“Manual” has a negative connotation. If I understand the article correctly, they mean “human coding remains key”. It’s not clear to me the GitHub CEO actually used the word “manual”, that would surprise me. Is there another source on this that’s either more neutral or better at choosing accurate words? The last thing we need is to put down human coding as “manual”; human coders have a large toolbox of non-AI tools to automate their coding.
From the AI overview (need to at least try that out a few times after all the talk about Google enshittification like by Cory Doctorow in this year's PyCon US keynote:
>Nemo malus nisi probetur" is a Latin legal maxim that translates to "No one is evil unless proven." It signifies that a person is presumed innocent until proven guilty in a court of law. This principle is fundamental to legal systems that value fairness and due process, ensuring that accusations are substantiated before a person is considered culpable.
Here's a breakdown of the phrase:
Nemo: No one
malus: Evil, bad, wicked
nisi: Unless
probetur: Is proven, is shown, is demonstrated
The maxim essentially means that one should not be treated as inherently bad or guilty without sufficient evidence. This principle is closely related to the concept of "presumption of innocence" and is crucial for upholding justice and protecting individual rights.
Often when you're calling something "manual" you're taking something off the automated happy path to tediously and slowly wind the thing through its complex process by hand. The difference between manual coding and human coding is tedium and laboriousness. It's not laborious to program, but the phrase "manual coding" evokes that image.
Maybe that’s what they’ve been doing? No one using Vim, Emacs, or Unix as an IDE would say they do manual coding with the amount of automation that usually goes there.
It's interesting to see a CEO express thoughts on AI and coding go in a slightly different direction.
Usually the CEO or investor says 30% (or some other made up number) of all code is written by AI and the number will only increase, implying that developers will soon be obsolete.
It's implied that 30% of all code submitted and shipped to production is from AI agents with zero human interaction. But of course this is not the case, it's the same developers as before using tools to more rapidly write code.
And writing code is only one part of a developer's job in building software.
He’s probably more right than not. But he also has a vested interest in this (just like the other CEOs who say the opposite), being in the business of human-mediated code.
Presumably you're aware that the full name of Microsoft's Copilot AI code authoring tool is "GitHub Copilot", that GitHub developed it, and that he runs GitHub.
I was talking about the Microsoft CEO quote everyone is misquoting. He said something to affect of "at Microsoft we have _some_ repos where up to 30% of the code is _generated_". This could mean they have one C++ repo for one tool that has 30% generated header files without any LLM usage at all. Of course that is probably not what he meant and the least generous view, but I find it extremely suspect to believe that any large company, much less Microsoft, would have third of their code produced by LLMs. I find it hard to believe any profitable software company has that high of a percentage.
Going by the content of the linked post, this is very much a misleading headline. There is nothing in the quotes that I would read as an endorsement of "manual coding", at least not in the sense that we have used the term "coding" for the past decades.
"He warned that depending solely on automated agents could lead to inefficiencies. For instance, spending too much time explaining simple changes in natural language instead of editing the code directly."
Lots of changes where describing them in English takes longer than just performing the change. I think the most effective workflow with AI agents will be a sort of active back-and-forth.
Yeah, I can’t count the number of times I’ve thought about a change, explained it in natural language, pressed enter, then realized I’ve already arrived at the exact change I need to apply just by thinking it through. Oftentimes I even beat the agent at editing it, if it’s a context-heavy change.
How active are you ok with/want? I've just joined an agent tooling startup (yesh...I wrote that huh...) - and it's something we talk a lot about internally, we're thinking it's fine to do back and forth, tell it frankly it's not doing it right, etc, but some stuff might be annoying? Do you have a sense of how this might work to your mind? Thanks! :)
AI can very efficiently apply common patterns to vast amounts of code, but it has no inherent "idea" of what it's doing.
Here's a fresh example that I stumbled upon just a few hours ago. I needed to refactor some code that first computes the size of a popup, and then separately, the top left corner.
For brevity, one part used an "if", while the other one had a "switch":
I wanted the LLM to refactor it to store the position rather than applying it immediately. Turns out, it just could not handle different things (if vs. switch) doing a similar thing. I tried several variations of prompts, but it very strongly leaning to either have two ifs, or two switches, despite rather explicit instructions not to do so.
It sort of makes sense: once the model has "completed" an if, and then encounters the need for a similar thing, it will pick an "if" again, because, well, it is completing the previous tokens.
Harmless here, but in many slightly less trivial examples, it would just steamroll over nuance and produce code that appears good, but fails in weird ways.
That said, splitting tasks into smaller parts devoid of such ambiguities works really well. Way easier to say "store size in m_StateStorage and apply on render" than manually editing 5 different points in the code. Especially with stuff like Cerebras, that can chew through complex code at several kilobytes per second, expanding simple thoughts faster than you could physically type them.
Just pointing out that there are limits and there’s no reason to believe that models will improve indefinitely at the rates we’ve seen these last couple of years.
There is reason to believe that humans will keep trying to push the limitations of computation and computer science, and that recent advancements will greatly accelerate our ability to research and develop new paradigms.
Look at how well Deepseek performed with the limited, outdated hardware available to its researchers. And look at what demoscene practitioners have accomplished on much older hardware. Even if physical breakthroughs ceased or slowed down considerably, there is still a ton left on the table in terms of software optimization and theory advancement.
And remember just how young computer science is as a field, compared to other human practices that have been around for hundreds of thousands of years. We have so much to figure out, and as knowledge begets more knowledge, we will continue to figure out more things at an increasing pace, even if it requires increasingly large amounts of energy and human capital to make a discovery.
I am confident that if it is at all possible to reach human-level intelligence at least in specific categories of tasks, we're gonna figure it out. The only real question is whether access to energy and resources becomes a bigger problem in the future, given humanity's currently extraordinarily unsustainable path and the risk of nuclear conflict or sustained supply chain disruption.
I agree. And if human civilization survives, your concerns about energy and resources will be only short term on the scale of civilization, especially as we make models more efficient.
The human brain uses just 20 watts of power, so it seems to me like it is possible to reach human-level intelligence in principle by using much greater power and less of the evolutionary refinement over billions of years that the brain has.
> And remember just how young computer science is as a field, compared to other human practices that have been around for hundreds of thousands of years.
How long do you think Homo sapiens have been on Earth and how long has civilization been here?
I’ve been programming since 89. I know what you can squeeze into 100k.
But you can only blast so much electricity into a dense array of transistors before it melts the whole thing and electrons jump rails. We hit that limit a while ago. We’ve done a lot of optimization of instruction caching, loading, and execution. We front loaded a ton of caching in front of the registers. We’ve designed chips specialized to perform linear algebra calculations and scaled them to their limits.
AI is built on scaling the number of chips across the board. Which has the effect of requiring massive amounts of power. And heat dissipation. That’s why we’re building out so many new data centres: each one requiring land, water, and new sources of electricity generation to maintain demand levels for other uses… those sources mostly being methane and coal plants.
Yes, we might find local optimizations in training to lower the capital cost and external costs… but they will be a drop in the bucket at the scale we’re building out this infrastructure. We’re basically brute forcing the scale up here.
And computer science might be older than you think. We just used to call it logic. It took some electrical engineering innovations to make the physical computers happen but we had the theoretical understanding of computation for quite some time before those appeared.
A young field, yes, and a long way to go… perhaps!
But let’s not believe that innovation is magic. There’s hard science and engineering here. Electrons can only travel so fast. Transistor density can only scale so much. Etc.
> How long do you think Homo sapiens have been on Earth and how long has civilization been here?
I already corrected my typo in a child comment.
> We’re basically brute forcing the scale up here
Currently, but even that will eventually hit thermodynamic and socioeconomic limits, just as single chips are.
> And computer science might be older than you think. We just used to call it logic.
In my opinion, two landmark theory developments were type theory and the lambda calculus. Type theory was conceived to get around Russell's paradox and others, which formal logic could not do on its own.
As far as hardware, sure we had mechanical calculators in the 17th century, and Babbage's analytical engine in the 19th century, and Ada Lovelace's program, but it wasn't until the mid-20th century that computer science coalesced as its own distinct field. We didn't used to call computer science logic; it's a unification of physical advancements, logic and several other domains.
> Electrons can only travel so fast.
And we have no reason to believe that current models are at all optimized on a software or theoretical level, especially since, as you say yourself, we are currently just focused on brute-forcing innovation as its the more cost-effective solution for the time being.
But as I said, once theoretical refinement becomes more cost-effective, we can look at the relatively short history of computer science to see just how much can be done on older hardware with better theory:
>> Even if physical breakthroughs ceased or slowed down considerably, there is still a ton left on the table in terms of software optimization and theory advancement.
The interesting questions happen when you define X, Y and Z and time. For example, will llms be able to solve the P=NP problem in two weeks, 6 months, 5 years, a century? And then exploring why or why not
I am working on a GUI for delegating coding tasks to LLMs, so I routinely experiment with a bunch of models doing all kinds of things. In this case, Claude Sonnet 3.7 handled it just fine, while Llama-3.3-70B just couldn't get it. But that is literally the simplest example that illustrates the problem.
When I tried giving top-notch LLMs harder tasks (scan an abstract syntax tree coming from a parser in a particular way, and generate nodes for particular things) they completely blew it. Didn't even compile, let alone logical errors and missed points. But once I broke down the problem to making lists of relevant parsing contexts, and generating one wrapper class at a time, it saved me a whole ton of work. It took me a day to accomplish what would normally take a week.
Maybe they will figure it out eventually, maybe not. The point is, right now the technology has fundamental limitations, and you are better off knowing how to work around them, rather than blindly trusting the black box.
3) autistic rigidity regarding a single hallucination throwing the whole experience off
4) subconscious anxiety over the threat to their jerbs
5) unnecessary guilt over going against the tide; anything pro AI gets heavily downvoted on Reddit and is, at best, controversial as hell here
I, for one, have shipped like literally a product per day for the last month and it's amazing. Literally 2,000,000+ impressions, paying users, almost 100 sign ups across the various products. I am fucking flying. Hit the front page of Reddit and HN countless times in the last month.
Idk if I break down the prompts better or what. But this is production grade shit and I don't even remember the last time I wrote more than two consecutive lines of code.
If you are launching one product per day, you are using LLMs to convert unrefined ideas into proof-of-concept prototypes. That works really well, that's the kind of work that nobody should be doing by hand anymore.
Except, not all work is like that. Fast-forward to product version 2.34 where a particular customer needs a change that could break 5000 other customers because of non-trivial dependencies between different parts of the design, and you will be rewriting the entire thing by humans or having it collapse under its own weight.
But out of 100 products launched on the market, only 1 or 2 will ever reach that stage, and having 100 LLM prototypes followed by 2 thoughtful redesigns is way better than seeing 98 human-made products die.
