While setting up the Claude Science container, I learned about a new way of using NVIDIA GPUs inside containers, so I’m noting it down (most of it was written by AI, used as reference material for my own research).

I had already managed to get claude-science running in a container, and basic functionality worked without major issues. But when I wanted to do things like molecular docking, I couldn’t—the container simply had no GPU. However, when I pulled NVIDIA’s official image to build the container the same way I had before, something bizarre happened: every language kernel in CS—Python, Perl, R—stopped working, and the entire runtime environment was rendered useless.

This was truly beyond my understanding. After consulting Gemini manually with no luck, I decided to let opencode + deepseek-v4-flash handle it on its own—not me remotely typing commands and directing it, but an AI agent living inside the container that read the logs, bisected parameters, decompiled the parser, and edited the entrypoint itself. It was a strange and interesting experience; here’s a record of it.

I recently worked on a small project: putting the Linux version of Claude Science into a Docker container, paired with an API Bridge so it can run through third-party API backends without needing to hold an Anthropic API Key directly.

The process was more eventful than expected—also quite interesting. Here’s a record of it.

DevPod is an open-source development environment management tool that allows you to create development environments similar to GitHub Codespaces on any Kubernetes cluster or Docker host. This article will introduce how to use the DevPod CLI to create workspaces and provide a detailed explanation of writing DevContainer configuration files.