FFS. What is it with these weirdo "leader" types and the all lower case writing? Are they trying to come off as "cool" in the "hello fellow children" way or are they just implying they are so important, they don´t even have to observe the rules of writing and grammar?
Code reviews are pseudo-science now? Computer unfriendly code? What are you talking about? Do you understand that this babble makes zero sense ? Are you one of those product managers who recently learned to vibe-code? If so, make sure your latest Replit project does not delete your production database..
Splitting your code up into multiple functions across multiple files is computer unfriendly code. It'll cause L1, L2 and L3 cache misses. Yet it's heailed as very human friendly and maintainable by Uncle Bob and his disciples. As far as code reviews go, do you have any form of evidence that it's not a pseudo science? If I look at our industry today, it's not like it's in better shape compared to where it was decades ago. Hell, some of our most important systems are still running COBOL. If all these methodologies and principles that people swear by actually worked, I'd argue that things would have improved over the previous 40 years.
I think AI is pretty terrible for a lot of things, and pretty great for a lot of things. Since I work in a NIS2 regulated field I can't have any form of agent running with any form of access. Which makes sense for any form of critical service we write, but I wouldn't have an issue having an AI deal with some "unimportant" internal application.
> Splitting your code up into multiple functions across multiple files is computer unfriendly code. It'll cause L1, L2 and L3 cache misses
I think you have no idea what you're talking about and trying to sound technical based on some concepts you misheard somewhere.
A lot of non-tech people got into "tech" in the last years not because they were passionate about technology but because they heard they could make more money there. This was possible due to VCs throwing around money at various software companies. As a result we get statements like yours. There is one thing that I am hopeful for with the AI bubble - which is the VCs panicking out because they think "everyone will vibecode an SaaS" - and pulling out of software companies investments, causing the folks like you to go back to whatever you were doing before and leaving software to people who actually know it and do it out of genuine interest and not primarily for the money.
> "Code has always been expensive. Producing a few hundred lines of clean, tested code takes most software developers a full day or more. Many of our engineering habits, at both the macro and micro level, are built around this core constraint."
Well, yes and no. While producing two screens worth of high quality code, a.k.a. software engineering was always expensive, "coding" as such, as in producing Nth React SPA or merely typing out code that you engineered in your head, was never that expensive - most of the work is applying existing patterns. But either way, as you wrote the code yourself, you mostly had a consistent mental model of how your code should work and the key contribution was evolving this model, first in your head, then in typing out code. Now here comes the real problem for the LLMs: I think most of us would be fine if the LLMs could actually just type out the code for us after we engineered it in our heads and explained it to the LLM in English language. Alas, they do produce some sort of code, but not always, or often enough not in a way we desribed it. So unfortunately "AI" boosters like Simon are reverting back to the argument of fast code generation and an appeal to us as unwilling adopters, to "change our ways", as it shows they have no real advantage of the LLMs to put forward - its only ever the "coding" speed and an appeal to us as professionals to "adapt", i.e. serve as cleaners of LLM sh*t. Where is the superintelligence we were promised and single-person-billion dollar unicorns, unique use cases etc? Are you telling us again these are just advanced text generators, Simon?
> I think most of us would be fine if the LLMs could actually just type out the code for us after we engineered it in our heads and explained it to the LLM in English language. Alas, they do produce some sort of code, but not always, or often enough not in a way we desribed it.
That's exactly what they do for me - especially since the November model releases (GPT-5.1, Opus 4.5).
> Where is the superintelligence we were promised and single-person-billion dollar unicorns, unique use cases etc? Are you telling us again these are just advanced text generators, Simon?
I never promised anyone a superintelligence, or single-person-billion dollar unicorns.
I do think these things are just advanced text generators, albeit the word "just" is doing a whole lot of work in that sentence.
> That's exactly what they do for me - especially since the November model releases (GPT-5.1, Opus 4.5).