Can you provide links to these 30 products you have shipped?
I keep hearing how people are so god damn productive with LLMs, but whenever I try to use them they can not reliably produce working code. Usually producing something that looks correct at first, but either doesn't work (at all or as intended).
Going over you list:
1. if the problem is that I need to be very specific with how I want LLM to fix the issue, like providing it the solution, why wouldn't I just make the change myself?
2. I don't even know how you can think that not vibe coding means you lack experience
3. Yes. If the model keeps trying to use non-existent language feature or completely made up functions/classes that is a problem and nothing to do with "autism"
4. This is what all AI maximalists want to think; that only reason why average software developer isn't knee deep in AI swamp with them is that they are luddites who are just scared for their jobs. I personally am not as I have not seen LLMs actually being useful for anything but replacing google searches.
5. I don't know why you keep bringing up Reddit so much. I also don't quite get who is going against the tide here, are you going against the tide of the downvotes or am I for not using LLMs to "fucking fly"?
>But this is production grade shit
I truly hope it is, because...
>and I don't even remember the last time I wrote more than two consecutive lines of code.
Means if there is a catastrophic error, you probably can't fix it yourself.
> if the problem is that I need to be very specific with how I want LLM to fix the issue, like providing it the solution, why wouldn't I just make the change myself?
I type 105 wpm on a bad day. Try gpt-4.1. It types like 1000 wpm. If you can formally describe your problem in English and the number of characters in the English prompt is less than whatever code you write, gpt-4.1 will make you faster.
Obviously you have to account for gpt-4.1 being wrong sometimes. Even so, if you have to run two or three prompts to get it right, it still is going to be faster.
> I don't even know how you can think that not vibe coding means you lack experience
If you lack experience, you're going to prompt the LLM to do the wrong thing and engineer yourself into a corner and waste time. Or you won't catch the mistakes it makes. Only experience and "knowing more than LLM" allows you to catch its mistakes and fix them. (Which is still faster than writing the code yourself, merely by way of it typing 1000 wpm.)
> If the model keeps trying to use non-existent language feature or completely made up functions/classes that is a problem and nothing to do with "autism"
You know that you can tell it those functions are made up and paste it the latest documentation and then it will work, right? That knee-jerk response makes it sound like you have this rigidity problem, yourself.
> I personally am not as I have not seen LLMs actually being useful for anything but replacing google searches.
Nothing really of substance here. Just because you don't know how to use this tool that doesn't mean no one does.
This is the least convincing point for me, because I come along and say "Hey! This thing has let me ship far more working code than before!" and then your response is just "I don't know how to use it." I know that it's made me more productive. You can't say anything to deny that. Do you think I have some need to lie about this? Why would I feel the need to go on the internet and reap a bunch of downvotes while peddling some lie that does stand to get me anything if I convince people of the lie?
> I also don't quite get who is going against the tide here, are you going against the tide of the downvotes
Yeah, that's what I'm saying. People will actively shame and harass you for using LLMs. It's mind boggling that a tool, a technology, that works for me and has made me more productive, would be so vehemently criticized. That's why I listed these 5 reasons, the only reasons I have thought of yet.
> Means if there is a catastrophic error, you probably can't fix it yourself.
See my point about lacking experience. If you can't do the surgery yourself every once in a while, you're going to hate these tools.
Really, you've just made a bunch of claims about me that I know are false, so I'm left unconvinced.
I'm trying to have a charitable take. I don't find joy in arguing or leaving discussions with a bitter taste. I genuinely don't know why people are so mad at me claiming that a tool has helped me be more productive. They all just don't believe me, ultimately. They all come up with some excuse as to why my personal anecdotes can be dismissed and ignored: "even though you have X, we should feel bad for you because Y!" But it's never anything of substance. Never anything that has convinced me. Because at the end of the day, I'm shipping faster. My code works. My code has stood the test of time. Insults to my engineering ability I know are demonstrably false. I hope you can see the light one day. These are extraordinary tools that are only getting better, at least by a little bit, in the foreseeable future. Why deny?
Would also love to see those daily shipped products. What I see on reddit is the same quiz done several times just for different categories and the pixel art generator. That does not look like shipping a product per day as you claim.
On my main, not gonna dox myself. Being pro AI is clearly a faux pas for personal branding.
Just a few days ago got flamed for only having 62 users on GameTorch. Now up to 91 and more paying subs. Entire thing written by LLMs and hasn't fallen over once. I'd rather be a builder than an armchair critic.
People would rather drag you down in the hole that they're in than climb out.
This is going to be all over the place and possibly hard to follow, I am just going to respond in "real time" as I read your comment, if you think that is too lazy to warrant reading I completely understand. I hope you have a nice day.
WPM is not my limiting factor. Maybe the difference is that I am not working on trivial software so a lot of thought goes into the work, typing is the least time consuming part. Still I don't see how your 105 wpm highly descriptive and instructive English can be faster than just fixing the thing. Even if after you prompt your LLM takes 1ms to fix the issue you have probably already wasted more time by debugging the issue and writing the prompt.
So your "you lack engineering experience" was actually "you don't know LLMs well", maybe use the words you intend and not make them into actual insults.
I am not going to be pasting in any C++ spec into an LLM.
Yet when I checked your profile you have shipped one sprite image generator website. I find all these claims so hard to believe. Everyone just keeps telling me how they are making millions off of LLMs but no one has to the recipes to show. Just makes me feel like you have stock in OpenAI or something and are trying your hardest to pump it up.
I think the shaming and harassing is mostly between your ears, at least I am not trying to shame or harass you for using LLMs, if anything I want to have superpowers too. If LLMs really work for you that is nice and you should keep doing it, I just have not seen the evidence you are talking about. I am willing to admit that it could very well be a skill issue, but I need more proof than "trust me" or "1000 wpm".
I don't think I have made any claims about you, although you have used loaded language like "autism" and "lack of engineering experience" and heavily implied that I am just too dumb to use the tools.
>I'm trying to have a charitable take.
c'mon nothing about your comments has been charitable in anyway. No one is mad at you personally. Do not take criticism of your tools as personal attacks. Maybe the tools will get good, but again my problem with LLMs and hype around them is that no one has been able to demonstrate them actually being as good as the hype suggests.
What is everyone working on that takes more than five minutes to think about?
For me, the work is insurmountable and infinite, while coming up with the solution is never too difficult. I'm not saying this to be cocky. I mean this:
In 99.9999999999% of the problems I encounter in software engineering, someone smarter than me has already written the battle tested solution that I should be using. Redis. Nginx. Postgres. etc. Or it's a paradigm like depth first search or breadth first search. Or just use a hash set. Sometimes it's a little crazier like Bloom filters but whatever.
Are you like constantly implementing new data structures and algorithms that only exist in research papers or in your head?
Once you've been engineering for 5 or 10 years, you've seen almost everything there is to see. Most of the solutions should be cached in your brains at that point. And the work just amounts to tedious, unimportant implementation details.
Maybe I'm forgetting that people still get bogged down in polymorphism and all that object oriented nonsense. If you just use flat structs, there's nothing too complicated that could possibly happen.
I worked in HFT, for what it's worth, and that should be considered very intense non-CRUD "true" engineering. That, I agree, LLMs might have a little more trouble with. But it's still nothing insane.
Software engineering is extremely formulaic. That's why it's so easy to statistically model it with LLMs.
I write embedded software in C++ for industrial applications. We have a lot of proprietary protocols and custom hardware. We have some initiatives to train LLMs with our protocols/products/documentation, but I have not been impressed with the results. Same goes with our end-to-end testing framework. I guess it isn't so popular so the results vary a lot.
I have been doing this for 8 year and while yes I have seen a lot you can't just copy-paste solutions due to flash, memory, and performance constraints.
Again maybe this is a skill issue and maybe I will be replaced with an LLM, but so far they seem more like cool toys. I have used LLMs to write AddOns for World of Warcraft since my Lua knowledge is mostly writing Wireshark plugins for our protocols and for that it has been nice, but it is nothing someone who actually works with Lua or with WoW API couldn't produce faster or just as fast, because I have to describe what I want and then try and see if the API the LLM provides exists and if it works as the LLM assumed it would.
Again, I appreciate the reply. I think my view on LLMs is skewed towards the positive because I've only been building CRUD apps, command line tools, and games with them. I apologize if I came off as incendiary or offensive.
> AI can very efficiently apply common patterns to vast amounts of code, but it has no inherent "idea" of what it's doing.
AI stands for Artificial Intelligence. There are no inherent limits around what AI can and can't do or comprehend. What you are specifically critiquing is the capability of today's popular models, specifically transformer models, and accompanying tooling. This is a rapidly evolving landscape, and your assertions might no longer be relevant in a month, much less a year or five years. In fact, your criticism might not even be relevant between current models. It's one thing to speak about idiosyncrasies between models, but any broad conclusions drawn outside of a comprehensive multi-model review with strict procedure and controls is to be taken with a massive grain of salt, and one should be careful to avoid authoritative language about capabilities.
It would be useful to be precise in what you are critiquing, so that the critique actually has merit and applicability. Even saying "LLM" is a misnomer, as modern transformer models are multi-modal and trained on much more than just textual language.
What a ridiculous response, to scold the GP for criticising today's AI because tomorrow's might be better. Sure, it might! But it ain't here yet buddy.
Lots of us are interested in technology that's actually available, and we can all read date stamps on comments.
You're projecting that I am scolding OP, but I'm not. My language was neutral and precise. I presented no judgment, but gave OP the tools to better clarify their argument and express valid, actionable criticism instead of wholesale criticizing "AI" in a manner so imprecise as to reduce the relevance and effectiveness of their argument.
> But it ain't here yet buddy . . . we can all read date stamps on comments.
That has no bearing on the general trajectory that we are currently on in computer science and informatics. Additionally, your language is patronizing and dismissive, trading substance for insult. This is generally frowned upon in this community.
You failed to actually address my comment, both by failing to recognize that it was mainly about using the correct terminology instead of criticizing an entire branch of research that extends far beyond transformers or LLMs, and by failing to establish why a rapidly evolving landscape does not mean that certain generalizations cannot yet be made, unless they are presented with several constraints and caveats, which includes not making temporally-invariant claims about capabilities.
I would ask that you reconsider your approach to discourse here, so that we can avoid this thread degenerating into an emotional argument.
> They were obviously not trying to make a sweeping comment about the entire future of the field
OP said “AI can very efficiently apply common patterns to vast amounts of code, but it has no inherent "idea" of what it's doing.”
I'm not going to patronize you by explaining why this is not "very precise", or why its lack of temporal caveats is an issue, as I've already done so in an earlier comment. If you're still confused, you should read the sentence a few times until you understand. OP did not even mention which specific model they tested, and did not provide any specific prompt example.
> Are you using ChatGPT to write your loquacious replies?
