get_flat_ccs_offset() reads the base of the flat CCS storage from the
hardware, scales it by the number of enabled L3 nodes, and rounds the
result up to 128K. Everything below that offset is then ...
If an AI can find the bug and point it out, I don’t see a problem, as long as it is checked thoroughly enough by an actual skilled programmer, to make sure that doing what the AI suggest is not making things worse.
Hey, Mr. Self-righteous. Tell us about how you live in a small efficient urban dwelling served nearly entirely by renewable energy, that you don’t own a car, don’t eat meat, and never take longer than a 5 minute shower.
And if even one of those things isn’t true, I demand a full explanation for why you hate the planet and want it to die.
Especially if you own a car. If you own a car and you’re shaming people for the environmental impact of AI, you owe it to us all to sell that thing today and prove your superiority to the rest of us.
EDIT: Nevermind, just reviewed your comment history to see that you are an enthusiastic spectator of the Russia/Ukraine war. Given that war is the single most ecologically destructive force on the planet, it is clear that you are completely irredeemable, and should keep your snide comments to yourself.
And this was a debug session from hell, enormously helped by an AI
doing much of the grunt-work.
I’d like to call it my tireless helper, but the AI several times
stated flat out that this was impossible and unsolvable and that we
should just write a report about it.
I suspect those things have been trained by people who may not be
quite as stubborn as I am.
But while the AI was ready to give up several times, it did keep
adding debug code and analyzing it faithfully when I pushed. So credit
where credit is due and I let the AI write the commit message above.
This is basically a one-liner fixing a bogus “round_up()” to a
“round_down()”, but there were 24 patches adding more and more debug
information to this, and 18 kernel boot to finally narrow it down to
this. - Linus
That’s a long way short of finding the bug and pointing it out, and instead is just the tedious work of adding checks and then deciding where to add more checks when you have the result of the previous checks. I suspect if we’d spent a trillion dollars on a tool for that, we could have come up with something much more efficient than an LLM.
“who may not be quite as stubborn as I am” that is the most Linus torvalds thing to say. good to see his passive agressive nature being put in a press release.
I have found that LLM agents can monitor ALL the logs, synthesize information that takes me hours to put together in minutes - and when there’s a problem in there it can spot it faster and more often than me. It can also miss things, not as many as I miss given equal time to work the problem, but it’s not perfect - just better. Like cutting down a tree with a chainsaw, instead of a bare hacksaw blade.
For every bug the AI does not find and I have to spend 1 hour to realize the AI is not able to find and then spend 2 hours to identify myself, there are 10 bugs the AI finds in a couple minutes.
I can gladly spend the extra time when it doesn’t work.
I find that the LLM agents find maybe 90% of “bugs” in their first pass, and 2/3 of those tend to be stuff I really wouldn’t sweat over in most cases, but O.K. they’re easy enough to button up so let’s do that, then the other 30% are more serious stuff I’m genuinely relieved got found sooner than later.
Then you get into that last 10%, and I’ll help it in the hunt, and probably 90% of them I’ll solve much more quickly with the LLM agent than I ever would have without it, even if I have to give it directions about where and how to look for the problems / solutions.
Then there’s the final 1% where the LLM just doesn’t figure it out, even with clues, and I can see it and fix it far faster than the LLM… those exist, and they make great “how dumb are LLMs these days???” stories, but that’s ignoring the other 99% where LLMs are clearly helpful.
Of course all the above is generalization, and if you roll back 12 months ago the LLMs weren’t nearly as good then - and any further than that and I thought they were a total waste of time, but starting about 12 months ago it was pretty clear they were useful, and improving, fast.
Last week I actually solved a network configuration problem on the other side of the planet by having the LLM: 1) create scripts to diagnose the problem, 2) have a test engineer run those scripts on problem machines and control (non problem) machines, 3) get the logs from those script-runs and have the LLM analyze them, 4) repeat the process, based on information from 3 make new scripts focused on the problem area, 5) more test engineer runs, this time generating many megabytes of log files, 6) LLM analyzes the log files from 5 and identifies the issue, 7) LLM makes a script to reconfigure the terminal machine to solve the issue, 8) test engineer deploys it and retests - problem solved, 9) I discover the “solution” broke something else, describe the problem to the LLM, 10) LLM adjusts the “solution” to fix the problem without creating the new problem.
What’s more remarkable about all that is: we first observed this problem 5 years ago, it was kind of a minor annoyance how it manifested but it’s recently become more important that we solve it. In those 5 years, nobody here or there figured out what was going wrong, but a couple of rounds of applied LLM diagnosis and treatment led to a solution inside a week.