I mean it's inherently impossible, given the statistical nature of LLMs, so I am not sure are you claiming this out of ignorance or other interests, but again, what you claim is impossible due to the very nature of LLMs.
It's impossible for human developers too. Natural language descriptions of a program are either much more painful and time consuming to write than the code they describe, or contain some degree of ambiguity which the thing translating the description into code has to resolve (and the probability of the resolution the entity writing the description would have chosen and the one entity translating it into code chose matching perfectly approach zero).
It can make sense to trade off some amount of control for productivity, but the tradeoff is inherent as soon as a project moves beyond a single developer hand writing all of the code.
I agree - the whole BS of "Hottest new programming language is English" is complete nonsense. There is something about writing the code directly from your mind that skips over the "language circuits" and makes it much more precise. Perhaps as humans with education we obtain an ability to think in programming language itself I suppose? It's probably similar to what happens in the mind of a composer or painter. This is why the natural language will never be the interface the big "AI" companies are making it to be.
What you've experienced is different from what was originally mentioned though. Even with the best human developers, you can't provide a normal natural language prompt and get back the exact code you would have written, because natural language has ambiguities and the probability that the other person (or LLM) will resolve all of them exactly as you would is approaches zero.
Collaborating with someone/something else via natural language in a programming project inherently trades control for productivity (or the promise of it). That tradeoff can be worth it depending on how much productivity you gain and how competent the collaborator is, but it can't be avoided.
Ah, the old "you suck at prompting" angle again, isn't it? If you're going to shill this hard, at least come up with something new and original, this is sounding more than desperate.
Most people suck at playing the piano. Most people suck at prompting coding agents. If you practice either of those things you'll get better at them.
I really don't understand the "stop telling me I'm holding it wrong" argument. You probably are holding it wrong!
Is this born out of some weird belief that "AI" is meant to be science fiction technology that you don't ever need to learn how to use?
That would help explain why conversations like this are full of people who claim to get great results and other people who say every time they've tried it the results have been terrible.
> I really don't understand the "stop telling me I'm holding it wrong" argument. You probably are holding it wrong!
I can't speak for others, but from my end it really seems like there's no actual way to detect whether someone is holding it right or wrong until after the implications for LLMs are known. If someone is enthusiastic about LLMs, we don't see claims that they're holding it wrong. It's only if an LLM project fails, or someone tries them and concludes they don't work as well as proponents say, that the accusations come out, even if the person in question had been using these tools for a long time and previously been a supporter. This makes it seem like "holding it wrong" is a post hoc justification for ignoring evidence that would tend to contradict the pro-LLM narrative, not a measurable fact someone's LLM usage.
> Most people suck at playing the piano. Most people suck at prompting coding agents. If you practice either of those things you'll get better at them.
It would be funny, if by now I weren't convinced you are pushing these false analogies on purpose. The key difference between a piano and LLMs being, the piano will produce the same sounds to a same sequence of keys. Every single time. A piano is deterministic. The LLMs are not, and you know it, which makes your constant comparison of deterministic with non-deterministic tools sound a bit dishonest. So please stop using these very weak analogies.
> I really don't understand the "stop telling me I'm holding it wrong" argument. You probably are holding it wrong!
Right, another weak argument. Writing English language paragraphs is not a science you seem to imply it is. You're not the only person using the LLMs intensively for the last years, and it's not like there this huge secret to using them - after all they use natural language as their primary interface. But that's besides the point. We're not discussing if they are hard or easy to use or whatever. We are discussing if I should replace the magnificent supercomputer already placed in my head by mother nature or God or Aliens or whatever you believe in, for a very shitty, downgraded version 0.0.1 of it sitting in someone's datacenter, all for the sake of sometimes cutting some corners by getting that quick awk/sed oneliner or some boilerplate code? I don't think that's a worthy tradeoff, especially when the relevant reports indicate an objective slowdown, which probably also explains the so-called LLM-fatigue.