If you can't handle a few short paragraphs as a reply, or find it unworthy of your time, you are free to stop arguing. The Hacker News guidelines actually encourage substantive responses.
I also assume that in the future, accusing a user of using ChatGPT will be against site guidelines, so you may as well start phasing that out of your repertoire now.
Here are some highlights from the Hacker News guidelines regarding comments:
- Don't be snarky
- Comments should get more thoughtful and substantive, not less, as a topic gets more divisive.
- Assume good faith
- Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken.
This is a lot of words, but does any of it contradict this:
> AI can very efficiently apply common patterns to vast amounts of code, but it has no inherent "idea" of what it's doing.”
Are you saying that AI does have an inherent idea of what it's doing or is doing more than that? Today?
We're in an informal discussion forum. I don't think the bar we're looking for is some rigorous deductive proof. The above matches my experience as well. Its a handy applied interactive version of an Internet search.
If someone has a different experience that would be interesting. But this just seems like navel-gazing over semantics.
> Are you saying that AI does have an inherent idea of what it's doing or is doing more than that?
No. I stated that OP cannot make that kind of blanket, non-temporally constrained statements about artificial intelligence.
> We're in an informal discussion forum. I don't think the bar we're looking for is some rigorous deductive proof
We're in a technology-oriented discussion forum, the minimum bar to any claim should be that it is supported by evidence, otherwise it should be presented as what it is: opinion.
> this just seems like navel-gazing over semantics.
In my opinion, conversation is much easier when we can agree that words should mean something. Imprecise language matched with an authoritative tone can mislead an audience. This topic in particular is rife with imprecise and uninformed arguments, and so we should take more care to use our words correctly, not less.
Furthermore, my argument goes beyond semantics, as it also deals with the importance of constraints when making broad, unbacked claims.
I learned neural networks around 2000, and it was old technology then. The last real jump we saw was going from ChatGPT 3.5 to 4, and that is more than 2 years ago.
It seems you don't recollect how much time passed without any big revolutions in AI. Deep learning was a big jump. But when the next jump comes? Might be tomorrow, but looking at history, might be in 2035.
According to what I see, the curve has already flattened and now only a new revolution could get us to the next big step.
Agree, the AI companies aren’t able to improve the base models so they’re pivoting to making add-ons like “agents” which seem to only be instructions atop the base models.
Since I can't seem to add an edit to my post, here's a realization:
My 2035 prediction actually seems pretty optimistic. It was more than 20 years that we haven't seen any big AI revolutions, so 2045 would be more realistic.
And it seems our current AI is also not going to get us there any faster.
> AI stands for Artificial Intelligence. There are no inherent limits around what AI can and can't do or comprehend.
Artificial, as in Artificial sand or artificial grass. Sure, it appears as sand or grass at first, but upon closer examination, it becomes very apparent that it's not real. Artificial is basically a similar word to magic - as in, it offers enough misdirection in order for people to think there might be intelligence, but upon closer examination, it's found lacking.
It's still impressive that it can do that, going all the way back to gaming AIs, but it's also a veil that is lifted easily.
What's amazing with "vibe coding" is the number of non tecchie who now decide they want to code. They understand that a new tool is on the block and they realize how it could help their business but they also realize that to really harness this superpower you need to code.
So out of nowhere both my brother and my wife, without concerting themselves (they don't live on the same continent), are now taking coding lessons. Not AI related: just learning to code in Python.
And suddenly I'm winning lots of cookie points: my wife realizes that coding is not something that's that simple and that it's really the coders who have it easy with AI.
As for the AI boom: it's another tool. I use it daily (and pay for it). But it's still just a tool.
Wake me up when 95% of Linux's kernel code is written by an AI.
My guess is they will settle for 2x the productivity as a before-AI developer as their skill floor, but then not take a look at how long meetings and other processes take.
Why not look at Bob who takes like 2 weeks to write tickets on what they actually want in a feature? Or Alice who's really slow getting Figma designs done and validated? How nice would having a "someone's bothered a developer" metric be and having the business seek to get that to zero and talk very loudly about it as they have about developers?
Because everyone who bothers the developers is actually in charge, and they would never want to reduce their utilization, all of this removal of need to focus on the code makes them excited that you can be in even more meetings.
I think most programmers would agree that thinking represents the majority of our time. Writing code is no different than writing down your thoughts, and that process in itself can be immensely productive -- it can spark new ideas, grant epiphanies, or take you in an entirely new direction altogether. Writing is thinking.
I think an over-reliance, or perhaps any reliance, on AI tools will turn good programmers into slop factories, as they consistently skip over a vital part of creating high-quality software.
You could argue that the prompt == code, but then you are adding an intermediary step between you and the code, and something will always be lost in translation.
I think this misses the point. You're right that programmers still need to think. But you're wrong thinking that AI does not help with that.
With AI, instead of starting with zero and building up, you can start with a result and iterate on it straight away. This process really shines when you have a good idea of what you want to do, and how you want it implemented. In these cases, it is really easy to review the code, because you knew what you wanted it to look like. And so, it lets me implement some basic features in 15 minutes instead of an hour. This is awesome.
For more complex ideas, AI can also be a great idea sparring partner. Claude Code can take a paragraph or two from me, and then generate a 200-800 line planning document fleshing out all the details. That document: 1) helps me to quickly spot roadblocks using my own knowledge, and 2) helps me iterate quickly in the design space. This lets me spend more time thinking about the design of the system. And Claude 4 Opus is near-perfect at taking one of these big planning specifications and implementing it, because the feature is so well specified.
So, the reality is that AI opens up new possible workflows. They aren't always appropriate. Sometimes the process of writing the code yourself and iterating on it is important to helping you build your mental model of a piece of functionality. But a lot of the time, there's no mystery in what I want to write. And in these cases, AI is brilliant at speeding up design and implementation.
Based on your workflow, I think there is considerable risk of you being wooed by AI into believing what you are doing is worthwhile. The plan AI offers is coherent, specific, it sounds good. It's validation. Sugar.
I know the tools and environments I am working in. I verify the implementations I make by testing them. I review everything I am generating.
The idea that AI is going to trick me is absurd. I'm a professional, not some vibe coding script kiddie. I can recognise when the AI makes mistakes.
Have the humility to see that not everyone using AI is someone who doesn't know what they are doing and just clicks accept on every idea from the AI. That's not how this works.
We're talking about software development here, not misinformation about politics or something.
Software is incredibly easy to verify compared to other domains. First, my own expertise can pick up most mistakes during review. Second, all of the automated linting, unit testing, integration testing, and manual testing is near guaranteed to pick up a problem with the functionality being wrong.
So, how exactly do you think AI is going to trick me when I'm asking it to write a new migration to add a new table, link that into a model, and expose that in an API? I have done each of these things 100 times. It is so obvious to me when it makes a mistake, because this process is so routine. So how exactly is AI going to trick me? It's an absurd notion.
AI does have risks with people being lulled into a false sense of security. But that is a concern in areas like getting it to explain how a codebase works for you, or getting it to try to teach you about technologies. Then you can end up with a false idea about how something works. But in software development itself? When I already have worked with all of these tools for years? It just isn't a big issue. And the benefits far outweigh it occasionally telling me that an API exists that actually doesn't exist, which I will realise almost immediately when the code fails to run.
People who dismiss AI because it makes mistakes are tiresome. The lack of reliability of LLMs is just another constraint to engineer around. It's not magic.
Yes, maybe using the word "verify" here is a bit confusing. The point was to compare software, where it is very easy to verify the positive case, to other domains where it is not possible to verify anything at all, and manual review is all you get.
For example, a research document could sound good, but be complete nonsense. There's no realistic way to verify that an English document is correct other than to verify it manually. Whereas, software has huge amounts of investment into testing whether a piece of software does what it should for a given environment and test cases.
Now, this is a bit different to formally verifying that the software is correct for all environments and inputs. But we definitely have a lot more verification tools at our disposal than most other domains.
> So, the reality is that AI opens up new possible workflows. They aren't always appropriate. Sometimes the process of writing the code yourself and iterating on it is important to helping you build your mental model of a piece of functionality. But a lot of the time, there's no mystery in what I want to write. And in these cases, AI is brilliant at speeding up design and implementation.
I agree but I have a hunch we're all gonna be pushed by higher ups to use AI always and for everything. Headcounts will drop, the amount of work will rise and deadlines will become ever so tight. What the resulting codebases would look like years from now will be interesting.
Yeah, I am grateful that I work with a lot of other engineers and managers who care a lot about quality. If you have a manager who just cares about speed, the corner cutting that AI enables could become a nightmare.
It was only 2 years ago we were still taking about GPTs making up completely nonsense, and now hallucinations are almost gone from the discussions. I assume it will get even better, but I also think there is an inherent plateau. Just like how machines solved mass manufacturing work, but we still have factory workers and overseers. Also, "manually" hand crafted pieces like fashion and watches continue to be the most expensive luxury goods. So I don't believe good design architects and consulting will ever be fully replaced.
Hallucinations are now plausibly wrong which is in some ways harder to deal with. GPT4.1 still generates Rust with imaginary crates and says “your tests passed, we can now move on” to a completely failed test run.
"hallucinations are almost gone from the discussions" because everyone now accepts that LLM hallucinate all the times, no point in mentioning it anymore.
… because programming languages are the right level of precision for specifying a program you want. Natural language isn’t it. Of course you need to review and edit what it generates. Of course it’s often easier to make the change yourself instead of describing how to make the change.
I wonder if the independent studies that show Copilot increasing the rate of errors in software have anything to do with this less bold attitude. Most people selling AI are predicting the obsolescence of human authors.
Transformers can be used to automate testing, create deeper and broader specification, accelerate greenfield projects, rapidly and precisely expand a developer's knowledge as needed, navigate unfamiliar APIs without relying on reference, build out initial features, do code review and so much more.
Even if code is the right medium for specifying a program, transformers act as an automated interface between that medium and natural language. Modern high-end transformers have no problem producing code, while benefiting from a wealth of knowledge that far surpasses any individual.
> Most people selling AI are predicting the obsolescence of human authors.
It's entirely possible that we do become obsolete for a wide variety of programming domains. That's simply a reality, just as weavers saw massive layoffs in the wake of the automated loom, or scribes lost work after the printing press, or human calculators became pointless after high-precision calculators became commonplace.
This replacement might not happen tomorrow, or next year, or even in the next decade, but it's clear that we are able to build capable models. What remains to be done is R&D around things like hallucinations, accuracy, affordability, etc. as well as tooling and infrastructure built around this new paradigm. But the cat's out of the bag, and we are not returning to a paradigm that doesn't involve intelligent automation in our daily work; programming is literally about automating things and transformers are a massive forward step.
