AI is also great at looking for its own quality problems.
Yesterday on an entirely LLM generated codebase
Prompt: > SEARCH FOR ANTIPATTERNS
Found 17 antipatterns across the codebase:
And then what followed was a detailed list, about a third of them I thought were pretty important, a third of them were arguably issues or not, and the rest were either not important or effectively "this project isn't fully functional"
As an engineer, I didn't have to find code errors or fix code errors, I had to pick which errors were important and then give instructions to have them fixed.
The limit of product manager as "extra technical context" approaches infinity is programmer. Because the best, most specific way to specify extra technical context is just plain old code.
And I think it's less about non-deterministic code (the code is actually still deterministic) but more about this new-fangled tool out there that finally allows non-coders to generate something that looks like it works. And in many cases it does.
Like a movie set. Viewed from the right angle it looks just right. Peek behind the curtain and it's all wood, thinly painted, and it's usually easier to rebuild from scratch than to add a layer on top.
Good principle. This is exactly why we research vaccines and bioweapons side by side in the labs, for example.
Somewhat unfortunately, the sheer amount of money being poured into AI means that it's being forced upon many of us, even if we didn't want it. Which results in a stark, vast gap like the author is describing, where things are moving so fast that it can feel like we may never have time to catch up.
And what's even worse, because of this industry and individuals are now trying to have the tool correct and moderate itself, which intuitively seems wrong from both a technical and societal standpoint.
The problem is, you have to know enough about the subject on which you're asking a question to land in the right place in the embedding. If you don't, you'll just get bunk. (I know it's popular to call AI bunk "hallucinations" these days, but really if it was being spouted by a half wit human we'd just call it "bunk".)
So you really have to be an expert in order to maximize your use of an LLM. And even then, you'll only be able to maximize your use of that LLM in the field in which your expertise lies.
A programmer, for instance, will likely never be able to ask a coherent enough question about economics or oncology for an LLM to give a reliable answer. Similarly, an oncologist will never be able to give a coherent enough software specification for an LLM to write an application for him or her.
That's the achilles heel of AI today as implemented by LLMs.
That’s not true.
A super intelligent ai would have agency, and when incentives are not aligned would be adversarial.
In the caricature scenario, we'd ask, "super ai, how to achieve world peace?" It would answer the same way, but then solve it in a non-human centric approach: reducing humanities autonomy over the world.
Fixed: anthropogenic climate change resolved, inequality and discrimination reduced (by reducing population by 90%, and putting the rest in virtual reality)
Because you asked the wrong question. The most likely question would be "How do I make a quadrillion dollars and humiliate my super rich peers?".
But realistically, it gave you an answer according to its capacity. A real super intelligent AI, and I mean oh-god-we-are-but-insects-in-its-shadow super intelligence, would give you a roadmap and blueprint, and it would take account for our deep-rooted human flaws, so no one reading it seriously could dismiss it as superficial. in fact, anyone world elite reading it would see it as a chance to humiliate their world elite peers and get all the glory for themselves.
You know how adults can fool little children to do what they don't want to? We would be the toddlers in that scenario. I hope this hypothetical AI has humans in high regard, because that would be the only thing saving us from ourselves.
>I hope this hypothetical AI has humans in high regard
This is invented. This is a human concept, rooted in your evolutionary relationships with other humans.
It's not your fault, it's very difficult or impossible to escape the simulation of human-ly modelling intelligence. You need only understand that all of your models are category errors.
Why is the Bagger 288 a servant to miners, given the unimaginable difference in their strenght? Because engineers made it. Give humanity's wellbeing the highest weight on its training, and hope it carries over when they start training on their own.
I work on a large product with two decades of accumulated legacy, maybe that's the problem. I can see though how generating and editing a simple greenfield web frontend project could work much better, as long as actual complexity is low.
When I give AI a smaller or more focused project, it's magical. I've been using Claude Code to write code for ESP32 projects and it's really impressive. OTOH, it failed to tell me about a standard device driver I could be using instead of a community device driver I found. I think any human who works on ESP-IDF projects would have pointed that out.
AI's failings are always a little weird.
This is not the case for most monoliths, unless they are structured into LLM-friendly components that resemble patterns the models have seen millions of times in their training data, such as React components.
Definitely. I've found Claude at least isn't so good at working in large existing projects, but great at greenfielding.
Most of my use these days is having it write specific functions and tests for them, which in fairness, saves me a ton of time.
Without such automation and guard rails, AI generated code eventually becomes a burden on your team because you simply can't manually verify every scenario.
If you can make as a rule "no AI for tests", then you can simply make the rule "no AI" or just learn to cope with it.
rogerkirkness•1h ago
airstrike•1h ago
Bold claim. They said the same thing at the start of this year.
adventured•27m ago
It doesn't matter if it takes another 12 or 36 months to make that claim true. It doesn't matter if it takes five years.
Is AI coming for most of the software jobs? Yes it is. It's moving very quickly, and nothing can stop it. The progress has been particularly exceptionally clear (early GPT to Gemini 3 / Opus 4.5 / Codex).
bdangubic•24m ago
be cool to start with one before we move to most…
yuedongze•1h ago
drlobster•57m ago
yuedongze•55m ago
dontlikeyoueith•44m ago
I've heard the same claim every year since GPT-3.
It's still just as irrational as it was then.
adventured•30m ago
They're already far faster than anybody on HN could ever be. Whether it takes another five years or ten, in that span of time nobody on HN will be able to keep up with the top tier models. It's not irrational, it's guaranteed. The progress has been extraordinary and obvious, the direction is certain, the outcome is certain. All that is left is to debate whether it's a couple of years or closer to a decade.
Arainach•22m ago