I think this post (and the OSS projects that he mentions that ban AI) are very reactionary.
> But the idea that AI has or will surpass humans any time soon in either capabilities or efficiency is simply not true
AI is already better than most developers. I'm not sure what alternative reality people are remembering, but human coders for the most part have been really awful at writing code. I think the average PR from an LLM is head and shoulders above the average PR from a human. Does it write code in the preferred style and architecture of the project maintainer 100% of the time? No, and neither did humans.
I think there are many legitimate criticisms of AI, but "they suck at coding" isn't one of them. The progress we've seen in the last couple of years alone suggest that very soon they will be better at coding than any person. As a coder of over 30 years, I've embraced this fact and come to terms with it. Leverage your knowledge of systems, software engineering and product design and you can be living in a golden era for software development. That's how it feels to me at least.
The problem is that skill at coding is not exactly the same thing as skill at developing and maintaining software, and AI can help there as well, but a swarm of cowboy coder agents will get you to a legacy codebase very very quickly.
And even if the AI is better than most humans, the speed means that you get more defects and issues! If a human developer has a change failure rate of say 10%, (1 in 10 changes causes a defect or issue), and AI is twice as good and only introduces bugs 5% of the time, but submits 10x as many changes, then you go from 1 bug per unit of time to 5 bugs per unit of time, so your velocity is up 10x but your defect rate is up 5x...
I agree, and I think what you’re describing is only scratching the surface of what’s possible today.
It’s even more powerful with large data and knowledge sources connected.
Takes a lot of work to set up effectively, but when connected to Slack _properly_ (not their MCP; but API which is more powerful), a database of your repo’s PRs/comments, data warehouses including analytics/telemetry and logs; and in a strong harness (including using multiple models simultaneously; like the OMP advisor pattern), what AI can achieve combined your domain expertise and human intelligence is just mind bogglingly crazy.
The larger your codebase / product / volume is; the more powerful it gets. AI has found many needles in haystacks that’s just impossible for a single person or team in large companies; because nobody has all the context.
I’ve embraced it too now. Initially I felt a bit disempowered and just somewhat uncomfortable.
Over time, I realised that I’m still doing serious and interesting engineering: just at a higher level of abstraction.
And for the craft and passion of software engineering, I have a couple of pet projects where I use ‘limited AI’. Good to still keep your wits sharp.
I generally agree with this. One of the strangest things about LLM driven engineering is holding two seemingly contradictory positions in your head: they’re both better than the median developer, and they’re also much worse at producing artifacts that are comprehensible to humans.
I often find myself throwing away large amounts of LLM driven code not because it’s bad, but because it doesn’t fit within my attention span. The code itself looks very reasonable, passes tests, benchmarks well, etc. But I throw it away because the models don’t yet “explain” their decisions in ways that elicit psychological safety. Humans are still very good at that, even when their engineering is worse.
My experience is the same. That is why I write technical specifications for the LLM to follow, and treat the actual code they generate in the same way I treat the assembly produced by a compiler: a black box I rarely peek into.
If the code passes the (extensive) tests, I don't need to read or understand it. That said, I retired before LLMs became popular, so my experience is limited to vibe coding at home.
We (at least some of us) sort of figured out 30ish years ago that waterfall-style software development doesn’t quite work in practice. I don’t think LLMs have substantially changed that.
Did I mention waterfall at all? You are fighting a strawman.
You can write specs for a MVP, write a test plan, yadda yadda, then progressively iterate. I have no idea where you got the idea that I was proposing a waterfall lifecycle.
It's the same with any tool. You can buy the most expensive drill but if its used by an inexperienced worker, the only result will be more wrong drilled holes.
Charitably, we could say that “agentic” software engineering is less than 4 years old. I say charitably because I think even that’s an extraordinary stretch. But even at 4 years, I don’t think anybody can fairly claim to be experienced in it in a way that’s going to be stable and fungible for, say, the next 30 years.
(My experience has been the polar opposite: the people I know who are the most “AI pilled” are also the ones who have the shortest technical horizons in terms of how transferable they expect their LLM skills to be.)
True, but thats explains why the gap is so huge at the moment. There are some experienced (in coding) devs with a talent in using agent, inexperienced devs with talent, experienced devs without talent and inexperienced devs without talent. And I don't mean talent in a judgmental sense; it's perfectly normal for people to have different aptitudes, and we simply weren't prepared for using computers in a natural language.
The fact that there are currently hardly any established methods, and that the combination of all the LLMs, harnesses, MCP servers, etc., results in extremely different experiences, and that nobody really has a comprehensive overview, only exacerbates the situation.
I agree. It remains to be seen whether the field will even stabilize, beyond some local maxima of everything being a chat interface. I for one hope it does.
In my feeling, it is decades old. With LLMs and agentic coding, it's deja vu of some 200x working with with inexperienced offshore teams. The same misunderstanding problems, the same corners cut, the same attempts to present bullshit as a "production ready", and the same "Yes, Sir, you are absolutely right" answer to criticue.
I've been really struggling to get AI to write good quality c# code, or to be precise, what I see as good quality.
I'm in two minds on if it matters or not.
On one hand I think, I want to be proud of it, I want to be able to explain it, if it breaks I want to be able to figure out why.
On the other hand, AI can do all of that with badly written code, so who cares.
Edit: however, it still feels like an amazing power tool, but it has taken me months to figure out how to use it.
I have the opposite experience of everyone else I follow online, I find it terrible at green field and great at brownfield. Green field it makes horrible choices as it has nothing to follow.
I'm in no way a very good programmer, or very smart, but the code I saw most of my co-workers writing was about the same quality as AI, not very good.
> Leverage your knowledge of systems, software engineering and product design and you can be living in a golden era for software development.
For a short while, maybe. If an LLM can keep track of a 500KSLoC codebase, it's gonna easily replace systems knowledge workers, software designers and product designers.
None of systems knowledge, software engineering and product design is a moat against this.
It hasn’t climbed the complexity bar for hard software engineering yet, fable still can’t build a fully functioning C Compiler, I think in the long horizon eval it can sometimes build a C pre-processor (not deterministic) given all the tests and a spec. And given the tests is a big deal, humans actually write the tests on their own while developing btw. Anthropics marketing stunt C compiler doesn’t count (that one didn’t even type check).
Now the thing to claim “a better coder than humans”, is you can’t just stop at making a production grade C Compiler, you then also need to make the leap to make something new that is a definite improvement over everything that existed before it. This is an also a question of taste not just implementation chops. Think Zigs cross platform C Compiler, Rusts memory safety opinionated compiler and more.
