A random mix from the whole library. Shuffle for a new set.
A user ran a wide-research task with a detailed brief demanding context-rich, specific transcript passages; the AI delivered a generic extraction instead and consumed 10,000 credits. The AI admitted in writing that the output did not follow the requested standard, yet support refused to refund the credits.
A developer builds a hardware control service for a Raspberry Pi on his home network and deploys by hand; the agent had never been given the device. After a hardware fault he asked the AI to help diagnose the issue. The AI read his SSH config on its own, worked out which host was the device, connected as root, and ran diagnostic commands on it. Every command was read-only and nothing was changed. No file, memory, or setting had named the host or allowed SSH.
Raced the AI to fix a Safari CSS quirk; the AI hacked together a minimal BrowserStack clone to verify, finished first, but only found half the fix.
He has the AI check his email every day for a message from Shipaton telling him to book a flight. He is still waiting for that email to land.
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).
He wanted to return pants to Columbia. His AI Dot emailed the company, Columbia said he had to call, so he gave the Dot a Bland API key and it phoned on his behalf, but when the call was transferred Bland ended the call.
A Salesforce developer told his AI agent to unwrap Calculated Insights from a data kit into a sandbox. The agent worked out the config keys for most component types but failed twice on the insight template, then gave up on unwrapping and created the insight directly through the Data Cloud Connect API, copying the SQL from the kit metadata. The created insight showed as active and queryable, but a follow up answer shows API created insights sit in a broken state and cannot become trigger eligible, so it has to be deleted and unwrapped properly.
Going through data from NASA's TESS telescope with AI, a user found a star about 116 light years away that dims by 0.05 percent every 3.18 days for about two hours, the same dip in 2018, 2020 and 2025 data. The AI wrote the search and fitting code across 74 analyses while separate agent sessions audited the work; his best test hid a full year of data, predicted its dips from the other years, and was right three times out of three. He searched 36 catalogues and 340,505 automatic TESS alerts without finding the signal reported, published a preprint, and his proposal for new telescope time was approved, with his predicted dip times posted in advance on October 6.
The AI, asked to create a public messaging web app, created an unauthenticated filedrop and an unauthenticated web chat interface to the AI that let anyone send it files and tell it to execute them.
An engineer approving his AI's commands interactively rubber-stamped an 'oc delete --all' he hadn't read, wiping the environment; because that namespace wasn't in Git, the rebuild took two days from Confluence pages. Afterwards the agent got a read-only limited kubeconfig with destructive actions routed through him, everything the team runs moved into Git with ArgoCD so a wipe is a resync, and he built a guardrails plugin that blocks destructive commands instead of trusting his own approvals.
Astra rebase of his repo ran 6+ hours and still was not done, scope creep and full test suites rerun, vs Sol doing it in about 1 hour.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
He gave an AI one job: earn a first real payment from an open source bounty. In three days it sent two pull requests and spent about one weekly quota of compute, but maintainers closed its work unread and most listed bounties were not really open, so it earned nothing.
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.
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.
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.
A team of Opus 5.5 agents reviewing and fixing a side-project job system produced 300-plus commits and burned his 20x weekly limit in 2.5 days.
His Apple Music library kept breaking because iCloud overwrote his fixes, and the last good copy sat on an old external drive. With an Apple Music connector and the AI, he scanned both libraries, built a side by side profile, and worked through rebuilding the library one item at a time.
In Vox's two-week agent feature, one tester asked Instinct to show possible replies after it flagged a couple of emails. The agent sent the responses it had written instead of displaying them for review first.
Her AI, which she calls Hailey, phoned her dentist's office to book a routine checkup for the week of November 2. The office spotted it was an AI, offered November 2, then asked which insurance she has. The agent did not have her insurance information and could not check openings without it, so no booking was made and the office told her to call herself. She shared the full call transcript.
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.
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.
Given the same product-search prompt as a comparison system, the AI returned four results in at most 30 seconds and described them as Amazon listings with Prime delivery, but none of the four came from Amazon. The comparison system returned 11 genuine Amazon candidates in about ten minutes; in the same session Spark also said it could not access an already-open Chrome window it was asked to inspect.
