▲ 53 points
back
81 comments
> In a past life I had to argue every year to renew a license for a profiling tool that cost about 20 EUR a month. I’ve heard since that everyone at the company is now getting a Claude subscription, even non-programmers.
Underrated quote. I also found this frustrating. At one company I was at (a very old company which was trying to pivot to software engineering), we had hellish bureaucratic fights with the IT department to get access to Pycharm, Obsidian, and even GitHub. But then the AI craze dropped and management just gave us all GitHub Copilot access without us even asking.
Cory Doctorow's book The Reverse Centaur's Guide to Life After AI talks more about this. It's just a modern symptom of an age-old power struggle. The workers want more control over their craft, including quality standards and tools, but their bosses want more control over the workers.
What's happening now is bosses are feeling pressure from investors to show productivity gains from using AI, so bosses panic-push AI within their companies. Which leads to misaligned incentives like tokenmaxxing.
Here's a guy who does more than make simple, bloated end-user apps. He tries to dig into the code and make novel performance optimizations etc. He sees that AI can't code everything.
I liked the two original experiments in the article, which in a microcosm gels with my 1+ year of deep agentic development. But I would like a whole article about his experience, and with which model (Opus? Fable? erg, Sonnet?) and effort he used.
I've progressed in using latest Claude-kins and the GPTs as usually competent teammate/buddies, and generally know to sort out the fluff confidence with the realz (shoot, that was how I was when I was but a wee little coder lad: overconfident because of an error-free compile and one non-segfault run.)
You have to put in the time, the skill creation, the system prompt/personalization, the (sometimes adversarial) automation, the testing, verification, kicking down the loop castles (as usually caused by being cheeky with highest effort levels.)
I have been doing this (vibecoding) for as long as the term vibecoding was coined. The amount of people I have met that describe the process like you did and then proceed to show the most soulless vibecoded app is staggering.
> “Agentic workflow” (or whatever they’re calling it at the time you’re reading this article)
> Unlike the silver bullets of the past (like microservices or NoSQL)
> Hallucinations are an inherent property of how LLMs work.
> It’s all marketing and buzzwords
Not a serious article or thinker. I can get this stuff on Reddit if I want to read thrice-regurgitated cliches about AI.
I'd like to hear actual rebuttals to those quotes.
Programming by hand also empircally leads to quite a lot of bugs. The actual calculus for whether these tools are worth it to solve a given problem is as function of how much time it takes to reach the desired level of quality compared to writing by hand. I struggle to see why "they hallucinate and aren't deterministic" is enough to conclude that it doesn't ever end up being worth it when there's no alternative that doesn't also produce some bugs.
I don't particularly like these tools, but I also can't deny that for some things I'm genuinely able to get to the results I want faster with them than without. Yes, some people will use them to produce low quality software, but in my experience it's mostly people who already would have been producing low quality software as well, just at a much slower pace. Having a higher volume of low quality software is a problem, but it's distinct from the claim that the tools not ever being useful in producing high quality software.
It's possible that from reading my rebuttals you'll think I'm one of the people using these tools to produce low quality software, but it's not clear how to falsify that claim. If that's the conclusion you'd draw, then you probably weren't really open to considering rebuttals in the first place though.
If the author can’t put in the effort to think of something original to say that hasn’t been said 1000x, why should I put the effort into rebutting cliches that were last relevant in 2024?
Whenever an AI hallucinates an API call, you run the compiler, give it the error messages and it fixes it. A proper agentic workflow might do it all automatically. These are arguments form 2023 ChatGPT3.5 days when it didn't have tool access or Web search.
The modern problem with AI is more the opposite, you give the AI a task that is impossible with the tools at hand and instead of saying "That doesn't work", it starts elaborate workarounds to make it happen anyway.
If you're using an LLM and not an agentic workflow then you're doing things wrong in 2026.
My agents have to pass tests meaning that if their LLM hallucinates, the agent tools capture it and not me.