> Is this born out of some weird belief that "AI" is meant to be science fiction technology that you don't ever need to learn how to use?
No, actually it is born out of the weird belief which your sponsors have been either explicitly or implicitly promoting, now for the 4th year, in various intensities and frequencies, that the LLM technology will be equal to a "country of PhDs in a datacenter". All of this based on the super weird transhumanist ideology a lot of the people directly or indirectly sponsoring your writing actively believe in. And whether you like it or not, even if you have never implied the same, you have been a useful helper by providing a more "rational" sounding voice, commenting on the supposed incremental improvements and progress and what not.
Most people suck at falconry. If you practice at falconry you'll get better at it.
Falcons certainly aren't deterministic.
> it's not like there this huge secret to using them - after all they use natural language as their primary interface
That's what makes them hard to use! A programming language has like ~30 keywords and does what you tell it to do. An LLM accepts input in 100+ human languages and, as you've already pointed out many times, responds in non-deterministic ways. That makes figuring out how to use them effectively really difficult.
> We are discussing if I should replace the magnificent supercomputer already placed in my head by mother nature or God or Aliens or whatever you believe in, for a very shitty, downgraded version 0.0.1 of it sitting in someone's datacenter
We really aren't. I consistently argue for LLMs as tools that augment and amplify human expertise, not as tools that replace it.
I never repeat the "country of PhDs" stuff because I think it's over-hyped nonsense. I talk about what LLMs can actually do.
Well falcons are not deterministic and are trained to do something in the art of falconry, yes. Still I fail to see an analogy here as it is the falcon gets trained to execute a few specific tasks triggered by specific commands. Much like a dog. The human more or less needs to remember those few commands. We don't teach dogs and falcons to do everything do we ? Although we do teach specific dogs do to specific tasks in various domains. But no one ever claimed Fido was superintelligent and that we needed to figure him out better.
> That's what makes them hard to use! A programming language has like ~30 keywords and does what you tell it to do. An LLM accepts input in 100+ human languages and, as you've already pointed out many times, responds in non-deterministic ways. That makes figuring out how to use them effectively really difficult.
Well yes and no. The problem with figuring out how to use them (LLMs) effectively is exactly caused by their inherent un-predictability, which is a feature of their architecture further exacerbated by whatever datasets they were trained on. And so since we have no f*ing clue as to what the glorified slot machines might pop out next, and it is not even sure as recently measured, that they make us more productive, the logical question is - why should we, as you propose in your latest blog, bend our minds to try and "figure them out" ? If they are un-predictable, that means effectively that we do not control them, so what good is our effort in "figuring them out"? How can you figure out a slot machine? And why the hell should we use it for anything else other than a shittier replacement for pre-2019 Google? In this state they are neither augmentation nor amplification. They are a drag on productivity and it shows, hint - AWS December outage. How is that amplifying anything other than toil and work for the humans?
I've found that using LLMs has had a very material effect on my productivity as a software developer. I write about them to help other people understand how I'm getting such great results and that this is a learnable skill that they can pick up.
I know about the METR paper that says people over-estimate the productivity gains. Taking that into account, I am still 100% certain that the productivity gains I'm seeing are real.
The other day I knocked out a custom macOS app for presenting web-pages-as-slides in Swift UI in 40 minutes, complete with a Tailscale-backed remote presenter control interface I could run from my phone. I've never touched Swift before. Nobody on earth will convince me that I could have done that without assistance from an LLM.
(And I'm sure you could say that's a bad example and a toy, but I've got several hundred more like that, many of which are useful, robust software I run in production.)