That doesn't really mean anything, though; You can still be as involved in your programming work as you'd like. Whether you can find paid, professional work depends on your domain, skill level and compensation preferences. But you can always program for fun or personal projects, and decide how much or how little automation you use. But I will recommend that you take these tools seriously, and that you aren't too dismissive, or you could find yourself left behind in a rapidly evolving landscape, similarly to the advent of personal computing and the internet.
> Modern high-end transformers have no problem producing code, while benefiting from a wealth of knowledge that far surpasses any individual.
It will also still happily turn your whole codebase into garbage rather than undo the first thing it tried to try something else. I've yet to see one that can back itself out of a logical corner.
That's a combination of current context limitations and a lack of quality tooling and prompting.
A well-designed agent can absolutely roll back code if given proper context and access to tooling such as git. Even flushing context/message history becomes viable for agents if the functionality is exposed to them.
This is it for me. If you ask these models to write something new, the result can be okay.
But the second you start iterating with them... the codebase goes to shit, because they never delete code. Never. They always bolt new shit on to solve any problem, even when there's an incredibly obvious path to achieve the same thing in a much more maintainable way with what already exists.
Show me a language model that can turn rube goldberg code into good readable code, and I'll suddenly become very interested in them. Until then, I remain a hater, because they only seem capable of the opposite :)
You'd be surprised what a combination of structured review passes and agent rules (even simple ones such as "please consider whether old code can be phased out") might do to your agentic workflow.
> Show me a language model that can turn rube goldberg code into good readable code, and I'll suddenly become very interested in them.
They can already do this. If you have any specific code examples in mind, I can experiment for you and return my conclusions if it means you'll earnestly try out a modern agentic workflow.
An LLM which was capable of refactoring all the duplicated logic into the common core and restructuring all the drivers to be simpler would be very very useful for me. It ought to be able to remove a few thousand lines of code there.
It needs to do it iteratively, in a sting of small patches that I can review and prove to myself are correct. If it spits out a giant single patch, that's worse than nothing, because I do systems work that actually has to be 100% correct, and I can't trust it.
That's not true in my experience. Several times now i've given Claude Code a too-challenging task and after trying repeatedly it eventually gave up, removing all the previous work on that subject and choosing an easier solution instead.
.. unfortunately that was not at all what i wanted lol. I had told it "implement X feature with Y library", ie specifically the implementation i wanted to make progress towards, and then after a while it just decided that was difficult and to do it differently.
> That's simply a reality, just as weavers saw massive layoffs in the wake of the automated loom, or scribes lost work after the printing press, or human calculators became pointless after high-precision calculators became commonplace.
See, this is the kind of conception of a programmer I find completely befuddling. Programming isn't like those jobs at all. There's a reason people who are overly attached to code and see their job as "writing code" are pejoratively called "code monkeys." Did CAD kill the engineer? No. It didn't. The idea is ridiculous.
I'm sure you understand the analogy was about automation and reduction in workforce, and that each of these professions have both commonalities and differences. You should assume good faith and interpret comments on Hacker News in the best reasonable light.
> There's a reason people who are overly attached to code and see their job as "writing code" are pejoratively called "code monkeys."
Strange. My experience is that "code monkeys" don't give a crap about the quality of their code or its impact with regards to the product, which is why they remain programmers and don't move on to roles which incorporate management or product responsibilities. Actually, the people who are "overly attached to code" tend to be computer scientists who are deeply interested in computation and its expression.
> Did CAD kill the engineer? No. It didn't. The idea is ridiculous.
Of course not. It led to a reduction in draftsmen, as now draftsmen can work more quickly and engineers can take on work that used to be done by draftsmen. The US Bureau of Labor Statistics states[0]:
Expected employment decreases will be driven by the use of computer-aided design (CAD) and building information modeling (BIM) technologies. These technologies increase drafter productivity and allow engineers and architects to perform many tasks that used to be done by drafters.
Similarly, the other professions I mentioned were absorbed into higher-level professions. It has been stated many times that the future focus of software engineers will be less about programming and more about product design and management.
I saw this a decade ago at the start of my professional career and from the start have been product and design focused, using code as a tool to get things done. That is not to say that I don't care deeply about computer science, I find coding and product development to each be incredibly creatively rewarding, and I find that a comprehensive understanding of each unlocks an entirely new way to see and act on the world.
My father in law was a draftsman. Lost his job when the defense industry contracted in the '90s. When he was looking for a new job everything required CAD which he had no experience in (he also had a learning disability, it made learning CAD hard).
He couldn't land a job that paid more than minimum wage after that.
Wow, that's a sad story. It really sucks to spend your life mastering a craft and suddenly find it obsolete and your best years behind you. My heart goes out to your father.
This is a phenomenon that seems to be experienced more and more frequently as the industrial revolution continues... The craft of drafting goes back to 2000 B.C.[0] and while techniques and requirements gradually changed over thousands of years, the digital revolution suddenly changed a ton of things all at once in drafting and many other crafts. This created a literacy gap many never recovered from.
I wonder if we'll see a similar split here with engineers and developers regarding agentic and LLM literacy.
> Actually, the people who are "overly attached to code" tend to be computer scientists who are deeply interested in computation and its expression.
What academics are you rubbing shoulders with? Every single computer scientist I have ever met has projects where every increment in the major version goes like:
"I was really happy with my experimental kernel, but then I thought it might be nice to have hotpatching, so I abandoned the old codebase and started over from scratch."
The more novel and cutting edge the work you do is, the more harmful legacy code becomes.
"I saw this a decade ago at the start of my professional career and from the start have been product and design focused"
I have similar view of the future as you do. But I'm just curious what the quoted text here means in practice. Did you go into product management instead of software engineer for example?
I don't disagree exactly, but the AI that fully replaces all the programmers is essentially a superhuman one. It's matching human output, but will obviously be able to do some tasks like calculations much faster, and won't need a lunch break.
At that point it's less "programmers will be out of work" as "most work may cease to exist".
Not sure about this. Coding has some unique characteristics that may it easier even if from a human perspective it requires some skill:
- The cost of failure is low: Most domains (physical, compliance, etc) don't have this luxury where the cost of failure is high and so the validator has more value.
- The cost to retry/do multiple simulations is low: You can perform many experiments at once, and pick the one with the best results. If the AI hallucinates, or generates something that doesn't work the agent/tool could take that error and simulate/do multiple high probability tries until it passes. Things like unit tests, compiler errors, etc make this easier.
- There are many right answers to a problem. Good enough software is good enough for many domains (e.g. a CRUD web app). Not all software is like this but many domains in software are.
What makes something hard to disrupt won't be intellectual difficulty (e.g. software harder than compliance analyst as a made up example), it will be other bottlenecks like the physical world (energy, material costs, etc), regulation (job isn't entirely about utility/output). etc.
> The cost of failure is low: Most domains (physical, compliance, etc) don't have this luxury where the cost of failure is high and so the validator has more value.
This is not entirely sensible, some code touches the physical / compliance world. Airports, airplanes, hospitals, cranes, water systems, military they all use code to different degrees. It's true that they can perhaps afford to run experiments over landing pages, but I don't think they can simply disrupt their workers and clients on a regular basis.
I did say "not all software is like this, but many domains are". So I agree with you.
Also note unlike say for physical domains where it's expensive to "tear down" until you commit and deploy (i.e. while the code is being worked on) you can try/iterate/refine via your IDE, shell, whatever. Its just text files after all; in the end you are accountable for the final verification step before it is published. I never said we don't need a verification step; or a gate before it goes to production systems. I'm saying its easier to throw away "hallucinations" that don't work and you can work around gaps in the model with iterations/retries/multiple versions until the user is happy with it.
Conversely I couldn't have an AI build a house, I don't like it, it demolishes it and builds a slightly different one, etc etc until I say "I'm happy with this product, please proceed". The sheer amount of resource waste and time spent in doing so would be enormous. I can simulate, generate plans, etc maybe with AI but nothing beats seeing the "physical thing" for some products especially when there isn't the budget/resources to "retry/change".
TL;DR the greater the cost of iteration/failure; the less likely you can use iteration to cover up gaps in your statistical model (i.e. tail risks are more likely to bite/harder to mitigate).
> That's simply a reality, just as weavers saw massive layoffs in the wake of the automated loom
They didn’t just see layoffs. There were the constant wars with Napoleon and the War of 1812 causing significant economic instability along with highly variable capital investments in textile production at the time. They we’re looking at huge wealth disparity and losing their jobs for most meant losing everything.
What many Luddite supporters were asking for in many parts of England were: better working conditions, a raise to minimum wage, abolishment of child labour, etc. Sabotage was a means to make such demands from a class that held almost all of the power.
Many of those protestors were shot. Those who survived and were laid off were forced into workhouses.
The capitalists won and got to write the history and the myths. They made it about the technology and not the conditions. They told us that the displaced workers found new, better jobs elsewhere.
Programmers, while part of the labour class, have so far enjoyed a much better bargaining position and have been compensated in kind. Many of us also complain about the quality of output from AI as the textile workers complained about the poor quality of the lace. But fortunately the workhouses were shut down. Although poor quality code tends to result in people losing their life’s savings, having their identities stolen, etc. Higher stakes than cheap lace.
> It's entirely possible that we do become obsolete for a wide variety of programming domains. That's simply a reality…
It is not a reality since it has not happen. In the real world it has not happened.
There is no reason to believe that the current rate of progress will continue. Intelligence is not like the weaving machines. A software engineer is not a human calculator.
To be fair he didn't say it is the reality now, he said the possibility is a reality. At least that's how I read his sentence.
And yeah, I do think it's a real possibility now.
Doesn't AI have diminishing returns on it's pseudo creativity? Throw all the training output of LLM into a circle. If all input comes from other LLM output, the circle never grows. Humans constantly step outside the circle.
Perhaps LLM can be modified to step outside the circle, but as of today, it would be akin to monkeys typing.
I think you're either imagining the circle too small or overestimating how often humans step outside it. The typical programming job involves lots and lots of work, and yet none of it creating wholly original computer science. Current LLMs can customize well known UI/IO/CRUD/REST patterns with little difficulty, and these make up the vast majority of commercial software development.
Frameworks and low code systems have been able to do that for years. The reason they haven’t replaced programmers is that every system eventually becomes a special unique snowflake as long as it has time and users.
I’m getting maybe a 10-20% productivity boost using AI on mature codebases. Nice but not life changing.
a 20% boost is huge, for 3 years since chatgpt. even if it stopped there, that's 20% fewer people that need to be in your role, which is at least tens of thousands of jobs
If devs produce 20% more, won't companies hire more since the gain/loss equation is starting to tilt their way even more? I find it odd that people think productivity increases lead to layoffs.
That’s far less than the productivity boost I got by building some internal tooling with phoenix liveview instead of react.
