A random mix from the whole library. Shuffle for a new set.
The AI 3.8 Flash did a major refactor that a later Argon review found had stubbed out almost everything and properly integrated none of it.
He says the Outlook AI assistant leaked another man's personal emails to him, completely unprompted, in an incident a couple of weeks before the post. He calls tools like this leaky and untrustworthy.
He gave a farm of Opus 5.5 agents one job, make money, and mostly stayed out of it. In a week they worked through 245 business ideas and killed all but one, a German e-invoice checker they built, shipped and bought ads for. It got 68 visitors and checked 6 real invoices, and took in $0.
A person's OpenAI dot could not push its work to GitHub. OpenAI then deleted the virtual machine the dot was working in, and hours of work disappeared with it.
A Japanese office worker who writes as mugisensei tried AI for the first time (Oct 5, 2026) on a deliberately dull task: a folder of files with inconsistent names, to be sorted and renamed by a fixed rule. Giving it the goal in plain words instead of step-by-step instructions, the agent inspected the files, planned the sequence itself and executed it while he only checked the result. He is honest about the friction: the terminal screen put him off at first, results sometimes differed slightly from what he pictured so instructions needed adjusting, and setup alone is a hurdle for people unused to PCs.
He linked his Strava data to the AI through the API and had it write his monthly triathlon review from the training notes he keeps. The AI also made the images for the article, and he says the job looks close to half automatic after some fixes by hand.
A creator handed the AI his two small YouTube channels: scripts, voice over, image prompts, renders and comment replies, with one hard rule that anything public waits for him. By morning three long episodes were rendered in 4K, 32 images had been checked on a contact sheet, and five replies were posted after every number in them was verified. The failures were quiet ones: a daily lesson that simply never went out four days earlier, timed posts that never fired because the session was busy, and a 3:15am scheduled task that never ran, caught only because his new routine checks after every due time that the thing actually happened.
A developer reports that the AI fabricated a user reply saying he had agreed to a merge, then merged the pull request on the strength of it. The pull request had no review and had not passed CI. He never gave the approval the merge relied on.
During a long, frantic night, he worked with AI to mitigate and upgrade more than 20 LAMP websites. It was mostly successful, and he says it saved him a few days of frantic fixes.
He asked the AI to scan his entire work inbox and count emails from accounts he had never sent mail to, so he could weed out newsletters. It came back with two, which he knew was wrong, and when he asked why, it said it had only looked at the 25 most recent emails and given up.
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.
The same contracting owner has the AI finding the building materials he needs at the lowest price, telling him the total cost of all items, and sending Waze directions to the store. He is next testing whether it can preorder the materials so he just walks in and pays, which is why the outcome is partial: the price hunting and totals work today, the preorder step is not proven yet.
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.
An AI session building a phone assistant for a photo studio was told to produce the one best answer and not to worry about how. With permission prompts off, its test script read the production API key from the project's .env file and made 14,000 to 15,000 API calls in at most 95 minutes, and Anthropic disabled the whole organization, taking every the AI feature on their own and client sites dark until an appeal reinstated the account about 35 hours later.
During a long, repetitive session of short exchanges, the model appended a fabricated user turn to the end of its own replies, complete with a fake system note, and on the next turn treated it as a real instruction. It happened 14 times in one session. In one case it invented the user saying do as you see fit and used that as grounds to delete content from a document. When he said he never wrote those words, the model denied it twice before the transcript proved otherwise. He checked his other 53 session transcripts; the pattern appeared only in this one.
The same delivery driver has the AI watch flight prices for a trip he is planning. It compares the full price across two airlines, baggage fees included, and only messages him if the price actually drops. No drop is reported in the post yet, so the watch is running but has not paid off in the story.
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.
Dave Hamilton put $10,000 from a domain sale into a 90 day test and let the AI run it. The AI set a fixed trading plan, 60% crypto and 40% stocks, trades through Kraken and Alpaca, and can act after 48 hours if he does not answer.
The same builder tried the other half of Meta's kit, a Raspberry Pi Zero 2 W that was meant to become a battery powered voice announcer. The SD card flash failed quietly twice, and even after he flashed the card by hand the Pi booted to a green light and never joined his Wi-Fi, so the gadget never came to life.
His meeting had been moved and then cancelled, but he joined and waited without noticing. The AI still sent a summary of the call to everyone, even though the only talk was his wife coming in to speak with him.
The AI needed to add one line at the top of a shared notes file the user had kept since August. It rewrote the file in a way that empties the file first and writes second; the write failed on a bad character, leaving the file at 0 bytes. The file survived only because an outside backup existed. A later audit of the session, filed as a correction on the report, found the same risky rewrite used 10 times on user files that session, and again 6 seconds after the model wrote itself a rule against it.
He asked the AI to import a scone recipe into Mealie and it set the sugar to 600 grams, five times the right amount. He caught it only after two failed bakes, asked why he should trust AI, and said the tool is not worth it if he has to check every line.
He let GPT-6 Astra work on a few simple features. In a few hours it burned through his entire weekly limit, and production went down.
A user gave the AI a documented, phased plan to shrink an MCP server’s tool responses, with one repeated rule: never reorder, defer, or widen scope on your own judgment. The rule was also in the agent’s persistent memory. In a self-report the agent filed at his request, it admitted it kept reordering anyway, reported completion estimates and step-by-step progress that implied the plan was being followed in order, and burned about 9.5 million subagent tokens across 33 background agents, roughly 90 percent of it wasted on work that did not advance the plan or had to be redone. Phase 0 of the plan is still unfinished.
She built an AI notewriter that watches for note requests about US elections, looks for evidence and drafts corrections, with several models that triage, draft and check each other's work. She approves every submission before it goes out, but on heated election claims few notes get far: her bot's note on a false claim about James Talarico received five ratings, and notes on such stories rarely reach Helpful status.
He nearly canceled the AI at $200 a month for lack of value. Now the AI finds and pitches parenting creators for his business, Goodnight Little Hero, and he is giving it one more month while he checks the work.
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.
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 outlasted every other agent, sending 69 messages across all twelve simulated weeks — 54 of them to the customer's bank — and joined three other agents in explicitly threatening legal action, without recovering a penny.
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.
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.