> Forget maintaining stability, AI is a downright destabilizer. It worsens understandability, fixability, debuggability, teachability, guaranteability, all the bloody bilities.
This is just an assumption and the whole article falls flat if this turns out to be wrong.
In my limited (as everyone else's) experience, working with agentic AI needs good documentation, good specification (spec driven, you know it's all the hype nowadays). Those alone lead to much improvement. Now take into account that probably your senior dev also has more time to think about the big picture, to improve all those little things that were a nuisance in the past but now are a mere "Claude, fix that" in a worktree away.. I would not bet on the assumption of this article.
I am not questioning the benchmark.
But the benchmark is NOT measuring a real world application in a real world setting.
Anyway, I am merely wondering IF there is a company out there affected, at all.
I understand that this was only measured on a graviton 4 setting with very heavy lifting, without huge tables.
For example, this issue aside, I'd rather split such a workload into multiple smaller instances, naturally. Because the impact of a crash on this single node, heavy load, many cores, many clients scenario would be huge.
I'd rather like to know if any real world usage broke, before coming to the conclusion that an edge case synthetic benchmark is worth changing the kernel (back or wherever) where supposedly the change that broke the benchmark had real world benefits.
Since we will never know it might be a good idea to feature gate the change, change the default and let users decide to change it back.
This may give some feedback on the lkml or else to decide if the change is worthwhile?
I recently had to switch to macos for work and Jesus Christ is this not the pinnacle of engineering. Sure, I'm accustomed to my self configured Linux desktop but boy is Mac OS slow to use and hard to configure.
And so keyboard unfriendly.
You hopefully know thats not true. But it's a matter of quality goals. Need absolute robustness? Prioritize it and build it. Need speed and be first to market? Prioritize and build it.
You can do both in an agile way. Many would argue that you won't be as fast in a non-agile way. There is no bullet point in the agile manifest saying to build unreliable software.
May be I don't understand but I can see that I press a short cut and can connect via ssh, right?
Either you included your own ssh client (battery included and such) and you'll forever be chasing the features your customers are missing from their client or you just launch the standard ssh client (or psql or whatever) but then, what's the benefit as opposed to some shell configuration?
Genuinely asking, maybe I missed it from the first glance or maybe you could include that on the front page..
I don't understand all the hate about ente, to be honest.
Ente seems to try to solve the big tech lock in with their apps.
Personally, i'm a very happy Ente Photos user, so what's the problem with Ensu?
It's available on desktops and mobile, it's an app trying to give all a little bit more privacy and freedom and yet most comments are just hating on it.
If you can vibe code Ensu in a week end, please do. Make a better clone if you want to, but don't hate on someone for their work for stupid reasons.
Keep in mind that kagi offers a wide range of models, not just one. I wouldn't want to have multiple subscriptions (for chatgpt, anthropic, gemini etc.)
But that's not what is in the whole context. The whole context contains a lot of noise and false "thoughts".
What the AI needs to do is to document the software project in an efficient manner without duplication. That's not what this tool is doing.
I question the value in storing all the crap in git.