10-20% productivity posts have been happening regularly over the course of my career. They are normally either squandered by inefficient processes or we start building more complex systems.
When Rails was released, for certain types of projects, you could move 3 or 4x faster almost overnight.
I agree humans only rarely step outside the circle, but I do have this intuition that some people sometimes do, whereas LLMs never do. This distinction seems important over long time horizons when thinking about LLM vs human work.
But I can't quite articulate why I believe LLMs never step outside the circle, because they are seeded with some random noise via temperature. I could just be wrong.
In my experience the best use of AI is to stay in the flow state when you get blocked by an API you don't understand or a feature you don't want to implement for whatever reason.
I wasn’t in the industry to see it first hand, but was this same criticism levied against higher level languages when they first hit the scene? Something to the effect of high level languages not being the right level of precision, because the programmer wanted to directly manipulate what was happening in memory, and the high level languages are not the right level of precision for that?
The issue with natural language isn’t that it’s impossible to be precise, it’s that most people aren’t, or they are precise about what they want it to do for them, but not what the computer needs to do to make it happen. This leads to a lot of guessing by engineerings as they try to translate the business requirements into code. Now the LLM is doing that guessing, often with less context about the broader business objectives, or an understanding of the people writing those requirements.
> was this same criticism levied against higher level languages when they first hit the scene?
No.
Some were concerned that the output of compilers couldn’t match the quality of what could be done by a competent programmer at the time. That was true for a time. Then compilers got better.
Nobody was concerned that compilers were going to be used by capitalists to lay them off and seize the means of producing programs by turning it into property.
> Of course you need to review and edit what it generates.
Treating LLMs as a scaffolding tool yields better results at least for me personally. I just brain dump what I'm thinking of building and ask for it to give me models, and basic controllers using said models, then I just worry about the views and business logic.
For me, too. On the poorly-documented stuff that I get into, where I really need help, it flails.
I wanted some jq script and it made an incomprehensible thing that usually worked. And every fix it made broke something else. It just kept digging a deeper hole.
After a while of that, I bit the bullet and learned jq script. I wrote a solution that was more capable, easier to read, and, importantly, always worked.
The thing is, jq is completely documented. Everything I needed was online. But there just aren't many examples to for LLMs, so they choke.
Start asking it questions about complexity classes and you can get it to contradict itself in no time.
Most of the code in the world right now is very formulaic, and it's great at that. Sorry, WordPress devs.
One of the most useful properties of computers is that they enable reliable, eminently reproducible automation. Formal languages (like programming languages) not only allow to unambiguously specify the desired automation to the upmost level of precision, they also allow humans to reason about the automation with precision and confidence. Natural language is a poor substitute for that. The ground truth of programs will always be the code, and if humans want to precisely control what a program does, they’ll be best served by understanding, manipulating, and reasoning about the code.
I think these are coordinated posts by Microsoft execs. First their director of product, now this. Its like they're trying to calm the auto coding hype until they catchup and thus keep OpenAI from running away.
I had a fun result the other day from Claude. I opened a script in Zed and asked it to "fix the error on line 71".
Claude happily went and fixed the error on line 91....
1. There was no error on line 91, it did some inconsequential formatting on that line
2. More importantly, it just ignored the very specific line I told it to go to. It's like I was playing telephone with the LLM which felt so strange with text-based communication.
This was me trying to get better at using the LLM while coding and seeing if I could "one-shot" some very simple things. Of course me doing this _very_ tiny fix myself would have been faster. Just felt weird and reinforces this idea that the LLM isn't actually thinking at all.
I suspect if OP highlighted line 71 and added it to chat and said fix the error, they’d get a much better response. I assume Cursor could create a tool to help it interpret line numbers, but that’s not how they expect you to use it really.
I do not code/program, but I do read thousands of fiction pages annually. LLMs (Perplexity, specifically) have been my lifetime favorite book club member — I can ask anything.
However, I can't just say "on page 123..." I've found it's better to either provide the quote, or describe the context, and then ask how it relates to [another concept]. Or I'll say "at the end of chapter 6, Bob does X, then why Y?" (perhaps this is similar to asking a coding LLM to fix a specific function instead of a specific line?).
My favorite examples of this have been sitting with living human authors and discussing their books — usually to jaw-dropped creators, particularly to Unknowns.
Works for non-fiction, too (of course). But for all those books you didn't read in HS English classes, you can somewhat recreate all that class discussion your teachers always attempted to foster — at your own discretion/direction.
That's the thing. We're expecting the tool to have a clear understanding of its own limitations by now and ask for better prompts (or say: I don't know, I can't etc). The fact it just does something wacky is not good at all to the consistency of these tools.
> This was me trying to get better at using the LLM while coding
And now you've learned that LLMs can't count lines. Next time, try asking it to "fix the error in function XYZ" or copy/paste the line in question, and see if you get better results.
> reinforces this idea that the LLM isn't actually thinking at all.
Of course it's not thinking, how could it? It's just a (rather big) equation.
54 def dicts_to_table_string(
55 headings: List[str], dicts: List[Dict[str, str]]
56 ) -> List[str]:
57 max_lengths = [len(h) for h in headings]
58
59 # Compute maximum length for each column
60 for d in dicts:
That’s not what he’s saying there. There’s a separate tool that adds line numbers before feeding the prompt into the LLM. It’s not the LLM doing it itself.
It's a tool. It's not a human. A line number works great for humans. Today, they're terrible for LLMs.
I can choose to use a screwdriver to hammer in a nail and complain about how useless screwdrivers are. Or I can realize when and how to use it.
We (including marketing & execs) have made a huge mistake in anthropomorphizing these things, because then we stop treating them like tools that have specific use cases to be used in certain ways, and more like smart humans that don't need that.
Maybe one day they'll be there, but today they are screwdrivers. That doesn't make them useless.
Check the whole ecosystem around editors, grep tools, debuggers, linting and build tools. One common thing about all of this is line (and column) number so you can integrate them together if you want to automate stuff. Like jumping to errors (quickfix in vim,…), search all files and jump to the occurrences (grep mode in emacs,…), etc…
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[ 3.4 ms ] story [ 254 ms ] thread(Wow I sound triggered! sigh)
A man is a human.
“Manual” comes from Latin manus, meaning “hand”: https://en.wiktionary.org/wiki/manus. It literally means “by hand”.
I too can sling a bit of Latin. ;)
Here is one phrase, in which one word, malus, is like an off-by-one error from your manus word, so to speak :) :
nemo malus nisi probetur
https://www.google.com/search?q=nemo+malus+nisi+probetur
From the AI overview (need to at least try that out a few times after all the talk about Google enshittification like by Cory Doctorow in this year's PyCon US keynote:
>Nemo malus nisi probetur" is a Latin legal maxim that translates to "No one is evil unless proven." It signifies that a person is presumed innocent until proven guilty in a court of law. This principle is fundamental to legal systems that value fairness and due process, ensuring that accusations are substantiated before a person is considered culpable. Here's a breakdown of the phrase:
The maxim essentially means that one should not be treated as inherently bad or guilty without sufficient evidence. This principle is closely related to the concept of "presumption of innocence" and is crucial for upholding justice and protecting individual rights.https://xkcd.com/378/
this is okay! it's a sign of your humanity :)
Wait does it? This is a new idea to me, in what way could "manual" have a negative connotation?
Are people really that against doing things manually nowadays?
Usually the CEO or investor says 30% (or some other made up number) of all code is written by AI and the number will only increase, implying that developers will soon be obsolete.
It's implied that 30% of all code submitted and shipped to production is from AI agents with zero human interaction. But of course this is not the case, it's the same developers as before using tools to more rapidly write code.
And writing code is only one part of a developer's job in building software.
And those without understanding what the number means or how it was derived write misleading articles.
Lots of changes where describing them in English takes longer than just performing the change. I think the most effective workflow with AI agents will be a sort of active back-and-forth.
https://en.m.wikipedia.org/wiki/Rubber_duck_debugging
Here's a fresh example that I stumbled upon just a few hours ago. I needed to refactor some code that first computes the size of a popup, and then separately, the top left corner.
For brevity, one part used an "if", while the other one had a "switch":
I wanted the LLM to refactor it to store the position rather than applying it immediately. Turns out, it just could not handle different things (if vs. switch) doing a similar thing. I tried several variations of prompts, but it very strongly leaning to either have two ifs, or two switches, despite rather explicit instructions not to do so.It sort of makes sense: once the model has "completed" an if, and then encounters the need for a similar thing, it will pick an "if" again, because, well, it is completing the previous tokens.
Harmless here, but in many slightly less trivial examples, it would just steamroll over nuance and produce code that appears good, but fails in weird ways.
That said, splitting tasks into smaller parts devoid of such ambiguities works really well. Way easier to say "store size in m_StateStorage and apply on render" than manually editing 5 different points in the code. Especially with stuff like Cerebras, that can chew through complex code at several kilobytes per second, expanding simple thoughts faster than you could physically type them.
Or the current strategies to scale across boards instead of chips gets too expensive in terms of cost, capital, and externalities.
Look at how well Deepseek performed with the limited, outdated hardware available to its researchers. And look at what demoscene practitioners have accomplished on much older hardware. Even if physical breakthroughs ceased or slowed down considerably, there is still a ton left on the table in terms of software optimization and theory advancement.
And remember just how young computer science is as a field, compared to other human practices that have been around for hundreds of thousands of years. We have so much to figure out, and as knowledge begets more knowledge, we will continue to figure out more things at an increasing pace, even if it requires increasingly large amounts of energy and human capital to make a discovery.
I am confident that if it is at all possible to reach human-level intelligence at least in specific categories of tasks, we're gonna figure it out. The only real question is whether access to energy and resources becomes a bigger problem in the future, given humanity's currently extraordinarily unsustainable path and the risk of nuclear conflict or sustained supply chain disruption.
The human brain uses just 20 watts of power, so it seems to me like it is possible to reach human-level intelligence in principle by using much greater power and less of the evolutionary refinement over billions of years that the brain has.
How long do you think Homo sapiens have been on Earth and how long has civilization been here?
I’ve been programming since 89. I know what you can squeeze into 100k.
But you can only blast so much electricity into a dense array of transistors before it melts the whole thing and electrons jump rails. We hit that limit a while ago. We’ve done a lot of optimization of instruction caching, loading, and execution. We front loaded a ton of caching in front of the registers. We’ve designed chips specialized to perform linear algebra calculations and scaled them to their limits.
AI is built on scaling the number of chips across the board. Which has the effect of requiring massive amounts of power. And heat dissipation. That’s why we’re building out so many new data centres: each one requiring land, water, and new sources of electricity generation to maintain demand levels for other uses… those sources mostly being methane and coal plants.
Yes, we might find local optimizations in training to lower the capital cost and external costs… but they will be a drop in the bucket at the scale we’re building out this infrastructure. We’re basically brute forcing the scale up here.
