LLM cannot reason about anything.
It can provide text that can be plausibly interpreted as reasoning by someone reading that text.
When human provides a plausible explanation then it means they either had someone else provide it to them or they actually understand the issue.
LLM cannot understand anything, it can only provide output based on the training data where similar input has likely to have produced similar output in the past.
Human can tell you they don't understand or don't know something but LLM is unlikely to have training that will produce that kind of output, it is more likely you will always get something that looks correct but it might not be.
LLM can automate a part of the process where human might take slightly but, ultimately, any output generated by LLM cannot be trusted and should be checked by human that understands the issue...and that is actually the hard part where humans will struggle so they won't actually do it.
When human is producing the output that human is performing the following actions:
-analysing the issue
-analysing the exiting process
-building the understanding of the existing process
-building the understanding of how issue affects the existing process
-producing the output to address the issue in the existing process
-checking the output as it is being produced
-updating the understanding of the existing process with lessons learned from the above
-checking the final product to ensure that it has solved the original issue and hasn't broken some other part of the system
LLM can help speed up one of those steps (producing the output) at the expense of slowing down the other parts (which were already slow) and reducing the understanding and reliability of the existing system which will make future iterations even slower.
LLM can be used to speed up the generation of examples but just like in the past you could not just copy the example from some random internet search result, you should not just copy the LLM output without understanding it...and that is the slow part where LLM might not help (and might actually make worse) for most people.
And when in the past you encountered comprehensive and well documented output you could assume human that put that amount of effort actually understood what they were doing and wouldn't have expended that much effort to generate garbage, you cannot make that same assumption now with LLMs.
I hate losing access to software just because it is "unmaintained".
If module is "endorsed" now, since it is included in current version, and there is no maintenance, so no changes made to it, why is it suddenly not good enough to "endorse" in the future?
No, security issues do not count as they don't magically appear, either they are in there now and debian is fine with distributing "insecure" code or they don't matter.
Debian is fine with shipping broken version of software for years as long as they consider it "stable" so why drop working "stable" software just because no one is making changes to it?
As a pedestrian I find cyclist are worse than cars for obstructing my path.
Riding on the footpath (illegal here) even with bike lane available right next to it, not respectig the traffic lights (mowing through pedestrians on crossings or blocking pedestrian crossings when stopped on red light), parking by blocking the footpath (must leave 1.5m of footpath unobstructed), riding the wrong way through traffic, flying down bike lanes (40kmh limit) and raging when anyone infringes their "rights" when they respect noone.
In my experience, I estimate that 20% of car drivers are a-holes, 50% or truck drivers and 80% of cyclists.
I would like for Linux to be able to replace Windows.
I run Linux on some of my computers with various levels of success.
But even with Windows 11 being as annoying as it is and Ubuntu/Mint/Cachy/Fedora/etc having some really good points they are not as easy to use as Windows.
Sure, web browsing is almost the same and simple home office tasks are close enough.
But all of the complaints that GP has mentioned are valid.
Windows file chooser is essentially small Windows Explorer and you can do almost everything that you can in the explorer while you are in file chooser mode. None of the Linux desktops have anything close.
HiDPI and multi monitor scaling on Linux has gotten better and it might approach what Windows had for the last 10 years but it is not 100% there yet.
Wayland is just a protocol with many incomplete and incompatible extensions that may or may not be implemented by your DE.
VNC is not even remotely close to RDP in features or performance. It just isn't.
I have used RDP over dial-up that was more responsive that VNC over LAN.
Not to mention sound, printer, disks, USB, etc all being available over one RDP connection.
Accessibility on Linux is a joke. On screen keyboard may work 80% of the time, screen reader might work 20% of the time. Sound might come out of random output or it might not. You may have to play with random settings, good luck with that if you are vision impaired.
One big reason Linux isn't there yet is people who just dismiss all of the above and go with "it works for me so it must be good for everyone."
Having seen LLMs so many time produce incoherent, nonsense, invalid answers to even simplest of questions I cannot agree with categorization of "thinking" or "intelligence" that applies to these models.
LLMs do not understand what they "know" or what they output. All they "know" is that based on training data this is most likely what they should output + some intentional randomization to make it seem more "human like".
This also makes it seem like they create new and previously unseen outputs but that could be achieved with simple dictionary and random number generator and no-one would call that thinking or intelligent as it is obvious that it isn't.
LLMs are better at obfuscating this fact by producing more sensible output than just random words.
LLMs can still be useful but they are a dead-end as far as "true" AI goes. They can and will get better but they will never be intelligent or think in the sense that most humans would agree those terms apply.
Some other form of hardware/software combination might get closer to AI or even achieve full AI and even sentience but that will not happen with LLMs and current hardware and software.
If the payload expands to something too large then it is easy to detect and ignore. Serve up thousands of 10kb or 100kb files that expand to 10s of MB with random garbage inside...possibly the same text but slightly modified. That will waste the time and CPU cycles and provide no value to them. Maybe also add a message you want to amplify so AI bots train on it.
We currently don't really know what intelligence is so we don't have a good definition of what to expect from "AI" but anyone who has used current "AI" for anything other than chat or search should recognize that "AI" is not "I" at all.
The "AI" does not "know" anything. It is really a fuzzy search on an "mp3" database (compressed with loss resulting in poor quality).
Based on that, everyone who is claiming current "AI" technology is any kind of intelligence has either fallen for the hype sold by the "AI" tech companies or is the "AI" tech company (or associated) and is trying to sell you their "AI" model subscription or getting you to invest in it.
UTF8 is a horrible design.
The only reason it was widely adopted was backwards compatibility with ASCII.
There are large number of invalid byte combinations that have to be discarded.
Parsing forward is complex even before taking invalid byte combinations in account and parsing backwards is even worse.
Compare that to UTF16 where parsing forward and backwards are simpler than UTF8 and if there is invalid surrogate combination, one can assume it is valid UCS2 char.
USB is faster and it is better when there is nothing saturating the USB with traffic and CPU can keep up.
Most of the time there will be no noticeable difference using the USB mouse or PS/2 mouse.
But if CPU is busy and USB bus is saturated, USB mouse will start to be less responsive than PS/2 mouse.
In the extreme cases even USB keyboard will become unresponsive where the old PS/2 keyboard still works fine.
This can make a difference between being able to terminate a misbehaving process and resume working or having to power-cycle the computer.
LLM can automate a part of the process where human might take slightly but, ultimately, any output generated by LLM cannot be trusted and should be checked by human that understands the issue...and that is actually the hard part where humans will struggle so they won't actually do it.
When human is producing the output that human is performing the following actions: -analysing the issue -analysing the exiting process -building the understanding of the existing process -building the understanding of how issue affects the existing process -producing the output to address the issue in the existing process -checking the output as it is being produced -updating the understanding of the existing process with lessons learned from the above -checking the final product to ensure that it has solved the original issue and hasn't broken some other part of the system
LLM can help speed up one of those steps (producing the output) at the expense of slowing down the other parts (which were already slow) and reducing the understanding and reliability of the existing system which will make future iterations even slower.
LLM can be used to speed up the generation of examples but just like in the past you could not just copy the example from some random internet search result, you should not just copy the LLM output without understanding it...and that is the slow part where LLM might not help (and might actually make worse) for most people.
And when in the past you encountered comprehensive and well documented output you could assume human that put that amount of effort actually understood what they were doing and wouldn't have expended that much effort to generate garbage, you cannot make that same assumption now with LLMs.