The day AI can do both, implement a complex production grade project, and make a conceptual actual improvement upon SOTA is the day I’ll agree AI has become better than humans at coding. I’ve underestimated AI in the past, maybe with 10T of compute they’ll get there, maybe they won’t , we’ll know in the coming years
I think LLMs can be used productively, but I also think the average PR from an LLM is crap. They can be decent (or even excellent) at writing code, but they're mediocre at deciding what code to write, and terrible at deciding what not to write.
Make your own benchmark on your own work, keep them to yourself. Try one typical not completely unambiguous spec document like you're likely to have seen. See if the AI asks the right questions, and how it navigates its unknown unknowns.
I have yet to see such research. I've seen research that says the exact opposite. The devs I see making such pronouncements usually aren't exactly the top devs on their teams. Seems like the result of the double burden to me.
My experience, no its not. It just doesn't fight back as much when you tell it that its wrong.
My sister team is vibecoding the shit out of a couple of product PoCs. There is only one person on that team that appears to understand how to vibe code properly. the rest are just producing shite and breaking the service everytime they deploy. However, the code it creates is fine enough, just the architecture is bad, or the prompter is bad.
_however_ the problem with the post is that its using tangential metrics to prove the point. The opensource maintainer bit doesn't always mean that the output is bad, it means that either:
1) the maintainers hate AI
2) the shit they are getting is huge and takes too long to review
3) The shite they are getting solves a specific problem for one user at the expense of everyone else
4) the PR is nonsense.
only one of those options area signal for code quality from LLMs. the rest are about the skill of the creator, or attitude/time budget of the maintainer.
I kind of agree that the post is a bit too far away from the trenches to be able to claim it will reveal "software engineering reality".
From where I snipe, I see a big divide between those...
1) that try to surrender to AI, aiming to fully replace value-added intellectual effort and often also to augment enterprise value-mask slop busywork...
...they fail, and succeed, and the collective suffers.
2) those that ride AI to get more challenged, more feedback if any kind, to tread further but with attention to the right details
I think one problem with this whole conversation is that “coding” is not one thing, and skill at it can mean many different things depending on the context.
In my experience, the top models still generate tons of useless slop on any non-trivial implementation request that I do not essentially solve in the prompt beforehand (change this class, this function, etc). They also still make trivial errors that no human would make (although the inverse is also true). In this sense, they do suck at coding.
On the other hand, even weaker models can understand large sections of code, come up with correct implementations of changes, and catch non-trivial edge cases in many situations that is obviously better than most devs. In this sense, they are better than almost all human devs, especially when considering the time and cost.
Perhaps in the long term AI will help us distinguish better between different types of coding tasks and programming disciplines
How do you know your average AI PR is better than a human developer? Most of the teams that I see touting the benefits almost never review the code that's output, or they offload that process to another agent.
Like I see people say this, and yet the teams that are AI maxing produce worse code than ever. Software has rapidly gotten more unstable and unsustainable over the past three or so years than I've experienced in the past 20.
> ... you can be living in a golden era for software development.
Let's assume for a moment that you're correct. That AI is already better than most developers, for whatever definition of "better" you like, and they will very soon be better than any person. (I think this is a total fantasy and you've failed to recognise the limitations as the article points out, but I digress.)
In that case the end goal of these companies is to replace all software engineers. Do you not see that? They've not been hiding this fact. It's a good thing for you and I that these models don't work, because if they did the "golden age" is not coming for us, it's coming for people who own compute capacity and the rest of us will become labourers.
Additionally, if this is true companies that have replaced all their software engineers will discover that they themself can be completely replaced by AI by their former customers.
Humans have value judgement. AI doesn't. AI doesn't "know" what good code even is. For something to be good, there has to be a purpose. Good is the measure of how well that purpose is fulfilled.
Humans can write better or worse code, but AI is not even in the category of things that can write better or worse code. A human needs to be there to tell bad code from good code.
* * *
Also, in my personal experience with AI code: I'm yet to see good code (but I haven't worked with people who are good at directing AI towards their goals). All code I've seen generated by AI so far ranged from "absolute garbage" to "passable". Which, most likely, reflects the ability of those who managed the tool: before they did that, their code was also atrocious. It was easier to deal with, because the velocity at which these people produced garbage didn't cause a deluge in the same way they do it with AI help.
* * *
A note on what I believe to be good code and its distribution. First of all, I agree with you on that the vast majority of code produced to date is very bad. There are many reasons for it: until few years ago the demand for programmers was smaller than supply and the industry was on course to create conditions for very bad programmers to succeed anyhow (help the losers lose less, tee-hee!). It still didn't recover from all the "paradigms" it created to support bad programmers.
Unlike in well-established fields, where you'd expect normal distribution in terms of how skillful the workers are (i.e. you'd expect very few to be very bad and very few to be very good, but most would be good enough), the distribution in programming is exponential: overwhelming majority are at the proverbial bottom of the barrel, only a few are OK, and you probably will never meet a truly good one. This defies intuition and leads us to assume that the barely palatable is the best it can possibly be. And that's, roughly, where AI is at at the moment.
Exactly the post you'd expect at this stage in the technology adoption and hype cycle. People got over hyped not understanding how technology and technology adaption works. Then they get a bunch of like 6-12 month lagging indicators further convincing them of the worse. Right when they become most certain the technology is useless is precisely when the people that have adopted it and truly understand it leave them in the dust. There's gotta be a name for it?
That wasn't necessarily caused by AI, in fact quite a lot of the layoffs came well before AI was useful. Most of the layoffs happened because companies over-hired during covid, and a lot of other economic issues causing people to spend less money, which causes top-heavy companies to lay off employees they hired when people had more money to spend.
Your comment is a little overdramatic and lacks and grounding in reality. Lots of companies overhired, including mine, and we also had layoffs that were not a result of AI, it pre-dated anyone using AI at our company.
But whatever, this pointless internet interaction is over.
Tangentially related, I'm starting to think LLM assisted coding will increase the jobs in software engineering.
Think about it: code is cheap now. You'll have accountants realizing they can create scripts to automate their work flow, so they hack together something. These scripts will become the backbone of the accounting pipeline of a company. Now someone needs to productionalize and maintain these scripts, but the accountants don't even have the vocabulary required for that, so they hire a software engineer to do so.
A simplified example, but I can see that happening. The only (personal) issue I have with this is that the LLMs get to take the fun part of the job.