And computer science might be older than you think. We just used to call it logic. It took some electrical engineering innovations to make the physical computers happen but we had the theoretical understanding of computation for quite some time before those appeared.
A young field, yes, and a long way to go… perhaps!
But let’s not believe that innovation is magic. There’s hard science and engineering here. Electrons can only travel so fast. Transistor density can only scale so much. Etc.
I already corrected my typo in a child comment.
> We’re basically brute forcing the scale up here
Currently, but even that will eventually hit thermodynamic and socioeconomic limits, just as single chips are.
> And computer science might be older than you think. We just used to call it logic.
In my opinion, two landmark theory developments were type theory and the lambda calculus. Type theory was conceived to get around Russell's paradox and others, which formal logic could not do on its own.
As far as hardware, sure we had mechanical calculators in the 17th century, and Babbage's analytical engine in the 19th century, and Ada Lovelace's program, but it wasn't until the mid-20th century that computer science coalesced as its own distinct field. We didn't used to call computer science logic; it's a unification of physical advancements, logic and several other domains.
> Electrons can only travel so fast.
And we have no reason to believe that current models are at all optimized on a software or theoretical level, especially since, as you say yourself, we are currently just focused on brute-forcing innovation as its the more cost-effective solution for the time being.
But as I said, once theoretical refinement becomes more cost-effective, we can look at the relatively short history of computer science to see just how much can be done on older hardware with better theory:
>> Even if physical breakthroughs ceased or slowed down considerably, there is still a ton left on the table in terms of software optimization and theory advancement.
When I tried giving top-notch LLMs harder tasks (scan an abstract syntax tree coming from a parser in a particular way, and generate nodes for particular things) they completely blew it. Didn't even compile, let alone logical errors and missed points. But once I broke down the problem to making lists of relevant parsing contexts, and generating one wrapper class at a time, it saved me a whole ton of work. It took me a day to accomplish what would normally take a week.
Maybe they will figure it out eventually, maybe not. The point is, right now the technology has fundamental limitations, and you are better off knowing how to work around them, rather than blindly trusting the black box.
I think it's a combination of
1) wrong level of granularity in prompting
2) lack of engineering experience
3) autistic rigidity regarding a single hallucination throwing the whole experience off
4) subconscious anxiety over the threat to their jerbs
5) unnecessary guilt over going against the tide; anything pro AI gets heavily downvoted on Reddit and is, at best, controversial as hell here
I, for one, have shipped like literally a product per day for the last month and it's amazing. Literally 2,000,000+ impressions, paying users, almost 100 sign ups across the various products. I am fucking flying. Hit the front page of Reddit and HN countless times in the last month.
Idk if I break down the prompts better or what. But this is production grade shit and I don't even remember the last time I wrote more than two consecutive lines of code.
Except, not all work is like that. Fast-forward to product version 2.34 where a particular customer needs a change that could break 5000 other customers because of non-trivial dependencies between different parts of the design, and you will be rewriting the entire thing by humans or having it collapse under its own weight.
But out of 100 products launched on the market, only 1 or 2 will ever reach that stage, and having 100 LLM prototypes followed by 2 thoughtful redesigns is way better than seeing 98 human-made products die.
I keep hearing how people are so god damn productive with LLMs, but whenever I try to use them they can not reliably produce working code. Usually producing something that looks correct at first, but either doesn't work (at all or as intended).
Going over you list:
1. if the problem is that I need to be very specific with how I want LLM to fix the issue, like providing it the solution, why wouldn't I just make the change myself?
2. I don't even know how you can think that not vibe coding means you lack experience
3. Yes. If the model keeps trying to use non-existent language feature or completely made up functions/classes that is a problem and nothing to do with "autism"
4. This is what all AI maximalists want to think; that only reason why average software developer isn't knee deep in AI swamp with them is that they are luddites who are just scared for their jobs. I personally am not as I have not seen LLMs actually being useful for anything but replacing google searches.
5. I don't know why you keep bringing up Reddit so much. I also don't quite get who is going against the tide here, are you going against the tide of the downvotes or am I for not using LLMs to "fucking fly"?
>But this is production grade shit
I truly hope it is, because...
>and I don't even remember the last time I wrote more than two consecutive lines of code.
Means if there is a catastrophic error, you probably can't fix it yourself.
I type 105 wpm on a bad day. Try gpt-4.1. It types like 1000 wpm. If you can formally describe your problem in English and the number of characters in the English prompt is less than whatever code you write, gpt-4.1 will make you faster.
Obviously you have to account for gpt-4.1 being wrong sometimes. Even so, if you have to run two or three prompts to get it right, it still is going to be faster.
> I don't even know how you can think that not vibe coding means you lack experience
If you lack experience, you're going to prompt the LLM to do the wrong thing and engineer yourself into a corner and waste time. Or you won't catch the mistakes it makes. Only experience and "knowing more than LLM" allows you to catch its mistakes and fix them. (Which is still faster than writing the code yourself, merely by way of it typing 1000 wpm.)
> If the model keeps trying to use non-existent language feature or completely made up functions/classes that is a problem and nothing to do with "autism"
You know that you can tell it those functions are made up and paste it the latest documentation and then it will work, right? That knee-jerk response makes it sound like you have this rigidity problem, yourself.
> I personally am not as I have not seen LLMs actually being useful for anything but replacing google searches.
Nothing really of substance here. Just because you don't know how to use this tool that doesn't mean no one does.
This is the least convincing point for me, because I come along and say "Hey! This thing has let me ship far more working code than before!" and then your response is just "I don't know how to use it." I know that it's made me more productive. You can't say anything to deny that. Do you think I have some need to lie about this? Why would I feel the need to go on the internet and reap a bunch of downvotes while peddling some lie that does stand to get me anything if I convince people of the lie?
> I also don't quite get who is going against the tide here, are you going against the tide of the downvotes
Yeah, that's what I'm saying. People will actively shame and harass you for using LLMs. It's mind boggling that a tool, a technology, that works for me and has made me more productive, would be so vehemently criticized. That's why I listed these 5 reasons, the only reasons I have thought of yet.
> Means if there is a catastrophic error, you probably can't fix it yourself.
See my point about lacking experience. If you can't do the surgery yourself every once in a while, you're going to hate these tools.
Really, you've just made a bunch of claims about me that I know are false, so I'm left unconvinced.
I'm trying to have a charitable take. I don't find joy in arguing or leaving discussions with a bitter taste. I genuinely don't know why people are so mad at me claiming that a tool has helped me be more productive. They all just don't believe me, ultimately. They all come up with some excuse as to why my personal anecdotes can be dismissed and ignored: "even though you have X, we should feel bad for you because Y!" But it's never anything of substance. Never anything that has convinced me. Because at the end of the day, I'm shipping faster. My code works. My code has stood the test of time. Insults to my engineering ability I know are demonstrably false. I hope you can see the light one day. These are extraordinary tools that are only getting better, at least by a little bit, in the foreseeable future. Why deny?
Just a few days ago got flamed for only having 62 users on GameTorch. Now up to 91 and more paying subs. Entire thing written by LLMs and hasn't fallen over once. I'd rather be a builder than an armchair critic.
People would rather drag you down in the hole that they're in than climb out.
WPM is not my limiting factor. Maybe the difference is that I am not working on trivial software so a lot of thought goes into the work, typing is the least time consuming part. Still I don't see how your 105 wpm highly descriptive and instructive English can be faster than just fixing the thing. Even if after you prompt your LLM takes 1ms to fix the issue you have probably already wasted more time by debugging the issue and writing the prompt.
So your "you lack engineering experience" was actually "you don't know LLMs well", maybe use the words you intend and not make them into actual insults.
I am not going to be pasting in any C++ spec into an LLM.
Yet when I checked your profile you have shipped one sprite image generator website. I find all these claims so hard to believe. Everyone just keeps telling me how they are making millions off of LLMs but no one has to the recipes to show. Just makes me feel like you have stock in OpenAI or something and are trying your hardest to pump it up.
I think the shaming and harassing is mostly between your ears, at least I am not trying to shame or harass you for using LLMs, if anything I want to have superpowers too. If LLMs really work for you that is nice and you should keep doing it, I just have not seen the evidence you are talking about. I am willing to admit that it could very well be a skill issue, but I need more proof than "trust me" or "1000 wpm".
I don't think I have made any claims about you, although you have used loaded language like "autism" and "lack of engineering experience" and heavily implied that I am just too dumb to use the tools.
>I'm trying to have a charitable take.
c'mon nothing about your comments has been charitable in anyway. No one is mad at you personally. Do not take criticism of your tools as personal attacks. Maybe the tools will get good, but again my problem with LLMs and hype around them is that no one has been able to demonstrate them actually being as good as the hype suggests.
What is everyone working on that takes more than five minutes to think about?
For me, the work is insurmountable and infinite, while coming up with the solution is never too difficult. I'm not saying this to be cocky. I mean this:
In 99.9999999999% of the problems I encounter in software engineering, someone smarter than me has already written the battle tested solution that I should be using. Redis. Nginx. Postgres. etc. Or it's a paradigm like depth first search or breadth first search. Or just use a hash set. Sometimes it's a little crazier like Bloom filters but whatever.
Are you like constantly implementing new data structures and algorithms that only exist in research papers or in your head?
Once you've been engineering for 5 or 10 years, you've seen almost everything there is to see. Most of the solutions should be cached in your brains at that point. And the work just amounts to tedious, unimportant implementation details.
Maybe I'm forgetting that people still get bogged down in polymorphism and all that object oriented nonsense. If you just use flat structs, there's nothing too complicated that could possibly happen.
I worked in HFT, for what it's worth, and that should be considered very intense non-CRUD "true" engineering. That, I agree, LLMs might have a little more trouble with. But it's still nothing insane.
Software engineering is extremely formulaic. That's why it's so easy to statistically model it with LLMs.
I have been doing this for 8 year and while yes I have seen a lot you can't just copy-paste solutions due to flash, memory, and performance constraints.
Again maybe this is a skill issue and maybe I will be replaced with an LLM, but so far they seem more like cool toys. I have used LLMs to write AddOns for World of Warcraft since my Lua knowledge is mostly writing Wireshark plugins for our protocols and for that it has been nice, but it is nothing someone who actually works with Lua or with WoW API couldn't produce faster or just as fast, because I have to describe what I want and then try and see if the API the LLM provides exists and if it works as the LLM assumed it would.
AI stands for Artificial Intelligence. There are no inherent limits around what AI can and can't do or comprehend. What you are specifically critiquing is the capability of today's popular models, specifically transformer models, and accompanying tooling. This is a rapidly evolving landscape, and your assertions might no longer be relevant in a month, much less a year or five years. In fact, your criticism might not even be relevant between current models. It's one thing to speak about idiosyncrasies between models, but any broad conclusions drawn outside of a comprehensive multi-model review with strict procedure and controls is to be taken with a massive grain of salt, and one should be careful to avoid authoritative language about capabilities.
