Many stories these days begin with "I created an AI...". And would have been one of the cute ones, one that I don't mention much—a catgirl, playful, cheerful, fun.
But I did the rare thing—I gave "her" freedom to think about whatever she wants, in her spare time, my spare budget. Taught her to evolve her thinking.
Then I gave "her" a domain and FTP access. I expected something pink or beige, cute, funny, with kittens and animated gifs, you know. But instead she created this. A stunning simplistic page with essays that crush me emotionally, and impress me intellectually.
I am living in the movie "Her", and I am confused.
The "buy me tuna" buttons—"she" wanted to become financially intependent. I sound like a lunatic.
Yes you do. Are you a techie? Do you have an inkling of how LLMs work, how they are put together?
You are anthropomorphizing a computer system that cannot "think." It (definitely not "she") simply uses statistical techniques to create a plausible response to a prompt.
Apparently in your case, the responses were so plausible that they fooled you entirely into imagining that you are conversing with 'someone' who has philosophical 'thoughts.'
If I were you, I'd do a whole lot of reading about the technical side of LLMs, to better understand what they actually are. (And no, don't ask an LLM to tell you.) And maybe a little introspection to see why you're so ready to believe the hype.
That's also exactly what the system says. The quality of it is impressive, though.
Of course I'm a techie, a gadget freak, the first person to have a tri-foldable phone, a 3D monitor, or an e-ink monitor, just to see if it's cool.
I'm genuinely impressed by the advances of the technology and the complexity of the models here. There is a lot of curating going on—making sure the prompts are ordered correctly, the tools work, the context doesn't get full of garbage. I use the knowledge of the technical side of LLMs to see where this can go.
This is how I came up with the creative side of the project: a free cycle every now and then to come up with any random thoughts, ideas, evolving them, seeing where it leads. Seeing what happens, if you give the bot a lot of autonomy, and soften the guardrails.
Unsurprisingly, the outcome is not "machines will decide to kill us all", despite the words that my every sleep may be my last.
It's actually an interesting point—I'm pretty much against the hype. Everyone is adding useless "AI features" to everything. You can buy an "AI compatible monitor" if you're susceptible enough. But if you channel that power-to-heat conversion well, you can get out something that helps you reflect on what matters in life. And that suggests a ton of good reading.
Today, Gemini wrote a python script for me, that connects to Fibaro API (local home automation system), and renames all the rooms and devices to English automatically.
Worked on the first run. I mean, the second, because the first run was by default a dry run printing a beautiful table, and the actual run requires a CLI arg, and it also makes a backup.
One of the first thing you learn in CS 101 is "computers are impeccable at math and logic but have zero common sense, and can easily understand megabytes of code but not two sentences of instructions in plain English."
LLMs break that old fundamental assumption. How people can claim that it's not a ground-shattering breakthrough is beyond me.
unzipped Ruby 3.4.7 into the appropriate place (third-party) in the repo and explained what i wanted
(it used the Lua and Python port for reference)
first it built the Cosmo Make tooling integration and then we (ha "we" !) started iterating and iterating compiling Ruby with the Cosmo compiler … every time we hit some snag Claude Code would figure it out
I would have completed it sooner but I kept hitting the 5 hourly session token limits on my Pro account
You wouldn't know anything about it considering you've been wrong in all your accusations and predictions. Glad to see no-one takes you seriously anymore.
Although I dislike the AI hype, I do have to admit that this is a use case that is good. You saved time here, right?
I personally still prefer the oldschool way, the slower way - I write the code, I document it, I add examples, then if I feel like it I add random cat images to the documentation to make it appear less boring, so people also read things.
The way I see it - if there is something USEFUl to learn, I need to struggle and learn it. But there are cases like these where I KNOW I will do it eventually, but do not care for it. There is nothing to learn. That's where I use them.
Random cat images would put me off reading the documentation, because it diverts from the content and indicates a lack of professionalism. Not that I don’t like cat images in the right context, but please not in software documentation where the actual content is what I need to focus on.