I wish developers could just walk out the door en masse tomorrow and let orgs replace them all with AI as has been touted for so long, then see what happens. It gets boring trying to explain that developers don’t just write code. “What do they do then?” I can almost hear a manager saying in a smug tone. If you have to ask, you’ll never knowww… ♬ ♬ ♬ ♬
On the "Stop saying please" part. I personally like to use polite language, as an exercise. According to this one paper on arxiv, toxic behavior gets better accuracy
Not being polite just further nourished our basest animal instincts, which the internet has already enflamed. I’ve watched people slide into severe impatience and antisocial knee jerks as a result of too much time online, including myself. Driving and synthetic (online/ai) social interaction make many of us a little more assholeish. And of course, there’s a negative feedback loop, as this further polarized online discussion angering people even more. It’s rather depressing, and it seems no online realm is safe (including HN)
Weird article. Feels very 2025 for a 2026 article (both in ways it reads too anti AI to me and ways it seems not critical enough of ai)
>Also stop saying “please” to an LLM. It does not have any feelings.
It emulates having feelings and it's a next token predictor. The next token in a dataset where you respond to an engineer rudely and call them stupid is rarely said engineer locking in and delivering incredible code. You have to play along to get the output you want.
> Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves.
This is like saying you should experiment with a paper airplane to see why fighter jets are overrated. Also the general understanding of temperature is not super solid here - it's not just that its "too boring" without it - random sampling is required for the models to work.
>Why are benchmarks showing they're still improving?
Ironically this section is far too generous to LLMs and benchmarks. lLMs cheat and companies benchmax. Don't trust benchmarks. They lie
Also in the what I do section - are these using local LLMs too? "Sometimes looping llms on itself can make it fix its own errors" feels very 2025 - the modern state of things is more "we've given up on one shots and getting it to produce the right answer immediately, set it up with a test harness so it can fix its own mistakes and let it loop otherwise it won't work." Also "asked fellow developers to make sure their code is well structured, easy to follow and documented. LLMs unfortunately make it easier for people to cheat in this regard" - really? Easy to follow? Maybe gpt models with good steering but trying to get an anthropic model to speak coherently and clearly and write documentation that isn't incomprehensible slop is a Herculean effort
First, I don’t think it’s widely accepted financial markets are in an AI bubble or there is actually any evidence to support this, most market cap growth is supported by real earnings per share. It is debatable how long that will continue and if it is sustainable, but that isn’t a bubble it’s a cycle. Even the most skeptical investors to bubbles like Buffet and Burry, have large stakes in Google and Microsoft, front line bets on AI.
Second, I agree with the distinction evals are not enough, and this is dangerously making people reckless, I think the conclusion is still quite incorrect.
There is essentially nothing magical about being human. Our primitive brains were trained on logic, reasoning and processes. An LLM is simply this on less efficient hardware, but, improving at a speed faster than human evolution.
> I don’t think it’s widely accepted financial markets are in an AI bubble or there is actually any evidence to support this
It's widely reported on and discussed. You'll find plenty of people saying it isn't one, but I'm sure you can find an economist willing to take any position you like if you look hard enough. Not to mention everyone who's financially incentivised to continue to claim that there isn't a bubble.
> Even the most skeptical investors to bubbles like Buffet and Burry, have large stakes in Google and Microsoft, front line bets on AI.
Burry has been warning about an AI bubble for years.
> There is essentially nothing magical about being human. Our primitive brains were trained on logic, reasoning and processes. An LLM is simply this on less efficient hardware, but, improving at a speed faster than human evolution.
There is no basis for this claim. There is no evidence that human brains and the process of evolution that led to modern humans is at all similar to how LLMs function. If you're going to evaluate an LLM then evaluate an LLM, you can't say "Humans can do X, LLMs are like humans, therefore LLMs can/will do X" because there is no evidence to connect them.
> It's widely reported on and discussed. You'll find plenty of people saying it isn't one, but I'm sure you can find an economist willing to take any position you like if you look hard enough. Not to mention everyone who's financially incentivised to continue to claim that there isn't a bubble.
Our media is a weaponized crap factory. There is literally nothing but propaganda and insider knowledge. You're network is how you survive today, mass media and social media and anyone that uses these medias as guidance is weaponized against their own interests. Your own critical analysis, secondary considerations, knowledge of real factual history, and your effective communications skills are how you survive. Not by listening to others and their parrot echo of some media propaganda trying to control the narrative.
There's something to say about the extreme confidence people have that AI is a bubble with perpetually pushed up timelines.
It doesn't matter that AI is useful (but so was internet).
It doesn't matter that revenues exist (mostly fake and circular).
It doesn't matter that real unit costs go down (models are getting commoditised).
It doesn't matter that the margins are high (companies are secretly hiding the real costs because that would pop the bubble).
In their world view, the bubble is predestined. There's nothing that could prevent it - even with higher demand, even with higher revenues, even with higher margins. Nothing.
I think, if the bubble pops, LLM assisted coding is here to stay. It's novel & useful enough to offer a real advantage, but can also be misused easily by folks who think software is now "solved" obviously. Either way - the middle ground is the sweet spot, and I think you're probably just looking at the disappearance of the bottom layer: grunt work, templates, straightforward SaaS apps, tooling, utils, etc. Job-wise though, I think juniors & interns are the ones who's caught in the storm unfortunately.
I really want to agree but the arguments he brings up make that extremely hard
> Also, it seems that many don’t want to learn but instead expect to have all understanding outsourced to LLMs. Many seniors have noticed this and have stopped teaching juniors as the seniors don’t like the feeling of having their time wasted by teaching people who don’t want to learn.
Or may be it is because now a junior dev is expected to deliver to output of a senior?
> Also stop saying “please” to an LLM. It does not have any feelings.
Yes, but I still prefer a nice tone. Like why should I change my manners just because it has no feelings? If anything the statistic predicts a friendlier answer when I say "please".
> Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves. It is a good antidote to falling for the illusion that LLMs would actually be intelligent.
It's like recommending someone to buy the cheapest Lenovo Thinkpad to prove that Lenovo sucks.
> However, the best models still have a pass rate of only about 50% on the Humanity’s Last Exam.
Haha, as if he (or really most people) even would understand 50% of those questions. I find it rather mind blowing that it's possible to put such diverse knowledge into a couple of TB. Or may be I'm just an idiot and it's common knowledge, "how many paired tendons are supported by the sesamoid bone of hummingbirds within Apodiformes".
> LLMs are not a scam, but a useful tool and technology that has its uses. But the idea that AI has or will surpass humans any time soon in either capabilities or efficiency is simply not true
He's not wrong that LLMs are just useful tools but isn't it the purpose of a tool to surpass human capabilities and efficiency? Like even a bicycle makes traveling more efficient than just walking and a car has the capability to transport more items than any human. And its know more than two decades since computer surpassed human capabilities in chess. If a tool is neither more capable nor efficient, it's just a useless tool.