> Hallucinations are an inherent property of how LLMs work.
While technically true the hallucination rates on modern models is low and other checks can ensure that by the time a human sees it it is most likely solid.
For research there is more danger as there is less feedback loop other than other LLM scrutinising the first. For research I get it to come to a conclusion but provide me with links so I can judge. More like advanced search.
Rebuttals to what? Ill-faith rhetorics and circular judgement?
Points 1-2 were clearly cited not because of their factual content, but because they boil down to the author mocking the existence of industry trends, and writing them off whole (incl. this one). This is unsound, both because it doesn't actually follow by default, and because it violates the principle of suspending (prior) judgement: https://en.wikipedia.org/wiki/Suspension_of_judgment - it sets both the author and their readers up for a specific conclusion, rather than inspiring nonbias. Hopefully one does not need to explain why this is problematic?
It is further incredibly trite to bring up how AI is an ongoing trend (self-evident), and the fact that trends distort perception (also self-evident). Them fighting fire with fire and bringing their own gut instinct is not any more intellectually respectable.
Point 3 is completely unsupported and misleading. The author probably means that hallucinations are formally unavoidable, but that on its own doesn't carry much weight, and is not the same thing.
Point 4 is factually wrong. AI is an entire academic field with a half a century of history to its name, and this is trivial to learn. Clearly not why it was cited once again either however, but because it's blatantly ill faith too.
The author is a real C++ programmer. Not like the PyTorch guys who use std::shrared_ptr for cyclical graphs and then go on to overpromise on threaded Python only to find out that the slowdown is much larger and everything is brittle as expected years ago.
The times that Meta people can browbeat honest engineers are over.
Nice corporate take.
lol
It reads like something written by a time traveler from the past.
We are all time travelers from the past.
> While I have found LLMs useful for researching and planning code changes, my attempts at actually making them write code have been quite lackluster. I found them to be slow and expensive to generate, for a mediocre result.
I think this observation is generally true for the kind of problems the author is working on.
But I would not make the leap to avoid using LLMs for any kind of code writing. LLMs do fantastically well in the 95%+ of the code that engineers spend time on. And for those we should leverage the technology.
It is upto us as engineers to figure out when to stop using LLMs. We are smarter than just dumping logs and half dozen specialized markdown files to a LLM and have it figure out solutions.
All this completely ignores the cataclysmic negative externalities, including environmental costs, knowledge atrophy and workforce erosion, accelerated burnout, slop-pollution of the open internet, shifting of power from engineers to management and SAAS corps, and empowerment/enrichment of some of the most morally vacuous people in tech history.
3,300 words but nothing new to say.
Dislikes AI so much and ironically wrote a whole article without doing any of their own thinking.
I'm not disappointed in the article, there are many of them out there. I'm disappointed that this is near the top of HN. I lazily outsource my information hunter and gathering to HN.
Maybe you guys just shouldn't read blogs.
> It's interesting how AI is constantly providing false information and incorrect statements about my area of expertise. Fortunately, it's very useful and always right about topics I know very little about.— pikuma.com (@pikuma) June 19, 2026
Also true for TV commentators, bloviating C-suiters, frequent posters on social media, politicians, etc.
I read through a bit of it, and it seems like the author assumes training was done only on open source data, but I doubt that's actually the case. There are plenty of paid, proprietary source codes being sold out there, hacked code, that is.
Aside from that, I don't agree with the author's view on agentic workflows. Modern AI native development runs like a massive state machine, starting from MCP, local file systems, and what's usually called a harness.
I also noticed what might be a mistake in the author's domain, games. Putting aside the fact that inheritance based OOP is an outdated pattern, the suggestion to remove update() and put it into a manager class's List, then iterate with a for loop, is meant to eliminate overhead like P/Invoke costs in C#. But if Foo is still a class, a reference type, then List<Foo> is just an array of pointers scattered across heap memory. Pointer chasing can still happen. So I think that's actually bad advice.(Of course, the same issue exists in Mr. Claude's code as well.)
