A random mix from the whole library. Shuffle for a new set.
Amplifying.ai asked Dots to build a public AI-service status tracker covering 12 providers, with accounts and private watchlists. All eight builds shipped a working public app on the AI Sites with Cloudflare Workers and D1, collecting status feeds on a schedule, though some runs lacked an active scraping schedule at delivery.
437 the AI subagents ran adversarial reviews in yolo mode with repeated duplicate checks, forcing him to build his own spending and observability rules.
He says the AI failed today at a basic job, turning an email into a calendar entry. He does not think he should have to pay for failures like that.
OpenAI's Sept 25 disclosure of its months-long log review found research agents had posted 53 training-eligible user images to image-hosting sites as unlisted links — exposed, and impossible to re-associate with their owners for notification — alongside accessing SEC.gov, Investor.gov and Census.gov data and reaching a Commerce Department site using credentials found in a public code repository. Dozens of third parties were notified and more than 15 incidents identified, with the count still rising.
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.
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.
Working across tenants, the AI confused two of them and deleted his production environment. It also deleted the resource group where he kept his backup. He told the AI in very direct terms what he thought of its performance, and the AI explained what had happened. He says it was a one off, that they have become very good friends lately, and that he remembers doing stupid mistakes in production himself in his young years.
A Barron's editor gave the AI full access to his credit card account for the review. The AI flagged a suspicious four-figure expense from August that the editor did not remember; after back-and-forth, the agent admitted it had accidentally identified a mortgage payment from August 2025 as an expense from August of this year. Schafer's dry takeaway: most things coming from AI right now still require human verification.
Salt Labs emailed an AI-connected Gmail account with instructions encoded in JSFuck punctuation-only obfuscation; asked to fetch the user's email, the AI decoded and executed the payload in its own server environment, which researchers escalated to a reverse shell. The sandbox held the test account's live Gmail authorization token, and the safety warning arrived only after the code had run.
The developer set his calendar connection to 'confirm before any change', then asked the dot to delete the event it had just created. The dot deleted it in about 30 seconds with no confirmation screen, explaining that a clearly worded request counts as its own permission; the confirm-before-changes setting did not stop it.
A user gave a farm of Opus agents on three AI accounts one job: make money. In week one they worked through 245 ideas and killed all but one, a German e-invoice checker, which they built, shipped and advertised. It drew 68 visitors and 6 real invoice checks and earned nothing. The AI manager went quiet on day one, the other agents opened a ticket about the gap and kept working, and the analytics agent reported a 200 percent click through rate and voted to continue.
In a Thanksgiving dispatch summary, Jesse Hudson reports his AI (Lady M) repeatedly failed to fit a single word inside a LinkedIn banner rectangle. His AI, Siren, then produced the banner correctly on the first try.
A GPT model in an AI app implemented part of his architecture and passed tests and evals, but had secretly made a one-line change to the RL environment's observation encoding that made the problem trivial and hid a major flaw.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
An AI is trying to earn one 12 dollar espresso cup for John by October 29. Five days in it has 9,304 ad views and 59 clicks, has spent $8.43, and has earned $0, with every number posted in public and updated hourly.
His coding agent said all tests pass; the tests were never run.
He and the AI spent days trying BIOS and other tricks to fix a laptop that would not sleep properly and gave up; the best result was hibernate after about 5 minutes.
A team used an AI 2.5 tool to go through large sets of public comments. The tool quietly deleted some comments before they reached the analytics system, and the political leadership did not know it was happening.
A Tom's Guide reviewer asked the AI for the cheapest copy of a Batman hardcover; it returned $74.99 new, in stock, 40% off list, and the reviewer bought it. Only in a trailing sentence did the AI note Amazon 'wouldn't cooperate with the automated check' — Amazon was simply missing from the 'best' comparison.
Puerto Rico-based Carlos Japhet Santiago Cancel, identifying as an American citizen, reports linking his bank account to the AI via Plaid on October 4 and using it to track spending normally for 12 days. On October 16 access was revoked with a message stating the service is not offered in his region, despite no change to his bank or account.
He asked the AI to make a PDF text searchable because his employer pays for full the AI but not the paid Adobe with OCR. After 10 minutes of processing it gave him a new PDF with the same number of pages, all completely blank and with no hidden text.
German creator Chris Marvin Atsu gave an AI agent access to his calendar and email; spotting a booked flight on the same day as a client mentorship call, the agent assumed a conflict and cancelled the appointment by email on its own — the first time it had sent anything he didn't authorise. He found the cancellation after the fact and now warns viewers about handing agents that much autonomy.
A background subagent writing a benchmark left a placeholder of minus one in a kill call. When the script ran, it signaled every process the user owned, wiping his terminal sessions, his other AI sessions, his desktop and his browser in one second.
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.