I mean I get his point that GenAI is to some degree over hyped but his arguments just dont hold in my opinion.
> stop saying “please” to an LLM. It does not have any feelings.
> Yes, but I still prefer a nice tone. Like why should I change my manners just because it has no feelings? If anything the statistic predicts a friendlier answer when I say "please".
It's a probabilistic generator. If you set the tone of the conversation it will follow it.
Try inserting a few jokes, puns etc into a conversation and you'll see that it responds kn kind.
Just like if you ask highly specific technical questions, it responds using highly technical language. If you ask questions in legalese it responds using precise legal terms.
That's what happens when a token predictor has a conversation: it can't have any tone other than what you give it.
So continue saying please, and you'll have more pleasant conversations.
> Yes, but I still prefer a nice tone. Like why should I change my manners just because it has no feelings? If anything the statistic predicts a friendlier answer when I say "please".
Like you I generally prefer polite phrasings over rude ones, and I'm certainly not going to tell you how you should communicate to LLM models, however it's interesting to note that being somewhat rude and to the point seems to have some objective advantages, see for example:
Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
I would hate for this style of communication to leak through to daily communications with humans (e.g. colleagues) though, so even just for that it can make sense to hold on to a polite form of communication :-).
They go nowhere because people are using wildly different definitions and contexts. There's one already in here about how ai is better than humans at coding.
- Yes llms are better at the mechanics of coding
- no they're not good enough for overall software dev.
- yes, this stuff should've been automated years ago in frameworks and in libs, or through sane programming langs that dealt with memory and logic flow better.
- yes, they're much more useful for documentation, search, etc. than they are at actual coding
- no, your stats aren't useful - 90% of coding is meaningless if you've also massively increased the amount of slop produced
- yes, llms do make you more productive overall, whether it's 10-30% or 1000% is context dependent
- yes and no that AI will change everything; no it doens't make sense to keep comparing pre-ai and post-ai worlds, it is very likely that the gains cancel each other out and we all just move up a layer of abstraction, and end up in a simlar situation to now.
- no we don't know what will actually happen to the job market, things can remain irrational longer than you can remain solvent. We've had the technology to be where are 20-30 years ago if we were focused as a society. Things take time, and real world is complicated.
I'm biased, being a software technical writer and not a dev, but at least from my side of the fence I think LLMs are no better at generating docs than they are at generating code, and might actually be way worse :)
My devs keep throwing Claude-generated writeups at me that look okay at first glance but fall apart when you actually try to follow the instructions they lay out (which makes them useless for anyone who's not already familiar with the product or feature they're describing), and/or have major structural and logical gaps, and/or gloss over messy details in a way that makes the product or feature sound better but doesn't reflect the reality of what we actually built.
Which turns into an ironic ouroboros where I then have to punch my own queries into an LLM and ask it to read our codebase, compare those details against the original writeup, try to decipher what the hell it was trying to say, etc. And even that turns into an extended back-and-forth where my LLM is like "The original writeup is inaccurate; it should say X" and I'm like "Are you sure? That contradicts what I know about related concept Y", and then the LLM is like "Good point, I dug into it more and the real answer is Z." (And even then, god only knows if Z is correct. I still have to get real developer eyeballs on anything before it goes live.)
Documentation was difficult to keep in sync with code because the tools didn't operate at a natural language level. Things like refactoring names, or terminology, or concept changes (foo is now fooGroup that contains bar) were tedious.
The granularity at which documentation parity is kept makes a big difference.
For example, brekaign things down into explicit assertion blocks in the code, and a link to the documentaiton that covers that assertion. So block-to-block tracking works well. Tracking completely separate documentation to independent codebase... idk I haven't tried it but my guess it that the context windows are too small for that.
But minor things like making sure there are unique error codes, that the codes somewhat make sense, etc. are all helpful. If nothing else, the model being stupid and acting as a rubber duck for pair-programming is useful for knowing what to document given a different perspective.
"this stuff should've been automated years ago in frameworks and in libs, or through sane programming langs that dealt with memory and logic flow better."
70 years of working on the 'how do I reuse code?'. And the solution just takes all the electricity in the world.
I'm confused by the situation. I'm the last manual coder of my company, and am shipping projects faster than my colleagues who are spending fortunes in tokens.
I was intrigued by the hype and gave a chance this week to codex+sol 5.6 and cc+opus 5. They cheated, lied, disobeyed, and shipped subtle bugs so often, it wasted more time that if I did it myself.
Is half of the industry under AI psychosis right now ? Will models get better ?
> It is widely accepted that there is an AI bubble in the financial markets at the moment
No its not. No one serious truly believes
1. AI is going away
2. its obvious that the total market cap of AI related stocks will go down more than 60-70%
Its obviously true that there are some related AI companies that will go bust as with any new technology. And its also true that the rise won't be monotonic.
What we instead get is perpetually pushed timelines for the bubbles and a religious belief that the end will come and people would pay for it.
To drive my point further, I ask for a single metric you would like to see that should be a certain way for you to believe it is not a bubble. Should the revenues have been 2x higher? Should the costs be 3x lower? Anything?
I think this post (and the OSS projects that he mentions that ban AI) are very reactionary.
> But the idea that AI has or will surpass humans any time soon in either capabilities or efficiency is simply not true
AI is already better than most developers. I'm not sure what alternative reality people are remembering, but human coders for the most part have been really awful at writing code. I think the average PR from an LLM is head and shoulders above the average PR from a human. Does it write code in the preferred style and architecture of the project maintainer 100% of the time? No, and neither did humans.
I think there are many legitimate criticisms of AI, but "they suck at coding" isn't one of them. The progress we've seen in the last couple of years alone suggest that very soon they will be better at coding than any person. As a coder of over 30 years, I've embraced this fact and come to terms with it. Leverage your knowledge of systems, software engineering and product design and you can be living in a golden era for software development. That's how it feels to me at least.
The problem is that skill at coding is not exactly the same thing as skill at developing and maintaining software, and AI can help there as well, but a swarm of cowboy coder agents will get you to a legacy codebase very very quickly.
And even if the AI is better than most humans, the speed means that you get more defects and issues! If a human developer has a change failure rate of say 10%, (1 in 10 changes causes a defect or issue), and AI is twice as good and only introduces bugs 5% of the time, but submits 10x as many changes, then you go from 1 bug per unit of time to 5 bugs per unit of time, so your velocity is up 10x but your defect rate is up 5x...
