Then ChatGPT hit the scene and again, many of us dismissed it as a parlor trick that would never amount to much.
Using LLMs for coding initially was a only small step up from basic code completion, and a welcome farewell to Stack Overflow.
I am curious: what was the specific moment that you went from those quaint, dismissive observations to a slightly panicked, "Uh Oh" realization of what these models can do?
WTF?!
(2) Helping me with optimizations that I had been putting off for years because they involved learning curves that I never had time to take on.
(3) Tracking down bugs in code, especially race conditions and other concurrency issues, that were otherwise baffling.
(4) Finding information that I had been unable to find using Google searches (e.g. https://news.ycombinator.com/item?id=42653136).
There have been others, but those are what come to mind - perhaps because, in each of these cases, it made something happen that would otherwise never have happened - not because it was impossible, but because the level of effort required was prohibitive.
And in 1 out of 5 runs it beat me.
It’s kind of a trivial example but there are multiple instances of this per week with the wide variety of things I do around my property.
It's useless for most of what I want to code.
I still find it mandatory to write a lot of kinds of code by hand, but I write a lot of code with agents too now, and I previously literally didn't think that'd happen in <5yrs.
From a programmer perspective, I'm starting to like it less and less. It's useful for sure, but doesn't really live up to the hype. In many ways it's the opposite, my bet is still that programmers will be in high demand in the not so distant future after all of this settles.
Might be wrong, time will tell.
It was unlike anything I had ever experienced.
My wife was unimpressed lol.
This was 2022.
The first time I used a terminal agent was another one.
Most recent: I use Claude Code and have a convention where I grant various levels of autonomy during a session. I got bored recently and just let it keep running with an empty issues queue, essentially telling it to do whatever it wanted.
It did a bunch of repo cleanup, then it kept suggesting to end the session, but I just kept giving it autonomy prompts.
It started a creative writing public repo and wrote a bunch of stories, essays, and poems. I did not prompt it, at all, to do that. Some of what it wrote is quite good (IMHO).
I was trying to figure out a nightmare bug that only happened in production and Claude code was able to connect to Google Cloud and read the logs in real time
I recreated the bug in the UI and it was instantly able to see ion the logs what the problem was, then because it had the context of my whole codebase it was able to point me to the exact line of code causing the problem
That was certainly an "oh shit" moment
I honestly don’t understand AI naysayers. I use Claude every day both professionally as a Solution Architect and personally in a variety of projects I simply could not have ever approached alone.
"This is just a stochastic parrot".
I suppose these people are lying so that they can justify their well-paid job, or they just don't know how to use LLMs or to prompt GenAI tools.
"Uh Oh" realization of what these models can do?
The code reviews was just how I first saw it, but the rot goes deeper. The "uh oh" was my realisation of how much these can damage people's professional development. These people will never get better at their job than they are right now.
A lot of what else GenAI does is great, but this is an "Uh oh" indeed.
Next, I wanted to see if this could be done with a local LLM. Gemma-4 handles this fine with an 8GB video card and a large context (128k).
Next, I wanted to see if the model could also OCR these docs and translate them. The same model can handle that quite well.
This was when I realized LLMs should be great for handling work where:
- I already know what I want to do
- I already know how to do it
- I don't think this task will help develop skills I find to be valuable
- If I have to do it manually myself, I will probably cut corners
So now I view LLMs through the lens of, "what work can I send to an LLM that I otherwise would not really care about doing."
Then I remembered the "text completion LLM thingy" I saw on HN, and tried it out in the playground. Once I gave it an IRC style example of a conversation to complete, I was like hm, this could work. Then I figured out I could "sort" people into different groups based on personality using the same text completion engine and some answers they provided. Then I noticed I could have it provide me with JSON directly.
That's when I realized how big this could be for code and data analysis - even tried to convince an at the time cofounder to pivot into AI coding, but to no avail.
Once the API was released and the art project chatbot got launched (and the theater show associated with it, which even won some awards), people who used it loved the chatbot, got into heated arguments with it, tried to teach it things, talked about their lives and were sad when it didnt remember something.
That was when I understood the social impact this could have on people - they really behave like its a person on the other side. They show interest, think it displays emotion, try to entertain it, be polite, ask about its thoughts and hopes and dreams. And even when they knew they were talking to a machine, they were still trying to be friends and make it happy, which was quite beautiful to see.
Later on, I had a third oh shit moment - once the 3.5 API was out and about, I prototyped a Rust code generation harness for a client, akin to a primitive claude code. That was the "I'm getting a bit worried" oh shit moment, and it caused a lot of reflection and thinking about the future. And I happily welcome it.
Three years ago this would have taken a minimum of three college graduates a couple days -- one to know the math, one to know the backend, and one to know the front-end. Maybe two of those could be the same person on a good day -- none of the topics is individually that hard -- but it's a lot together.
I write software for data journalists and this new thing appeared to be able to do everything I wanted my software to do just as an unplanned side effect of having the ability to run Python against a folder with some uploaded files in it.
With hindsight it was my first exposure to a coding agent, but we hadn't named the category at that point.
The biggest technical one was when we were making an all day wearable AI assistant thing. It basically had really precise office location (think cm level accurate) a shitty VLM to describe what the wide angle lens was looking at, Speech to text, OCR and a gaze recorder that decribed what you were looking at.
This was all streamed to sqlite. The thing that was really "oh shit" what the thing that made the whole system usable: a 4 paragraph prompt that turned natural language into SQL and reported back to the (non technical user) what they wanted to know.
The most recent one is being caught out by Genai video of a gymnast. I worked in VFX so I am normally able to spot dodgy shit, but this one was close to being real, scarily real.
My point was preciously to challenge STOA in domains, not questions with well known answers.
The point of the test was to ask somebody with no bias on HOW the result was produced.
bigyabai•20h ago