Launching today
TryCase
Disposable test environments for AI coding agents
26 followers
Disposable test environments for AI coding agents
26 followers
TryCase gives AI coding agents disposable Linux environments to run apps, test changes end to end, capture screenshots and recordings, and return verified code instead of asking you to test manually.




Hey Product Hunt,
I’m Ben, and I’m building TryCase.
This came from my own workflow. I’ll often have a bunch of agents running at once across different worktrees, each trying changes, spinning up the app, testing, iterating, taking screenshots, or recording proof.
That gets messy quickly. My laptop becomes the bottleneck, ports collide, installs overlap, browser sessions get reused in weird ways, and I still end up doing a lot of the final verification myself.
So I built TryCase to give each agent its own disposable Linux environment in the cloud. The agent can run the app, test the change end to end, capture screenshots or video, and come back with proof instead of just code.
It’s also useful for longer-running tasks. You can give an agent a goal, let it work inside a clean disposable environment, and ask it to come back with screenshots, logs, and recordings. Secrets can be passed in deliberately, and each run is isolated from your laptop and from other agents.
TryCase is still early, but the goal is simple: agents should only say “done” once they’ve actually run and verified the work.
It’s easy to try. Just ask your coding agent to use TryCase at trycase.dev:
- Fix this bug, test it end to end with TryCase, iterate until it works, and send me screenshots and a video recording as proof.
- Implement this feature, run the app in TryCase, iterate on any failures, and prove it’s working with screenshots, logs, and a recording.
- Use TryCase to run this repo in a clean environment, verify the main flow, and show me what the app looks like.
- Test this branch in TryCase, find anything that breaks, fix it, and prove the final version works.
- If manual login is needed, use desktop mode and give me the take-control link.
I’d love feedback from people using Codex, Claude Code, Cursor, or other coding agents. What would make you trust an agent’s “done” more?
GPTEverywhere
Love the framing around agents only saying done after screenshots/video proof in an isolated env — that is exactly the gap when running multiple worktrees locally. Good luck with the launch.