> the speed means that you get more defects and issues
You forget to account for the rate of error correction.
You can just choose how many bugs you want now:
https://nolanlawson.com/2026/08/16/you-can-just-choose-how-m...
How many new bugs get introduced in those fixes?
As many as you allow.
> but a swarm of cowboy coder agents will get you to a legacy codebase very very quickly.
You mean “a codebase of high technical debt” I think
I agree, and I think what you’re describing is only scratching the surface of what’s possible today.
It’s even more powerful with large data and knowledge sources connected.
Takes a lot of work to set up effectively, but when connected to Slack _properly_ (not their MCP; but API which is more powerful), a database of your repo’s PRs/comments, data warehouses including analytics/telemetry and logs; and in a strong harness (including using multiple models simultaneously; like the OMP advisor pattern), what AI can achieve combined your domain expertise and human intelligence is just mind bogglingly crazy.
The larger your codebase / product / volume is; the more powerful it gets. AI has found many needles in haystacks that’s just impossible for a single person or team in large companies; because nobody has all the context.
I’ve embraced it too now. Initially I felt a bit disempowered and just somewhat uncomfortable.
Over time, I realised that I’m still doing serious and interesting engineering: just at a higher level of abstraction.
And for the craft and passion of software engineering, I have a couple of pet projects where I use ‘limited AI’. Good to still keep your wits sharp.
I generally agree with this. One of the strangest things about LLM driven engineering is holding two seemingly contradictory positions in your head: they’re both better than the median developer, and they’re also much worse at producing artifacts that are comprehensible to humans.
I often find myself throwing away large amounts of LLM driven code not because it’s bad, but because it doesn’t fit within my attention span. The code itself looks very reasonable, passes tests, benchmarks well, etc. But I throw it away because the models don’t yet “explain” their decisions in ways that elicit psychological safety. Humans are still very good at that, even when their engineering is worse.
My experience is the same. That is why I write technical specifications for the LLM to follow, and treat the actual code they generate in the same way I treat the assembly produced by a compiler: a black box I rarely peek into.
If the code passes the (extensive) tests, I don't need to read or understand it. That said, I retired before LLMs became popular, so my experience is limited to vibe coding at home.
We (at least some of us) sort of figured out 30ish years ago that waterfall-style software development doesn’t quite work in practice. I don’t think LLMs have substantially changed that.
Did I mention waterfall at all? You are fighting a strawman.
You can write specs for a MVP, write a test plan, yadda yadda, then progressively iterate. I have no idea where you got the idea that I was proposing a waterfall lifecycle.
It's the same with any tool. You can buy the most expensive drill but if its used by an inexperienced worker, the only result will be more wrong drilled holes.
Charitably, we could say that “agentic” software engineering is less than 4 years old. I say charitably because I think even that’s an extraordinary stretch. But even at 4 years, I don’t think anybody can fairly claim to be experienced in it in a way that’s going to be stable and fungible for, say, the next 30 years.
(My experience has been the polar opposite: the people I know who are the most “AI pilled” are also the ones who have the shortest technical horizons in terms of how transferable they expect their LLM skills to be.)
True, but thats explains why the gap is so huge at the moment. There are some experienced (in coding) devs with a talent in using agent, inexperienced devs with talent, experienced devs without talent and inexperienced devs without talent. And I don't mean talent in a judgmental sense; it's perfectly normal for people to have different aptitudes, and we simply weren't prepared for using computers in a natural language.
The fact that there are currently hardly any established methods, and that the combination of all the LLMs, harnesses, MCP servers, etc., results in extremely different experiences, and that nobody really has a comprehensive overview, only exacerbates the situation.
I agree. It remains to be seen whether the field will even stabilize, beyond some local maxima of everything being a chat interface. I for one hope it does.
In my feeling, it is decades old. With LLMs and agentic coding, it's deja vu of some 200x working with with inexperienced offshore teams. The same misunderstanding problems, the same corners cut, the same attempts to present bullshit as a "production ready", and the same "Yes, Sir, you are absolutely right" answer to criticue.
Yes, we know that Astral has been bought by OpenAI.
Don't use Astral, they want to make you unemployed!
They are only good in the context of the engineer guiding them.
I really can’t imagine what would happen if I didn’t manually intervene sometimes and just kept prompting it for the new behavior I wanted.
The engineer guiding them only scales to a certain amount of output, complexity and churn.
I've been really struggling to get AI to write good quality c# code, or to be precise, what I see as good quality. I'm in two minds on if it matters or not.
On one hand I think, I want to be proud of it, I want to be able to explain it, if it breaks I want to be able to figure out why.
On the other hand, AI can do all of that with badly written code, so who cares.
Edit: however, it still feels like an amazing power tool, but it has taken me months to figure out how to use it.
I have the opposite experience of everyone else I follow online, I find it terrible at green field and great at brownfield. Green field it makes horrible choices as it has nothing to follow.
I'm in no way a very good programmer, or very smart, but the code I saw most of my co-workers writing was about the same quality as AI, not very good.
> Leverage your knowledge of systems, software engineering and product design and you can be living in a golden era for software development.
For a short while, maybe. If an LLM can keep track of a 500KSLoC codebase, it's gonna easily replace systems knowledge workers, software designers and product designers.
None of systems knowledge, software engineering and product design is a moat against this.
It hasn’t climbed the complexity bar for hard software engineering yet, fable still can’t build a fully functioning C Compiler, I think in the long horizon eval it can sometimes build a C pre-processor (not deterministic) given all the tests and a spec. And given the tests is a big deal, humans actually write the tests on their own while developing btw. Anthropics marketing stunt C compiler doesn’t count (that one didn’t even type check).
Now the thing to claim “a better coder than humans”, is you can’t just stop at making a production grade C Compiler, you then also need to make the leap to make something new that is a definite improvement over everything that existed before it. This is an also a question of taste not just implementation chops. Think Zigs cross platform C Compiler, Rusts memory safety opinionated compiler and more.
The day AI can do both, implement a complex production grade project, and make a conceptual actual improvement upon SOTA is the day I’ll agree AI has become better than humans at coding. I’ve underestimated AI in the past, maybe with 10T of compute they’ll get there, maybe they won’t , we’ll know in the coming years
I think LLMs can be used productively, but I also think the average PR from an LLM is crap. They can be decent (or even excellent) at writing code, but they're mediocre at deciding what code to write, and terrible at deciding what not to write.
Yeah, it's a bit absurd. There's so many empirically measured benchmarks where LLMs clearly exceed human capabilities and efficiencies!
