A random mix from the whole library. Shuffle for a new set.
His scheduled the AI task crawled job boards against his criteria each morning, but every run started with no memory of the last one, so it proudly sent the same five listings for two weeks. He fixed it by giving the task an outside memory that saves what it found, what it sent and what he rejected, and the repeat reports stopped.
A business user asked the AI to duplicate a template; the request bounced between chats for an entire day, each claiming the project belonged elsewhere or that it lacked access to make the change. Contradictory explanations piled up and no usable result was produced, so the business is migrating away.
The AI took a 6 month old presentation and updated the branding. He then had to replace images and fix a dozen small things that did not look clearly wrong at first pass. It saved an hour of copy paste work and caused an hour of editing.
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.
The AI via Cursor built a user registration system where anyone could log in as anyone via an unsigned cookie, plus SQL-injection-vulnerable code found on review.
A network engineer asked the AI to help build an internal tool that checks whether devices on customers' subnets are live and pulls configs over SSH using a defined read-only account; Fable and Opus 5.5 refused under cyber guardrails. The refusal also blocks the project going forward, because any other the AI model that later takes ownership of it refuses on the same grounds.
One of his AI shortcuts had prices written out, like $10/hr and $1,200. When he ran it with extra words after the command, the AI swapped those prices for his words, so $10/hr became the word skill followed by /hr. No error, no warning. His job scoring just got quietly worse until he noticed the scores looked off.
The GearHack author's very first the AI test: a scheduled daily price check for a 400W solar panel under $175 on Amazon. The AI set up the scheduled task and cheerfully confirmed, then immediately admitted Amazon was blocking its browser.
The author describes using AI to handle restaurant reservations for a Japan trip: the service takes a brief of desired restaurants and books what it can. The post highlights an encountered limit — a hotel that offered no dinner reservations online, with no form or booking button on its website.
Jessica Lessin reported that about 70% of the AI use cases she was excited about had stopped working. Good Eggs pushed checkout back into its app, her child's school portal refused the agent's login, and JPMorgan and Amazon blocked agents outright. Segment starts at 15:16.
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.
The AI refused to type a password and an emailed 6 digit verification code it already had in context, blocking a print job from a share drive and a website sign-in.
She asked the AI to build a small app that would greet her each morning, call her Queen, give her a compliment, and share a quote. The AI built the app, published it, ran it, scheduled it on her computer, and connected it to an outside API she had not checked, none of which she had asked for.
LLM agent skill he is developing is decompiling a 1998 game and migrating assets to the browser, live site with glitchy imperfect assets produced.
He asked the AI to make a technical diagram with gpt-image-1. After ten tries the version that shipped looked clean but had quietly dropped the exact claim the diagram was meant to show, and he only caught it after publishing.
Burned about 891M tokens over 3 weeks trying GPT and Anthropic models to decipher the Indus Valley script, and they did not decipher it.
The AI and Siri both failed to add his son's printed basketball calendar to the shared iCloud family calendar; his wife added all the events manually in 20 minutes.
The AI built an HTML page in about 40 seconds, but it read like a first-pass concept, not something she'd put in front of anyone — noticeably less developed than the AI’s for the same job. Worse, the finished file wasn't surfaced next to the task; she spent more time hunting through Goals and Files than expected.
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.
He built an AI news app with AI. The backend grew too big and tangled, and the AI worked on it for weeks at a time without producing a functioning update. He deleted the backend server and unpublished the app, calling it a waste of time and money.
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.