#direnv — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #direnv, aggregated by home.social.
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New post for the July #EmacsBlogCarnival on Programming: **How Emacs Uses Nix**.
A few years ago I wrote about my #direnv + #Nix setup. I finally wrote up what that post glossed over: how #Emacs actually _uses_ it.
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New post for the July #EmacsBlogCarnival on Programming: **How Emacs Uses Nix**.
A few years ago I wrote about my #direnv + #Nix setup. I finally wrote up what that post glossed over: how #Emacs actually _uses_ it.
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New post for the July #EmacsBlogCarnival on Programming: **How Emacs Uses Nix**.
A few years ago I wrote about my #direnv + #Nix setup. I finally wrote up what that post glossed over: how #Emacs actually _uses_ it.
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New post for the July #EmacsBlogCarnival on Programming: **How Emacs Uses Nix**.
A few years ago I wrote about my #direnv + #Nix setup. I finally wrote up what that post glossed over: how #Emacs actually _uses_ it.
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New post for the July #EmacsBlogCarnival on Programming: **How Emacs Uses Nix**.
A few years ago I wrote about my #direnv + #Nix setup. I finally wrote up what that post glossed over: how #Emacs actually _uses_ it.
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Tired of supplying my own `layout uv` for `direnv` I made an issue and a matching PR. Lot of previous work on this in the community, but it stalled in 2024.
#direnv #uv #OpenSource
https://github.com/direnv/direnv/issues/1593 -
Tired of supplying my own `layout uv` for `direnv` I made an issue and a matching PR. Lot of previous work on this in the community, but it stalled in 2024.
#direnv #uv #OpenSource
https://github.com/direnv/direnv/issues/1593 -
Tired of supplying my own `layout uv` for `direnv` I made an issue and a matching PR. Lot of previous work on this in the community, but it stalled in 2024.
#direnv #uv #OpenSource
https://github.com/direnv/direnv/issues/1593 -
Tired of supplying my own `layout uv` for `direnv` I made an issue and a matching PR. Lot of previous work on this in the community, but it stalled in 2024.
#direnv #uv #OpenSource
https://github.com/direnv/direnv/issues/1593 -
So tonight I found out about direnv. How did I not know about this handy tool before? So, so unbelievably useful #direnv
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So tonight I found out about direnv. How did I not know about this handy tool before? So, so unbelievably useful #direnv
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So tonight I found out about direnv. How did I not know about this handy tool before? So, so unbelievably useful #direnv
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VSCode-Based Extensions Can Be Taught! https://medium.com/p/vscode-based-extensions-can-be-taught-aba138fdbf0c?source=social.tw
#direnv #vscode #cursor #extensions #environmentvariables #sdkman #nvm #pyenv
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VSCode-Based Extensions Can Be Taught! https://medium.com/p/vscode-based-extensions-can-be-taught-aba138fdbf0c?source=social.tw
#direnv #vscode #cursor #extensions #environmentvariables #sdkman #nvm #pyenv
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My day job is all about #Python (which I love). Here are some personal rules, specific to working with Python projects:
* Do **not** install or modify global tools, especially Python itself or any packages. This means a given system might not even **have** a global Python
* Always use virtual environments (`uv` agrees with me, and doesn't need this but). I always set the global environment variable `PIP_REQUIRE_VIRTUALENV`.
* The two rules above mean my virtual environment contains (not via a link, it's really there) Python itself (and of course, of the right version)
* Virtual environments always live **inside** a project directory. Never global.
