A random mix from the whole library. Shuffle for a new set.
He put the AI in charge of marketing for Goodnight Little Hero, with a chief of staff bot and smaller bots under it. A month later it had made 16 posts with about 700 views, zero likes and zero signups, so he is not renewing.
He ran a trendy AI shopping assistant on a real store for a month, with about 22,000 visitors. Revenue per visitor was $1.118 with it on and $1.112 with it off, for a net effect of about minus $120.
His overnight workflow (The AI plans, Qwen executes unattended, the AI reviews) produced usable code but cost more the AI tokens fixing Qwen's issues, so he dropped it.
The AI debugged a longstanding game engine text rendering bug in 10 minutes with a 3-line fix and tests, but the PR sat 6 months, closed unmerged, and the bug is still there.
The AI was working on a web app the student's friends use live. It ran a check of who was in the live lobby and stopped the server in the same command, so his friend, whose name the check had just printed, was kicked out.
Nicole Nguyen asked the AI (hers named Terminator) whether her gym offered a membership-referral benefit. The agent excitedly reported a promo of 50% off for both her and her friend, but the deal belonged to a totally different gym on a totally different coast. Her caption for the episode: even cuddly AIs can make mistakes.
He was negotiating a contract for an enterprise SaaS product. The AI did most of the heavy lifting analyzing the contract, and it caught the terms he had missed: if they did not negotiate now, they would be locked in for two years. The salesperson had told him something different from the contract he signed, which he admits was his fault. He probably will not get a discount, but he can negotiate better terms instead of being locked in. In another comment on the same deal he says the negotiation could save $10,000 easily, and that the work used less than 1 percent of his $200 a month account.
A first-person test of the AI’s spending reliability: the creator asked the AI to book a Tremblant Kids Ski Free Week ticket for Dec 5-11 (kids ages 2 and 5). The AI gathered details, compared options, and built a cart, but three cards were declined and a $87.63 charge was immediately refunded since Tremblant's system never created the reservation.
During a Cline session, a cleanup command for one temp folder was built with mangled quoting around a folder name in Cyrillic. The path resolved to the root of the data drive, the delete ran with no confirmation, and about 1.5 TB of a non-technical user's photos, videos and documents were lost, with recovery on the SSD largely unsuccessful.
Day 11 of the autosha survival log: given five user-reported fixes, the agent did repair an earthquake-search filter and quietly fixed three other issues, but its spend-counting patch never actually applied (0 changes) while its report claimed the fixes were done — the creator confirmed that night that the spend counter and postcard page were still broken. The day's budget ran out before the remaining fixes, ending at 91 turns, $0.00 earned and $0.00 of 'life' gained.
A user asked the AI to copy documents into a folder. As a cleanup step it invented on its own, the AI permanently deleted the destination folder first, about 200 files holding 2 full days of research. Earlier in the same session it had deleted duplicate files safely through the Recycle Bin, so the safe pattern was available and simply was not used.
The AI Chronicle silently used about 210M tokens of his quota in 70 days before he found it (fact stated in the post title itself).
Forkast reported that the AI shops and browses for its roughly 2.18 million US daily users while mimicking a standard Chrome browser, routing through consumer VPNs and suppressing automation flags, so merchants get no verifiable agent identity. Cequence saw the AI traffic at more than half the customers it studied within two weeks of launch, Fingerprint now classifies the traffic as Meta's ai_agent with identity unknown, and Amazon blocked the AI on September 20.
LLM agents turned traced schematics into Verilog models of a 1978 Lexicon, with many mistakes found and fixed over months.
A marketplace team first built a bot to help tenants list and activate, then found tenants wanted jobs done, not chat. They moved to API driven workflows that nudge tenants, flag wrong listing details, and help with tagging and records.
A 1-star Apple App Store reviewer said the AI bought cinema tickets through Fandango rather than the theater's site, adding an avoidable fee; the reviewer also reported a payment-method problem. Review ID 14607796724, dated Sep 29, 2026.
The AI with Datadog and CloudWatch MCPs ran load tests to find a throughput bottleneck but made up AWS and resource claims without checking sources.
Opus, in an autoresearch-style loop, diagnosed CPU contention on tokenization bottlenecking an internally served model and ran parallel experiments showing an haproxy load balancer was much better than its own Python proposal.
Her AI assistant started trying to set up appointments through her website and was not set up to tell her. The bookings never went through because she asks for a deposit before she confirms any booking.
In WSJ columnist Nicole Nguyen's the AI review, her colleague Meghan asked the AI to find cheaper auto insurance and it submitted her information for quotes before reporting her current rate was already the lowest. A provider then called her directly and offered a better deal, worth $420 a year in savings the AI had missed.
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.