It would be useful to be precise in what you are critiquing, so that the critique actually has merit and applicability. Even saying "LLM" is a misnomer, as modern transformer models are multi-modal and trained on much more than just textual language.
Lots of us are interested in technology that's actually available, and we can all read date stamps on comments.
> But it ain't here yet buddy . . . we can all read date stamps on comments.
That has no bearing on the general trajectory that we are currently on in computer science and informatics. Additionally, your language is patronizing and dismissive, trading substance for insult. This is generally frowned upon in this community.
You failed to actually address my comment, both by failing to recognize that it was mainly about using the correct terminology instead of criticizing an entire branch of research that extends far beyond transformers or LLMs, and by failing to establish why a rapidly evolving landscape does not mean that certain generalizations cannot yet be made, unless they are presented with several constraints and caveats, which includes not making temporally-invariant claims about capabilities.
I would ask that you reconsider your approach to discourse here, so that we can avoid this thread degenerating into an emotional argument.
They were obviously not trying to make a sweeping comment about the entire future of the field.
Are you using ChatGPT to write your loquacious replies?
OP said “AI can very efficiently apply common patterns to vast amounts of code, but it has no inherent "idea" of what it's doing.”
I'm not going to patronize you by explaining why this is not "very precise", or why its lack of temporal caveats is an issue, as I've already done so in an earlier comment. If you're still confused, you should read the sentence a few times until you understand. OP did not even mention which specific model they tested, and did not provide any specific prompt example.
> Are you using ChatGPT to write your loquacious replies?
If you can't handle a few short paragraphs as a reply, or find it unworthy of your time, you are free to stop arguing. The Hacker News guidelines actually encourage substantive responses.
I also assume that in the future, accusing a user of using ChatGPT will be against site guidelines, so you may as well start phasing that out of your repertoire now.
Here are some highlights from the Hacker News guidelines regarding comments:
- Don't be snarky
- Comments should get more thoughtful and substantive, not less, as a topic gets more divisive.
- Assume good faith
- Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken.
https://news.ycombinator.com/newsguidelines.html
> AI can very efficiently apply common patterns to vast amounts of code, but it has no inherent "idea" of what it's doing.”
Are you saying that AI does have an inherent idea of what it's doing or is doing more than that? Today?
We're in an informal discussion forum. I don't think the bar we're looking for is some rigorous deductive proof. The above matches my experience as well. Its a handy applied interactive version of an Internet search.
If someone has a different experience that would be interesting. But this just seems like navel-gazing over semantics.
No. I stated that OP cannot make that kind of blanket, non-temporally constrained statements about artificial intelligence.
> We're in an informal discussion forum. I don't think the bar we're looking for is some rigorous deductive proof
We're in a technology-oriented discussion forum, the minimum bar to any claim should be that it is supported by evidence, otherwise it should be presented as what it is: opinion.
> this just seems like navel-gazing over semantics.
In my opinion, conversation is much easier when we can agree that words should mean something. Imprecise language matched with an authoritative tone can mislead an audience. This topic in particular is rife with imprecise and uninformed arguments, and so we should take more care to use our words correctly, not less.
Furthermore, my argument goes beyond semantics, as it also deals with the importance of constraints when making broad, unbacked claims.
It seems you don't recollect how much time passed without any big revolutions in AI. Deep learning was a big jump. But when the next jump comes? Might be tomorrow, but looking at history, might be in 2035.
According to what I see, the curve has already flattened and now only a new revolution could get us to the next big step.
My 2035 prediction actually seems pretty optimistic. It was more than 20 years that we haven't seen any big AI revolutions, so 2045 would be more realistic.
And it seems our current AI is also not going to get us there any faster.
Artificial, as in Artificial sand or artificial grass. Sure, it appears as sand or grass at first, but upon closer examination, it becomes very apparent that it's not real. Artificial is basically a similar word to magic - as in, it offers enough misdirection in order for people to think there might be intelligence, but upon closer examination, it's found lacking.
It's still impressive that it can do that, going all the way back to gaming AIs, but it's also a veil that is lifted easily.
So out of nowhere both my brother and my wife, without concerting themselves (they don't live on the same continent), are now taking coding lessons. Not AI related: just learning to code in Python.
And suddenly I'm winning lots of cookie points: my wife realizes that coding is not something that's that simple and that it's really the coders who have it easy with AI.
As for the AI boom: it's another tool. I use it daily (and pay for it). But it's still just a tool.
Wake me up when 95% of Linux's kernel code is written by an AI.
Why not look at Bob who takes like 2 weeks to write tickets on what they actually want in a feature? Or Alice who's really slow getting Figma designs done and validated? How nice would having a "someone's bothered a developer" metric be and having the business seek to get that to zero and talk very loudly about it as they have about developers?
I think an over-reliance, or perhaps any reliance, on AI tools will turn good programmers into slop factories, as they consistently skip over a vital part of creating high-quality software.
You could argue that the prompt == code, but then you are adding an intermediary step between you and the code, and something will always be lost in translation.
I'd say just write the code.
With AI, instead of starting with zero and building up, you can start with a result and iterate on it straight away. This process really shines when you have a good idea of what you want to do, and how you want it implemented. In these cases, it is really easy to review the code, because you knew what you wanted it to look like. And so, it lets me implement some basic features in 15 minutes instead of an hour. This is awesome.
For more complex ideas, AI can also be a great idea sparring partner. Claude Code can take a paragraph or two from me, and then generate a 200-800 line planning document fleshing out all the details. That document: 1) helps me to quickly spot roadblocks using my own knowledge, and 2) helps me iterate quickly in the design space. This lets me spend more time thinking about the design of the system. And Claude 4 Opus is near-perfect at taking one of these big planning specifications and implementing it, because the feature is so well specified.
So, the reality is that AI opens up new possible workflows. They aren't always appropriate. Sometimes the process of writing the code yourself and iterating on it is important to helping you build your mental model of a piece of functionality. But a lot of the time, there's no mystery in what I want to write. And in these cases, AI is brilliant at speeding up design and implementation.
I know the tools and environments I am working in. I verify the implementations I make by testing them. I review everything I am generating.
The idea that AI is going to trick me is absurd. I'm a professional, not some vibe coding script kiddie. I can recognise when the AI makes mistakes.
Have the humility to see that not everyone using AI is someone who doesn't know what they are doing and just clicks accept on every idea from the AI. That's not how this works.
Software is incredibly easy to verify compared to other domains. First, my own expertise can pick up most mistakes during review. Second, all of the automated linting, unit testing, integration testing, and manual testing is near guaranteed to pick up a problem with the functionality being wrong.
So, how exactly do you think AI is going to trick me when I'm asking it to write a new migration to add a new table, link that into a model, and expose that in an API? I have done each of these things 100 times. It is so obvious to me when it makes a mistake, because this process is so routine. So how exactly is AI going to trick me? It's an absurd notion.
AI does have risks with people being lulled into a false sense of security. But that is a concern in areas like getting it to explain how a codebase works for you, or getting it to try to teach you about technologies. Then you can end up with a false idea about how something works. But in software development itself? When I already have worked with all of these tools for years? It just isn't a big issue. And the benefits far outweigh it occasionally telling me that an API exists that actually doesn't exist, which I will realise almost immediately when the code fails to run.
People who dismiss AI because it makes mistakes are tiresome. The lack of reliability of LLMs is just another constraint to engineer around. It's not magic.
This strikes me as an absurd thing to believe when there's almost no such thing as bug-free software
For example, a research document could sound good, but be complete nonsense. There's no realistic way to verify that an English document is correct other than to verify it manually. Whereas, software has huge amounts of investment into testing whether a piece of software does what it should for a given environment and test cases.
Now, this is a bit different to formally verifying that the software is correct for all environments and inputs. But we definitely have a lot more verification tools at our disposal than most other domains.
It's right there in the quote.
I agree but I have a hunch we're all gonna be pushed by higher ups to use AI always and for everything. Headcounts will drop, the amount of work will rise and deadlines will become ever so tight. What the resulting codebases would look like years from now will be interesting.
I wonder if the independent studies that show Copilot increasing the rate of errors in software have anything to do with this less bold attitude. Most people selling AI are predicting the obsolescence of human authors.
Even if code is the right medium for specifying a program, transformers act as an automated interface between that medium and natural language. Modern high-end transformers have no problem producing code, while benefiting from a wealth of knowledge that far surpasses any individual.
> Most people selling AI are predicting the obsolescence of human authors.
It's entirely possible that we do become obsolete for a wide variety of programming domains. That's simply a reality, just as weavers saw massive layoffs in the wake of the automated loom, or scribes lost work after the printing press, or human calculators became pointless after high-precision calculators became commonplace.
This replacement might not happen tomorrow, or next year, or even in the next decade, but it's clear that we are able to build capable models. What remains to be done is R&D around things like hallucinations, accuracy, affordability, etc. as well as tooling and infrastructure built around this new paradigm. But the cat's out of the bag, and we are not returning to a paradigm that doesn't involve intelligent automation in our daily work; programming is literally about automating things and transformers are a massive forward step.
That doesn't really mean anything, though; You can still be as involved in your programming work as you'd like. Whether you can find paid, professional work depends on your domain, skill level and compensation preferences. But you can always program for fun or personal projects, and decide how much or how little automation you use. But I will recommend that you take these tools seriously, and that you aren't too dismissive, or you could find yourself left behind in a rapidly evolving landscape, similarly to the advent of personal computing and the internet.
It will also still happily turn your whole codebase into garbage rather than undo the first thing it tried to try something else. I've yet to see one that can back itself out of a logical corner.
That's not true at all.
...
It's only pretending to be happy.
A well-designed agent can absolutely roll back code if given proper context and access to tooling such as git. Even flushing context/message history becomes viable for agents if the functionality is exposed to them.
Will they fail to do it in practice once they poison their own context hallucinating libraries or functions that don’t exist? Absolutely.
That’s the tricky part of working with agents.
But the second you start iterating with them... the codebase goes to shit, because they never delete code. Never. They always bolt new shit on to solve any problem, even when there's an incredibly obvious path to achieve the same thing in a much more maintainable way with what already exists.
Show me a language model that can turn rube goldberg code into good readable code, and I'll suddenly become very interested in them. Until then, I remain a hater, because they only seem capable of the opposite :)
> Show me a language model that can turn rube goldberg code into good readable code, and I'll suddenly become very interested in them.
They can already do this. If you have any specific code examples in mind, I can experiment for you and return my conclusions if it means you'll earnestly try out a modern agentic workflow.
I doubt it. I've experimented with most of them extensively, and worked with people who use them. The atrocious results speak for themselves.
