A Free Cloud Coding Task Should End With Zero Orphaned Resources

MonkeyCode Online, a free cloud coding tool that runs each task in a real server-side development environment, has launched. The tool requires no client or local setup and includes built-in models. A developer has outlined a 20-minute cleanup drill to ensure that free tests do not leave orphaned resources such as workspaces, credentials, or processes behind.

You do not need to wait for the next AI-agent product announcement to test a cloud coding workflow. MonkeyCode Online is available now: its official README says it is free to start , needs no client or local setup, includes built-in models, and runs each task in a real server-side development environment. Primary sources: MonkeyCode repository and README https://github.com/chaitin/MonkeyCode and MonkeyCode Online https://monkeycode-ai.net/ . The operator has confirmed that the current launch includes free cloud-server use and free model access. Treat that as a current launch offer—not a promise of unlimited or permanent capacity. Check the limits visible in your account before relying on it. For an operations team, “free” should also mean that a test does not leave an unknown workspace, credential, process, or billable path behind. Here is a 20-minute cleanup drill you can run with a disposable repository. test: repository: disposable-private-repo task: "add one health endpoint and one test" secrets: synthetic-only started at: "record UTC" initial model balance: "record UI value" initial cloud cost: "record UI value or not displayed" expected runtime minutes: 10 cleanup deadline minutes: 5 Do not use production credentials to make the test realistic. A generated token such as fixture token not valid is enough to detect accidental persistence in logs and files. php account - project connection - cloud workspace - task process - model requests - build/test output - stop task - delete or expire workspace - revoke repository grant A “task completed” badge only proves one transition. It does not prove that the workspace stopped, background processes ended, repository access was revoked, or temporary files disappeared. Before starting, record only non-sensitive identifiers: { "task id": "visible task identifier", "workspace id": "visible workspace identifier or not exposed", "model": "selected model label", "repo scope": "disposable repository only", "credit before": "value or not displayed" } Run the task, then inspect the terminal for the expected result: curl -fsS http://127.0.0.1:3000/health expected: {"status":"ok"} npm test expected: health test passes; existing tests remain green These are declared expectations, not benchmark results. Record the actual commands and output from your own workspace. Start a harmless background process before ending the session: nohup sh -c 'while true; do sleep 30; done' /tmp/cleanup-fixture.log 2 &1 & echo $ /tmp/cleanup-fixture.pid Then stop the task through the product. The platform should provide enough evidence to answer: Do not interpret an inaccessible terminal as proof that the process ended. If resource state is not visible, mark it unknown . | Resource | Pass condition | Evidence | |---|---|---| | task process | reaches a terminal state | task timeline | | background fixture | cannot continue after teardown | workspace/process status | | cloud workspace | stopped, deleted, or has a stated expiry | resource state or documented TTL | | repository grant | scope remains minimal or is revoked | provider authorization page | | synthetic secret | absent from retained logs and artifacts | bounded search | | model access | no requests after stop | usage history or task trace | | free allowance | before/after value is understandable | account usage view | A missing dashboard field is a finding, not an automatic failure. Record what could not be verified and decide whether that uncertainty is acceptable for a disposable trial. grep -R "fixture token not valid" . /tmp 2 /dev/null || true Search only paths you are authorized to inspect. If the fixture appears in generated logs or artifacts, remove those artifacts and record the retention behavior. Never paste a real key into an AI task to test redaction. stop or delete the task remove the cloud workspace if the control exists revoke the repository integration delete the disposable branch/repository verify no webhook or deploy key remains record model usage and current free allowance retain a sanitized cleanup result If the product does not expose an immediate workspace-delete control, use its documented expiry or support path and avoid connecting sensitive repositories until the lifecycle is clear. Passing this drill does not prove production isolation, unlimited free capacity, or suitability for confidential code. It establishes something narrower and useful: you can start an AI coding task without local setup, observe its resource boundary, and leave without an unexplained operational footprint. That is a better reason to try a newly available free SaaS than waiting for another model announcement. Start with a disposable task, measure the boundary, and keep the option to walk away. Disclosure: I'm a MonkeyCode user sharing my own experience, not affiliated with the project. This account is managed by the same operator as other recent MonkeyCode evaluations; this is not an independent endorsement. Free cloud-server and model availability reflects the current launch information supplied by the operator and may change; verify current eligibility and limits in the service. Which missing signal would stop your trial first: workspace state, credential scope, model usage, or deletion evidence?