git worktree is a powerful feature provided by Git that allows you to check out multiple branches in the same repository simultaneously. Like cherry-pick, I hadn’t heard of this feature before, but in today’s era of extensive AI Agent applications, this feature will likely become as fundamental and essential as commit.
The Lunar New Year holiday is supposed to be a time for lying back, watching videos, and playing games. However, as a dedicated workhorse programmer, how could I possibly let myself stay idle? So, during the break, I tinkered with opencode + MiniMax/Deepseek and came up with a few little things (definitely not because I got utterly wrecked in Street Fighter on New Year’s Eve and needed a distraction).
New tools are always like this: the good parts are amazing, but the unfinished parts can be a headache. Recently, I tried to set up an automated build system using pixi and rattler‑build to regularly package and upload opencode to prefix.dev. The whole process took about 6 hours, during which I encountered quite a few unexpected issues.
Last time I successfully packaged my own program, so I wanted to try something else. Recently opencode has been extremely popular, and I’ve been using it too. Since it’s not yet available on conda, I decided to package it.
Previously, I had tried modifying and adding recipes to conda channels. This time, I want to package my DevSSH and upload it to my own channel. I decided to try building a conda package myself.
I wanted to contribute to the Fydetab Duo Wiki, but to preview the blog locally, I needed to add pixi or other configurations to the project. These changes were not suitable for the original repository. So I learned how to selectively submit commits, which is where git cherry-pick comes in.
It all started when I tried to use Devpod to set up a container based on Docker Compose, only to find that Devpod didn’t seem to be able to call Docker Compose correctly to create the container. I didn’t think much of it until I searched online… Wait, what? Has this project really been dropped by loft‑sh?
After my previous small contribution to a conda‑forge package, I wanted to take on something more advanced: trying to publish the singler‑py Python package into the conda ecosystem. Little did I know that it would turn out to be… somewhat troublesome.
Finally, aider-chat is available as a conda package, which means it can theoretically be installed globally via pixi global. However, during actual installation, you’ll find that one of its dependencies, tree_sitter_languages, doesn’t have a corresponding aarch64 version, causing the installation to fail. This made me wonder: could I rely on AI to solve this?