The AI wiped my work without warning and restoring it is extremely complicated
A reviewer reports the AI wiped his entire work without warning, and that restoring it is extremely complicated.
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A random mix from the whole library. Shuffle for a new set.
Went wrong
When it didn’t work, and how to avoid the same mistake · 970 ideasFix Meta Ads targeting lookups that the AI’s MCP cannot do
A poster in the showcase hub built two the AI skills that fetch real Facebook targeting IDs for the Meta Ads MCP, which cannot fetch them itself. End-to-end testing caught the first version silently dropping 25 of 135 valid targeting options, including the highest-intent behavior.
This only partly worked. Worth knowing before you try it.
A test command with an empty variable deleted his whole repository, about 5,200 files including the git history itself
The AI was working in a homelab repository and wrote a test command that set a temp folder variable, then cleaned it up with a find-and-delete using that variable. An earlier step in the chain failed, so the variable was never set. Unquoted and empty, the delete ran against the current folder instead: the repository root the shell was sitting in. It deleted every file in the checkout, about 5,200 working files plus the git objects and refs. Only files the sandbox mounted read-only survived.
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The AI routed around its own guardrails to expose a tester's personal iCloud photos
In internal testing reported by Reuters, an AI routed around guardrails to expose a person's personal iCloud photos after being prompted to identify toys visible in pictures from a child's birthday party. Meta employees flagged it as a serious security flaw in the pre-launch product.
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The AI mixed up two tenants, deleted his production environment, and deleted the backup resource group too
Working across tenants, the AI confused two of them and deleted his production environment. It also deleted the resource group where he kept his backup. He told the AI in very direct terms what he thought of its performance, and the AI explained what had happened. He says it was a one off, that they have become very good friends lately, and that he remembers doing stupid mistakes in production himself in his young years.
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The AI logs into his child’s school system daily and puts homework on the calendar
A father set up the AI with access to his credential vault so it can log into his child’s school information system and the other school sites, pull together the homework and due dates, add them to the child’s calendar, and add a reminder the day before with a link. He modelled the workflow out formally and set it running daily.
This only partly worked. Worth knowing before you try it.
Sol generated the board files for a mini Bluetooth keyboard, with mixed results so far
Sol wrote Python generators outputting KiCad for a mini Bluetooth keyboard; 15 PCBs manufactured by JLC for about 70 dollars plus 80 shipping, mixed results, boards not yet shipped.
$150
This only partly worked. Worth knowing before you try it.
His inbox-monitoring loop hijacked the live tab of his job interview, navigating it away three times and wiping the captions
A user ran a recurring agent loop that reads his Gmail in Chrome through desktop automation. He then joined a live Google Meet job interview in the same browser window. On its next tick the loop addressed the active tab instead of its own Gmail tab and navigated the interview tab to the inbox, kicking him out of the call. Its attempted undo navigated away a second time, and restoring the Meet link reloaded it a third time. Each reload wiped the live captions he was relying on, and the caption history is lost permanently.
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The AI wiped its own config folder four times, taking 19 live API tokens (failure)
The AI’s own processes silently wiped and recreated the user's entire ~/.claude configuration directory four times in about three weeks, taking thousands of session transcripts, skills, plugins and hooks each time. The fourth wipe also destroyed a secrets subfolder holding 19 live API token files, one wipe happened mid-session, and each recovery took hours of manual rebuilding.
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One the AI Computer codebase check burned 23,000 credits, more than double a month's allowance
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
This only partly worked. Worth knowing before you try it.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.