That's beside my point. You are trading off the LoC for quality of code. You're not onto some big secret here - I've also built complete fullstack web applications with LLMs, complete with ORM data models and payment integrations. With the issue being....the LLMs will often produce the laziest code possible, such as putting the stripe secret directly into the frontend for anyone with two neurons in their brain to see.... or mixing up TS and JS code...or suggesting an outdated library version.... or for the thousandth time not using the auth functions in the backend we already implemented, and instead adding again session authentication in the expressjs handlers...etc etc. etc. We all know how to "knock out" major applications with them. Again you are not sitting on a big secret that the rest of us have yet to find out. "Knocking out" an application with an LLM most of us have done several times over the last few years, most of them not being toy examples like yours. The issue is the quality of the code and the question whether the effort we have to put into controlling the slot machine is worth the effort.
Part of the argument I'm developing in my writing here is that LLMs should enable us to write better code, and if that's not happening we need to reevaluate and improve the way we are putting them to use. That chapter is still in my drafts.
> Again you are not sitting on a big secret that the rest of us have yet to find out. "Knocking out" an application with an LLM most of us have done several times over the last few years, most of them not being toy examples like yours.
That's still a very tiny portion of the software developer population. I know that because I talk to people - there is a desperate need for grounded, hype-free guidance to help the rest of our industry navigate this stuff and that's what I intend to provide.
The hardest part is exactly what you're describing here: figuring out how to get great results despite the models often using outdated libraries, writing lazy code, leaking API tokens, messing up details etc.
> Part of the argument I'm developing in my writing here is that LLMs should enable us to write better code, and if that's not happening we need to reevaluate and improve the way we are putting them to use. That chapter is still in my drafts.
So you see, after so much hype and hard and soft promotion efforts ( I count your writing in the latter category), you'd think it should not be "us" figuring it out - should it not be the people who are shoving this crap down our throats?
> That's still a very tiny portion of the software developer population. I know that because I talk to people - there is a desperate need for grounded, hype-free guidance to help the rest of our industry navigate this stuff and that's what I intend to provide.
That's a very arrogant position to assume - on the one hand there is no big secret to using these tools provided you can express yourself at all in written language. However some people for various reasons, I suspect mostly those who wandered into this profession as "coders" in the last years from other, less-paid disciplines, and lacking in basic understanding of computers, plus being motivated purely extrinsically - by money - I suspect those people may treat these tools as wonder oracles and may be stupid enough to think the problem is their "prompting" and not inherent un-reliability of LLMs. But everyone else, that is those of us who understand computers at a bit deeper level, do not want to fix Sams and Darios shit LLMs. These folks promised us no less than superintelligent systems, doing this, doing that, curing cancer, writing all the code in 6 months (or is it now 5 months already), creating a society where "work is optional" etc. So again - where TF is all of this shit promised by people sponsoring your soft promotion of LLMs? Why should we develop dependence on tools built by people who obviously dont know WTF they are talking about and who have been fundamentally wrong on several ocassions over the past few years. Whatever you are trying to do, whether you honestly believe in it or not I am afraid is a fool's errand at best.
> you'd think it should not be "us" figuring it out - should it not be the people who are shoving this crap down our throats?
If they're "shoveling this crap down our throats" why should we expect them to help here?
More to the point: a consistent pattern over the last four years has been that the AI labs don't know what their stuff can do yet.. They will openly admit that. They have clearly established that the best way to find out what models can do is to put them out into the world and wait to hear back from their users.
> That's a very arrogant position to assume - on the one hand there is no big secret to using these tools provided you can express yourself at all in written language. However some people for various reasons, I suspect mostly those who wandered into this profession as "coders" in the last years from other, less-paid disciplines, and lacking in basic understanding of computers
I can't take you calling me "arrogant" seriously when in the very next breath you declare coding agents trivial to use and suggest that anyone having trouble with them is a coder and not a proper software engineer!
A hill I will happily die on is that LLM tools, including coding agents, are deceptively difficult to use. If you accepted that was true yourself, maybe you would be able to get better results out of them.
> If they're "shoveling this crap down our throats" why should we expect them to help here?
