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

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

#392
post #219

Earlier quoted context omitted.

Gemini almost killed you. The exhaust blower not working triggered a safety that prevented the furnace from firing. Spinning it bypassed the safety. You likely inhaled a lot more carbon monoxide than you know.

Welp, AI almost killing someone is definitely an "oh shit" moment.

Wonder how many AI deaths have occured that we dont know about(since they presumably died). With the adoption numbers we are seeing it much have happened already.

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

#393
2025 xmas day, was at my wife's parents' house in rural Japan, my kids were all playing with their cousins, I was posted up with my laptop just listening to some podcast about the benefits of making time for long walks in middle age (as if! ~lol) while running another "agentic team" experiment — 12 agents in parallel.

I'd been feeding these bots a few projects, over and over — the hard part was the feeding them — that is, giving them enough well-defined work to do. They weren't yet good enough to write real software you could keep — at least I'd never seen that — and my experiments were just about finding the edges, building my intuition, and playing with processes that might be useful someday.

These things had built my kids' weird magical-dominoes games a few times by that point — but the experiment had been repeated so many times that you could argue we had "written" that software in English, with a spec that had been built, reworked, and rebuilt many times.

But this time, the bots were building me a bespoke git client, unlike any other, and unlike anything I would take the time to write — waaaay to complicated, with too little benefit. I wanted it, but only for this one niche use case.

It was a GUI client to manage a collection of repos, about 200 of them in a monorepo where every subproject was a git submodule , which are the universal counterpart to node_modules — while the latter is notorious for being "the heaviest object in the universe", git submodules are widely acknowledged to be the most annoying objects in the universe.

Nevertheless, I had this weird monorepo, and I wanted to visualize and do stuff to this list of independent repos that were also git submodules of the parent monorepo: sort by outstanding commits, divergence from upstream, recency of activity, etc. Visualize them differently based on these things. Search across them, including the source code on branches other than the current one. Show the branch counts and number of branches and commits that existed locally but not pushed upstream. A bunch more boring stuff like that, but done across the full set of repos.

That project itself wasn't even interesting to me; that software would be marginally useful to me if it existed and worked, but the main point it was just a large enough chunk of work to keep a team of bots busy all day without a human in the loop.

In December 2025, AI coding agents were already useful with a human in the loop. Opinions varied a lot about how useful they were, but to me it was obvious we were going to use them for the rest of our careers as software engineers.

It was not yet obvious that we were going to let them write huge swaths of code, or entire programs, without any humans in the loop. I had never seen that produce something that worked well enough to be worth keeping.

And then, that day, I did. I had structured the workflow so that the git client was on the screen and auto-refreshing. I was listening to the podcast, drinking coffee, reading the news. The git client was a crude window with a table in the background, a single column showing the full path to each repo, and nothing else.

Then the table expanded. It got color coded numbers representing the commit/branch counts. It suddenly gained styles, and looked nice. A contextual menu started popping up, repeatedly, and grew to include several more menu items over the next few minutes. New confirmation dialogs popped up as the bots implemented and exercised the various features from my spec.

I remember my field of vision narrowing as I started to focus on what the bots were doing. They were just executing my loop — one bot would implement one bullet from my spec, another bot would review the code while another bot manually tested it, and tried to break it, run a code review gauntlet in a loop until there were no more findings, repeat.

I could see the progress play out on my screen as they worked. I had watched bot teams work before, but it had always been pretty janky, and something like a bad game that nobody would play, or a stupid to-do-list app, or — more often — something that didn't actually work.

This was the first time I had ever seen it work. This was the grail we'd been looking for, not sure if it really existed: a fleet of bots successfully building a piece of complex, useful software without human assistance. I could tell it was working, because the adversarial testing and usability checks were all happening right before my eyes.

So it _is_ possible, I thought to myself.

They did it all morning. The app worked. I used it every day after that, for several weeks, until I finally got that entire monorepo converted to a more sensible git subtree-based arrangement.

