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iceberg
Twitter hit an iceberg, let's replace the ship by Thanksgiving (Nov 24, 2022) (by mikewarot)
> Visual Studio is a bloated mess, and has been for many years.
Yes and? First of all, it looks like you don't need the IDE, just the compiler tool chain for the linker:
> Have Visual Studio installed (MSVC 2010 or later, for the linker)[1]
The MSVC toolchain isn't nearly as big as Visual Studio. Microsoft recommends 2.1GB-60GB of disk space depending which add-ons you select[2]. If you're only using one version of C++ it usually comes out to around 5GB of disk space.
LLVM has similar requirements:
> Note that Debug builds require a lot of time and disk space. An LLVM-only build will need about 1-3 GB of space. A full build of LLVM and Clang will need around 15-20 GB of disk space... If you are space-constrained, you can build only selected tools or only selected targets. The Release build requires considerably less space.[3]
I don't know how much "considerably less space" constitutes, but I'm willing to bet it's about the same. So calling MSVC bloated and then recommending LLVM + MinGW is a bit odd. All C++ compilers are bloated, because the language itself is bloated and complex.
You may believe there are better options then MSVC, and that's fine. MSVC is probably the most used compiler toolset on windows. Once again, even if it is bloated in comparison to other options, that means nothing in this argument. If MSVC is the most popular toolcahin on Windows, bloated or not, then Odin using MSVC sounds like supporting the majority of windows developers. As opposed to the hundreds of Unix-centered open source libraries out there that really don't care about supporting windows.
...
I like using premake. Most C++ projects use CMake. CMake is much more popular than premake. This doesn't mean that I can go around having the expectation that everyone abandons the well established standards to accommodate my development style. It means that if I want to continue using premake, it will be an uphill battle since I refuse to use the industry standards.
Visual Studio may be bloated, and you may refuse to use the MSVC toolset for whatever reasons you have. This just means it will be an uphill battle if you want to develop on windows. And people won't subvert accepted industry standards to accommodate a minority of developers.
Finally, if this really upsets you enough to open an issue for your niche use case, then you can submit a PR or fork the repo and add it yourself. It is open source after all :)
[1]: https://github.com/odin-lang/Odin/tree/master/bin
[2]:https://learn.microsoft.com/en-us/visualstudio/releases/2019...
[3]: https://llvm.org/docs/GettingStarted.html#hardware
This really speaks to me. Modern software is too hard to assemble from source. If you're shipping sources, every moving part you add increases the odds of something going wrong on other people's computers.
It's worth having some skepticism of tools. By making some operations easy, tools encourage them. Build systems make it easy to bloat software. Package managers make it easy to bloat dependencies. This dynamic explains why Python in particular has such a terrible package management story. It's been around longer than Node or Rust, so if they seem better -- wait 10 years!
For many of my side projects I try to minimize moving parts for anyone (usually the '1' is literally true) who tries them out. I work in Unix, and one thing I built is a portable shell script that acts like a build system while being much more transparent about what it does: https://codeberg.org/akkartik/basic-build
When I use this script my build instructions are more verbose, but I think that's a good thing. They're more explicit for newcomers, and they also impose costs that nudge me to keep my programs minimalist.
You can see this build system evolve to add partial builds and parallel builds in one of my projects:
https://github.com/akkartik/mu1/blob/master/build0
https://github.com/akkartik/mu1/blob/master/build1
https://github.com/akkartik/mu1/blob/master/build2
https://github.com/akkartik/mu1/blob/master/build3
https://github.com/akkartik/mu1/blob/master/build4
Each of these does the same thing for this one repo -- build it -- but adding successively more bells and whistles.
I think providing just the most advanced version, build4, would do my users a disservice. It's also the most likely to break, where build0 is rock solid. If my builds do break for someone, they can poke around and downgrade to a simpler version.
Not really the thing you’re looking for, but for those looking for a toolless approach static web apps are a possibility. Host a folder on github pages, put an index.html file in there, start coding.
Plugging my own repo: https://github.com/jsebrech/create-react-app-zero
It is a version of create react app that works in that way, no build tools needed, only a static web server for local development.
> trying to build a lifeboat for Twitter, Python works, but then modules require builds that break.
> Alternatively, any good resources for the above?
There are many, _unbelievably many_ writeups and tools for Python building and packaging. Some of them are really neat! But paralysis of choice is real. So is the reality that many of the new/fully integrated/cutting edge tools, however superior they may be, just won't get long term support to catch on and stay relevant.
