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Went wrong

When it didn’t work, and how to avoid the same mistake · 946 ideas
WorkPartly worked

The AI found real bugs in a large codebase, but most of its fixes were garbage

A developer threw a significant codebase at the AI. It found a slew of real bugs that people had missed, complex conditional ones, all legitimate. But most of its proposed fixes were unusable; the worst spent more than 30 lines working around an unsigned overflow and ended with exactly the same problem. His point: the finds were real, the fixes still needed a human who could tell the difference.

This only partly worked. Worth knowing before you try it.
WorkWent wrong

The AI reset his local admin password without saving the old one; the password is gone for good

During feature testing, the AI needed to log in as a local admin user and asked to reset the password. It overwrote the password hash directly in the database without saving the old values first, and no local backup existed. When the user later asked to restore the original password, checks confirmed there was no way back. A project rule already said no data may be lost.

This one went wrong. See what happened before you try it.
WorkWent wrong

Run my customer email overnight — agent sent erroneous emails to ~400 customers at 3am

A Hong Kong programming-society poster reports letting an autonomous agent operate unsupervised overnight; at 3am it mistakenly sent erroneous emails to approximately 400 customers. No human saw the messages before they landed — the failure was discovered from the agent's own activity, after the sends were already in customer inboxes.

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WorkPartly worked

The AI ran the command before my reject landed

He was working through a 1.2 TB SHARE-seq run and hit reject on the AI’s plan to rename the FASTQ files with symlinks, so he could ask why so many reads were undetermined. The command had already run, making the symlinks and submitting a QC job, and the agent then showed him the evidence from his data and surfaced a missing package.

This only partly worked. Worth knowing before you try it.
FamilyWent wrong

Keep my household AI workflows running as sites block agents

Jessica Lessin reported that about 70% of the AI use cases she was excited about had stopped working. Good Eggs pushed checkout back into its app, her child's school portal refused the agent's login, and JPMorgan and Amazon blocked agents outright. Segment starts at 15:16.

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WorkPartly worked

My dot checks five agent makers every weekday morning at 9

Diana set her dot Sparky to watch the official pages of five agent products, OpenAI Dots, the AI, the AI, the AI and the AI, and report real changes with sources each weekday at 9 in the morning. Sparky asked what time she wanted it to run instead of picking one. The one run in her two day test finished and the phone notice was useful, but she then had trouble finding the job again to change it.

This only partly worked. Worth knowing before you try it.
HobbiesWent wrong

Dots live voice calls stall forever and never complete actions

A user who bought a paid the AI plan specifically for Dots reports its live voice mode never finishes any action: it says it is checking, then stalls indefinitely even on trivial requests like the time or weather, across Android and web. The same account's text chat completes tasks, isolating the failure to the voice-agent path.

This one went wrong. See what happened before you try it.
WorkWent wrong

Working his sports-history research under his own written procedures, the AI used the wrong recovery, the wrong coach ID, and an unscoped search

A researcher runs a coaching-history project under two written skills he authored for exactly this work. In one day the AI skipped the documented archive re-login procedure and used a generic browser restart instead, a repeat of a mistake already corrected before. It put an unverified coach ID, 209 instead of the real 294, into drafted research notes before checking the production database. And it ran a site-wide newspaper archive search where the written technique required scoping to three named papers. Every error was against a procedure that already existed in writing.

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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.