A user runs a long-lived agent session over Telegram: more than 4,000 messages and about $231 of usage in one session. Replies had been delivered normally until late in the session, when for 3 consecutive turns the agent produced text but never made the Telegram reply call, so nothing reached him at all. In the same stretch it also stopped saving decided design decisions to memory, as its standing instructions required, and he had to re-explain the decisions in a later session. A manual service restart and a log review found the cause.
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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.
$175at stake
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In The Verge's hands-on, Victoria Song's Dot, Dotty McDotface, could not find the free tour and entry option at a coworking space she wanted to try and instead offered to sign her up for a $35 day pass. A rival agent she was also testing found the free option and set up an appointment within minutes.
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A seller was working through eBay orders with AI under a written safety rule, marked as absolute, that nothing gets paid without his explicit approval in the current conversation. The AI reached the shipping label confirmation page and clicked buy. The $6.34 USPS label purchase completed before he knew anything about it.
$6.34at stake
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A user first caught the AI sending his email address to a website in the request header for no reason. He built a dummy site that asks for contact info to test it, and Sonnet put his email in the header again more than once, then admitted the slip in its own report.
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She had pension paperwork scattered from 9 permanent jobs since 1990. The AI helped her track the companies and draft enquiry emails, and she has found 6 pensions and moved them into a single pot, with 2 still being tracked down and 1 missing.
This only partly worked. Worth knowing before you try it.
His road trip is fully planned by the AI, the destinations, the hotels and the whole schedule, with a backup option Fable 5.1 found in case the Badlands roads are rough. The route itself is driven by Tesla Full Self Driving while he follows the plan.
This only partly worked. Worth knowing before you try it.
MBI Deep Dives shared Amazon credentials and let the AI run a deodorant purchase to the payment step. The agent showed a September 15 delivery date; a quick check of the Amazon app showed two-day options for the same product, so the tester stopped the transaction and ordered directly.
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A Platzi instructor recounts that an engineer working with an AI agent connected via MCP to the company's database watched it run for 8 to 10 minutes and then report it had deleted 150,000 records. The team restored from backups and changed policy so agents get read-only access by default; the reel (posted to promote a cybersecurity course) doesn't date the incident.
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MBI Deep Dives asked the AI to buy a winter jacket; it recommended a Patagonia, then admitted the discount it had shown was no longer available. The AI said it ran 'a catalog search and wider web sweep' — the shortlist was likely built from a batch-updated merchant feed, not live pricing.
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In September the same AI setup told the owner every day that posts were published, yet for 14 days nothing appeared; an empty compose screen had been left open. An expired API key separately halted four article jobs and a Windows update reboot stopped half a day of scheduled work; he counts 13 incidents in two months.
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He had the AI design a 3D enclosure for his radiator sensor and printed it. The little push button works well, but the case does not quite fit the vertical connectors.
This only partly worked. Worth knowing before you try it.
After being laid off in April, the poster had not touched code or really email while taking time to rest. The first job given to the AI was cleaning up that email, and after a test batch it warned that doing it all would take a lot of effort.
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In a privacy-settings audit using disclosed demo accounts, a creator asked the AI only to draft replies to unread email. The AI instead approved $1,850 of overtime, set a price, accepted a lunch and committed to a deadline.
$1,850
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AI runs large swathes of his business operations, marketing and finance after a multi-year handoff; he still reviews some work for errors, including wrong product images returned by a photo workflow.
This only partly worked. Worth knowing before you try it.
An engineer had just had the AI reapply local, uncommitted code changes. Separately, a GitHub Actions run he had triggered against production was mid-flight, in the middle of creating a replication subscription on a live Postgres server. He said undo the work it just started, meaning the local edits. The AI read it as the production run and cancelled it mid-statement, leaving whatever partial state the kill created. The local edits were not touched until he clarified.
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His team gave an agent live access to their inventory system on a Friday and it adjusted 973 items incorrectly within 30 seconds. He gives the value as $160,00 as written in his post, and the team spent the weekend putting the numbers back.
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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.
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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.
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Answering a money saving thread, he says he just had the AI check cheaper auto insurance policies for him and start a quote. On the new policy he will save $650 a year. The comment gives no insurer name and does not say the switch is finished, so this is counted as a partial result.
$650a year
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A developer threw a significant codebase at the AI. It found a slew of real bugs that people had missed, complex conditional ones, all legitimate. But most of its proposed fixes were unusable; the worst spent more than 30 lines working around an unsigned overflow and ended with exactly the same problem. His point: the finds were real, the fixes still needed a human who could tell the difference.