No no no - they are not supposed "to help". They own this complete timeline of LLMs. Dario Amodei said several times over that the agents will be writing ALL CODE in 6 months. We are now at least one month into his latest instance of this promise. He also babbled a lot about "PhD" level intelligence, just like the other ghoul at that other company. THEY are the ones who promote the supposed superintelligence creeping up on us closer each day. Whatever benchmarks they always push out with new release. But we should cut them some slack, accept that we are stupid for not wanting to burn our brains in multihour sessions with LLMs and just try to figure it out? We should not accept explaining it away as merely some cheap "hype". These people are not some C-list celebrities. They are billionaire CEOs, running companies supposedly worth into high hundreds of billions of dollars, making huge market influencing statements. I expect those statements to be true. Because if they are not, and they are smart people and will know if they are pushing out untruths on purpose, well that's just criminal behaviour. Now tell me more about how "we" should figure it out.
> A hill I will happily die on is that LLM tools, including coding agents, are deceptively difficult to use. If you accepted that was true yourself, maybe you would be able to get better results out of them.
:) No mate, please drop that "getting good results" nonsense. I have been getting good results too if I babysit them, and for the record, have done a bit more with them than just various model use cases. The issue for me and a lot of other people, that with a lot of care and safeguarding and attention etc, yes you can even build something to deploy in production - and myself and my team have done so - however it is so that they are not worth all the babysitting and especially the immense mental fatigue that comes out of working with them in continuity over a longer time span. At the end of the day, for complex projects its actually faster if I shortcircuit my thinking machine to my code-writing executors and skip the natural language bollocks altogether (save for the original spec). Using LLMs is like putting additional friction in between my brain and my hands.
The most impressive part is the remote control mechanism from my phone but yeah, it's not meant to be amazing, it's meant to be something useful that I couldn't have built myself (not knowing SwiftUI) and I knocked out in 40 minutes with Claude Code.
> DRAM is priced based on supply and demand, like every other market.
Please don't explain it away like that - you are referring to the theoretical "ideal" market where a bunch of small companies compete with low margins to the benefit of the wider customer base. This is not what is happening. We have a couple of intrinsically worthless, LLM-whale companies, working literally to swallow and entshittify literally everything in their weird transhumanist/accelerationist/weirdo way. To add to the insult, the whole creation of artificial scarcity is almost a political construct, paid for with "monopoly-the-game-money" that these companies DO NOT EARN but instead BORROW based on vague and dishonest promises of achieving a "Country of PhDs in a datacenter"/"Pocket PhDs"/"AGI by 2025" (oops, now apparently by 2028 according to the OpenAI CEO). In their weird vision, as humans we should be merely cattle to be managed, not independent spirits with interest and aspirations. That ghoul Karpathy speaks about "ghost in the machine", overlooking the magnificence of the already existing "ghost in the machine" in the form of human beings. We should not have to swallow the increasingly crappier future these folks are insisting on pushing on all of us.
Right, because as seen over the last several years, the Big Tech CEOs should totally be trusted on their promises, especially if it is related to how our sensitive personal data is stored and processed. This goes even wtihout knowing who is one of the better known "personas" investing in Persona.
It is a rather attractive view, and I used to hold it too. However, seeing as Alphabet recently issued 100-year bonds to finance the AI CapEx bloat, means they are not that far off from the rest of the AI "YOLO"s currently jumping off the cliff ...
They have over $100B in cash on hand. I can't pretend to understand their financial dealings, but they have a lot more runway before that cliff than most of the other companies.
Well Bill Gates declared it 20 years ago : "Content is king", so here we are. "Content" everywhere, and not just the Internet - go to anny tourist hotspot, there is a ratio of 5:1 of idiot influencers all stretching their faces in front of the same landmarks. Your utility company now produces "content" for some reason. Every lazy Tom, Dick and Joe produce "content" instead of doing their trade. Apps that summarise your books so you can "read" them on your commute. To the ghouls who are driving the "content" economy, everything is "content". They don't really understand either long form or novels, or music or anything creative. To them it's all just content, to be bought, sold and consumed by the pound.
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