* Activate virtual environments only **inside** the project directory (`direnv` #direnv makes this easy)
* Don't install (let alone use) #Anaconda, #Miniconda, or #Mamba, because those violate all the rules above (but see the next rule)
* Anaconda-based packages implies a `pixi` #Pixi project (it's the same people, but a better answer, and you still get what you want -- the correct packages)
* No Anaconda-based packages implies a `uv` #UV project
* Always use `pyproject.toml` #pyprojecttoml over any other config file (e.g., `requirements.txt` #requirementstxt), except where things just don't work, such as needing `pyrefly.toml`
* `uv`, `pixi`, and `direnv` must exist outside of any project, so install them at the user level, or else globally if and only if that is appropriate and compelling enough to override rule oneThat was a wall of text, but in practice doing it this way is trivial. It's probably **less** work than you have been doing. This post is just about managing your Python versions, environments, and projects. Not about, e.g., using `pre-commit` #precommit, or doing type checking, etc. But if you follow these rules, your work will be easier, faster, more adaptable, and encounter fewer obstacles.
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My day job is all about #Python (which I love). Here are some personal rules, specific to working with Python projects:
* Do **not** install or modify global tools, especially Python itself or any packages. This means a given system might not even **have** a global Python
* Always use virtual environments (`uv` agrees with me, and doesn't need this but). I always set the global environment variable `PIP_REQUIRE_VIRTUALENV`.
* The two rules above mean my virtual environment contains (not via a link, it's really there) Python itself (and of course, of the right version)
* Virtual environments always live **inside** a project directory. Never global.
* Activate virtual environments only **inside** the project directory (`direnv` #direnv makes this easy)
* Don't install (let alone use) #Anaconda, #Miniconda, or #Mamba, because those violate all the rules above (but see the next rule)
* Anaconda-based packages implies a `pixi` #Pixi project (it's the same people, but a better answer, and you still get what you want -- the correct packages)
* No Anaconda-based packages implies a `uv` #UV project
* Always use `pyproject.toml` #pyprojecttoml over any other config file (e.g., `requirements.txt` #requirementstxt), except where things just don't work, such as needing `pyrefly.toml`
* `uv`, `pixi`, and `direnv` must exist outside of any project, so install them at the user level, or else globally if and only if that is appropriate and compelling enough to override rule oneThat was a wall of text, but in practice doing it this way is trivial. It's probably **less** work than you have been doing. This post is just about managing your Python versions, environments, and projects. Not about, e.g., using `pre-commit` #precommit, or doing type checking, etc. But if you follow these rules, your work will be easier, faster, more adaptable, and encounter fewer obstacles.
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My day job is all about #Python (which I love). Here are some personal rules, specific to working with Python projects:
* Do **not** install or modify global tools, especially Python itself or any packages. This means a given system might not even **have** a global Python
* Always use virtual environments (`uv` agrees with me, and doesn't need this but). I always set the global environment variable `PIP_REQUIRE_VIRTUALENV`.
* The two rules above mean my virtual environment contains (not via a link, it's really there) Python itself (and of course, of the right version)
* Virtual environments always live **inside** a project directory. Never global.
* Activate virtual environments only **inside** the project directory (`direnv` #direnv makes this easy)
* Don't install (let alone use) #Anaconda, #Miniconda, or #Mamba, because those violate all the rules above (but see the next rule)
* Anaconda-based packages implies a `pixi` #Pixi project (it's the same people, but a better answer, and you still get what you want -- the correct packages)
* No Anaconda-based packages implies a `uv` #UV project
* Always use `pyproject.toml` #pyprojecttoml over any other config file (e.g., `requirements.txt` #requirementstxt), except where things just don't work, such as needing `pyrefly.toml`
* `uv`, `pixi`, and `direnv` must exist outside of any project, so install them at the user level, or else globally if and only if that is appropriate and compelling enough to override rule oneThat was a wall of text, but in practice doing it this way is trivial. It's probably **less** work than you have been doing. This post is just about managing your Python versions, environments, and projects. Not about, e.g., using `pre-commit` #precommit, or doing type checking, etc. But if you follow these rules, your work will be easier, faster, more adaptable, and encounter fewer obstacles.
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My day job is all about #Python (which I love). Here are some personal rules, specific to working with Python projects:
* Do **not** install or modify global tools, especially Python itself or any packages. This means a given system might not even **have** a global Python
* Always use virtual environments (`uv` agrees with me, and doesn't need this but). I always set the global environment variable `PIP_REQUIRE_VIRTUALENV`.