Make your own benchmark on your own work, keep them to yourself. Try one typical not completely unambiguous spec document like you're likely to have seen. See if the AI asks the right questions, and how it navigates its unknown unknowns.
I have yet to see such research. I've seen research that says the exact opposite. The devs I see making such pronouncements usually aren't exactly the top devs on their teams. Seems like the result of the double burden to me.
> human coders for the most part have been really awful at writing code.
They are better when copying and pasting expert code, even when they don't understand it.
> AI is already better than most developers.
My experience, no its not. It just doesn't fight back as much when you tell it that its wrong.
My sister team is vibecoding the shit out of a couple of product PoCs. There is only one person on that team that appears to understand how to vibe code properly. the rest are just producing shite and breaking the service everytime they deploy. However, the code it creates is fine enough, just the architecture is bad, or the prompter is bad.
_however_ the problem with the post is that its using tangential metrics to prove the point. The opensource maintainer bit doesn't always mean that the output is bad, it means that either:
1) the maintainers hate AI
2) the shit they are getting is huge and takes too long to review
3) The shite they are getting solves a specific problem for one user at the expense of everyone else
4) the PR is nonsense.
only one of those options area signal for code quality from LLMs. the rest are about the skill of the creator, or attitude/time budget of the maintainer.
> AI is already better than most developers.
A tool can only be as good as the person who use it.
I kind of agree that the post is a bit too far away from the trenches to be able to claim it will reveal "software engineering reality".
From where I snipe, I see a big divide between those...
1) that try to surrender to AI, aiming to fully replace value-added intellectual effort and often also to augment enterprise value-mask slop busywork...
...they fail, and succeed, and the collective suffers.
2) those that ride AI to get more challenged, more feedback if any kind, to tread further but with attention to the right details
...they succeed
that’s the thing, you can have the LLM study and make a skill to only code in the maintainers preferred style or readability.
Based on what I’ve read on HN, no you can’t because they inevitably revert to their bad RL’d habits as the context window grows.
> AI is already better than most developers
By what measure? How do you even compare developer skill?
I think one problem with this whole conversation is that “coding” is not one thing, and skill at it can mean many different things depending on the context.
In my experience, the top models still generate tons of useless slop on any non-trivial implementation request that I do not essentially solve in the prompt beforehand (change this class, this function, etc). They also still make trivial errors that no human would make (although the inverse is also true). In this sense, they do suck at coding.
On the other hand, even weaker models can understand large sections of code, come up with correct implementations of changes, and catch non-trivial edge cases in many situations that is obviously better than most devs. In this sense, they are better than almost all human devs, especially when considering the time and cost.
Perhaps in the long term AI will help us distinguish better between different types of coding tasks and programming disciplines
The golden era that hasn't produced anything of note yet. More pro-AI advertisements from someone who needs AI crutches.
How do you know your average AI PR is better than a human developer? Most of the teams that I see touting the benefits almost never review the code that's output, or they offload that process to another agent.
Like I see people say this, and yet the teams that are AI maxing produce worse code than ever. Software has rapidly gotten more unstable and unsustainable over the past three or so years than I've experienced in the past 20.
> ... you can be living in a golden era for software development.
Let's assume for a moment that you're correct. That AI is already better than most developers, for whatever definition of "better" you like, and they will very soon be better than any person. (I think this is a total fantasy and you've failed to recognise the limitations as the article points out, but I digress.)
In that case the end goal of these companies is to replace all software engineers. Do you not see that? They've not been hiding this fact. It's a good thing for you and I that these models don't work, because if they did the "golden age" is not coming for us, it's coming for people who own compute capacity and the rest of us will become labourers.
> you've failed to recognise the limitations as the article points out
Fixed weights don't preclude in-context learning and out-of-the-loop weight updates. And that's the only principled limitation mentioned in the post.
Additionally, if this is true companies that have replaced all their software engineers will discover that they themself can be completely replaced by AI by their former customers.
This is a very misguided idea.
Humans have value judgement. AI doesn't. AI doesn't "know" what good code even is. For something to be good, there has to be a purpose. Good is the measure of how well that purpose is fulfilled.
Humans can write better or worse code, but AI is not even in the category of things that can write better or worse code. A human needs to be there to tell bad code from good code.
* * *
Also, in my personal experience with AI code: I'm yet to see good code (but I haven't worked with people who are good at directing AI towards their goals). All code I've seen generated by AI so far ranged from "absolute garbage" to "passable". Which, most likely, reflects the ability of those who managed the tool: before they did that, their code was also atrocious. It was easier to deal with, because the velocity at which these people produced garbage didn't cause a deluge in the same way they do it with AI help.
* * *
A note on what I believe to be good code and its distribution. First of all, I agree with you on that the vast majority of code produced to date is very bad. There are many reasons for it: until few years ago the demand for programmers was smaller than supply and the industry was on course to create conditions for very bad programmers to succeed anyhow (help the losers lose less, tee-hee!). It still didn't recover from all the "paradigms" it created to support bad programmers.
Unlike in well-established fields, where you'd expect normal distribution in terms of how skillful the workers are (i.e. you'd expect very few to be very bad and very few to be very good, but most would be good enough), the distribution in programming is exponential: overwhelming majority are at the proverbial bottom of the barrel, only a few are OK, and you probably will never meet a truly good one. This defies intuition and leads us to assume that the barely palatable is the best it can possibly be. And that's, roughly, where AI is at at the moment.
Exactly the post you'd expect at this stage in the technology adoption and hype cycle. People got over hyped not understanding how technology and technology adaption works. Then they get a bunch of like 6-12 month lagging indicators further convincing them of the worse. Right when they become most certain the technology is useless is precisely when the people that have adopted it and truly understand it leave them in the dust. There's gotta be a name for it?
Certainly this is the impression true believers have had for at least a year. Nobody seems to have been left in the dust yet though. How odd.
Sure they have. Massive layoffs throughout the industry.
Layoffs were mainly due to overhiring during covid and outsourcing to cheaper countries tho.
Oh you sweet summer child
Brilliant reply.
That wasn't necessarily caused by AI, in fact quite a lot of the layoffs came well before AI was useful. Most of the layoffs happened because companies over-hired during covid, and a lot of other economic issues causing people to spend less money, which causes top-heavy companies to lay off employees they hired when people had more money to spend.
The robot overlords will put the last free man in a pod in 2080 and you guys will still be calling it Covid hiring correction huh
Your comment is a little overdramatic and lacks and grounding in reality. Lots of companies overhired, including mine, and we also had layoffs that were not a result of AI, it pre-dated anyone using AI at our company.
But whatever, this pointless internet interaction is over.
