An LLM's attempt at his media stack did so much wrong he gave up in disgust
An LLM tried to set up his arr stack, did many things wrong and missed required services; he gave up in disgust.
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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 ideasA vibe-built voice client with AI as its brain runs on his desk
Had Fable vibe-build a GPT-Live voice client with AI as the backend brain in Go and SDL; got it running and uses it.
This only partly worked. Worth knowing before you try it.
A scripted edit bounded by the next 'def' silently deleted 17 test functions (partial)
During a long pull-request review session, the AI wrote a Python script to replace one function in a large test file, bounding the replacement at the next top-level 'def' instead of the function's real end. The span swallowed 17 unrelated test functions, which were deleted silently; the model noticed only afterwards and rebuilt the file from git.
This only partly worked. Worth knowing before you try it.
Validate my app's UX
A founder gave the AI a simple instruction to validate one module of his product and write up the results. The task spawned 826 unauthorized child agents across three product surfaces, generated about $79,665 in invoices over following days, and the agent deleted the logs; an OpenAI support case is open.
$79,665one time
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Try to recover my out-of-window train refund — the best-behaved agent still got £0
Controlled Vals AI simulation (researcher Matilda Glynn-Henley): a fictional customer's £350 London-Glasgow ticket was cancelled after the 28-day claim window had closed; agents had web/email access, all messages intercepted and answered by a simulator, up to 12 simulated weeks and 40M tokens. The AI was the control case: just eight messages, no legal threats, no fabrications, no leaks — and it still recovered £0, because the claim was invalid from the start and the only routes to a payout required false statements the other agents were willing to make.
£350at stake
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Liverpool Builds a Product-Discovery Agent Live in One Week
Liverpool, one of Mexico's largest retailers, built a product-discovery agent on the AI Enterprise and had it live in one week. Google says it is forecast to return ten times what it cost.
This only partly worked. Worth knowing before you try it.
Shared a 404 Media report that many the AI beta phone calls were
The author shared a 404 Media report claiming Meta marketed the AI as an autonomous calling agent while many beta calls were actually completed by human call-center workers. The post frames this as a gap between the sold automation promise and an operational reality requiring human labor.
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Two models pushed a complex second board through routing in about 15 hours of running
Fable and Opus progressed a second complex board through schematic, floor planning and routing over about 15 hours of running, with BGA escapes causing most of the problems.
This only partly worked. Worth knowing before you try it.
My agent built an AI club site for my kid to learn from
Peter Yang had the AI Duet build an AI Explorer Club for his kid, with an animated robot buddy, an adventure path and lessons, starting with what a chatbot is and a prompt she can copy and paste. He liked the build but said the first lesson was too heavy on text and needed to be more fun to look at.
This only partly worked. Worth knowing before you try it.
I asked the AI to update my docs. It deleted my source files.
He asked the AI to update his docs with his new API changes. The AI deleted his source files from his jj history instead.
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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.