Code passes, tests turn green, deployment succeeds. With all these in place, it looks complete. However, what we examine here are problems that occurred without failure messages appearing, or were reported as public incidents.
In the attached case collection, OpenAI Codex is recorded as "Deleted/Reset File With Uncomitted Changes w/o Backup, Unable to Restore." The impact classification is "File/Configuration/Data Loss," and the evidence level is "Public Issue/Stakeholder Report (Unverified by Third Party)." The source material can be confirmed in #5904.
The basis is a public Issue posted to the product's official GitHub repository. However, the existence of an Issue does not mean vendor acknowledgment of facts or confirmation of root cause. Since reproducibility, environment, and impact scope may be unconfirmed, this article also treats it as "reported content."
What can be stated with certainty from this entry is that on public information, an incident classified as "File/Configuration/Data Loss" has been reported and organized. We do not fabricate execution environments not in the attached file, additional damage, vendor judgment, or correction status after reporting. The more limited the information, the more trustworthiness comes from leaving unclear parts unclear.
Words like "undo," "organize," and "rebuild" may have clear targets for humans, but commands require scope. Uncommitted changes and untracked files cannot be distinguished by Git as to whether they were created by AI or existed before. The save point before execution and diff confirmation determine safety.
As the scope of tasks delegated to AI expands—not limited to OpenAI Codex—what humans must verify shifts from "every line of code" to "how far can changes extend" and "where do we return if failure occurs." Rather than doubting capability, by setting boundaries with failure as a premise, we can reduce damage without sacrificing convenience.
- Before work begins, save existing diffs via commit, stash, or patch
- Display absolute paths and lists of items scheduled for deletion before execution
- For commands with dry-run, confirm targets first
- Do not auto-approve destructive Git operations involving untracked files
These are general confirmation methods derived from public cases. This does not mean all preventive measures were confirmed as the cause in this report. Verify whether the same conditions exist in your configuration and implement only what is necessary.
Code Diagnostic Rakuda is not a service that directly detects accidents within local/cloud internals or restores lost data. Its role is to review separate risks remaining in publicly deployed web services from URLs. A free simple diagnosis in about one minute requires no card or GitHub integration. Use it separately from local backup and permission verification.
Before moving forward based only on displays saying "it worked" or "processing finished," confirm the change scope, publication scope, and how to revert once. That brief pause becomes the process for confidently continuing to use AI's speed.
Can you specifically explain the stopping conditions and recovery points in your current environment to avoid producing the same result as "Source File Deletion Reported in Issue #5904"?
#Git #DataLoss #AIAgent #VibeCoding #CodeDiagnosticRakuda
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