When getting started with Python, I very personally like to choose from a few simple options (others are likely to pipe up with their own, and that's great; mine aren't The One Right Way, just some fairly cold/mainstream takes).
1. First pick what stack you'll be using to develop and test software. In Python this is sadly often going to be different from the stack you'll use to deploy/run it in production, but here we are. There are two sub-choices to be made here:
1.a. How will you be running the _python interpreter_ in dev/test? "I just want to use the Python that came with my laptop" is fine to a point, but breaks down a lot sooner than folks expect (again, the reasons for this are variously reasonable and stupid, but here we are). Personally, I like pyenv (https://github.com/pyenv/pyenv) here. It's a simple tool that builds interpreters on your system and provides shell aliases to adjust pathing so they can optionally be used. At the opposite extreme from pyenv, some folks choose Python-in-Docker here (pros: reproducible, makes deployment environments very consistent with dev; cons: IDE/quick build-and-run automations get tricker). There are some other tools that wrap/automate the same stuff that pyenv does.
1.b. How will you be isolating your project's dependencies? "I want to install dependencies globally" breaks down (or worse, breaks your laptop!) pretty quickly, yes it's a bummer. There are three options here: if you really eschew automations/wrappers/thick tools in general, you can do this yourself (i.e. via "pip install --local", optionally in a dedicated development workstation user account); you can use venv (https://docs.python.org/3/library/venv.html stdlib version of virtualenv, yes the names suck and confusing, here we are etc. etc.), which is widely standardized upon and manually use "pip install" while inside your virtualenv, and you can optionally integrate your virtualenv with pyenv so "inside your virtualenv" is easy to achieve via pyenv-virtualenv (https://github.com/pyenv/pyenv-virtualenv); or you can say "hell with this, I want maximum convenience via a wrapper that manages my whole project" and use Poetry (https://python-poetry.org/). There's no right point on that spectrum, it's up to you to decide where you fall on the "I want an integrated experience and to start prototyping quickly" versus "I want to reduce customizations/wrappers/tooling layers" spectrum.
2. Then, pick how you'll be developing said software: what frameworks or tools you'll be using. A Twitter lifeboat sounds like a webapp, so you'll likely want a web framework. Python has a spectrum of those of varying "thickness"/batteries-included-ness. At the minimum of thickness are tools like Flask (https://flask.palletsprojects.com/en/2.2.x/) and Sanic (like Flask, but with a bias towards performance at the cost of using async and some newer Python programming techniques which tend, in Python, to be harder than the traditional Flask approach: https://sanic.dev). At the maximum of thickness are things like Django/Pyramid. With the minimally-thick frameworks you'll end up plugging together other libraries for things like e.g. database access or web content serving/templating, with the maximally-thick approach that is included but opinionated. Same as before: no right answers, but be clear on the axis (or axes) along with you're choosing.
3. Choose how you'll be deploying/running the software, maybe after prototyping for awhile. This isn't "lock yourself into a cloud provider/hosting platform", but rather a choice about what tools you use with the hosting environment. Docker is pretty uncontentious here, if you want a generic way to run your Python app on many environments. So is "configure Linux instances to run equivalent Python/package versions to your dev/test environment". If you choose the latter, be aware that (and this is very important/often not discussed) many tools that the Python community suggests for local development or testing are very unsuitable for managing production environments (e.g. a tool based around shell state mutation is going to be extremely inconvenient to productionize).
Yeah, that's a lot of choices, but in general there are some pretty obvious/uncontentious paths there. Pyenv-for-interpreters/Poetry-for-packaging-and-project-management/Flask-for-web-serving/Docker-for-production is not going to surprise anyone or break any assumptions. Docker/raw-venv/Django is going to be just as easy to Google your way through.
Again, no one obvious right way (ha!) but plenty of valid options!
Not sure if that's what you were after. If you want a "just show me how to get started"-type writeup rather than an overview on the choices involved, I'm sure folks here or some quick googling will turn up many!
> trying to build a lifeboat for Twitter, Python works, but then modules require builds that break.
> Alternatively, any good resources for the above?
There are many, _unbelievably many_ writeups and tools for Python building and packaging. Some of them are really neat! But paralysis of choice is real. So is the reality that many of the new/fully integrated/cutting edge tools, however superior they may be, just won't get long term support to catch on and stay relevant.