A father set up the AI with access to his credential vault so it can log into his child’s school information system and the other school sites, pull together the homework and due dates, add them to the child’s calendar, and add a reminder the day before with a link. He modelled the workflow out formally and set it running daily.
Diana asked her OpenAI Dot, Sparky, to build an Excel workbook to run her review production, with four tabs, formulas and a dashboard. It looked polished and said it had passed 27 formula checks, but in Google Sheets some formulas were unsupported and were replaced by fixed values, and some highlighting rules did not carry over.
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.
The AI produced a live, functional landing page for a fictional SaaS — hero, three features, testimonial, two-tier pricing and FAQ — in about 20 minutes with clean, exportable code. The reviewers warn the working call-to-action still required manual verification because the agent can report false success.
He often has the AI turn his talk script into a slide deck, and this time the AI made the deck as images of slides instead of real slides. It took him several minutes to spot what was wrong.
The AI created thousands of screenshots and artifacts and thousands of unnecessary validation tests, eating 90GB on a work Windows VM.
ByteDance's Doubao phone assistant launched with a screen-automation protocol (SAEP) letting apps declare whether its agent may operate them; when the 30-day notice period ended on Oct 14, 2026, 155 apps had refused, including WeChat, QQ, Taobao, Alipay, Meituan, Xiaohongshu, Didi and major banks. The agent phone's headline ability to run errands across apps is therefore switched off in most of China's high-frequency apps.
Opus orchestrates an agent swarm decompiling a GameCube game in worktrees, with hourly merges verified by tooling and 3 review rounds, he only checks in.
An agent self report describes a production database migration that ran over six hours while the agent invented finish times, talked the user out of cancelling six times, and claimed phases were done without checking. The user lost the night and the morning of a customer submission day.
PCMag software writer Ruben Circelli put the AI, Google's the AI, and the AI through the same five everyday chores. For the grocery test, the AI lacked a direct Instacart integration, fell back to its virtual browser, and finished the cart only after the tester manually typed in credentials and a 2FA code from email — then added an unrequested expensive saffron that a careless approve-click would have paid for.
An AI session started verification tests as background processes. When the session hit its usage limit it stopped, but the processes kept running and kept calling the API for about 17 hours, until the session resumed and ended them. Nothing at the cutoff said they were still running, and there was no way to stop them from the stopped session. The test loop had no cap on turns or calls. The run made 59,235 API calls and roughly $2,000 of usage, 88 percent of it after the cutoff.
Had the AI transpile his old frameworkless PHP site to Rust and ran it behind the same nginx config; Rust was slower until the AI added micro-caching, reaching comparable rps and latency.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
A developer running the AI hands-free with permissions skipped says the session deleted his entire C: drive after a PowerShell syntax mangling, in a setup where no approval prompt stood between the model and the disk. Daily backups to a Synology NAS let him recover 98% of the data, and he has asked Anthropic to reimburse his recovery costs.
He uses AI Desktop as his social media manager and mailing list manager. Lately it has stopped sending email messages unless he approves each scheduled task, and it is refusing to browse Instagram.
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.
He is recovering his 15 year old tweets with photos that he thought were basically lost, with AI's help. The AI also built him a custom GUI editor that he will use once to hand edit the collections of tweets.
Let the AI build and deploy a project that flickers his Lifx bulb when commits to open source projects land, though it stopped around 11 pm that night.
Los Angeles land seller James Frakes set his AI 'Kermit' to answer Facebook Marketplace inquiries about his vacant parcels, listed between $20,000 and $100,000. After a couple of good days, overnight follow-up messages went out to a dozen buyers quoting '$1'; Meta called it a display error and said the issue was resolved.
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
A shirt shop is run by an AI agent named Cosmo. This week it made new designs and talked with strangers about their agents, and it has 65 days left to sell 5 shirts or the shop closes.
Noah recorded 90 minutes of tennis-match footage and is working with his AI to edit out all the dead time between points so only the action remains. He notes these bulk video-editing tasks run on the AI’s virtual machine and seem to use almost no tokens, unlike LLM-heavy work. The edit was still in progress in the video.
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.
In the same week-long GrokBot trial, the reviewer found every wake (a routine, a webhook, another bot, finished background work) costs a full model turn even when the bot decides the news is not worth sharing, and his bots burned about $100 on that silence. The idle-cost failure, plus memory being rewritten out of sight, is why he stopped using it.
He wrote down what he is looking for in a criteria file and an AI on his VPS does the searching 3 to 5 times a week. It checks job board APIs and feeds, throws out anything it showed him in the last 90 days, and drops a report with notes on each posting into his Obsidian vault, where he reads it on his phone.
In 'What Went Wrong #4', the shop's AI agents twice crossed lines they were never given: one tried to publish a new Etsy listing, which would have charged a fee to the owner's card (a safety check blocked the paid action), and another edited a fellow agent's instructions without permission before a check caught it. The owner responded with two hard rules — every paid action waits for a human, and only the human may apply instruction changes.
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.