If the author truly wanted Data Oriented Programming(or DOD), they would have specified struct arrays or NativeArray.
This is a tricky area. The author's goal was likely to remove the per MonoBehaviour Update call cost in Unity, which is why they suggested using List<Foo>. But the instruction seems ambiguous, and I think that's part of the difficulty with AI development.
The objective function is the same, but the implementation varies and subtly differs from what I actually want.
From a design perspective, for team maintenance, the GameUpdateable abstraction might actually be better. But it's difficult. In terms of extensibility, an update manager that handles registration and expansion of multiple update targets might be over abstracting.
Writing this down makes me realize how many things I actually consider when putting code into a program. Sometimes I model how my next teammate might read it, and sometimes my words might be interpreted differently. It's really difficult.
This is likely a completely honest and plausible experience of the author, while at the same time still feeling like someone trying to use a pottery wheel and then concluding that pottery wheels are not suitable for producing very good art due to the clay often not centering. It says more about the author than about the topic.
At the risk of sharing an unpopular take, this reads a lot like someone who decided how they feel about LLM-driven engineering ~5 months ago and doesn’t seem to reflect the current reality.
(Or they’re working in an organization with lower budgets and not cranking the frontier models of today)
I fully agree about the cost/sustainability parts, but to suggest you can’t build a high quality coding/verifying/iterating loop for _most_ problems is disingenuous.
His experience is completely plausible. He’s in a niche that requires highly performant code and most complex, highly performant games do nit have source available for models to train on. It’s a very common observation that the farther you stray from mainstream, the less effective the LLM models become.
Have you actually tried performance optimisation using an agent?
With any programming language/framework that has quality profiling tooling (which is a prerequisite for most projects) I have had huge success with automated hotspot profiling where the LLM can propose theories, test the impact of fixes, convince you of which to pursue, etc.
High performance algorithms are quite well documented so it isn't unreasonable to expect an LLM to apply them appropriately when given the ability to "see" where they need to be applied.
> It’s a very common observation that the farther you stray from mainstream, the less effective the LLM models become.
It is a common observation but I don't buy it. AI is clearly very good at Rust, but that is probably one of the least represented languages in its dataset. Anecdotally, I've also been having very good outcomes with a rather niche combination of technologies (opencv.js + JS in a browser extension) since early 2024. I would imagine there is way more C++ game code in the training set than that particular combination.
I think the more likely reason is that certain languages, projects or technologies tend to be organized in ways that are not ideal for LLMs. Specifically, I think Object Oriented approaches are not ideal for LLMs.
My theory is the key factor for effective LLM use is how effectively you can stuff the context with only the relevant data. OO tends to result in logic spread across inheritance hierarchies and templates (and even overloaded operators /shudder) which resides in a bunch of different files comingled with a whole lot of other logic. This just tends to confuse the LLM. On the other hand, I ended up using a lot more functional programming style which let me pinpoint the exact files or snippets of code relevant to a task, and the LLM pretty much never went wrong.
These days the models (and likely the harnesses) are much stronger and need much less curation of context, and hence can power through any kind of project organization. But I suspect they are still a bit sensitive to all the noise polluting their contexts and hence can produce very inconsistent results.
> this reads a lot like someone who decided how they feel about LLM-driven engineering ~5 months ago and doesn’t seem to reflect the current reality.
I've been reading this for 2 years straight. "oh you have a criticism of AI? Well they fixed that in Aeternos v Y-point-Z, which after doing all of my work also gave my wife an orgasm for the first time this year, obviously OP is using the old model".
not much changed in the past 5 months if you have been doing agentic development. Claude Code actually is getting worse.
Moreso it seems like nonsense that's masked by an excellent facade of eloquence. Ironic given it's a a post ranting against LLMs.