When getting started with Python, I very personally like to choose from a few simple options (others are likely to pipe up with their own, and that's great; mine aren't The One Right Way, just some fairly cold/mainstream takes).
1. First pick what stack you'll be using to develop and test software. In Python this is sadly often going to be different from the stack you'll use to deploy/run it in production, but here we are. There are two sub-choices to be made here:
1.a. How will you be running the _python interpreter_ in dev/test? "I just want to use the Python that came with my laptop" is fine to a point, but breaks down a lot sooner than folks expect (again, the reasons for this are variously reasonable and stupid, but here we are). Personally, I like pyenv (https://github.com/pyenv/pyenv) here. It's a simple tool that builds interpreters on your system and provides shell aliases to adjust pathing so they can optionally be used. At the opposite extreme from pyenv, some folks choose Python-in-Docker here (pros: reproducible, makes deployment environments very consistent with dev; cons: IDE/quick build-and-run automations get tricker). There are some other tools that wrap/automate the same stuff that pyenv does.
1.b. How will you be isolating your project's dependencies? "I want to install dependencies globally" breaks down (or worse, breaks your laptop!) pretty quickly, yes it's a bummer. There are three options here: if you really eschew automations/wrappers/thick tools in general, you can do this yourself (i.e. via "pip install --local", optionally in a dedicated development workstation user account); you can use venv (https://docs.python.org/3/library/venv.html stdlib version of virtualenv, yes the names suck and confusing, here we are etc. etc.), which is widely standardized upon and manually use "pip install" while inside your virtualenv, and you can optionally integrate your virtualenv with pyenv so "inside your virtualenv" is easy to achieve via pyenv-virtualenv (https://github.com/pyenv/pyenv-virtualenv); or you can say "hell with this, I want maximum convenience via a wrapper that manages my whole project" and use Poetry (https://python-poetry.org/). There's no right point on that spectrum, it's up to you to decide where you fall on the "I want an integrated experience and to start prototyping quickly" versus "I want to reduce customizations/wrappers/tooling layers" spectrum.
2. Then, pick how you'll be developing said software: what frameworks or tools you'll be using. A Twitter lifeboat sounds like a webapp, so you'll likely want a web framework. Python has a spectrum of those of varying "thickness"/batteries-included-ness. At the minimum of thickness are tools like Flask (https://flask.palletsprojects.com/en/2.2.x/) and Sanic (like Flask, but with a bias towards performance at the cost of using async and some newer Python programming techniques which tend, in Python, to be harder than the traditional Flask approach: https://sanic.dev). At the maximum of thickness are things like Django/Pyramid. With the minimally-thick frameworks you'll end up plugging together other libraries for things like e.g. database access or web content serving/templating, with the maximally-thick approach that is included but opinionated. Same as before: no right answers, but be clear on the axis (or axes) along with you're choosing.
3. Choose how you'll be deploying/running the software, maybe after prototyping for awhile. This isn't "lock yourself into a cloud provider/hosting platform", but rather a choice about what tools you use with the hosting environment. Docker is pretty uncontentious here, if you want a generic way to run your Python app on many environments. So is "configure Linux instances to run equivalent Python/package versions to your dev/test environment". If you choose the latter, be aware that (and this is very important/often not discussed) many tools that the Python community suggests for local development or testing are very unsuitable for managing production environments (e.g. a tool based around shell state mutation is going to be extremely inconvenient to productionize).
Yeah, that's a lot of choices, but in general there are some pretty obvious/uncontentious paths there. Pyenv-for-interpreters/Poetry-for-packaging-and-project-management/Flask-for-web-serving/Docker-for-production is not going to surprise anyone or break any assumptions. Docker/raw-venv/Django is going to be just as easy to Google your way through.
Again, no one obvious right way (ha!) but plenty of valid options!
Not sure if that's what you were after. If you want a "just show me how to get started"-type writeup rather than an overview on the choices involved, I'm sure folks here or some quick googling will turn up many!
> trying to build a lifeboat for Twitter, Python works, but then modules require builds that break.
> Alternatively, any good resources for the above?
There are many, _unbelievably many_ writeups and tools for Python building and packaging. Some of them are really neat! But paralysis of choice is real. So is the reality that many of the new/fully integrated/cutting edge tools, however superior they may be, just won't get long term support to catch on and stay relevant.
