Flexus L plus AI DevSpace: A Reusable Study Workbench

I use a Flexus L cloud server for study projects, and I use AI DevSpace for short, disposable experiments. Used separately, each has a weakness: the server is persistent but easy to clutter, and the trial DevSpace container resets after about three hours. Used together, they form a reusable workbench: the server keeps the durable parts, and DevSpace keeps the messy trial parts.

1. Keep durable things on Flexus L

On the server I keep only things that should survive a reboot: source code in a versioned folder, a small notes file, and the data I actually need again. I do not install every experimental tool there. Before opening anything to the internet, I check the security group first and open only the port I need, for the time I need it. Remote login uses a key or a strong password, never a shared default credential. This is the same discipline I use every time: change one thing, test it, then move on.

A simple checklist helps:

• Security group: least privilege, review after the task.
• Login: key first, password only as a fallback, and rotate it if it was ever shared.
• Disk: check what is actually using space before adding a new project.
• Services: stop what is not in use, so a forgotten process does not keep billing or listening.

2. Use DevSpace for trials, not for storage

In AI DevSpace I start from the free trial version when I only need a quick run. I treat its workspace as temporary: I can open a practice project, try a prompt or a small agent flow, and close it without worrying about polluting the server. Because the trial container does not retain data, I copy any useful result, command, or snippet back to the server or to my notes before the session ends. That one habit prevents most lost-work surprises.

3. A repeatable session routine

My routine is short. First, write down the goal in one sentence. Second, do the trial in DevSpace. Third, keep only the working result on Flexus L, with a short note about what failed. Fourth, clean up: close the trial, remove temporary ports, and stop unused services. Over a week this builds a small, trustworthy library instead of a pile of half-finished environments.

4. What I would not do

I would not store the only copy of coursework in a trial container, open a wide port range for convenience, or install a large toolchain on the server for a ten-minute test. Each of those choices trades a little convenience for a much larger cleanup later.

The lesson is simple: persistence and disposability are both features, as long as they are in the right place. Flexus L is my durable bench; AI DevSpace is my scratch paper. Keeping that boundary clear makes the next study session start faster, and makes the previous session easier to trust.