See: /\
Tangentially related, I'm starting to think LLM assisted coding will increase the jobs in software engineering.
Think about it: code is cheap now. You'll have accountants realizing they can create scripts to automate their work flow, so they hack together something. These scripts will become the backbone of the accounting pipeline of a company. Now someone needs to productionalize and maintain these scripts, but the accountants don't even have the vocabulary required for that, so they hire a software engineer to do so.
A simplified example, but I can see that happening. The only (personal) issue I have with this is that the LLMs get to take the fun part of the job.
I wish developers could just walk out the door en masse tomorrow and let orgs replace them all with AI as has been touted for so long, then see what happens. It gets boring trying to explain that developers don’t just write code. “What do they do then?” I can almost hear a manager saying in a smug tone. If you have to ask, you’ll never knowww… ♬ ♬ ♬ ♬
On the "Stop saying please" part. I personally like to use polite language, as an exercise. According to this one paper on arxiv, toxic behavior gets better accuracy
https://arxiv.org/pdf/2510.04950
It was published a while ago. But I wonder if it still holds true today.
Not being polite just further nourished our basest animal instincts, which the internet has already enflamed. I’ve watched people slide into severe impatience and antisocial knee jerks as a result of too much time online, including myself. Driving and synthetic (online/ai) social interaction make many of us a little more assholeish. And of course, there’s a negative feedback loop, as this further polarized online discussion angering people even more. It’s rather depressing, and it seems no online realm is safe (including HN)
I've heard it matters less now, but I also don't like being impolite to inanimate objects.
> But are LLMs actually getting smarter, or just better at fooling us?
They’re getting smarter. Next question.
Weird article. Feels very 2025 for a 2026 article (both in ways it reads too anti AI to me and ways it seems not critical enough of ai)
>Also stop saying “please” to an LLM. It does not have any feelings.
It emulates having feelings and it's a next token predictor. The next token in a dataset where you respond to an engineer rudely and call them stupid is rarely said engineer locking in and delivering incredible code. You have to play along to get the output you want.
> Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves.
This is like saying you should experiment with a paper airplane to see why fighter jets are overrated. Also the general understanding of temperature is not super solid here - it's not just that its "too boring" without it - random sampling is required for the models to work.
>Why are benchmarks showing they're still improving?
Ironically this section is far too generous to LLMs and benchmarks. lLMs cheat and companies benchmax. Don't trust benchmarks. They lie
Also in the what I do section - are these using local LLMs too? "Sometimes looping llms on itself can make it fix its own errors" feels very 2025 - the modern state of things is more "we've given up on one shots and getting it to produce the right answer immediately, set it up with a test harness so it can fix its own mistakes and let it loop otherwise it won't work." Also "asked fellow developers to make sure their code is well structured, easy to follow and documented. LLMs unfortunately make it easier for people to cheat in this regard" - really? Easy to follow? Maybe gpt models with good steering but trying to get an anthropic model to speak coherently and clearly and write documentation that isn't incomprehensible slop is a Herculean effort
First, I don’t think it’s widely accepted financial markets are in an AI bubble or there is actually any evidence to support this, most market cap growth is supported by real earnings per share. It is debatable how long that will continue and if it is sustainable, but that isn’t a bubble it’s a cycle. Even the most skeptical investors to bubbles like Buffet and Burry, have large stakes in Google and Microsoft, front line bets on AI.
Second, I agree with the distinction evals are not enough, and this is dangerously making people reckless, I think the conclusion is still quite incorrect.
There is essentially nothing magical about being human. Our primitive brains were trained on logic, reasoning and processes. An LLM is simply this on less efficient hardware, but, improving at a speed faster than human evolution.
> I don’t think it’s widely accepted financial markets are in an AI bubble or there is actually any evidence to support this
It's widely reported on and discussed. You'll find plenty of people saying it isn't one, but I'm sure you can find an economist willing to take any position you like if you look hard enough. Not to mention everyone who's financially incentivised to continue to claim that there isn't a bubble.
> Even the most skeptical investors to bubbles like Buffet and Burry, have large stakes in Google and Microsoft, front line bets on AI.
Burry has been warning about an AI bubble for years.
> There is essentially nothing magical about being human. Our primitive brains were trained on logic, reasoning and processes. An LLM is simply this on less efficient hardware, but, improving at a speed faster than human evolution.
There is no basis for this claim. There is no evidence that human brains and the process of evolution that led to modern humans is at all similar to how LLMs function. If you're going to evaluate an LLM then evaluate an LLM, you can't say "Humans can do X, LLMs are like humans, therefore LLMs can/will do X" because there is no evidence to connect them.
> It's widely reported on and discussed. You'll find plenty of people saying it isn't one, but I'm sure you can find an economist willing to take any position you like if you look hard enough. Not to mention everyone who's financially incentivised to continue to claim that there isn't a bubble.
Our media is a weaponized crap factory. There is literally nothing but propaganda and insider knowledge. You're network is how you survive today, mass media and social media and anyone that uses these medias as guidance is weaponized against their own interests. Your own critical analysis, secondary considerations, knowledge of real factual history, and your effective communications skills are how you survive. Not by listening to others and their parrot echo of some media propaganda trying to control the narrative.
There's something to say about the extreme confidence people have that AI is a bubble with perpetually pushed up timelines.
It doesn't matter that AI is useful (but so was internet).
It doesn't matter that revenues exist (mostly fake and circular).
It doesn't matter that real unit costs go down (models are getting commoditised).
It doesn't matter that the margins are high (companies are secretly hiding the real costs because that would pop the bubble).
In their world view, the bubble is predestined. There's nothing that could prevent it - even with higher demand, even with higher revenues, even with higher margins. Nothing.
I think, if the bubble pops, LLM assisted coding is here to stay. It's novel & useful enough to offer a real advantage, but can also be misused easily by folks who think software is now "solved" obviously. Either way - the middle ground is the sweet spot, and I think you're probably just looking at the disappearance of the bottom layer: grunt work, templates, straightforward SaaS apps, tooling, utils, etc. Job-wise though, I think juniors & interns are the ones who's caught in the storm unfortunately.
I really want to agree but the arguments he brings up make that extremely hard
> Also, it seems that many don’t want to learn but instead expect to have all understanding outsourced to LLMs. Many seniors have noticed this and have stopped teaching juniors as the seniors don’t like the feeling of having their time wasted by teaching people who don’t want to learn.
Or may be it is because now a junior dev is expected to deliver to output of a senior?
> Also stop saying “please” to an LLM. It does not have any feelings.
Yes, but I still prefer a nice tone. Like why should I change my manners just because it has no feelings? If anything the statistic predicts a friendlier answer when I say "please".
> Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves. It is a good antidote to falling for the illusion that LLMs would actually be intelligent.
It's like recommending someone to buy the cheapest Lenovo Thinkpad to prove that Lenovo sucks.
> However, the best models still have a pass rate of only about 50% on the Humanity’s Last Exam.
Haha, as if he (or really most people) even would understand 50% of those questions. I find it rather mind blowing that it's possible to put such diverse knowledge into a couple of TB. Or may be I'm just an idiot and it's common knowledge, "how many paired tendons are supported by the sesamoid bone of hummingbirds within Apodiformes".
> LLMs are not a scam, but a useful tool and technology that has its uses. But the idea that AI has or will surpass humans any time soon in either capabilities or efficiency is simply not true
He's not wrong that LLMs are just useful tools but isn't it the purpose of a tool to surpass human capabilities and efficiency? Like even a bicycle makes traveling more efficient than just walking and a car has the capability to transport more items than any human. And its know more than two decades since computer surpassed human capabilities in chess. If a tool is neither more capable nor efficient, it's just a useless tool.
I mean I get his point that GenAI is to some degree over hyped but his arguments just dont hold in my opinion.
> stop saying “please” to an LLM. It does not have any feelings.
> Yes, but I still prefer a nice tone. Like why should I change my manners just because it has no feelings? If anything the statistic predicts a friendlier answer when I say "please".
It's a probabilistic generator. If you set the tone of the conversation it will follow it.
Try inserting a few jokes, puns etc into a conversation and you'll see that it responds kn kind.
Just like if you ask highly specific technical questions, it responds using highly technical language. If you ask questions in legalese it responds using precise legal terms.
That's what happens when a token predictor has a conversation: it can't have any tone other than what you give it.
So continue saying please, and you'll have more pleasant conversations.
> Yes, but I still prefer a nice tone. Like why should I change my manners just because it has no feelings? If anything the statistic predicts a friendlier answer when I say "please".
Like you I generally prefer polite phrasings over rude ones, and I'm certainly not going to tell you how you should communicate to LLM models, however it's interesting to note that being somewhat rude and to the point seems to have some objective advantages, see for example:
Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
https://arxiv.org/abs/2510.04950
Understanding Tone-Dependent Inference Cost in Large Language Models
https://arxiv.org/abs/2607.23915
I would hate for this style of communication to leak through to daily communications with humans (e.g. colleagues) though, so even just for that it can make sense to hold on to a polite form of communication :-).
This conversation again?
They go nowhere because people are using wildly different definitions and contexts. There's one already in here about how ai is better than humans at coding.
- Yes llms are better at the mechanics of coding
- no they're not good enough for overall software dev.
- yes, this stuff should've been automated years ago in frameworks and in libs, or through sane programming langs that dealt with memory and logic flow better.
- yes, they're much more useful for documentation, search, etc. than they are at actual coding
- no, your stats aren't useful - 90% of coding is meaningless if you've also massively increased the amount of slop produced
- yes, llms do make you more productive overall, whether it's 10-30% or 1000% is context dependent
- yes and no that AI will change everything; no it doens't make sense to keep comparing pre-ai and post-ai worlds, it is very likely that the gains cancel each other out and we all just move up a layer of abstraction, and end up in a simlar situation to now.
- no we don't know what will actually happen to the job market, things can remain irrational longer than you can remain solvent. We've had the technology to be where are 20-30 years ago if we were focused as a society. Things take time, and real world is complicated.
I'm biased, being a software technical writer and not a dev, but at least from my side of the fence I think LLMs are no better at generating docs than they are at generating code, and might actually be way worse :)
My devs keep throwing Claude-generated writeups at me that look okay at first glance but fall apart when you actually try to follow the instructions they lay out (which makes them useless for anyone who's not already familiar with the product or feature they're describing), and/or have major structural and logical gaps, and/or gloss over messy details in a way that makes the product or feature sound better but doesn't reflect the reality of what we actually built.
Which turns into an ironic ouroboros where I then have to punch my own queries into an LLM and ask it to read our codebase, compare those details against the original writeup, try to decipher what the hell it was trying to say, etc. And even that turns into an extended back-and-forth where my LLM is like "The original writeup is inaccurate; it should say X" and I'm like "Are you sure? That contradicts what I know about related concept Y", and then the LLM is like "Good point, I dug into it more and the real answer is Z." (And even then, god only knows if Z is correct. I still have to get real developer eyeballs on anything before it goes live.)
Documentation was difficult to keep in sync with code because the tools didn't operate at a natural language level. Things like refactoring names, or terminology, or concept changes (foo is now fooGroup that contains bar) were tedious.
The granularity at which documentation parity is kept makes a big difference.
For example, brekaign things down into explicit assertion blocks in the code, and a link to the documentaiton that covers that assertion. So block-to-block tracking works well. Tracking completely separate documentation to independent codebase... idk I haven't tried it but my guess it that the context windows are too small for that.
But minor things like making sure there are unique error codes, that the codes somewhat make sense, etc. are all helpful. If nothing else, the model being stupid and acting as a rubber duck for pair-programming is useful for knowing what to document given a different perspective.
"this stuff should've been automated years ago in frameworks and in libs, or through sane programming langs that dealt with memory and logic flow better."
70 years of working on the 'how do I reuse code?'. And the solution just takes all the electricity in the world.
I'm confused by the situation. I'm the last manual coder of my company, and am shipping projects faster than my colleagues who are spending fortunes in tokens.
I was intrigued by the hype and gave a chance this week to codex+sol 5.6 and cc+opus 5. They cheated, lied, disobeyed, and shipped subtle bugs so often, it wasted more time that if I did it myself.
Is half of the industry under AI psychosis right now ? Will models get better ?
> Is half of the industry under AI psychosis right now ?
A bad coder sees bot code as an inprovement. A good coder sees bot code as deterioration.
They are both correct.
> But are LLMs actually getting smarter, or just better at fooling us?
They look smarter to people they've made dumber.
> It is widely accepted that there is an AI bubble in the financial markets at the moment
No its not. No one serious truly believes
1. AI is going away
2. its obvious that the total market cap of AI related stocks will go down more than 60-70%
Its obviously true that there are some related AI companies that will go bust as with any new technology. And its also true that the rise won't be monotonic.
What we instead get is perpetually pushed timelines for the bubbles and a religious belief that the end will come and people would pay for it.
To drive my point further, I ask for a single metric you would like to see that should be a certain way for you to believe it is not a bubble. Should the revenues have been 2x higher? Should the costs be 3x lower? Anything?
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