Readit News #!/home/tetha/Tools/uv run #!/usr/bin/env uv runf"" vs STR."" is really a quite minimal difference, and I think worth it. Having the "." operator here mean "interpolate RHS using LHS" seems a nice balance between terseness and explicitness.
As for "\{" being a divergent choice, frankly who cares.
https://developer.apple.com/documentation/swift/stringinterp...
So far I haven’t needed anything fancy like Htmx, but interested in looking into it in the future.
I’ve tried playing with things like Elixir or Go, but I know Python and am instantly productive in it. If I want to do something to play with a new stack, I will. But I want something done, then Python is the fastest way for me to get something useable.
alias gcm=git commit -m
alias gaugcm = git add -u && gcm
alias gfp=“git commit --amend --no-edit && git push --force-with-lease”
For those “whoops, for got to add $x file” moments or typos
It's downright shocking the difference in frequency between power losses I experienced on the regular in Texas vs. what my out of state family experience. I started keeping track of it a couple years ago. My family can have dozens of sporadic outages in a span of time with 100% uptime for me in TX. Smaller, less interconnected networks can be a godsend. Hell, it's a lesson that's immortalized in our interstate highway system.
...Then again, I suppose that there's the case to be made that more frequent outages keep your response teams sharp. There is a drop off among Operations people whereby knowledge tends to end up getting forgotten when too much time passes between completion of repeated periodic tasks.
In my family while growing up we kept flashlights and candles handy, and us kids were (very lightly) drilled on where to find them when the power went out. And again, this was in the middle of the metro, not out in any rural parts.
So yeah Python ain’t that bad. I can’t speak to frameworks like Django, though.
However, early versions of Python 3 were slower than Python 2, and also some breaking changes were getting rolled back (e.g. PEP-414, which was targeting Python 3.3), which contributed to a lot of library authors dragging their feet in upgrading their support.
So, yes, it was libraries causing the most headaches, but there was a sense at the time of wondering when the upgrade would become “real”. Depreciating py2 took 11 years after the release of 3.0.
So it behaves like an ICE cars ? Or am I missing something
By default, with no feet on pedals, the EV will decelerate. An ICE car will coast when nothing is depressed.
At the start, nothing more in the app. She would be happy to do the data analysis using her laptop.
I was thinking about a stupid CRUD app with an SQLite database, but I do not want to reinvent the wheel.
If some of you have ideas, we do not need banking integration.
If you want to do investment tracking, I don’t think it’s the best for that.