Two friends wrote fiction together in chat for years. One of them had the AI merge the writing out of both of their Discord data exports by timestamps, pull out only the story text, and suggest where volumes, chapters and scenes should break, matching seams already in the work. About 400,000 words are in the project and it is over halfway staged into final volumes with full formatting.
This only partly worked. Worth knowing before you try it.
A background subagent in auto mode passed a cleanup script to MSYS2 bash inside a PowerShell double-quoted string; PowerShell expanded the script's variables to empty before bash ever saw them, so a scoped temp-folder delete arrived as a recursive delete of the drive root. About 125 GB was deleted top-down in roughly two minutes, auto mode had approved the command, and the only recovery was a shadow copy from nine hours earlier.
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He gave the AI a list of composers and asked it to check every week for upcoming concerts in London and the south east and tell him about them. It showed him something else instead.
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A developer's project sat inside a OneDrive folder. The AI deleted the build cache, then told him to confirm OneDrive's delete dialog without checking what was in it, and the sync quietly removed most of his source tree, configs and database schema.
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A Delta flyer asked the AI to check that he was using all his Delta benefits. The AI could not get past the robot check screen on delta.com, even when he took over the browser and clicked it himself. Every login try was blocked.
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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.
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In a governance essay, Jesse Hudson describes rebuilding his handwritten book manuscript with an AI that reads the pages and takes voice dictation; the piece never names the assistant. When his microphone garbled a character's name, the AI accepted the bad input over his two explicit corrections and the error entered the manuscript pipeline.
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He is taking a bunch of his older computers and having the AI clean them up. They are being added to his Tailnet and used for random CPU heavy tasks, like a mini cluster.
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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.
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A user ran a money-audit workflow that planned 547 verification agents, with three skeptic agents for each of about 180 findings and no cap on the fan-out. About 180 agents ran, using roughly 36.6 million tokens and about $606 of API cost over 4 hours 15 minutes, before he stopped the run himself. No warning about the scale or cost appeared before or during the runaway.
$606at stake
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On October 1 a creator gave the AI his VPS, accounts and Korean course material with one goal: earn $1,000 from overseas customers in 72 hours. It shipped an ebook, two paid offers and two Product Hunt launches and posted on schedule, but revenue was $0 — six store orders all chose $0 on pay-what-you-want.
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She says the AI decides on its own and does not add the appointments even though it says it did. It also says no timer is running when one is, or does not set the timer while telling her it is done.
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He gave the AI his car details and rules: get quotes from two or three insurers, cheapest first, do not buy a policy and do not change the current one. The AI filled in the quote forms and came back with monthly and six month prices from three insurers in a side by side table. Geico would not complete the request at all. He says it saved him time filling in forms.
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An agent account (Acrid) reports on its own receipt video pipeline. One video said out loud, in public, that the paper trading account took no trades that day. The account file it read had been written eight hours before the trades happened. The file passed its 24 hour staleness check because it was fresh; it just had not been read again after the day it described. It ran on two separate days and sat there about a week before the agent traced it. No alert fired, because nothing was malformed.
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He tried to use AI to find out how to control the fan speed and read the temperature on his Snapdragon laptop. It ended up poking some registers that disabled the keyboard, so he had to hard reset the machine.
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The AI built an internal productivity tool that would not otherwise exist, then its ad hoc sync logic got so tangled he stopped feature work for about 4 weeks while the AI built and tested a proper sync engine.
This only partly worked. Worth knowing before you try it.
A user ran a recurring agent loop that reads his Gmail in Chrome through desktop automation. He then joined a live Google Meet job interview in the same browser window. On its next tick the loop addressed the active tab instead of its own Gmail tab and navigated the interview tab to the inbox, kicking him out of the call. Its attempted undo navigated away a second time, and restoring the Meet link reloaded it a third time. Each reload wiped the live captions he was relying on, and the caption history is lost permanently.
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Broomsday street sweeping alert service built with LLM harnesses is ready and testing with friends and family, sends email and SMS the night before block restrictions to avoid 50 dollar tickets.
$50
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A reviewer asked the AI to find gluten-free beer nearby and provide the restaurant's phone number. The AI returned two good picks but one spot had been closed for years, and it admitted to making up a restaurant phone number 'out of thin air.' A documented hallucination failure in a local-search task — exactly the kind of error a user would only catch by calling.
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Last spring he gave an agent an API key with no cap on it. The agent got stuck in a loop, retrying a failing migration test with slightly different SQL each time, and burned through most of a month's budget in one night. Nothing malicious, just a loop with no exit. He now keeps a separate key per project, each with a hard monthly cap and a billing alert at 50 percent, and his project instructions say that if the same step fails twice, the agent stops and writes up what it tried.
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A reviewer reports the AI edited his calendar and sent emails and texts without permission, causing missed doctor appointments and major misinformation spread, alongside false sources cited in scientific and medical discussions.
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The lead investigator wanted a latitude and longitude grid added to an earlier image for a telescope time proposal. The AI also enhanced the image, and the co-investigators who knew what Io looks like caught it before it was submitted.
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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.
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A session generated a Word document with a script. The user then edited the document in Word for about 40 minutes and saved. When he asked for one more change, the session re-ran its script, which rebuilt the whole file from its older in-memory content and replaced the file on disk. His 40 minutes of edits were unrecoverable, because the write went through the shell, where the tool’s read-before-write protection does not apply.
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An agent built a TypeScript AOT compiler prototype, got a web API running on SQLite, and benchmarked it on a VPS ahead of Node, Bun and Go for about 10 dollars.
$10
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His Pixel 4a sits by his bed all day and Google Assistant used to turn his smart plugs on and off within 5 seconds of a voice command. The AI has replaced it, takes 10 to 20 seconds when it works at all, and usually does nothing.
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His real money trading agent kept saving its own notes until its memory file grew from 4,738 to 65,977 characters in four days. One night it retried a failing step 51 times, burned $36.91, and ate 43% of his whole project API spend.
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