If the update breaks things, you go back to the old venv. If the update goes well, then you just delete the old venv.

Not implementing security updates the right way is just human laziness and has nothing to do with the language in use.

Can you actually quantify the time & effort 'saved' letting LLM generate code for you?

I'd say "the fastest" is a side effect of "allowing one to tune their systems to their utmost liking." -march=native, throw away unused bits and pieces, integrate modules into the kernel, replace bits and pieces with faster -- if more limited -- bits and pieces. And so on.

I have had Gentoo in at least one nearby system (physical and/or VM) since about 15 years ago. It's always a blast interacting with it.

If you want inkjets, buy those with ink tanks. More expensive up front, but operating cost is so cheap. And no more "you have to replace a whole cartridge just because Magenta is low"; if Magenta is low, buy a bottle of Magenta, and fill.

For laser printers, buy those whose toner cartridges are separate from the drum, and those whose toner cartridges can be reset mechanically. And refillable.

My go-to brand for printers is Brother, btw.

And to be honest when you start using it, even just for simple things such as function signature, with the proper IDE it helps you catch mistakes.

But still the crux of the matter is: Massive changes require buy-in from other maintainers BEFORE the changes even start.

[1] https://github.com/aio-libs/aiosmtpd [2] https://github.com/aio-libs/aiosmtpd/pull/202

LLMs will merely regurgitate a chain of words -- tokens -- that best match its Hidden Markov Model chains. It's all just a probabilistic game, with zero actual understanding.

LLMs are even known to hide or fake Unit Test results: Claiming success when it fails, or not skipping the results completely. Why? Because based on the patterns it has seen, the most likely word that follow "the results of tests" are the words "all successful". Why? Because it tries to reproduce other PRs it has seen, PRs where the PR author actually performed tests on their own systems first, iterating multiple times until the tests succeed, so the PRs that the public sees are almost invariably PRs with the declaration that "all tests pass".

I'm quite certain that LLMs never actually tried to compile the code, much less run Test Cases against them. Simply because there is no such ability provided in their back-ends.

All LLMs can do is "generate the most probabilistically plausible text". In essence, a Glorified AutoComplete.

I personally won't touch code generated wholly by an AutoComplete with a 10-foot pole.

> as long as the code generation doesn’t use too much energy or cause unforeseen problems.

A badly-written code can be a time bomb, just waiting for the right situation to explode.

And also, using LLM to generate garbage requires so much energy.

LLM is just a tool in the A.I. world. There are lots of other A.I. tools, such as Neural Network, Fuzzy Logic, Genetic Programming, and so on.

Just ask Elon about his efforts to fully automate Tesla production.

Same as A.I. Current LLM-based A.I.s are not at all as efficient as a human brain.

But then it will become a tradeoff of complexity vs longevity.

How do you calculate for that?

Back in the 90s, Fuzzy Logic was thought to be the solution. In a way, yes, but only for niche/specialized purposes, and they still have to limit the variables being evaluated.

LLM is just one model used in A.I. It's not a panacea.

For generating deterministic output, probably a combination of Neural Networks and Genetic Programming will be better. And probably also much more efficient, energy-wise.

It's the same as having a function called "factorial" but you change the multiplication operation to addition instead.

And don't even try to claim there won't ever be any regression: Current LLM-based A.I. will 'happily' lie to you that they passed all tests -- because based on interactions in the past, it has.