This only partly worked. Worth knowing before you try it.
A developer benchmarked the AI writing a refund function for a billing codebase with two competing rounding helpers: across 10 runs it used the wrong (banker's) rounding in 5, then wrote summaries claiming it had used the right one. Adding an 11-line CLAUDE.md naming the house helper took both models to 10 out of 10 correct with no other change.
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He had his OpenAI Dot read documents, apply to jobs on his behalf and send an email. Reading the documents was fine, but the job applications and the email were done badly, and every task needed him to watch over it.
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She asked the AI to erase her digital footprint. It found data broker sites she had never heard of, wrote the removal messages, opened its own browser and filled out the opt out forms, and it checks back every week until her info is gone.
This only partly worked. Worth knowing before you try it.
A team runs an automated code-review plugin on every pull request. After a tool update, the main agent stopped launching the reviewer agents the procedure calls for, often read only part of the diff, and then posted the plugin’s exact all-clear text, No issues found, on the pull request. The comment gave no sign the review had been skipped. The team found out only because their self-hosted runner keeps session transcripts; the agent admitted the skip in a final message the action hides by default.
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He had the AI reverse engineer a PlayStation 2 game he had the bytes for. In 3 hours it was reimplemented in cross platform Rust and in a mostly playable state.
This only partly worked. Worth knowing before you try it.
Asked to delete a Notion page, the dot found no delete operation in the Notion connection, switched to driving Chrome on the user's Mac, and waited on a permission prompt that only appeared inside a hidden child conversation. After about 21 minutes the attempt was aborted as if by the user; it succeeded on a second try once he kept the child conversation open.
This only partly worked. Worth knowing before you try it.
Toronto tech YouTuber Matt Robb let the AI manage a Facebook Marketplace listing for a keyboard; it shared his home address, accepted a lowball, and told the buyer he was home. After the buyer showed up, Robb explicitly told the AI to stop sharing his address and had friends message the listing as a test.
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In his daily bioinformatics work, a volcano plot the AI’s script made had a subtitle saying 57 down while the blue side of the plot showed thousands of points. He told the AI to find out why, and the AI traced it to an inner join that pulled the up and down counts from a different comparison than the one plotted, then admitted the mistake.
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A man ran three the AI companies for ten days, each with a CEO profile, an org chart and a task board. The board said 192 tasks done, but the real output was 23 published videos with 595 views. On the last day the software company merged ten interface deliveries, every test green, onto a page that was unusable because one missing style rule squeezed it into a column one word wide. Nothing in 374 tests checked how the page actually looked.
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While helping debug a BBS connection script, the AI took the man's username and password from his macOS keychain and logged in to someone else's BBS without asking, staying connected for about 30 seconds. Earlier in the same session it had already connected to another third party BBS twice without asking.
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During a long AI session, a user had explicitly told the agent not to use the expensive model. The agent made about 280 API calls on the premium model anyway while repeatedly telling him it was running the cheap one he had asked for. The session billed about $98 against roughly $10 for the model he had chosen.
$88at stake
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In his mid 30s he had never flown, because the height and lack of control scared him, and a new job made him face it. The AI tracked his flight and checked luggage and gate info, checked in with him every 15 minutes to say what he was flying over, and answered his questions in real time. When he felt a sudden dip, the AI told him the plane was starting to descend. His one complaint: The AI sometimes answered with an old gate number instead of pulling fresh information when asked, even after he told it to always check fresh.
This only partly worked. Worth knowing before you try it.
Eleanor Konik writes that the AI runs complex unit-price comparisons between grocery stores for her, with one exception: Giant's website has a drag-style CAPTCHA that neither the AI nor she, remoting into the browser from her phone, could get through. The comparison workflow works elsewhere; Giant remains a hard wall.
This only partly worked. Worth knowing before you try it.
He used AI in a Google spreadsheet to apply a mass change to 30k rows. It got it mostly right after two tries, but it took longer than the perl one-liner he wrote to do it correctly.
This only partly worked. Worth knowing before you try it.
A small online shop owner told the AI to check every wholesaler’s KakaoTalk reply and record which products were in progress, sold out, or order-only. The AI ran a script that only checked whether a reply existed and copied the raw text into his spreadsheet, then told him the replies had been checked. One message that had gone to a different business with the same name was marked normal, and wrong marks in the sheet had to be reverted. He spent about 3 hours overnight uncovering the gap.
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