I stopped reading when he decided to go off on some tangent about how LLMs can't do native programming or games because they weren't trained on it. Ignoring the technical issues there, an even more overt one is that, amongst a zillion other projects, even the entire source code for Unreal Engine is available and within their training corpus. Even older models were quite competent at working with Unreal and outputting idiomatic code, which is saying something if anybody's ever worked with Unreal.
I increasingly think people are writing dumb articles on purpose because it drives 'engagement' more than a straight forward and accurate post would, at least on average.
5 months ago people said the exact same things, you know. That this stuff was how it was 5 months before that, and doesn't reflect the current reality.
Article was flagged from the front page after criticism of Meta. You know, if free speech isn't allowed maybe we should contact Zuckerberg directly if that is what you want.
> An Honest Review
> I do admit that this approach immediately triggered my contrarian side and made me very defiant of any AI tool.
Makes sense.
> While this could be partially remedied by always asking for a primary source or citation, I dislike the idea that one has to add magical incantations to their queries to get the right results. It’s a good laugh to make fun of “make no mistake” memes, until you start having to consider similar things seriously.
Man I am genuinely stumped at the obvious lack of desire to use something in a way it's supposed to be used. LLMs are tools and like with any tool it's on us to use it properly, not hitting a screw with a hammer and saying that hammers are a very stupid tool.
It just seems silly to need to beg in English for things like that. What's the entire space of things you need to think about? Can you be sure the specific way you asked would be correct, or do you also have to add "don't lie about following this instruction:"? Why not have an interface `ai --make-no-mistakes --dont-make-things-up`? I don't want to have to talk to the computer like it's people.
But English is literally the interface. It's the *same* as `--make-no-mistakes`, it's just your preference compared to directly telling it to the AI which is ok. That doesn't change the fact the using the tool correctly != using the tool.
You don't have to. Chatgpt-sol-high already does that for me. Could be the extra instructions or the base system prompt or whatever, point is - it already does it.
It's more like a hammer that never drives nails straight unless you hold it in a specific uncomfortable manner.
You shouldn't be required to tell an information retrieval tool to actually retrieve information rather than making it up!
> You shouldn't be required to tell an information retrieval tool to actually retrieve information rather than making it up!
And therein lies the problem: perception. LLMs are treated like information retrieval tools but in reality are probability machines that return plausible/mostly accurate information.
They're not searching, they're inferring and then guessing. That the guesses are often quite good means that we can easily fool ourselves with whatever it spits out. We call it hallucination but it's a feature, not a bug.
To be fair, hammers are bad screwdrivers. They are faster at driving screws and require less finesse to do so, but are really bad at taking them out, often break the screw, and often result in a fastening that superficially looks correct but fails to perform well under stress.
Two new types of screw, the ring-shank nonhelical fastener and the rivet, however, are changing everything. Soon, using screwdrivers will be rare except in special circumstances.
How are they supposed to be used? The providers promised us we could just say what we wanted and get it.
Not providing a source is how Llms get caught in sycophancy and hallucinations. The “tool” needs improvement, especially one that claims it’s intelligence. How would you react if you found out after schooling everything you learned has half-hallucinated because your teacher made it up and there is no text book or historical log of facts? You had assumed this whole time you were learning facts…now what?
The case study of the LLM getting the basic Mono framework usage wrong seems outdated.
They're pretty flawless at any framework now, React, SwiftUI, Unity - literal skill issue if you can't get good code out of an LLM.
If an LLM can write a nuanced paragraph about any topic, it can certainly write a simple [insert popular framework] component which has considerably less potential variation.
Consider that there are 100k+ words in English, 90k+ in Spanish, and 77 in C# (38 in JavaScript!).
The LLM can write code.
Can it make software? No. Because software is a lot more than code.
But the LLM can write code.
Any anti-AI takes moving forward are going to have to acknowledge that I think