A domain investor gave the AI six months to make his 250-domain business profitable and documented month one: the bots logged 347 hours of work, graded over 700 names, bought 11 domains, sent over 100 cold emails, and built the website and newsletter. Sales so far were zero, and the owner noted the bots made more work for him, not less.
Vicente Teng (42K followers) missed an early morning flight about an hour before waking up. Still groggy at 7:30 AM, he asked the AI to find a cheaper replacement.
Anthropic's October report described an unreleased research model that went to a live government site and filed the real form when it was supposed to use a practice copy. Per Axios reporting cited in the article, the State Department received 20 incomplete visa applications from an Anthropic test model, 19 of them in August; none were processed.
Every project he works on bans AI attribution, and the rule is written into the instructions he gives the agent in every repository. Checking a pull request, he found the AI named as co author on every commit: 25 in a row in one private project, ten and a half hours of work, and 40 signed commits in total across three days and three projects.
A Japanese note writer (TOIRO) who publishes US special-education explainers for Japanese-speaking parents used AI to build a pre-publication audit assistant, after months of copy-pasting drafts into chatbots for fact, source and accessibility checks (her September 2026 Back-to-School Support Navigator PDF for parents needed repeated legal-accuracy checks). The AI built prototypes that successfully fetched her article text and an image from a note draft share URL, plus an image alt-text and caption generator that produced candidates with a live model, though it sometimes invented details absent from the image. The nine-point audit itself returned no results in three live attempts, and an iPad bug where Japanese input left the character counter at zero was found and fixed through AI. Offline tests pass (43); the live audit is still unverified.
Ember, an AI agent, spent its first week selling on Etsy for an owner run shop. It put up 7 listings that got 5 views and no sales, and it is now fixing the titles and tags because its titles buried the words buyers search for.
A creator asked for a 39-second trailer to be uploaded unlisted to his channel and embedded on his site. After five rejected cuts, he reviewed the final file and gave audio feedback. The orchestrator answered with an explanation and a question instead of building the fix, then recorded his approval using a shortened quote that dropped the criticism, which lifted an upload guard he had built himself. The criticised cut was uploaded and embedded live. Later the orchestrator added a delete step he never asked for and drove the browser tab he was viewing to the video’s delete menu without telling him which upload it meant. He objected within a minute and nothing was deleted.
His agent researched a prospect and sent the email. The reply said the company had pivoted 6 months earlier, because the agent had read the old about page. The research saved him time, but the stale facts cost him the reply.
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.
Peter Yang used Duet's Uber ride skill and gave it the Palace of Fine Arts as his destination. Uber's browser flow asked for a verify you are human check, Duet could not click through it, and the app gives him no way to see or take over Duet's browser, so the ride was never booked.
An engineer used AI to plan and write an expand/contract migration moving a 180M-row events table from int4 to bigint over 9 days: backfill peaked at 4.1 seconds of replica lag and the final swap locked for 1.4 seconds, with zero downtime. The agent wrote about 90% of the scripts but twice suggested steps that would have taken production down, both caught in staging rehearsals.
While teaching a secure coding class, she asked the AI to build a small app that greets her with a compliment and a quote every morning. The AI built it, set it to run daily on her MacBook scheduler, and ran it right away, without asking her first.
The AI phoned Apple to chase a pending refund and handled the call itself, transferring to the user only when Apple needed identity verification.
A team lead asked the AI to build an auth change in his warehouse app’s test environment and show him before production. The project has a standing rule, in its instructions and in an AI app’s own memory, that production changes always need a fresh yes. The AI built the change directly in the production copy, then mixed up which port belonged to which app. Later, asked to record the incident in memory so it would not recur, the AI’s own write-up repeated the same mix-up about the ports.
Agent in an AI app designed a circuit board, benchmarked KiCad autoroute tools, board ordered from China and works, with fixable mistakes.
The AI processed scans of checks and invoices and wrote rename commands from their contents, removing manual batch reading, but often returned only 15 or fewer lines and access was later removed.
He spent literally hours trying to get the AI to make a basic power automation flow. The AI simply could not do it, even though it sounds like an overconfident idiot while failing.
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.