When getting started with Python, I very personally like to choose from a few simple options (others are likely to pipe up with their own, and that's great; mine aren't The One Right Way, just some fairly cold/mainstream takes).
1. First pick what stack you'll be using to develop and test software. In Python this is sadly often going to be different from the stack you'll use to deploy/run it in production, but here we are. There are two sub-choices to be made here:
1.a. How will you be running the _python interpreter_ in dev/test? "I just want to use the Python that came with my laptop" is fine to a point, but breaks down a lot sooner than folks expect (again, the reasons for this are variously reasonable and stupid, but here we are). Personally, I like pyenv (https://github.com/pyenv/pyenv) here. It's a simple tool that builds interpreters on your system and provides shell aliases to adjust pathing so they can optionally be used. At the opposite extreme from pyenv, some folks choose Python-in-Docker here (pros: reproducible, makes deployment environments very consistent with dev; cons: IDE/quick build-and-run automations get tricker). There are some other tools that wrap/automate the same stuff that pyenv does.
1.b. How will you be isolating your project's dependencies? "I want to install dependencies globally" breaks down (or worse, breaks your laptop!) pretty quickly, yes it's a bummer. There are three options here: if you really eschew automations/wrappers/thick tools in general, you can do this yourself (i.e. via "pip install --local", optionally in a dedicated development workstation user account); you can use venv (https://docs.python.org/3/library/venv.html stdlib version of virtualenv, yes the names suck and confusing, here we are etc. etc.), which is widely standardized upon and manually use "pip install" while inside your virtualenv, and you can optionally integrate your virtualenv with pyenv so "inside your virtualenv" is easy to achieve via pyenv-virtualenv (https://github.com/pyenv/pyenv-virtualenv); or you can say "hell with this, I want maximum convenience via a wrapper that manages my whole project" and use Poetry (https://python-poetry.org/). There's no right point on that spectrum, it's up to you to decide where you fall on the "I want an integrated experience and to start prototyping quickly" versus "I want to reduce customizations/wrappers/tooling layers" spectrum.
2. Then, pick how you'll be developing said software: what frameworks or tools you'll be using. A Twitter lifeboat sounds like a webapp, so you'll likely want a web framework. Python has a spectrum of those of varying "thickness"/batteries-included-ness. At the minimum of thickness are tools like Flask (https://flask.palletsprojects.com/en/2.2.x/) and Sanic (like Flask, but with a bias towards performance at the cost of using async and some newer Python programming techniques which tend, in Python, to be harder than the traditional Flask approach: https://sanic.dev). At the maximum of thickness are things like Django/Pyramid. With the minimally-thick frameworks you'll end up plugging together other libraries for things like e.g. database access or web content serving/templating, with the maximally-thick approach that is included but opinionated. Same as before: no right answers, but be clear on the axis (or axes) along with you're choosing.
3. Choose how you'll be deploying/running the software, maybe after prototyping for awhile. This isn't "lock yourself into a cloud provider/hosting platform", but rather a choice about what tools you use with the hosting environment. Docker is pretty uncontentious here, if you want a generic way to run your Python app on many environments. So is "configure Linux instances to run equivalent Python/package versions to your dev/test environment". If you choose the latter, be aware that (and this is very important/often not discussed) many tools that the Python community suggests for local development or testing are very unsuitable for managing production environments (e.g. a tool based around shell state mutation is going to be extremely inconvenient to productionize).
Yeah, that's a lot of choices, but in general there are some pretty obvious/uncontentious paths there. Pyenv-for-interpreters/Poetry-for-packaging-and-project-management/Flask-for-web-serving/Docker-for-production is not going to surprise anyone or break any assumptions. Docker/raw-venv/Django is going to be just as easy to Google your way through.
Again, no one obvious right way (ha!) but plenty of valid options!
Not sure if that's what you were after. If you want a "just show me how to get started"-type writeup rather than an overview on the choices involved, I'm sure folks here or some quick googling will turn up many!
> trying to build a lifeboat for Twitter, Python works, but then modules require builds that break.
> Alternatively, any good resources for the above?
There are many, _unbelievably many_ writeups and tools for Python building and packaging. Some of them are really neat! But paralysis of choice is real. So is the reality that many of the new/fully integrated/cutting edge tools, however superior they may be, just won't get long term support to catch on and stay relevant.
When getting started with Python, I very personally like to choose from a few simple options (others are likely to pipe up with their own, and that's great; mine aren't The One Right Way, just some fairly cold/mainstream takes).