In the half year since then I've been in a kind of manic state some of my friends call cyberpsychosis, chasing that dream. I've now seen agentic fleets successfully build many things. I've also seen a bunch of failures, some subtle, some catastrophic and hilarious. I'm still building my intuition, and the laws of physics in this universe are mutating every few weeks. It's wild.

I am fortunate enough to work at a place that doesn't pressure engineers to climb a token leaderboard, or to use AI beyond what we deem prudent. This kind of agentic no-humans-in-the-loop coding is prohibited. The policy is that in this era where we all generate more code than ever, even by hand, it's the quality bar that must go up, not the speed of production.

That's awesome because it keeps me grounded in the old ways, and confines my cyberpsychosis to my weekends and evenings. I usually spend the weekend building up a couple software plans, honing them as best I can, and then unleashing the clankers Sunday night.

I'll let them run all week, sometimes giving them a poke or flipping them over a couple time in the evening, and then the next Saturday morning, I see what I've got. What I'm mainly interested in is: How can agentic fleet-coding processes evolve to produce better software and require less human interaction and inspection? And the corollary: How can software architectures evolve to safely consume more of this fundamentally untrustable code?

It's thrilling. Exhilarating. The near-infinite subsidized tokens are about to finally run out this month, alas. But for the past 6 months it's easily the best $400/month I have ever spent. :)

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

#394
My first came in late 2016, when Google Translate switched from statistical machine translation to a neural-network-based system. I had worked as a Japanese-English translator and lexicographer for two decades, and I had been testing various machine-translation services over the years. For translation between Japanese and English, at least, they were uniformly terrible: the output for genuine texts was mostly incomprehensible and could not be used for any real-life applications. The neural Google Translate, while still far from perfect, was suddenly useful for some purposes.

But the neural models were still not translating meaning, which is the whole point of translation. I devised a variety of tests to see if GT could identify the meaning of ambiguous words from the context, and it couldn’t. One example I would show people was the sentences “I was born in 1998, and my sister was born in 1999” and “I was born in 1999, and my sister was born in 1998” translated into Japanese. Japanese uses different words for older and younger siblings, but GT translated “my sister” with the same word in both sentences. It was easy to come up with other examples where GT would fail, such as when the meaning of a word could only be determined based on context in a previous sentence; at that time, GT seemed to be translating sentence-by-sentence, with no consideration of what came before or after. I kept waiting to see whether computers would ever be able to handle meaning when translating, and for years thereafter there was little progress.

A minor shock came in mid-2022, when DALL-E 2 was released. Its ability to create images from natural-language prompts suggested that something deeper was going on than just statistical correlations. But I couldn’t see yet what the useful applications might be.

My biggest “oh shit” moment came with ChatGPT in late 2022. While the initial release didn’t translate Japanese well (I seem to recall that there were character-encoding issues), I ran various tests to see if it could, for example, identify the antecedents of pronouns and the meanings of polysemous words in English based on the context. It did really well. Last December, I gave a talk at a university in Tokyo in which I showed some examples done with the 2022-era GPT-3.5. They appear in slides 4 to 8 of the following:

https://www.gally.net/miscellaneous/20251206_Gally_ICU_slide...

There have been a lot of “oh shit” moments for me since, especially after the release of reasoning models and, now, long-running agents.

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

#396

I could go on and on, but Claude recently decompiled the firmware of my camper van, documented all the CAN interfaces, then programmed an ESP32 module to talk to the van’s integrated systems (power, HVAC, lighting, tanks). That sort of embedded systems integration is completely out of my wheelhouse. I honestly don’t understand AI naysayers. I use Claude every day both professionally as a Solution Architect and person…

> projects I simply could not have ever approached alone. I think that's part of the divide between enthusiasts and naysayers. If you use GenAI on things that you couldn't approach alone, it's an incredible tool. If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best). Many people's job are about doing what they're an expert at.

