It has happened under both.

Even a stopped clock is right twice a day.

The descendants of Ogg have renewed the ongoing lawsuit against the descendants of Zogg alleging that Ogg dropped a rock on Zoggs foot in a cave one fine day and it was really ouchie! Anthropologists are working around the clock trying to restore the cave paintings documenting the original complaint made to the tribal chief in 30,001 BCE.

That's because of all of the inbreeding. A is the CEO at one place where B sits on the board. Next door, B is the CEO and A sits on the board...

Exactly that. Many seem to have forgotten that hospitals were absolutely slammed early in the pandemic and that medical care in general suffered.

Not to mention an increase in damaging severe weather.

Even people who like it warmer probably don't like having their house blown away to Oz.

Unfortunately, it has a way of becoming your problem as they strain medical resources, continue to drive global warming and run around spreading their infectious diseases.

The key is to use the AI more sensibly. Let it find things in code. Even let it look at possible bugs in the form of where does it "describe bug". Even let it propose changes. DO NOT let it make the changes, just show them. Do not just take it's word for it, take the shown code as a starting point but re-do it for maintainability after sanity checking it.

THEN it can be a useful tool.

Hereâ(TM)s the difference: if youâ(TM)re not a citizen, the government can get rid of you before your rights can be asserted. A judge can order a stay on your deportation and watch as youâ(TM)re taken anyway without consequence.

I agree, especially with that last part. They have no idea what they are missing. I'm a gamer, and I have nothing but respect for game devs. You guys make life *fun*. :) As I mentioned, I am a sysadmin, not a coder. But -- with the help of an LLM I vibe coded two successful mods for a couple of my favorite games and figured out (again, with the help of an LLM) to get one of them published to Steam's workshop. At my college commencement thirty-odd years ago, my CS advisor noted that in his 25 years on faculty, I was the only graduate who genuinely couldn't code his way out of a wet paper bag. Fair assessment. But an LLM bridges the gap between architectural intent and actual syntax, which lets me build things in my head. I keep getting the feeling that the anti-AI backlash is coming from the same kind of people that blame their tools when they screw up. I'm a baby with a flamethrower when it comes to using an LLM to write code for me, but I figured out (eventually) how to put guardrails around *everything* so that the LLM converges on my intent, and not the semantics I used to convey that intent. That's the real hard part, I think. Sounds like you figured that out as well.

Torvalds is right. This is a bit of a me too post, although my use case is a little less kernel-adjacent. :)

Up front -- I'm not a dev. I'm a retired sysadmin with a BS in CS and several decades in cubicle land managing systems and networks for a very, very large defense contractor. And I hate coding with a purple passion...if I couldn't solve it with a shell script, I would hand it off to a real coder. With that said, I've spent the last several days using an LLM as a development partner to build a fairly ridiculous personal music system. What surprised me wasn't that it could generate Python. That's useful, but not especially interesting.

What became interesting was using it as a persistent collaborator. My sysadmin lizard brain imposed enough engineering discipline that neither of us was allowed to hand-wave about system state. The result was actually amazingly useful to me: a user-specific taste model from actual listening evidence rather than genres. It (currently) has nine weighted dimensions: melodic continuity, spatial/textural architecture, emotional landscape, developmental architecture, background-to-foreground permeability, conceptual/lyrical reach, contextual integrity, arrangement integration, and register/timbral fit, and two engines, one cloud and one local, that actually can generate context aware playlists, one from the music mines available from my Spotify subscription, or one from my local mp3 media library managed by my wine-supported Winamp install. And fine, go ahead and insert Ok, boomer jokes here. :)

Both engines reduce requests like "I'm surfing slashdot for a couple of hours and don't want the music getting in my way" or "I'm about to try this damn slaughterspine fight for the umpteenth time and I need something to propel me into a flow state" into an activity envelope, apply the taste model, and then produce something that both Spotify's API and winamp can grok. The end result is a private playlist in my spotify account, or a .m3u8 playlist for winamp. The taste matrix guarantees the list will be generated from music that I genuinely like to listen to, and still match the context I want to listen to it in.

As a pleasant surprise when I was building the local model, I ended up creating a seriously functional ID3 metadata scrubber. Did you know that there is an entire cottage industry out there for figuring out what exact physical carrier your MP3s were derived from? I didn't, but the LLM did. It fingerprinted all my mp3s, matched them to online digital signatures, record label catalogs, and even pulled down and digitally fingerprinted free samples from Apple/Amazon music when it couldn't find a good fit elsewhere, including deep dives in USENET alt.music.mp3 archives. These forensic tactics were brand new to me, and they helped to completely fix my Winamp ID3 tag jungle. I ended up with a very decent forensic model with a control surface that let me find provenance and preserve it in a SQLite catalog for almost my entire winamp library; I'm down to just 17 holdouts out of the 914 mp3s I've ripped from CDs over the course of the last four decades or so -- many of which I no longer possess and can't consult for provenance.

But the part that made the LLM genuinely useful was the boundary between its world and me. The development loop was roughly:

wild ass idea -> generate helper -> run locally -> deterministic evidence -> inspect/adjudicate -> next operation

rather than:

AI says it probably will work -> continue

The LLM has been exceptionally useful for finding contradictions in my schemas, designing forensic queries, generating one-off analysis tools, keeping long dependency chains straight, spotting places where I was promoting an inference into a fact, and remembering why some ugly-looking safeguard exists after I had long forgotten the incident that created it.

TBH, It has also confidently proposed things that were just plain wrong, misunderstood what layer owned a fact, tried to solve problems more broadly than the evidence justified, and occasionally decided that some particularly unpleasant corner was probably not worth pursuing. Which is where Torvalds' comment made me laugh. The useful mode isn't "tell the AI to build a system." It's much closer to "keep track of this shit for me, please, and try to keep us from shooting ourselves in the foot."

It's like having a very fast, very sharp indefatigable summer intern with an absurdly large working notebook, broad technical knowledge, apparently no need for sleep, and a tendency to press shiny red buttons just to see what happens. If you give an LLM observable state, narrow authority, regression checks, and provenance, and are willing to accept that you might have to herd some hallucinating cats, it can become one hell of a collaborator.

Same argument, govt subsidies for buggy whips. The govt needs to set these rules for the benefit of the people, not to support companies trying to keep selling obsolete and uncompetitive legacy technology products.

It's not the SCREEN that does the damage, it's the brain rot the screen is used to access. Also the amount of time speant with the screen. Moderation is good. This shouldn't surprise people, but since it is a nuanced understanding and not just screen bad!, it will.

BOTH screwed up. The system shouldn't have made the match and the human should have done a better job seeing that the match was wrong. I'll go one larger and say the people who created the procedures screwed up because they didn't make allowances for the possibility of the first two screw-ups.

The problem is, people have been over-trusting technology since technology was invented. Consider the '70s when corporate representatives were so fond on the phrase "But the computer says...". Yes, you could hear the bold italics in their voice, as if it was the infallible final word on everything.

If THE COMPUTER says you shoplifted, rest assured, some dolt in a suit will treat that like God's own word booming from the clouds.