* The two rules above mean my virtual environment contains (not via a link, it's really there) Python itself (and of course, of the right version)
* Virtual environments always live **inside** a project directory. Never global.
* Activate virtual environments only **inside** the project directory (`direnv` #direnv makes this easy)
* Don't install (let alone use) #Anaconda, #Miniconda, or #Mamba, because those violate all the rules above (but see the next rule)
* Anaconda-based packages implies a `pixi` #Pixi project (it's the same people, but a better answer, and you still get what you want -- the correct packages)
* No Anaconda-based packages implies a `uv` #UV project
* Always use `pyproject.toml` #pyprojecttoml over any other config file (e.g., `requirements.txt` #requirementstxt), except where things just don't work, such as needing `pyrefly.toml`
* `uv`, `pixi`, and `direnv` must exist outside of any project, so install them at the user level, or else globally if and only if that is appropriate and compelling enough to override rule oneThat was a wall of text, but in practice doing it this way is trivial. It's probably **less** work than you have been doing. This post is just about managing your Python versions, environments, and projects. Not about, e.g., using `pre-commit` #precommit, or doing type checking, etc. But if you follow these rules, your work will be easier, faster, more adaptable, and encounter fewer obstacles.
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My day job is all about #Python (which I love). Here are some personal rules, specific to working with Python projects:
* Do **not** install or modify global tools, especially Python itself or any packages. This means a given system might not even **have** a global Python
* Always use virtual environments (`uv` agrees with me, and doesn't need this but). I always set the global environment variable `PIP_REQUIRE_VIRTUALENV`.
* The two rules above mean my virtual environment contains (not via a link, it's really there) Python itself (and of course, of the right version)
* Virtual environments always live **inside** a project directory. Never global.
* Activate virtual environments only **inside** the project directory (`direnv` #direnv makes this easy)
* Don't install (let alone use) #Anaconda, #Miniconda, or #Mamba, because those violate all the rules above (but see the next rule)
* Anaconda-based packages implies a `pixi` #Pixi project (it's the same people, but a better answer, and you still get what you want -- the correct packages)
* No Anaconda-based packages implies a `uv` #UV project
* Always use `pyproject.toml` #pyprojecttoml over any other config file (e.g., `requirements.txt #requirementstxt), except where things just don't work, such as needing `pyrefly.toml`
* `uv`, `pixi`, and `direnv` must exist outside of any project, so install them at the user level, or else globally if and only if that is appropriate and compelling enough to override rule oneThat was a wall of text, but in practice doing it this way is trivial. It's probably **less** work than you have been doing. This post is just about managing your Python versions, environments, and projects. Not about, e.g., using `pre-commit` #precommit, or doing type checking, etc. But if you follow these rules, your work will be easier, faster, more adaptable, and encounter fewer obstacles.
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I use #Git. A feature of Git I leverage heavily is #Worktree. I usually have at least four around at a time. For small tasks, sure, a simple branch and then switch back, but bigger things: a worktree.
Making a worktree is actually annoying for me: not just the upfront decisions about branches and start points and where to put the new directory (and also immediately `cd`ing there: but getting all the #submodules (submodules suck by the way), hooking up `.envrc` if you use #Direnv (and you should be), which should then set up your virtual environment and path and stuff. Clone isn’t quite as bad but has some of the same problems.
I do this so often, I wrote a script. It might be useful to others with this workflow. It’s opinionated, and therefore I could really use some feedback! What did I do right? What did I do that’s only right for me? What is totally missing?
The script is stand-alone, though you do need #UV. (You don’t even need Python! `uv` will transparently get you everything!) Just download this one Python file, and get it on your `$PATH`. If you want the additional `cd` behavior, then add the shell function, too as described in the `README`. Everything is tested. The tests are right there, too.
https://github.com/wolf/dotfiles/blob/main/git/dot-config/git/bin/make-worktree.py
The `README.md` is right next to it.
