I previously wrote a blog post about using pixi’s tasks feature to fix missing dependency issues with Bioconductor packages (like GenomeInfoDbData, BSgenome.Hsapiens.UCSC.hg38, etc.) after installation. At the time, I only knew the problem existed but didn’t understand the root cause — I was just providing a less-than-ideal workaround.

But recently, with insights from AI responses, I finally figured out the real reason — it all comes down to the post-link script mechanism in the Conda ecosystem.

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.

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?

Bioinformatics is an interdisciplinary field, and the toolkit or technology stack used in bioinformatics is also quite “interdisciplinary”. The level of fragmentation is, in my opinion, absolutely not less than that of Linux distributions… This also brings us a common challenge: the deployment of bioinformatics analysis environments.

Today, I needed to deploy an RStudio server for someone else. Here are the steps and key points of the deployment.

It seems I hit a bug again…

When installing software, I thought that the portability and feasibility of using conda were better, so I decided to try it out and see how Snakemake works…

Due to work requirements, I needed to install a package mentioned in a literature. Initially, I thought it would be done with two commands, but who knew there were so many dependencies to resolve… and R packages on Linux need to be compiled, which is quite time-consuming… Fortunately, there’s Conda!