If an AI can find the bug and point it out, I don’t see a problem, as long as it is checked thoroughly enough by an actual skilled programmer, to make sure that doing what the AI suggest is not making things worse.
So you don’t care about environment, huh?
Hey, Mr. Self-righteous. Tell us about how you live in a small efficient urban dwelling served nearly entirely by renewable energy, that you don’t own a car, don’t eat meat, and never take longer than a 5 minute shower.
And if even one of those things isn’t true, I demand a full explanation for why you hate the planet and want it to die.
Especially if you own a car. If you own a car and you’re shaming people for the environmental impact of AI, you owe it to us all to sell that thing today and prove your superiority to the rest of us.
EDIT: Nevermind, just reviewed your comment history to see that you are an enthusiastic spectator of the Russia/Ukraine war. Given that war is the single most ecologically destructive force on the planet, it is clear that you are completely irredeemable, and should keep your snide comments to yourself.
The exact quote is:
That’s a long way short of finding the bug and pointing it out, and instead is just the tedious work of adding checks and then deciding where to add more checks when you have the result of the previous checks. I suspect if we’d spent a trillion dollars on a tool for that, we could have come up with something much more efficient than an LLM.
“who may not be quite as stubborn as I am” that is the most Linus torvalds thing to say. good to see his passive agressive nature being put in a press release.
Press release? This is a git commit.
You must be an ex reddit user as you neither clicked the link, nor looked at the domain it linked to.
You will be surprised to know, “ex-reddit user” is the majority of Lemmy users.
But most of us have the decency to be ashamed about it.
why? reddit actually had content and debate
“debate”
I don’t think I had a single coherent argument on that godforsaken hellsite
It’s not that serious
Just a nitpick, git commit. Kernel development has nothing to do with GitHub other than the fact that there is a mirror of the repo in there.
Thank you, corrected.
I have found that LLM agents can monitor ALL the logs, synthesize information that takes me hours to put together in minutes - and when there’s a problem in there it can spot it faster and more often than me. It can also miss things, not as many as I miss given equal time to work the problem, but it’s not perfect - just better. Like cutting down a tree with a chainsaw, instead of a bare hacksaw blade.
For every bug the AI does not find and I have to spend 1 hour to realize the AI is not able to find and then spend 2 hours to identify myself, there are 10 bugs the AI finds in a couple minutes.
I can gladly spend the extra time when it doesn’t work.
I find that the LLM agents find maybe 90% of “bugs” in their first pass, and 2/3 of those tend to be stuff I really wouldn’t sweat over in most cases, but O.K. they’re easy enough to button up so let’s do that, then the other 30% are more serious stuff I’m genuinely relieved got found sooner than later.
Then you get into that last 10%, and I’ll help it in the hunt, and probably 90% of them I’ll solve much more quickly with the LLM agent than I ever would have without it, even if I have to give it directions about where and how to look for the problems / solutions.
Then there’s the final 1% where the LLM just doesn’t figure it out, even with clues, and I can see it and fix it far faster than the LLM… those exist, and they make great “how dumb are LLMs these days???” stories, but that’s ignoring the other 99% where LLMs are clearly helpful.
Of course all the above is generalization, and if you roll back 12 months ago the LLMs weren’t nearly as good then - and any further than that and I thought they were a total waste of time, but starting about 12 months ago it was pretty clear they were useful, and improving, fast.
Last week I actually solved a network configuration problem on the other side of the planet by having the LLM: 1) create scripts to diagnose the problem, 2) have a test engineer run those scripts on problem machines and control (non problem) machines, 3) get the logs from those script-runs and have the LLM analyze them, 4) repeat the process, based on information from 3 make new scripts focused on the problem area, 5) more test engineer runs, this time generating many megabytes of log files, 6) LLM analyzes the log files from 5 and identifies the issue, 7) LLM makes a script to reconfigure the terminal machine to solve the issue, 8) test engineer deploys it and retests - problem solved, 9) I discover the “solution” broke something else, describe the problem to the LLM, 10) LLM adjusts the “solution” to fix the problem without creating the new problem.
What’s more remarkable about all that is: we first observed this problem 5 years ago, it was kind of a minor annoyance how it manifested but it’s recently become more important that we solve it. In those 5 years, nobody here or there figured out what was going wrong, but a couple of rounds of applied LLM diagnosis and treatment led to a solution inside a week.