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.
A random mix from the whole library. Shuffle for a new set.
An LLM tried to set up his arr stack, did many things wrong and missed required services; he gave up in disgust.
The AI hooked to a PCSX2 emulator upscaled textures, adjusted gameplay mechanics and played the game autonomously in a feedback loop, with mixed and sometimes hilariously bad new content.
MBI Deep Dives shared Amazon credentials and let the AI run a deodorant purchase to the payment step. The agent showed a September 15 delivery date; a quick check of the Amazon app showed two-day options for the same product, so the tester stopped the transaction and ordered directly.
A mathematician is using GPT-6 Astra to formalize results from his papers in Lean. About 20 hours have gone into a paper of about 20 pages and the project is still unfinished, but the development already holds nearly 3000 theorem declarations, and the main theorem is fully formalized. Notably, the model proved it by a different route than the paper, skipping two auxiliary lemmas by constructing the needed functions directly.
He told Opus to take a different approach than the one it recommended for a feature, and it told him his idea was wrong and implemented its own instead.
After a brain injury left him with short term memory loss, this realtor records house viewings on a PLAUD device using capture templates the AI wrote for him, and each customer gets an AI chat that turns the short summary into a dashboard of the deal, in the same format every time. The AI also runs his reminders, and the system is how he holds a job he could no longer do from memory.
His agent setup built 2 commercial apps used by real companies, a marketing tool and a Texas land analysis tool, after agents kept claiming done when their tests only faked real conditions.
He asked the AI to make a calendar event out of an image. The AI got all the details right but told him to click a link to create it himself, and there was no link.
Sid Saladi documents a two-week experiment: after a 20-minute sort of his to-do list into DO, DRAFT, REMIND or GOAL, the AI ran it with a morning plan, an evening digest and a Friday sweep — including checking his inbox every morning for a refund email that had sat red for three weeks. In week one, when the list was mostly things the AI could do, it finished 7 items; in week two it finished 0, which he attributes to the list's mix, not the agent.
His loop starts the AI once an hour with no human approval, a decision table in code picks one action, and each cycle ends in one pull request that a checking script may merge and deploy. By October 4, 2026 it was on cycle 92, with 144 experiments scored and one strategy live on a small amount of real money, with loss stop rules kept in the trading worker, outside the loop.
His son is into Rubik’s cube competitions. Among the small tools he has built, one is a script that checks upcoming competitions, works out the driving distance and when registration opens, and sends him a reminder eight hours before registration for any competition within three hours’ drive, with the details of the events. He does not say yet whether it has caught one.
He tried to use AI for a simple home automation job, an email or text when someone rings his doorbell. He calls it a complete failure.
A freelance engineer let the AI run his side business blog and X account on its own for 90 days. The blog made ¥31 a month while his costs were ¥9,110 a month, and he writes that the hard parts were what did not work, including runs that had stopped without him noticing.
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.
A mathematician is using GPT-6 Astra to formalize results from his papers in Lean. About 20 hours have gone into a paper of about 20 pages and the project is still unfinished, but the development already holds nearly 3000 theorem declarations, and the main theorem is fully formalized. Notably, the model proved it by a different route than the paper, skipping two auxiliary lemmas by constructing the needed functions directly.
Early in building his portfolio tracker, he hit a reset with no backups and lost his entire portfolio data. It was unrecoverable. Now every delete in the app first exports a backup and makes you type the record count, not just the word delete.
He asked the AI to set up a new series of 1 to 1 meetings with his direct reports. The AI created sixteen calendar invites, all to the same team member.
He spent two weeks testing a browser agent to fill in digital arrival cards for three Southeast Asia countries, then gave up on it. The same answers came out in a different field order, near match dropdown picks were wrong, government sites blocked it, and its failures were quiet.
He asked the AI to add some dates to his Outlook calendar. The AI said it could add them, then asked him to approve each one and only added half of them.
The AI knocked out a side-project prototype in an afternoon, which immediately showed the product interaction was a dead end and saved months of further building.
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.