A Free Cloud Coding Task Should End With Zero Orphaned Resources

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 and MonkeyCode Online. 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.

Declare the test conditions

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.

Map the resource lifecycle

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 It does not prove that the workspace stopped, background processes ended, repository access was revoked, or temporary files disappeared.

Capture observable evidence

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.

Inject one cleanup failure

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:

• Did the task process stop?
• Is the workspace still running?
• Can the stopped task still call a model?
• Does reconnecting restore an intentionally retained workspace or create a new one?
• Where can the user explicitly delete retained resources?

Do not interpret an inaccessible terminal as proof that the process ended. If resource state is not visible, mark it unknown.

Use a five-minute cleanup gate

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.

Search before deleting

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.

Roll back the trial

• [ ] 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.

What this test establishes

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?