Ask HN: What was your "oh shit" moment with GenAI?

1) When I was testing one of the early coding agents, I gave it admin keys to a fresh AWS account and it configured everything beyond just building a demo site. That was, "oh shit, tool-use is going to be the killer feature of GenAI."

2) When I was still skeptical of the system as just a more-or-less dumb statistical predictor of the next token/word, I read the argument that even if it is a statistical predictor, the fact that it can reason means the intelligence is necessarily baked into the statistical model somewhere. That was "oh shit, intelligence is actually modeled."

It looked absolutely unmaintainable and horrible.

"oh shit" there are serious developers using this crap? As an industry, we are so fsck'd

Concrete: Last year I was DIYing a solar-power system for my home. I spent about an hour spitting out a Python tool that took (as inputs) drone photos and JSON and generated several proposed roof layouts for the panels and conduit. The tool helped me identify the exact railing attachment points and route around existing roof obstructions. Professionals already have these tools, and maybe they're available to DIYers, but you know what? It was faster to build my own than to do the product research on the web.

Abstract: This "oh shit" was more of a slow burn than a sudden realization. I see a lot of angst from developers who complain about their LLM agents. Agents write terrible code that barely works. They say things are done when they aren't. They misinterpret feature requests and ignore clear-cut project rules. They make assumptions that would have taken three seconds to research and invalidate. They suddenly quit because we're not paying them enough. And so on.

But you know what? All those complaints apply to humans, too! The industry has been dealing with these problems forever. Many of the same management techniques and software-development processes apply. This is why I discount a certain class of criticism about AI-generated code. If a fault of an LLM applies equally well to human engineers, and the person voicing the criticism hasn't managed a team, then I'd invite that person to wear a management hat for a while. Read some books/blogs, talk to an EM. Maybe this is a skill issue, which matters because we're all managers now.

The "oh shit" for me is that I have yet to hear a criticism that I can't map to one or more actual engineers I've worked with -- eventually successfully -- in my career. Which means that I'm still waiting for a new criticism, and eventually absence of evidence might be evidence of absence. LLMs fit too well into the giant machine of commercial software development for them to be a parlor trick.

I have a personal project: who's winning the race at 3 AM?

You see, I don't sleep well. I live in a busy city, with a busy freeway about a half mile away. Sometimes at 3 AM there are some very loud cars racing on the freeway. That's illegal for many reasons, not least of which is the fact that the noise pollution wakes people up from their precious sleep and causes knock-on affects to the population.

Anyway, now that I'm woken up, my only question is: who's winning the race?

I used this question as a way to explore a hyptothetical tech stack, with each part of the tech stack useful in some way to my work as a software engineer who's interested in robotics.

- run raspberry pis with microphones, collect audio data

- run a k8s cluster for audio collection and processing

- calculate and triangulate individual points, and give estimations of velocity based on position changes over time, and adjust for doppler shift

- estimate (poorly, but doable) engine power based on amplitude

- run a webserver in the k8s cluster showing an animation of the racers with color fields representing estimation error radiating from the position estimate, with arrow representing velocity

Great project, actually. It was really thought-provoking. I had this working in late 2018.

Since there was a lot of hype around this new "AI", I thought how smart could it be?

I threw the scenario to chat GPT. I did have to break the problem set into smaller parts for context window purposes. But the solution it came up with solved about 80% of the project correctly (and very close to solutions I already came up with), about 15% of the project remained "open until we have more data", with maybe about 5% of the project would have been incorrectly solved.

That was very much an "oh shit, AI is closer than the 20 years away that I've been telling people. It's more like 5 years away"

Here we are three, almost four, years later...