Replying to this one since that was my first impression. On closer look they did seem quite different.
Gameplay, goals, all of that is similar to screeps. But the game board and therefore the mental model itself is quite different. Where screeps feels like an endless multiverse along the lines of old school browser games like oGame, yare carries a feel of a more quick starcraft battle. Definitely not a copy even though it carries similar mechanics.
Basically, I feel like I could do games on yare randomly whereas screeps has always felt like a long term investment I need to make.
EDIT: Also also, yare seems to be far more simple for the moment in terms of environments and resources but that could change in the future and I'm looking forward to developments there.
back then IT was not the workhorse of 95% of modern life, and only a handful of physicists needed computers for very niche usecases that needed next to nothing in resources
You can run a fully functional voip and chat client within that order of magnitude (Ripcord) whereas the 'real client' (Discord) doing the same thing is 10x that.
Don't downplay the resource wastes on nothing of value.
I'd understand if this were about something taking half a giga of ram like too many applications do nowadays. But this is a wrapper adding 8mb of ram. Your efforts should be re-directed somewhere else.
My intention in dealing with planar networks is mostly the case of deploying a local mesh network. Most mesh networks behave like planar graphs. Not sure if this is an actual use case for matrix though.
question, you mention "no more AST parsing" as a selling line for semgrep. Isn't this actually a bad thing for a staric analyzer, as ASTs are actually... abstract, and closer to the real logic of what the code does? I know analyzing text is faster but is it worth?
how is this different than blackhats contributing to general awareness of web security practices? Opensource considered secure just because its up on github is no different than plaintext HTTP GET params being secure just because "who the hell will read your params in the browser", which would be still the status quo if some hackers hadn't done the "lowest of the low " and show the world this lesson.
The clickbait article makes people believe "you can create 3d models of ANY 2d object" but in reality, this would only come down to cars, cats and human faces. We have only so much datasets that are suitable for a GAN.
I do use and like both fastapi and pydantic but this situiation is something they brought to themselves by adopting experimenral features into their mainline code and sell it as production ready. Python designers are not responsible for time-to-market decisions of other projects. It comes with the "no liability" part of opensource.
Thought it has been there for some time, it was added to core Python on a "provisional" basis (it says so in the docs), meaning that they were not bound to keep backwards compatibility with it. In other words, it's an experimental feature.
It's definitely experimental. No other language has an established system like this, although Ruby seems to have acquired one in 3.0. We still aren't close to uncovering all the unsolved problems yet, let alone solving said problems.
You and I seem to have different definitions of "experimental feature" then. By your definition, I don't think they did anything wrong. dataclasses in the standard library also rely on runtime type annotations. (how they don't have the same issue with 3.10 I don't know). I would hope people would start relying on new language features years after their release.
Data classes don't do runtime type checking, but they do provide dynamic type info through dataclasses.fields(). This function returns a tuple of Field objects, and according to the documentation[1], Field includes the attribute "type":
type: The type of the field.
In Python 3.10, this now returns a string. In my opinion that's a bug (perhaps in the docs).
I verified this using a short test program:
import dataclasses
@dataclasses.dataclass
class Apa:
bepa: int
first_field = dataclasses.fields(Apa)[0]
print(type(first_field.type))
and what is the problem with that?
"Single guy made a good looking and so far working product in only 59 commits since september" is a different way to present the same facts.
Honestly, I also took it as throwing shade. I had to read the whole thing and realised that GP clearly wasn't throwing shade.
I am wondering why I initially thought that. Maybe a mindset of inherently better software created by Teams that's instilled within us these days could be a part of it. Trusting a lone programmer, it's harder because you have to reject the "programming".
I think it's because of several points that can be interpreted as good or bad:
- "relatively new project" > code is fresh and certainly well-organized / maybe lacks stability
- "all by one person" > a single vision is a good thing / if that person gives up the project, it's dead
- "no other project mentions it" > [no good aspect in that] / no real-world usage
If you have any experience in the frontend field, those points are more often bad than good. You don't want to invest time and/or money in that kind of project that has a high chance of being dead within a year.
Now, that does not mean that it's a bad project (it looks pretty good actually), but it's just very young.
Edit: actually I like this framework very much (but I have a thing for underdogs). It might be ideal to quickly throw POCs or toy projects
