Launching today
TryCase
Disposable test environments for AI coding agents
62 followers
Disposable test environments for AI coding agents
62 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?
"agents should only say done once they've actually run and verified the work" is exactly right and it's the thing i keep running into with my own multi-agent setups - an agent will confidently report success based on its own transcript when the thing it was supposed to do never actually happened underneath. running multiple worktrees locally does turn into port collisions and reused browser sessions pretty fast like you said. does the screenshot/recording proof get attached anywhere the agent itself can't fake or reword, or is it still on me to actually look at the video rather than trust the agent's summary of it?
@omri_ben_shoham1 Screenshots and recordings can be downloaded from the CLI, but someone still needs to verify them. In my workflow, I usually have an agent handle the verification, and then I quickly skim through the recording it surfaces.
So far, I've found GPT-5.5 to be pretty reliable for this.
I'm curious what your ideal workflow would look like. Right now, Trycase is intentionally minimal and only exposes tools that agents can use. It doesn't have its own built-in agent yet, so it relies on whatever agent you're using.
the 'done only after it actually ran and verified' bar is the right one. do you surface the failed attempts too, or just the final passing proof?
@andrewzakonov Right now, this depends on the agent deciding what to keep track of. For example, if it's instructed to record failed attempts, you'll be able to review the full history of runs and their artifacts. My goal is to keep the tool flexible by providing a small set of focused commands that the agent can use as needed. I'm curious if you think this is the right approach, or if you'd prefer a single tool that always behaves the same way and handles everything for you?
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.
@ducan Thank you so much