The full changelog for this release is here https://docs.python.org/release/3.11.7/whatsnew/changelog.html#python-3-11-7-final
Surprisingly not shown that obviously in the release announcements, but I guess that's fair since most of the changes will have no effect on 99.9999% of people.
Sounds fine, they're both immutable which helps.
UTF-8 is an encoding for unicode, that means it's a way of representing a unicode string as actual bytes on a computer.
It is variable length and works by using the first bits of each byte to indicate how many bytes are are needed to represent the current character.
Python also uses an encoding, as you describe in the article, but it's different to UTF-8. Unlike unicode, all characters in Python's representation of the unicode string use the same number of bytes, which is the maximum that any individual unicode character in the string needs.
I'd probably mess up a more detailed explanation of UTF-8 or Python's representation, so I'll let you look into how they work in more detail if you're interested.
The article says that CPython represents strings as UTF-8 encoded, which is not correct. The details about how it works are correct, just that's not UTF-8.
That's just a minor point though, nice article.
It's described in PEP 585, https://peps.python.org/pep-0585/#parameters-to-generics-are-available-at-runtime
Well I kept using it until Infinity died, which was only at the start of this month!
If I do decide to go back, it will be by compiling the infinity APK with my own API key, but I'm not feeling much of an urge to bother at the moment.
It'd be nice to have a rule specifically for the use of f-strings and template formatting in the same call, since that can easily be a security vulnerability.
I'm pretty sure most type checkers recognise both forms.
It probably really depends on the project, though I'd probably try and start with the tests that are easiest/nicest to write and those which will be most useful. Look for complex logic that is also quite self-contained.
That will probably help to convince others of the value of tests if they aren't onboard already.
Yeah they've put them in a couple places, It's pretty bad. Had to work out how to create a custom uBlock Origin rule to block them.
I think calling it just like a database of likely responses is too much of a simplification and downplays what it is capable of.
I also don't really see why the way it works is relevant to it being "smart" or not. It depends how you define "smart", but I don't see any proof of the assumptions people seem to make about the limitations of what an LLM could be capable of (with a larger model, better dataset, better training, etc).
I'm definitely not saying I can tell what LLMs could be capable of, but I think saying "people think ChatGPT is smart but it actually isn't because <simplification of what an LLM is>" is missing a vital step to make it a valid logical argument.
The argument is relying on incorrect intuition people have. Before seeing ChatGPT I reckon if you'd told people how an LLM worked they wouldn't have expected it to be able to do things it can do (for example if you ask it to write a rhyming poem about a niche subject it wouldn't have a comparable poem about in its dataset).
A better argument would be to pick something that LLMs can't currently do that it should be able to do if it's "smart", and explain the inherent limitation of an LLM which prevents it from doing that. This isn't something I've really seen, I guess because it's not easy to do. The closest I've seen is an explanation of why LLMs are bad at e.g. maths (like adding large numbers), but I've still not seen anything to convince me that this is an inherent limitation of LLMs.