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.
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?
The "come back with proof instead of just code" framing matches exactly where my agent workflow breaks. I ship an iOS app and the agent can pass tsc and lint all day, then I'm still the one clicking through the simulator to see if the thing actually renders. Since the environments are Linux, is mobile out of scope for the foreseeable, or do you have thoughts on a macOS/simulator story? Even screenshot-level proof for a web preview would change my loop.
The 'come back with proof instead of just code' framing is the right one, and the disposable-env angle solves a real mess. The gap I keep hitting past this: a run passing proves it ran, not that it did the right thing. The last mile is a pass criterion the agent doesn't get to write for itself, otherwise 'verified' quietly means 'it didn't crash.'
disposable sandboxes for agents is exactly whats needed rn 👏 super usefull
Congrats Ben — the part that resonates is “verified code instead of asking you to test manually.” In my own coding-agent loop, the weak point is not generation, it's proof quality: agents say “done” after lint passes, but the actual product flow is still broken.
A small thing I’d love to see in TryCase is a compact verification receipt: commands run, browser path tested, screenshots/video links, and what failed before the final pass. That would make it much easier to trust the result or review it later.
Curious if you're thinking about a standard “done packet” that agents can return to humans or CI?
@TryCase Giving coding agents an isolated, disposable Linux sandbox is exactly how we solve the local port collision and dependency nightmare. Since TryCase is exposed as a runtime tool layer for external agents, how are you handling base image caching? If an agent executes a massive npm install or updates a database schema across multiple iterative debugging runs, do you snapshot and delta-cache that specific container's filesystem state, or does it do a clean cold boot every time the agent invokes a test call?
As a solo dev shipping an iOS app, the final manual-verify step is exactly where my releases stall — I'm the QA team of one, and "done" from an agent usually just means "it compiled." The disposable box per agent is a smart way to move that check off my laptop. Does it handle mobile/app flows (simulator or a device) yet, or is it web + CLI apps only for now?
This maps to the messy part of agent coding for me: not the patch itself, but proving it ran in a clean environment. The useful constraint is keeping the proof lightweight enough that agents actually include it every time.
Love how the screenshots and recordings come back with the diff already attached, makes verifying what the agent actually did way less of a guessing game.
the disposable linux envs actually working from a single prompt blew me away, screenshots and all. finally lets the agent go end to end without me babysitting every shell command.
how does it handle cleanup of those disposable environments when an agent spins up a bunch in parallel, anything to worry about resource-wise?
finally something that lets my agent actually run the code instead of me playing QA. tried it on a small flask app and got back screenshots plus a clean diff, which honestly saved me a whole round trip.
Congrats on the launch, this is a real problem. My question is more about the iteration loop than the verification side - if an agent fails, tweaks the code, and needs to test again, does it get a brand new disposable box each time, or does it reuse the same one until the task is done? Fully fresh every retry sounds cleanest for isolation but on a repo with a slow install/build step that could add up fast if the agent is iterating 10+ times on one bug.
I’m curious about the differences between GPT-5.5 browsing and testing and TryCase. What makes TryCase unique, apart from just screenshots and videos? Thank you
How do you handle state between runs if the agent needs to verify something like a running database or queued background jobs from a previous step?
Such a smart concept for vibe coding. Testing AI-generated code safely without messing up my local environment is always a massive concern. Can these disposable environments be spun up locally, or is the platform entirely cloud-based?
Screenshots and recordings as verification artifacts are useful for UI changes, but for backend logic or API behavior the visual output doesn't tell you much about whether the code actually works correctly. What does TryCase return as verification evidence for non-visual changes, like a database write, a webhook handler, or a background job, and how does the agent know the difference between "it ran without error" and "it did the right thing"?
the disposable Linux environment idea is such a clean solve for the "I cant actually verify this" problem with coding agents. nice execution.
finally something that lets my coding agent actually run the app instead of just staring at it. the screenshot capture came back clean on the first try
The 'an agent handles verification' step is where I've watched this quietly break. When the same model family does the work and the check, the verifier tends to trust the doer's framing of what success looks like, so it happily confirms a screenshot of the wrong screen. What helped me was feeding the verification agent only the original task spec plus the artifact, never the doer's transcript, so it can't inherit the optimistic story of what happened. Does TryCase hand the checker the full run log, or just the recording and a fresh prompt?
About TryCase on Product Hunt
“Disposable test environments for AI coding agents”
TryCase launched on Product Hunt on July 5th, 2026 and earned 214 upvotes and 29 comments, placing #4 on the daily leaderboard. 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.
TryCase was featured in Software Engineering (42.7k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 190.3k products, making this a competitive space to launch in.
Who hunted TryCase?
TryCase was hunted by Ben Chomsang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Ben Chomsang
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?