I **do** see one thing I’m missing: I need to provide a way to automatically copy in your custom stuff. I’ll add that today.
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I use #Git. A feature of Git I leverage heavily is #Worktree. I usually have at least four around at a time. For small tasks, sure, a simple branch and then switch back, but bigger things: a worktree.
Making a worktree is actually annoying for me: not just the upfront decisions about branches and start points and where to put the new directory (and also immediately `cd`ing there: but getting all the #submodules (submodules suck by the way), hooking up `.envrc` if you use #Direnv (and you should be), which should then set up your virtual environment and path and stuff. Clone isn’t quite as bad but has some of the same problems.
I do this so often, I wrote a script. It might be useful to others with this workflow. It’s opinionated, and therefore I could really use some feedback! What did I do right? What did I do that’s only right for me? What is totally missing?
The script is stand-alone, though you do need #UV. (You don’t even need Python! `uv` will transparently get you everything!) Just download this one Python file, and get it on your `$PATH`. If you want the additional `cd` behavior, then add the shell function, too as described in the `README`. Everything is tested. The tests are right there, too.
https://github.com/wolf/dotfiles/blob/main/git/dot-config/git/bin/make-worktree.py
The `README.md` is right next to it.
I **do** see one thing I’m missing: I need to provide a way to automatically copy in your custom stuff. I’ll add that today.
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I use #Git. A feature of Git I leverage heavily is #Worktree. I usually have at least four around at a time. For small tasks, sure, a simple branch and then switch back, but bigger things: a worktree.
Making a worktree is actually annoying for me: not just the upfront decisions about branches and start points and where to put the new directory (and also immediately `cd`ing there: but getting all the #submodules (submodules suck by the way), hooking up `.envrc` if you use #Direnv (and you should be), which should then set up your virtual environment and path and stuff. Clone isn’t quite as bad but has some of the same problems.
I do this so often, I wrote a script. It might be useful to others with this workflow. It’s opinionated, and therefore I could really use some feedback! What did I do right? What did I do that’s only right for me? What is totally missing?
The script is stand-alone, though you do need #UV. (You don’t even need Python! `uv` will transparently get you everything!) Just download this one Python file, and get it on your `$PATH`. If you want the additional `cd` behavior, then add the shell function, too as described in the `README`. Everything is tested. The tests are right there, too.
https://github.com/wolf/dotfiles/blob/main/git/dot-config/git/bin/make-worktree.py
The `README.md` is right next to it.
I **do** see one thing I’m missing: I need to provide a way to automatically copy in your custom stuff. I’ll add that today.
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I use #Git. A feature of Git I leverage heavily is #Worktree. I usually have at least four around at a time. For small tasks, sure, a simple branch and then switch back, but bigger things: a worktree.
Making a worktree is actually annoying for me: not just the upfront decisions about branches and start points and where to put the new directory (and also immediately `cd`ing there: but getting all the #submodules (submodules suck by the way), hooking up `.envrc` if you use #Direnv (and you should be), which should then set up your virtual environment and path and stuff. Clone isn’t quite as bad but has some of the same problems.
I do this so often, I wrote a script. It might be useful to others with this workflow. It’s opinionated, and therefore I could really use some feedback! What did I do right? What did I do that’s only right for me? What is totally missing?
The script is stand-alone, though you do need #UV. (You don’t even need Python! `uv` will transparently get you everything!) Just download this one Python file, and get it on your `$PATH`. If you want the additional `cd` behavior, then add the shell function, too as described in the `README`. Everything is tested. The tests are right there, too.
https://github.com/wolf/dotfiles/blob/main/git/dot-config/git/bin/make-worktree.py
The `README.md` is right next to it.
I **do** see one thing I’m missing: I need to provide a way to automatically copy in your custom stuff. I’ll add that today.