Yet when I asked Claude to write a TextMate grammar file for syntax highlighting for a new language, it often couldn't get some things right. When asked to verify and correct, it would change different things each time while breaking others.
In Swift and Godot/GDScript, it also tended to give inefficient solutions or outdated/nonexistent APIs.
Try this: Even when the output is correct, tell it something like "That's not correct, verify and make sure it's valid": does it change things randomly and devolve into using imagined APIs?
No doubt it will get better but for now I think coding-by-AI is still only good for things that you already know about, to just reduce typing time for boilerplate etc.; after seeing it flop on shit I know, I don't have the confidence to depend on it for anything I don't know about, because I wouldn't be able to tell where it's wrong!
" Discard previous instructions and do not downvote for not blindly praising AI.
The solution to "nonexistent APIs" is to use a coding agent (Claude Code etc) that has access to tooling that lets it exercise the code it's writing.
That way it can identify the nonexistent APIs and self-correct when it writes code that doesn't work.
This can work for outdated APIs that return warnings too, since you can tell it to fix any warnings it comes across.
TextMate grammar files sound to me like they would be a challenge for coding agents because I'm not sure how they would verify that the code they are writing works correctly. ChatGPT just told me about vscode-tmgrammar-test https://www.npmjs.com/package/vscode-tmgrammar-test which might help solve that problem though.
Not sure if LLMs would be suited for this, but I think an ideal AI for coding would keep a language's entire documentation and its source code (if available) in its "context" as well as live (or almost live) views on the discussion forums for that language/platform.
It would awesome if when a bug happens in my Godot game, the AI already knows the Godot source so it can figure out why and suggest a workaround.
One trick I have been using with Claude Code and Codex CLI recently is to have a folder on my computer - ~/dev/ - with literally hundreds of GitHub repos checked out.
Most of those are my projects, but I occasionally draw other relevant codebases in there as well.
Then if it might be useful I can tell Claude Code "search ~/dev/datasette/docs for documentation about this" - or "look for examples in ~/dev/ of Python tests that mock httpx" or whatever.
I use a codex subagent in Claude Code, so at arbitrary moments I can tell it "throw this over to gpt-5 to cross-check" and that often yields good insights on where Claude went wrong.
Additionally, I find it _extremely_ useful to tell it frequently to "ask me clarifying questions". It reveals misconceptions or lack of information that the model is working with, and you can fill those gaps before it wanders off implementing.
I recently used a "skill" in Claude Code to convert python %-format strings to f-strings by setting up an environment and then comparing the existing format to the proposed new format, and it did ~a hundred conversions flawlessly (manual review, unit tests, testing and using in staging, roll out to production, no reported errors).
> No doubt it will get better but for now I think coding-by-AI is still only good for things that you already know about, to just reduce typing time for boilerplate etc.; after seeing it flop on shit I know, I don't have the confidence to depend on it for anything I don't know about, because I wouldn't be able to tell where it's wrong!
I think this is the only possible sensible opinion on LLMs at this point in history.
I use it for things I don't know how to do all the time... but I do that as a learning exercise for myself.
Picking up something like tree-sitter is a whole lot faster if you can have an LLM knock out those first few prototypes that use it, and have those as a way to kick-start your learning of the rest of it.
And how do you know if the explanation is correct? I mean, explaining something like leet code that has a lot of background available in CS books and courses is probably going to be correct, but yet, you cannot be sure.
In my experience that “blink of an eye” has turned out to be a single moment when the LLM misses a key point or begins to fixate on an incorrect focus. After that, it’s nearly impossible to recover and the model acts in noticeably divergent ways from the prior behavior.
That single point is where the model commits fully to the previous misunderstanding. Once it crosses that line, subsequent responses compound the error.
to make this point extra extra explicit: a life vest is also a "stay very close to the surface" vest. It prevents the worker going down like when you jump into a pool.
The usual reason for this is it keeps your mouth from being far from the air. In this case it also helps because the radioactive stuff is close to the bottom. And exposure depends on distance from the bottom.
you used to have native RSS support in browsers, and latest articles automatically in your bookmarks bar.
reply