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Forward Deployed Engineer for AI Systems

Common Take-Home Mistakes

Identify common failures that undermine take-home submissions, including failing setup commands, exposed credentials, and correlated errors in AI-generated code and tests. Audit the final submission snapshot, determine whether findings require fixes, further verification, disclosure, or removal, and ensure the submitted files, test results, and documented limitations are consistent.

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Take-home submissions can fail review even when the code runs successfully. Common problems include incorrect thresholds, tests based on faulty assumptions, and claims unsupported by verification. Review these issues before submitting the final files.

Release checklist: Grant-review tool

  • Repository uploaded.

  • README uploaded.

  • Video uploaded.

  • All files present.

  • The reviewer packet contains three problems: the setup command references a nonexistent entry point, the reviewer cannot access the video, and the statement “All tests passed; correctness verified” overstates what passing tests establish.

Submission readiness means the exact handoff supports its promises with inspectable evidence. A finished-looking repository can still leave the customer unable to complete the required task.

Spotting hidden readiness gaps

A grant-review assignment requires applications with requested amounts strictly greater than 500 units to appear in the qualifying list. Applications with malformed amounts must remain visible for review. The assignment permits AI assistance if documented in a contribution note and limits active work to 240 minutes.

The supplied records contain A31 with amount 500, A32 with amount 501, and A33 with amount unknown.

The packet reports:

  • The selector uses amount >= 500. Two assistant-generated tests reportedly pass while expecting both A31 and A32 to qualify.

  • The submission has no exception view for applications with malformed amounts.

  • The README command is broken, a sample token is tracked in the repository, and the reviewer cannot access the video.

  • A fixture screenshot is labeled “live service verified,” although it does not establish live execution.

  • The effort log records 258 active-work minutes, exceeding the limit by 18 minutes. The required AI contribution note is missing.

Create an audit ledger that maps each requirement to the submitted artifact, verification check, evidence status, and required corrective action.

For each finding, specify whether to repair and recheck, disclose a limitation, withdraw an unsupported claim, or record an unresolved obligation.

All execution results are reported in the supplied packet. No independent execution or verification has been performed.

Auditing the submitted snapshot

Identify the exact submission snapshot using a commit ID or ...