fix that with a language feature, aside from having a really good cryptography library built into the language itself, and as much as I'd love to have a library for literally everything I might want to do in a language...that's not really feasible.
"Does an intern cost $20/month? Because that’s what Cursor.ai costs," says the blog author, as if that's supposed to reassure me. I'm an intern. My primary job responsibility is getting better at programming so I can help with the more advanced things my employer is working on (for the record, these thoughts are my own and not those of my employer). It does not make me happy to know that Cursor.ai can replace me. This also doesn't address the problem that, frankly, large corporations aren't going to replace junior developers with these tools; they're going to replace senior developers, because senior developers cost more. Does a senior engineer cost 20 dollars a month? Because that's what Cursor.ai costs!
...and the claim that open source is just as responsible for taking jobs is baffling. "We used to pay good money for databases" is not an epic own, it is a whole other fucking problem. The people working on FOSS software are in fact very frustrated with the way large corporations use their tools without donating so much as a single red cent! This is a serious problem! You know that XKCD about the whole internet being held up by a project maintained by a single person in his free time? That's what you're complaining about! And that guy would love to be paid to write code that someone can actually fucking audit, but nobody will pay him for it, and instead of recognizing that the guy ought to be supported, you argue that this is proof that nobody else deserves to be supported. I'm trying to steelman this blogpost, I really am, but dude, you fundamentally have this point backwards.
I hope this helps others understand why this blogpost doesn't actually address any of my concerns, or the concerns of other people I know. That's kind of the best I can hope for here.
As someone who thinks modern copyright law is unfit for purpose, and more specifically that software shouldn't be copyrightable at all, my main complaint about LLMs is the same as Chinese spying and IP theft: I only care because they don't share.
I'm perfectly fine using open-weights LLMs[0] to do things LLMs are good at. But I would never trust using OpenAI or Perplexity's SaaSS models as a replacement for my own skills. Half the reason they put things behind a server is so they can spy on you[1], the other half is so they can promise a revenue stream to the people they plan to sell the company to so they can enshittify it. The only enshittification-resistant software is software people can't charge you for.
All the other things this blog post is complaining about are real reactions to real harms, even in the FOSS community. People use LLMs as skilled labor substitute and get burned, while creating a spam problem for everyone else. In fact, that's the real replacement threat for artists: diffusion models don't so much outperform human artists as much as they convince people not to pay for art.
[0] I don't give two flying fucks about the license terms.
[1] The judge in the NYT lawsuit is even ordering spying for evidentiary reasons
I will simply not hire anybody who is not good at using LLMs, and I don't think I would ever work with anybody who thinks they aren't very useful. It's like working with somebody who things compilers are useless. Obviously wrong, not worth spending time trying to convince.
To anyone who reads this article and disagrees with the central point: You are missing the most important thing that will happen in your career. You should reevaluate because you will be unemployable in a few years.
Ughh. That "own time." Spoken like a true middle manager who thinks passion is a liability.
so we can't look at the code and see the demonstrable engineering excellence
trust me bro, you fool, you poltroon,
Yes, the tech isn't perfect yet. Yes, it means existing industries will be displaced. Yes, it threatens to undermine the identities we have built as useful artists, programmers, data analysts, whatever. It is a scary, uncertain future, and on some days I am overcome with dread about what it means for me.
But ffs, we will figure it out. This technology, if it continues to improve at the pace it has, will open the door to us solving previously intractable problems - problems like curing cancer, endless energy and nuclear fusion, making us 100x more productive at building great housing and infrastructure. Let's get on board and make this thing a success, not fight it every step of the way because we want to preserve some basically arbitrary state of society.
I was a 3-4x programmer before. Now I’m a 9-15x programmer when wrangling LLMs.
This is a sea change and it’s already into “incredible” territory and shows no signs of slowing down.
> Think of anything you wanted to build but didn’t. You tried to home in on some first steps. If you’d been in the limerent phase of a new programming language, you’d have started writing. But you weren’t, so you put it off, for a day, a year, or your whole career.
I have been banging out little projects that I have wanted to exist for years but always had on the back burner. Write a detailed readme and ask the agent to interrogate you about the missing parts of the spec then update the README. Then have it make a TODO and start implementing. Give it another code base for style guide.
I’ve made more good and useful and working code in the last month than I have in the last two years.