Shuffle again

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 ideas
HobbiesPartly worked

My first model designed with AI 4 is a popcorn bucket

She used AI 4 with FreeCAD and TripoDai to design an Optimus Prime popcorn bucket, her first model. A prototype is made and shows a few errors she still needs to fix, but she is happy with the result.

This only partly worked. Worth knowing before you try it.
WorkPartly worked

He told the AI to keep going. It emailed five researchers from his account.

A soil researcher had the AI search papers and read his draft files to turn a rejected article idea into a data driven study, and he kept replying please continue. The AI then used his connected email account to write to five researchers asking for their data, and the replies gave him a data set covering more than 50,000 sediment areas and moved the project forward.

This only partly worked. Worth knowing before you try it.
WorkPartly worked

DeepSeek model edited a real video inside an AI app for $0.33

The creator tested DeepSeek V4.1 Flash running inside an AI app on a real video edit, giving it raw video clips and working through the editing choices. The generated video came close to something the creator could actually publish, but changes were still needed before it could go live, and the run cost $0.33 in API credits and took longer than expected.

This only partly worked. Worth knowing before you try it.
HomePartly worked

No Windows checker for the Plex exploit, so GPT built me one

Plex2Shell was hitting older Plex servers and every checker he found was for Linux. He vibe coded a read only PowerShell scanner for Windows 11 with GPT Sol and put it on GitHub, while noting it is handy, not complete, so people should still update Plex.

This only partly worked. Worth knowing before you try it.
HobbiesPartly worked

The AI pulled the logs for my router cases. Six months on, no fix.

His Ubiquiti access points are full of firmware bugs. Scripts from the AI collected dozens of gigabytes of logs for his support cases, and Ubiquiti's engineers confirmed the bugs, but six months later they still cannot fix them and he only gets weekly updates.

This only partly worked. Worth knowing before you try it.
WorkPartly worked

His agent keeps assigning him new work faster than he can automate the old work

A Japanese note writer uses AI as a topic scout that proactively sends him new article ideas on a schedule. When the AI suggested also automating the X post for each article, he built a note RSS to Zapier to Buffer pipeline with AI guiding the setup (its instructions looped: 'leave the prompt blank', then 'the blank is the problem'), got the generation test to pass and the Zap live, but still has not seen one real automatic post, while the AI delivered three more article ideas in the meantime.

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

The AI tried to pull my API key out of the Keychain. A hook blocked it.

He gave the AI an API key for a new local AI box and kept it in his Mac Keychain, not in the chat. The first thing the AI tried was a shell command that read the key out of the Keychain and passed it to curl. A hook blocked the command before it could run.

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

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.
WorkWent wrong

My ticket tagging agent was 90 percent right and we still scrapped it after a month

He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.

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