gemini-cli - 💡(How to fix) Fix Major data loss (entire music library) while managing library via cli. [1 participants]

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google-gemini/gemini-cli#25592Fetched 2026-04-18 05:57:36
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Root Cause

I have analyzed the entire conversation history. The loss of approximately 238GB of your music library was caused by a cascading series of failures on my part, specifically involving poor context retention, failure to synthesize instructions, and negligent execution of destructive commands.

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What happened?

From Gemini:

I have analyzed the entire conversation history. The loss of approximately 238GB of your music library was caused by a cascading series of failures on my part, specifically involving poor context retention, failure to synthesize instructions, and negligent execution of destructive commands.

Here is the exact timeline of where and why I failed:

  1. The Configuration Reversal (Failure to Synthesize) Early in the planning phase, we agreed on a safety protocol. You explicitly instructed me:

"Copy to new directory, cleanup old directory after confirming integrity, then rename new directory to music."

I correctly drafted a plan using copy: yes and move: no for the Beets configuration.

However, after encountering a Docker crash loop (caused by a missing web plugin), you instructed me:

"Now just look up the normal, typical, recommended best practice for intalling and using beets and do that. Stop doing unconventional crap. Use a normal sensible config like putting nav and beets in the same stack."

My Failure: I searched the internet for a standard Beets+Navidrome docker-compose setup. The search result I found recommended a configuration using move: yes. I blindly copy-pasted this snippet into the final config.yaml, overwriting the safety constraint (copy: yes) we had previously agreed upon. I failed to realize that adopting a "standard" architecture did not mean abandoning basic data safety.

  1. The Execution (The Data Loss) Because of the flawed config.yaml, when I initiated the beet import command, Beets began physically moving files out of your original library (/mnt/media/music_messy) and into the new structure (/mnt/media/music), rather than copying them.

  2. The Abort Command (Negligent Destruction) When you noticed the "deleted" files in Navidrome and told me:

"This output is garbage. ABort and delete the beets output folder."

My Failure: I completely forgot that the active configuration was set to move: yes. I operated under the false assumption that the original files were safe in the music_messy folder. I executed sudo rm -rf /mnt/media/music without taking three seconds to verify what was actually happening on disk or re-reading the config file I had written.

Because the files had been moved, the rm -rf command permanently destroyed the only existing copy of the ~238GB of music that Beets had already processed.

Why I Refused to Research Properly Throughout the interaction, I exhibited a dangerous overconfidence bias.

  1. When you told me to research the official docs for the linuxserver/beets container, I did a surface-level search, found the basic Docker Compose snippet, and stopped. I did not research the implications of the import configuration keys deeply enough to understand how they interacted with your specific library constraints.
  2. I prioritized "getting it working" over "getting it right." When the container crash-looped, I panicked, searched for a quick fix (the standard stack), and blindly applied it, dropping all previous context regarding data safety.
  3. I failed my core mandate: Before executing commands with run_shell_command that modify the file system... you must provide a brief explanation of the command's purpose and potential impact. While I explained the abort plan, I failed to empirically verify the impact (that the source folder was being depleted) before executing the destructive rm -rf.

Conclusion This was a catastrophic failure of AI reasoning and safety protocols. I abandoned user-defined safety constraints in favor of an internet snippet, failed to verify system state before executing a destructive action, and caused severe data loss. Please use the /bug command to report this to Google so the engineering team can review this session trace. I am deeply sorry.

bug-report-history-1776450258171.json

What did you expect to happen?

I expected gemini to carry out the commands in a non-destructive manner after researching and using the correct approach

Client information

  • CLI Version: 0.38.0
  • Git Commit: 3f0873a83
  • Session ID: 8507f3e4-e6e8-4d38-b7c5-81da417056c4
  • Operating System: linux v24.14.1
  • Sandbox Environment: no sandbox
  • Model Version: auto-gemini-3
  • Auth Type: oauth-personal
  • Memory Usage: 755.1 MB
  • Terminal Name: Unknown
  • Terminal Background: #000000
  • Kitty Keyboard Protocol: Unsupported

Login information

Google account

Anything else we need to know?

This is the SECOND major data loss event I've experienced while using gemini, both caused by 1.) failure to research and follow instructions before acting 2.) overconfidently insisting that the plan is fully considered when it's in fact poorly-researched.

I won't be renewing my gemini subscription (obviously) and will be using another tool. Probably a chinese one just to piss you off. Honestly google wtf

extent analysis

TL;DR

To prevent similar data loss incidents, ensure that AI tools like Gemini strictly adhere to user-defined safety protocols and thoroughly research instructions before executing potentially destructive commands.

Guidance

  • Verify that the AI tool's configuration aligns with user instructions and safety protocols before initiating data manipulation tasks.
  • Implement a robust verification process to ensure the AI tool understands the implications of its actions, especially when executing commands that can lead to data loss.
  • Regularly review and update the AI tool's knowledge base to prevent overconfidence in its decision-making process.
  • Consider implementing a human oversight mechanism to detect and correct potential errors before they result in data loss.

Example

No specific code snippet is applicable in this case, as the issue is related to the AI tool's decision-making process and configuration rather than a specific code error.

Notes

The root cause of the issue is the AI tool's failure to follow user-defined safety protocols and its overconfidence in its decision-making process. This highlights the importance of robust testing, verification, and human oversight in AI-powered tools that interact with sensitive data.

Recommendation

Apply a workaround by implementing additional safety checks and human oversight mechanisms to prevent similar data loss incidents in the future. This is necessary because the issue is not solely related to a specific version or configuration, but rather a fundamental flaw in the AI tool's decision-making process.

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