1. First pick what stack you'll be using to develop and test software. In Python this is sadly often going to be different from the stack you'll use to deploy/run it in production, but here we are. There are two sub-choices to be made here:
1.a. How will you be running the _python interpreter_ in dev/test? "I just want to use the Python that came with my laptop" is fine to a point, but breaks down a lot sooner than folks expect (again, the reasons for this are variously reasonable and stupid, but here we are). Personally, I like pyenv (https://github.com/pyenv/pyenv) here. It's a simple tool that builds interpreters on your system and provides shell aliases to adjust pathing so they can optionally be used. At the opposite extreme from pyenv, some folks choose Python-in-Docker here (pros: reproducible, makes deployment environments very consistent with dev; cons: IDE/quick build-and-run automations get tricker). There are some other tools that wrap/automate the same stuff that pyenv does.
1.b. How will you be isolating your project's dependencies? "I want to install dependencies globally" breaks down (or worse, breaks your laptop!) pretty quickly, yes it's a bummer. There are three options here: if you really eschew automations/wrappers/thick tools in general, you can do this yourself (i.e. via "pip install --local", optionally in a dedicated development workstation user account); you can use venv (https://docs.python.org/3/library/venv.html stdlib version of virtualenv, yes the names suck and confusing, here we are etc. etc.), which is widely standardized upon and manually use "pip install" while inside your virtualenv, and you can optionally integrate your virtualenv with pyenv so "inside your virtualenv" is easy to achieve via pyenv-virtualenv (https://github.com/pyenv/pyenv-virtualenv); or you can say "hell with this, I want maximum convenience via a wrapper that manages my whole project" and use Poetry (https://python-poetry.org/). There's no right point on that spectrum, it's up to you to decide where you fall on the "I want an integrated experience and to start prototyping quickly" versus "I want to reduce customizations/wrappers/tooling layers" spectrum.
2. Then, pick how you'll be developing said software: what frameworks or tools you'll be using. A Twitter lifeboat sounds like a webapp, so you'll likely want a web framework. Python has a spectrum of those of varying "thickness"/batteries-included-ness. At the minimum of thickness are tools like Flask (https://flask.palletsprojects.com/en/2.2.x/) and Sanic (like Flask, but with a bias towards performance at the cost of using async and some newer Python programming techniques which tend, in Python, to be harder than the traditional Flask approach: https://sanic.dev). At the maximum of thickness are things like Django/Pyramid. With the minimally-thick frameworks you'll end up plugging together other libraries for things like e.g. database access or web content serving/templating, with the maximally-thick approach that is included but opinionated. Same as before: no right answers, but be clear on the axis (or axes) along with you're choosing.
3. Choose how you'll be deploying/running the software, maybe after prototyping for awhile. This isn't "lock yourself into a cloud provider/hosting platform", but rather a choice about what tools you use with the hosting environment. Docker is pretty uncontentious here, if you want a generic way to run your Python app on many environments. So is "configure Linux instances to run equivalent Python/package versions to your dev/test environment". If you choose the latter, be aware that (and this is very important/often not discussed) many tools that the Python community suggests for local development or testing are very unsuitable for managing production environments (e.g. a tool based around shell state mutation is going to be extremely inconvenient to productionize).
Yeah, that's a lot of choices, but in general there are some pretty obvious/uncontentious paths there. Pyenv-for-interpreters/Poetry-for-packaging-and-project-management/Flask-for-web-serving/Docker-for-production is not going to surprise anyone or break any assumptions. Docker/raw-venv/Django is going to be just as easy to Google your way through.
Again, no one obvious right way (ha!) but plenty of valid options!
Not sure if that's what you were after. If you want a "just show me how to get started"-type writeup rather than an overview on the choices involved, I'm sure folks here or some quick googling will turn up many!
Came here to mention Smalltalk. In things like Smalltalk-80 and Squeak, there was no build system, there are no source code files, there isn't anything but the Smalltalk Development Environment. With something like ENVY/Developer, building involved generating an exported image from the environment.
If OP wants to try it: https://squeak.org/
Visual Studio is a bloated mess, and has been for many years. Its at least 10 times larger than other options, such as MinGW-LLVM:
https://github.com/mstorsjo/llvm-mingw
Details I didn't include but should have (I wasn't sure I'd have any replies at all... I should have had more faith, sorry)
It's a bit of a ramble, sorry about that.