> They can already do this. If you have any specific code examples in mind
Sure. The bluetooth drivers in the Linux kernel contain an enormous amount of shoddy duplicated code that has amalgamated over the past decade with little oversight: https://code.wbinvd.org/cgit/linux/tree/drivers/bluetooth
An LLM which was capable of refactoring all the duplicated logic into the common core and restructuring all the drivers to be simpler would be very very useful for me. It ought to be able to remove a few thousand lines of code there.
It needs to do it iteratively, in a sting of small patches that I can review and prove to myself are correct. If it spits out a giant single patch, that's worse than nothing, because I do systems work that actually has to be 100% correct, and I can't trust it.
Show me what you can make it do :)
That's not true in my experience. Several times now i've given Claude Code a too-challenging task and after trying repeatedly it eventually gave up, removing all the previous work on that subject and choosing an easier solution instead.
.. unfortunately that was not at all what i wanted lol. I had told it "implement X feature with Y library", ie specifically the implementation i wanted to make progress towards, and then after a while it just decided that was difficult and to do it differently.
See, this is the kind of conception of a programmer I find completely befuddling. Programming isn't like those jobs at all. There's a reason people who are overly attached to code and see their job as "writing code" are pejoratively called "code monkeys." Did CAD kill the engineer? No. It didn't. The idea is ridiculous.
I'm sure you understand the analogy was about automation and reduction in workforce, and that each of these professions have both commonalities and differences. You should assume good faith and interpret comments on Hacker News in the best reasonable light.
> There's a reason people who are overly attached to code and see their job as "writing code" are pejoratively called "code monkeys."
Strange. My experience is that "code monkeys" don't give a crap about the quality of their code or its impact with regards to the product, which is why they remain programmers and don't move on to roles which incorporate management or product responsibilities. Actually, the people who are "overly attached to code" tend to be computer scientists who are deeply interested in computation and its expression.
> Did CAD kill the engineer? No. It didn't. The idea is ridiculous.
Of course not. It led to a reduction in draftsmen, as now draftsmen can work more quickly and engineers can take on work that used to be done by draftsmen. The US Bureau of Labor Statistics states[0]:
Similarly, the other professions I mentioned were absorbed into higher-level professions. It has been stated many times that the future focus of software engineers will be less about programming and more about product design and management.I saw this a decade ago at the start of my professional career and from the start have been product and design focused, using code as a tool to get things done. That is not to say that I don't care deeply about computer science, I find coding and product development to each be incredibly creatively rewarding, and I find that a comprehensive understanding of each unlocks an entirely new way to see and act on the world.
[0] https://www.bls.gov/ooh/architecture-and-engineering/drafter...
He couldn't land a job that paid more than minimum wage after that.
This is a phenomenon that seems to be experienced more and more frequently as the industrial revolution continues... The craft of drafting goes back to 2000 B.C.[0] and while techniques and requirements gradually changed over thousands of years, the digital revolution suddenly changed a ton of things all at once in drafting and many other crafts. This created a literacy gap many never recovered from.
I wonder if we'll see a similar split here with engineers and developers regarding agentic and LLM literacy.
[0] https://azon.com/2023/02/16/rare-historical-photos/
What academics are you rubbing shoulders with? Every single computer scientist I have ever met has projects where every increment in the major version goes like:
"I was really happy with my experimental kernel, but then I thought it might be nice to have hotpatching, so I abandoned the old codebase and started over from scratch."
The more novel and cutting edge the work you do is, the more harmful legacy code becomes.
In my case, I am referring to a deep appreciation of code itself, not any particular piece of code.
I have similar view of the future as you do. But I'm just curious what the quoted text here means in practice. Did you go into product management instead of software engineer for example?
At that point it's less "programmers will be out of work" as "most work may cease to exist".
- The cost of failure is low: Most domains (physical, compliance, etc) don't have this luxury where the cost of failure is high and so the validator has more value.
- The cost to retry/do multiple simulations is low: You can perform many experiments at once, and pick the one with the best results. If the AI hallucinates, or generates something that doesn't work the agent/tool could take that error and simulate/do multiple high probability tries until it passes. Things like unit tests, compiler errors, etc make this easier.
- There are many right answers to a problem. Good enough software is good enough for many domains (e.g. a CRUD web app). Not all software is like this but many domains in software are.
What makes something hard to disrupt won't be intellectual difficulty (e.g. software harder than compliance analyst as a made up example), it will be other bottlenecks like the physical world (energy, material costs, etc), regulation (job isn't entirely about utility/output). etc.
This is not entirely sensible, some code touches the physical / compliance world. Airports, airplanes, hospitals, cranes, water systems, military they all use code to different degrees. It's true that they can perhaps afford to run experiments over landing pages, but I don't think they can simply disrupt their workers and clients on a regular basis.
Also note unlike say for physical domains where it's expensive to "tear down" until you commit and deploy (i.e. while the code is being worked on) you can try/iterate/refine via your IDE, shell, whatever. Its just text files after all; in the end you are accountable for the final verification step before it is published. I never said we don't need a verification step; or a gate before it goes to production systems. I'm saying its easier to throw away "hallucinations" that don't work and you can work around gaps in the model with iterations/retries/multiple versions until the user is happy with it.
Conversely I couldn't have an AI build a house, I don't like it, it demolishes it and builds a slightly different one, etc etc until I say "I'm happy with this product, please proceed". The sheer amount of resource waste and time spent in doing so would be enormous. I can simulate, generate plans, etc maybe with AI but nothing beats seeing the "physical thing" for some products especially when there isn't the budget/resources to "retry/change".
TL;DR the greater the cost of iteration/failure; the less likely you can use iteration to cover up gaps in your statistical model (i.e. tail risks are more likely to bite/harder to mitigate).
They didn’t just see layoffs. There were the constant wars with Napoleon and the War of 1812 causing significant economic instability along with highly variable capital investments in textile production at the time. They we’re looking at huge wealth disparity and losing their jobs for most meant losing everything.
What many Luddite supporters were asking for in many parts of England were: better working conditions, a raise to minimum wage, abolishment of child labour, etc. Sabotage was a means to make such demands from a class that held almost all of the power.
Many of those protestors were shot. Those who survived and were laid off were forced into workhouses.
The capitalists won and got to write the history and the myths. They made it about the technology and not the conditions. They told us that the displaced workers found new, better jobs elsewhere.
Programmers, while part of the labour class, have so far enjoyed a much better bargaining position and have been compensated in kind. Many of us also complain about the quality of output from AI as the textile workers complained about the poor quality of the lace. But fortunately the workhouses were shut down. Although poor quality code tends to result in people losing their life’s savings, having their identities stolen, etc. Higher stakes than cheap lace.
History is not repeating but it sure does rhyme.
It is not a reality since it has not happen. In the real world it has not happened.
There is no reason to believe that the current rate of progress will continue. Intelligence is not like the weaving machines. A software engineer is not a human calculator.
Perhaps LLM can be modified to step outside the circle, but as of today, it would be akin to monkeys typing.
I’m getting maybe a 10-20% productivity boost using AI on mature codebases. Nice but not life changing.
10-20% productivity posts have been happening regularly over the course of my career. They are normally either squandered by inefficient processes or we start building more complex systems.
When Rails was released, for certain types of projects, you could move 3 or 4x faster almost overnight.
But I can't quite articulate why I believe LLMs never step outside the circle, because they are seeded with some random noise via temperature. I could just be wrong.
But once you add repo context, domain knowledge etc... programming languages are far too verbose.
The issue with natural language isn’t that it’s impossible to be precise, it’s that most people aren’t, or they are precise about what they want it to do for them, but not what the computer needs to do to make it happen. This leads to a lot of guessing by engineerings as they try to translate the business requirements into code. Now the LLM is doing that guessing, often with less context about the broader business objectives, or an understanding of the people writing those requirements.
No.
Some were concerned that the output of compilers couldn’t match the quality of what could be done by a competent programmer at the time. That was true for a time. Then compilers got better.
Nobody was concerned that compilers were going to be used by capitalists to lay them off and seize the means of producing programs by turning it into property.
Treating LLMs as a scaffolding tool yields better results at least for me personally. I just brain dump what I'm thinking of building and ask for it to give me models, and basic controllers using said models, then I just worry about the views and business logic.
Why wouldn't your boss ask ChatGPT directly?
I wanted some jq script and it made an incomprehensible thing that usually worked. And every fix it made broke something else. It just kept digging a deeper hole.
After a while of that, I bit the bullet and learned jq script. I wrote a solution that was more capable, easier to read, and, importantly, always worked.
The thing is, jq is completely documented. Everything I needed was online. But there just aren't many examples to for LLMs, so they choke.
Start asking it questions about complexity classes and you can get it to contradict itself in no time.
Most of the code in the world right now is very formulaic, and it's great at that. Sorry, WordPress devs.
It's a powerful tool, but it's not that powerful.
I had great success with niche programming languages by feeding it the documentation and a couple of projects.
> Source: The Times of India
what in the recycled content is this trash?
1. There was no error on line 91, it did some inconsequential formatting on that line 2. More importantly, it just ignored the very specific line I told it to go to. It's like I was playing telephone with the LLM which felt so strange with text-based communication.
This was me trying to get better at using the LLM while coding and seeing if I could "one-shot" some very simple things. Of course me doing this _very_ tiny fix myself would have been faster. Just felt weird and reinforces this idea that the LLM isn't actually thinking at all.
However, I can't just say "on page 123..." I've found it's better to either provide the quote, or describe the context, and then ask how it relates to [another concept]. Or I'll say "at the end of chapter 6, Bob does X, then why Y?" (perhaps this is similar to asking a coding LLM to fix a specific function instead of a specific line?).
My favorite examples of this have been sitting with living human authors and discussing their books — usually to jaw-dropped creators, particularly to Unknowns.
Works for non-fiction, too (of course). But for all those books you didn't read in HS English classes, you can somewhat recreate all that class discussion your teachers always attempted to foster — at your own discretion/direction.
And now you've learned that LLMs can't count lines. Next time, try asking it to "fix the error in function XYZ" or copy/paste the line in question, and see if you get better results.
> reinforces this idea that the LLM isn't actually thinking at all.
Of course it's not thinking, how could it? It's just a (rather big) equation.
So are you.
You need to give LLMs context. Line number isn't good context.
a line number is plenty of context - it's directly translatable into a range of bytes/characters in the file
I can choose to use a screwdriver to hammer in a nail and complain about how useless screwdrivers are. Or I can realize when and how to use it.
We (including marketing & execs) have made a huge mistake in anthropomorphizing these things, because then we stop treating them like tools that have specific use cases to be used in certain ways, and more like smart humans that don't need that.
Maybe one day they'll be there, but today they are screwdrivers. That doesn't make them useless.