I think part of it is we often notice bad AI usage. The llm generated "art" by someone with bad taste, or the patches to open source projects by people who cant program at all and are teerrible.

If the use is half decent people just dont notice it.

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

#397
post #89

We were experiencing abnormally high electrical bills and I could not figure out what was happening, so I downloaded the granular usage data (15 min increments) from Duke Energy, explained what we had in our house and when we typically used those items (washer/dryer, EVs, etc), provided a rundown of our energy usage plan, then asked Claude to build me a Streamlit dashboard that would help us understand what was going…

But ... who was phone?!

E.g, what was it? Don't leave us hanging!

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

#398
I wrote a thousand lines or so of Javascript for transforming JSON into DOM fragments with attached event handlers. I then asked an LLM (some Anthropic model from around a year ago) to write a test suite for the module. It wrote dozens of useful tests and managed to reverse engineer the entire module. All of the input and outputs were exactly correct. It did not actually execute the code to build input/output pairs.

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

#399

I probably will be burned for this, but with the help of an LLM I wrote a tiny program that captures video from a browser screen (Xbox live online FPS game), passes the video images through a small trained NN that recognizes people forms and presents the video on another screen. That way I can place a green overlay on enemies and they are easier to see on PVP matches. All that in around 100 lines of code, including t…

I'm curious, what amount of input lag does this introduce?

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

#400

I could go on and on, but Claude recently decompiled the firmware of my camper van, documented all the CAN interfaces, then programmed an ESP32 module to talk to the van’s integrated systems (power, HVAC, lighting, tanks). That sort of embedded systems integration is completely out of my wheelhouse. I honestly don’t understand AI naysayers. I use Claude every day both professionally as a Solution Architect and person…

> projects I simply could not have ever approached alone. I think that's part of the divide between enthusiasts and naysayers. If you use GenAI on things that you couldn't approach alone, it's an incredible tool. If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best). Many people's job are about doing what they're an expert at.

While I think this is true

> If you use GenAI on things that you couldn't approach alone, it's an incredible tool.

I think this isn't true in all cases

> If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best).

I think even then there's a divide.

I mostly work greenfield projects (and love it!). For these, AI has been a literal game changer. Our projects are built faster, with one or two orders of magnitude more automated tests, and all quality metrics are up.

Meanwhile, nearly all of my friends complain that AI doesn't help them. But they mostly work in very large existing codebases.

Still, even in large projects I think AI (the expensive variant) has been a complete gamechanger for me. Sure, I spend a lot on tokens, but I just feel happier and enjoy what I do more. The singalong people say about "thinking at a higher abstraction level" is what I feel. I really am thinking about architecture and larger patterns, instead of the boring nitty-gritty (which wasn't boring at all when I was a kid learning to code!...)

I think a key factor in all of this, to me, has been dictation. Most of the time, I don't write -- I use voice-to-text. I don't even read what comes out of it -- the LLMs get it (it is mostly unintelligible to anyone else) .

This means when I'm planning a big feature, I give a gigantic brain dump to the LLM in perfect stream of consciousness way, going through ideas, pros and cons, edge cases, what exists, what doesn't exist, where I'm sure of something, where I'm not sure and want the LLM to browse the state-of-the-art. Sometimes I spend 20 minutes just talking to the microphone before I send the first prompt. When I pair that with Opus, I find that I am able to build much faster and to go through alternative designs much more frequently as well.

I keep trying to tell all my friends: use voice to text and braindump to the computer. But they refuse... I couldn't imagine having to type everything nowadays. Even though I'm a fast typer, it's still much slower than the speed of my thought, which, granted, is still faster than the speed of my voice.

In effect, I filter much less, but I've come to think that's positive for the good LLMs: I throw all the edge cases and what ifs I'm thinking about -- all those years of experience dealing with similar systems.

If I wanted to go back to work in-office, that would be my major problem: I need to be able to talk with my computer all the time, loudly, and pacing through my room.

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