I have mixed feelings about macOS. I grew up using it and I talked my previous employer into getting me a mac for work but I’ve barely used my mac laptop in the last four years, even more so in the last year since I bought a Linux laptop.

You: judgmental meme dissing windows devs

Them: well-reasoned argument for not buying a mac

You: “You do you, boo”

Classy.

I mean, any time a Windows process needs to interact with resources within WSL it has to go through a translation layer. I didn’t realize you could run native GUI apps within WSL. But if I’m to the point of installing Wayland and GUI apps in WSL, I’d just wipe Windows and install Linux instead…

the machines are better value than Macs.

In my personal experience, Apple laptops are far more durable than the crap Dell, Lenovo, etc are selling. Either way though I’m done with Apple and Microsoft. I doubt I’ll ever buy another computer that comes preinstalled with an OS.

I have to strongly disagree with you. I’ve used WSL 2 with VSCode, and I experienced waaaaaaaay more weird broken shit than I ever have running Linux. And even if it weren’t for that, it’s still not at all worth it IMO because using WSL 2 means every interaction I have with my development environment has to go through a Linux-to-Windows translation layer. I will never use Windows again for anything beyond testing unless I’m forced to.

How are you using it for data crunching? That’s an honest question, based on my experiences with AI I can’t imagine how I’d use them to crunch data.

So I always have to check it’s work to some degree.

That goes without saying. Every AI I’ve seen or heard of generates some level of garbage.

My point is that I strongly feel that the kind of “AI” we have today is much closer to bacteria than to cats on that scale. Not that an LLM belongs on the same scale as biological life, but the point stands in so far as “is this thing intelligent” as far as I’m concerned.

it’s not inconceivable it could happen in the next two generations.

I am certain that it will happen eventually. And I am not arguing that something has to be human-level intelligent to be considered intelligent. See dogs, pigs, dolphins, etc. But IMO there is a huge qualitative difference between how an LLM operates and how animal intelligence operates. I am certain we will eventually create intelligent systems but there is a massive gulf between what LLMs are capable of and abstract reasoning. And it seems extremely unlikely to me that linear algebraic models will ever achieve that type of intelligence.

Intelligence is just responding to stimuli

Bacteria respond to stimuli. Would you call them intelligent?

I don’t know, have you ever used JavaScript? I’ve run into some really fucking weird bugs. I’ve also spent hours trying to find the source of an error message only to discover the error message was lying and caused by some other error.

The only part of copilot that was actually useful to me in the month I spent with the trial was the autocomplete feature. Chatting with it was fucking useless. ChatGPT can’t integrate into my IDE to provide autocomplete.

The point is that AI stands for “artificial intelligence” and these systems are not intelligent. You can argue that AI has come to mean something else, and that’s a reasonable argument. But LLMs are nothing but a shitload of vector data and matrix math. They are no more intelligent than an insect is intelligent. I don’t particularly care about the term “AI” but I will die on the “LLMs are not intelligent” hill.

I’m the opposite. AI is best (though not great) at boring shit I don’t want to do and sucks at the stuff I love - problem solving.

Their rules have stopped me from being able to do my job. Like the time the AV software quarantined executables as I was creating them so I literally could not run my code. When security enforcement prevents me from working, something needs to change.

My comment game has gotten far better since I started doing live code reviews. Essentially I ask myself, “Would I feel the need to explain this to someone during a code review?” and if the answer is yes I add a comment.

That’s a hot take. If you want your code to be maintainable at all, it needs comments. If you’re part of a team, write comments for them. If someone else may take over your project after you move on, leave comments for them. And have you ever tried to read uncommented code you wrote a year ago? Leave comments for yourself.

The con is that it’s not very powerful. I haven’t attempted to code on a gaming handheld, but I’ve had issues with a midrange laptop being under powered. RAM is probably the biggest issue. My life improved noticeably when I upgraded my main machine to 64 GB. Granted I was doing particularly heavy work. It really depends on what you’re doing. You could get away with it for some work, but it’s going to be painfully slow for other stuff.

The key difference is that compilers don’t fuck up, outside of the very rare compiler bug. LLMs do fuck up, quite often.

Copilot frequently produces results that need to be fixed. Compilers don’t do that. Anyone who uses copilot to generate code without understanding how that code works is a shit developer. The same is true of anyone who copies from stack overflow/etc without understanding what they’re copying.

I’d create my own macro or function for that. I have enough ADD that I cannot stand boring shit like that and I will almost immediately write a pile of code to avoid having to do boring crap like that, even with copilot.

Using git reset --keep would just make more work since I’ll have to throw away uncommitted changes anyways. Removing uncommitted changes is kind of the whole point, it is called ‘reset’ after all. If I want to preserve uncommitted changes, I’ll either stash them or commit them to a temporary branch. That has the added benefit of adding those changes to the reflog so if I screw up later I’ll be able to recover them.