Things I've Done with AI

Something like this would be anxiety inducing for most people, I bet. That'd be an excellent experiment, track heart rate, EEG, and performance on a range of cognitive tasks with 2 minute long breaks between each tasks, one group exposed to the irregular ticking, another exposed to regular ticking, another with silence, and one last one with pleasant white noise.

And it’s exactly what I expected - lines of code. Cute. But… so what? This is not good for the AI hype and nor any continued support for future investment.

On the other hand all this stuff is going to drive continual innovation. The more tokens generated the more model producers invest. And we might eventually get to a place of local models.

Moreover though, I'm not even saying you shouldn't do those things. I'm actually playing around with AI quite a bit, and certainly have created my share of useless/productivity tools. But it's not a flex to show off your own Flappy Birds or OpenNanoClaw clone, even if they are written in COBOL or MUMPS.

And they definitely do not have to be "extremely useful". But they should answer the question: what problem does it solve?

For example, I checked out their "Fictional Encyclopedia". It's an absolutely terrible project, much worse than useless, because it claims to be an "encyclopedia" right in the name (the tagline is "Everything about everything"), yet it's engineered to just completely make things up, and nowhere on the page does it indicate this! I looked up my own niche open-source project, and was prepared to be at least somewhat impressed that it pulled together facts on the fly into an encyclopedic form. For the first couple of paragraphs that seemed like it might be the case, then it veered into complete fantasy and just kept going.

Like what is the point of this? I can already ask a chatbot the same question and at least then I have explicit indicators that it might be hallucinating. But this page deliberately confuses truth and reality for absolutely zero purpose. It's a waste of brain cells, for both the creator and the consumer, with no redeeming value. It's neither interesting, nor different, nor valuable. AND it's burning tokens to boot!

I mean, come on, the bar is not that high. Some of stavros' projects may even be over it. But the first projects I checked were sub-basement, and I am not interested in searching through mounds of trash for what might be a quarter dollar. I'm actually kind of disappointed that stavros didn't have (or apply) the sense or taste to whittle down that list of 11 (!) projects to some 3 that show off the value of their work. Which I'm starting to understand is everyone's issue with AI brain rot; it seems to just encourage "here's everything, I dunno, you figure it out" which is maddening and deserves the pushback it gets.

The bar for this will just keep moving. Some people are heavily invested in the anti-stance, so human nature being what it is, you've little hope of changing their minds anyway.

Yeah they are interesting, I guess they do something but are any of them actually delivering value? That’s when you get into the argument of what is value and to whom, but as AIs role in society of generating productivity, that’s pretty disputable if every person being able to build their own train set that turns on the toaster and makes coffee is going to move us forward as a species like , say the internet.

That’s really the only argument, is the use of LLMs worth the trillions of dollars and selling out the future of humanity for. Not is it fun Bildungsroman quirky apps really fast

That’s not even mentioning that this tools doesn’t do much beyond wrap a call to Claude. And it’s using Claude to display blood test data to the end user. This is not something I’d trust an LLM to not mess up. You’d really want to double check every single result.

These AI tools are not hard to learn, in fact they're super easy when you have some experience programming, so the only people who are going to be left behind are the ones who simply refuse to use the tools out of principle. And why would they care about being "left behind"? They're making a conscious choice to not use the tools. They want to be left behind!

And not everyone who is skeptical are that out of principle, some just don't see the value yet or are slowly and cautiously adopting it into their workflow. If AI powered coding ends up being even half as good as promised, so good that denying the evidence is impossible, they can just start using it and catch right up. So who exactly is "being left behind" here? It's complete nonsense while simultaneously being extremely condescending and I get triggered every time I read the phrase.

I don't mean anything against you personally with my ranting, it's more a general observation. Perhaps you and some others do mean it as a genuine bit of advice, like "hey, you should learn these tools or else you might struggle to find work in the future", but the sense I get most of the time are people who are gleeful that the non-believers are soon to be homeless or whatever.

A major advantage of (certain) extension mechanisms is that you can update them in real-time. For example, in Emacs you can change functions without losing the current state of the application. In Processing or live coding environments, you can even update functions that affect real-time animation or audio.

Another advantage is that they can pose a very nice API, that allows for other people to learn an abstraction of the core application. If you are the sole developer, and if you can spend the time to keep an active memory of the core application, this does not help much. But it can certainly help others to build upon your foundation. Gimp and Emacs are great examples of this.

A disadvantage is that you have to keep supporting the extension mechanism, or otherwise extensions will break. That makes an ecosystem somewhat more slow to adapt. Emacs is the prime example here. We're still stuck with single-threaded text mode :)

1. The closer the context gets to full the worse it performs.

2. The more context it has the less it weights individual items.

That is Claude might learn you hate long functions and add a line about short functions. When that is the only thing in the function it is likely to follow other very closely. But when it’s 1 piece of such longer context, it is much more likely to ignore it.

3. Tokens cost money even you are currently being subsidized.

4. You have no idea how new models and new system prompt will perform with your current memory.md file.

5. Unlike learning something yourself, anything you teach Claude is likely to start being controlled by your employer. They might not let you take it with you when you go.

Not quite. I agree that companies will try to do this, but every company that has tried to treat engineering staff as replaceable units of person-hours has failed.

> Metrics, performance reviews, sprint velocity, delivery timelines, all orbit around observable artifacts because those are what management systems can actually track objectively and equitably. It's a handy abstraction just like looking only at the ins/outs of a logic gate as opposed to looking at the implementation and wiring.

Yes, and these metrics are, usually, worthless.

It's not that companies and managers will not try to replace engineers with AI. I'm sure they will. I'm sure many will be laid off because "AI does it cheaper now".

My point is that companies that have gone down this route in the past have failed, and AI is no different. Companies that lean strongly into AI as a workforce replacement will fail too.