Oh, along those lines, it's well reported that Hyundai used systemd in recent models of their Tuscon braking system, Samsung used systemd in the Galaxy Note 7, that Toyota used systemd in their 2009-2011 accelerator systems. In addition, GM is rumored to be using system in their recent models of Cadillac, Chevy, and GMC; especially in the braking system.

Systemd makes an explosion everywhere it is used. It keeps the old rust off the tires.

Because it is a tool aimed at distro builders. It does the things they want. That doesn't mean it's good design, and it doesn't follow the Unix way.

Lennart Poettering kissed distro builder's asses until they included his tool. Once he got it in, he did whatever he wanted.

Literally every monolith ever is a collection of smaller parts.

Your example proves you don't understand monoliths. For example, it is perfectly possible to use Google Maps without using Google search, even though they are both part of the same monolith. "using" and "not using" aren't what determine a monolith. What determines a monolith is improper separation of concerns, and that is a design mistake made by systemd.

Systemd is a monolith.

It is illogical to try to fit everything into a single-dimensional left/right framework.

Literally every issue has its own dimension, and probably more than one dimension.

But again that's backwards. I let my AI coding assistant write tests exactly so that I have a reproducible, deterministic ground truth that I can inspect for logic errors. If you let the AI do the testing directly, it's a toss-up if today it feels like it or it decides that your desired result is "everything green" so it'll just give you that answer without actually testing anything.

LLMs obviously aren't human. But that's not the point. The point is that you don't need to know a programming language to program a computer.

For example: Especially in the early days of smartphones, there were mountains of small tools that did something that's essentially a simple script. Egg timers, remind-me-tomorrow apps, tons of stuff that's basically a wrapper around a web search, and so on. Almost all of those are today something ChatGPT can do, and are easy enough to describe.

You can copyright the prompt.

That's why...if you are thinking of sending me an AI email, please just send me the prompt. It's protected.

Ok, you have a hypothesis, that the AI made your code faster.

Did you collect any evidence to back this up? Or did you approach it from a 'faith' perspective?

I didn't understand the summary.

This is the example that helped me understand what they built. It's a framework for benchmarking different configurations. If you want to know whether Claude or ChatGPT works better, this will help you. IF you want to know if adding a skill is worth it, then this will help you determine and keep track.

Then it will put it into a pretty graph that will make CEOs think you've done something useful.

That quote is fairly benign.

Since it bothered you so much, I can only assume that you inflict breaking changes on end-users merely because you are lazy, without consideration for the problems it will cause them.

"Breaking changes are an act of violence." Douglas Crockford.

Oh yeah? What kind of performance gain did you get?

Did you measure the performance gain, or did you leave your hypothesis untested?

Good point.

I'll add that to my prompt: "Dont' make it annoying."

I've actually outsourced the first iteration of that to AI. :-)

At the end of coding sessions, I generall ask it to spin up an adversial agent whose task it is to find flaws, inconsistencies, mistakes, bugs, etc.

I'd still strongly recommend to anyone shipping AI-generated code to have a thorough human code review, but for a first pass at surface level, the AI can check itself if it has some kind of separation between the generator and the reviewer.

Personally, after some experimenting, I've concluded that:

• AI is good at implementing known algorithms.
• AI is good in creating code that flows straightforward from a design document.
• AI is good at code reviews and finding bugs.
• AI (unlike me) doesn't mind writing a few hundred tests.
• But, it tends to over-engineering simple tasks to the point of "why does Hello World need enterprise-software levels of abstraction?"
• It struggles with code that doesn't have a known solution.
• It's difficult to get it into a different lane once it started going down the wrong way.

What I use it for these days is rapid prototyping. Using AI I can make a functioning prototype of an idea I have in a few days instead of a few weeks. I can quickly test out an idea for a feature or change in my existing codebase.

What I also have been using it for a while is as a code-reviewer and for someone like me who is mostly working solo, that's an insanely valuable thing. Not just the bugs themselves, but also the ability to discuss code with someone. Basically, AI is a rubber ducky that replies.