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Discussion (61 Comments)Read Original on HackerNews
Also true for TV commentators, bloviating C-suiters, frequent posters on social media, politicians, etc.
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.
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.)
> 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 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.
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.
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.
Isn’t this entirely context dependent? Where did you get the information that modern models have low hallucination rates? I’d love to see the benchmark if there is one, it seems like it would be useful to track.
Most likely? That’s not reassuring at all. So you’re saying the other checks can result in hallucinations?
My agents have to pass tests meaning that if their LLM hallucinates, the agent tools capture it and not me.
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, 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 judgement 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.
See also: https://en.wikipedia.org/wiki/Motivated_reasoning
The times that Meta people can browbeat honest engineers are over.
Nice corporate take.
(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.
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.
> High performance algorithms are quite well documented
That may be true for bloom filters or what have you. But the author states the obvious: all recent games are closed-source. So any algorithms or techniques for real-time 3D that an LLM was trained on are going to be a long way behind the state of the art. The author makes that point extremely clearly, and they have credibility.
> this reads a lot like someone who decided how they feel about LLM-driven engineering ~5 months ago
TFA? No. Your comments here? Yep. Try to imagine a world where different kinds of work have different applicability of tools.
Unless you yourself are doing commercial game development, calling the author disingenuous puts your own pro-AI bias on display. It’s based on speculation. I prefer to take his very specific examples and experiments at face value.
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".
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.
> 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.
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.
I mean sure, pick any analogy, still doesn't change the fact that there are right uses and wrong uses; right ways to use it and wrong ways to use it.
> information retrieval tool
How is an LLM this? It's a non-deterministic word generator.
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.
There is a middle ground between CEOisms "LLMs are literally Jesus" and people similar to this blog's author "LLMs are mostly trash."
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.
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