MSTOICAL[0] is a fork of an old C based Forth variant, it took some help from the HN community[1] to get it to compile in a modern 64 bit environment, for which I am very thankful. However, it uses AutoConf to configure, build, install, etc... and I can't for the life of me figure out how to remove all of that logic. (C isn't my primary language, I'm willing to learn that, but adding AutoConf on top of it was too much)
In order to work on that, I was willing to switch to Linux (Ubuntu)... got everything up and running for the most part, but then I couldn't access WikidPad[2], my local Wiki with my appointments, etc. I missed a doctors appointment because of that, so went back to Windows.
The issue is around wxWindows changing the names of variables in some calls. On Windows, you just download an EXE installer and you're good to go. I couldn't figure it out because the program seems to be unwilling to support newer Python versions. (I could be wrong)
I don't understand why they felt the need to make breaking changes to wxWindows, and the python is a bit too dense for me.
So finally... I'm back in Windows 10, and decided to try to craft together a twitter clone with a bunch of weird ideas that I tossed out at 3:30 am in a twitter thread, and put into a more coherent manifesto.[3]
[0] https://github.com/mikewarot/mstoical
[1] https://news.ycombinator.com/item?id=30957273
[2] https://github.com/WikidPad/WikidPad
[3] https://github.com/mikewarot/iceberg/blob/main/MANIFESTO.md
Details I didn't include but should have (I wasn't sure I'd have any replies at all... I should have had more faith, sorry)
It's a bit of a ramble, sorry about that.
MSTOICAL[0] is a fork of an old C based Forth variant, it took some help from the HN community[1] to get it to compile in a modern 64 bit environment, for which I am very thankful. However, it uses AutoConf to configure, build, install, etc... and I can't for the life of me figure out how to remove all of that logic. (C isn't my primary language, I'm willing to learn that, but adding AutoConf on top of it was too much)
In order to work on that, I was willing to switch to Linux (Ubuntu)... got everything up and running for the most part, but then I couldn't access WikidPad[2], my local Wiki with my appointments, etc. I missed a doctors appointment because of that, so went back to Windows.
The issue is around wxWindows changing the names of variables in some calls. On Windows, you just download an EXE installer and you're good to go. I couldn't figure it out because the program seems to be unwilling to support newer Python versions. (I could be wrong)
I don't understand why they felt the need to make breaking changes to wxWindows, and the python is a bit too dense for me.
So finally... I'm back in Windows 10, and decided to try to craft together a twitter clone with a bunch of weird ideas that I tossed out at 3:30 am in a twitter thread, and put into a more coherent manifesto.[3]
[0] https://github.com/mikewarot/mstoical
[1] https://news.ycombinator.com/item?id=30957273
[2] https://github.com/WikidPad/WikidPad
[3] https://github.com/mikewarot/iceberg/blob/main/MANIFESTO.md
Details I didn't include but should have (I wasn't sure I'd have any replies at all... I should have had more faith, sorry)
It's a bit of a ramble, sorry about that.
MSTOICAL[0] is a fork of an old C based Forth variant, it took some help from the HN community[1] to get it to compile in a modern 64 bit environment, for which I am very thankful. However, it uses AutoConf to configure, build, install, etc... and I can't for the life of me figure out how to remove all of that logic. (C isn't my primary language, I'm willing to learn that, but adding AutoConf on top of it was too much)
In order to work on that, I was willing to switch to Linux (Ubuntu)... got everything up and running for the most part, but then I couldn't access WikidPad[2], my local Wiki with my appointments, etc. I missed a doctors appointment because of that, so went back to Windows.
The issue is around wxWindows changing the names of variables in some calls. On Windows, you just download an EXE installer and you're good to go. I couldn't figure it out because the program seems to be unwilling to support newer Python versions. (I could be wrong)
I don't understand why they felt the need to make breaking changes to wxWindows, and the python is a bit too dense for me.
So finally... I'm back in Windows 10, and decided to try to craft together a twitter clone with a bunch of weird ideas that I tossed out at 3:30 am in a twitter thread, and put into a more coherent manifesto.[3]
[0] https://github.com/mikewarot/mstoical
[1] https://news.ycombinator.com/item?id=30957273
[2] https://github.com/WikidPad/WikidPad
[3] https://github.com/mikewarot/iceberg/blob/main/MANIFESTO.md