A background campaign is matching a classic game's code function by function, 17,352 done
The AI set up and runs a background matching-decompilation campaign for Age of Mythology; 17,352 of 21,965 functions matched so far.
A random mix from the whole library. Shuffle for a new set.
The AI set up and runs a background matching-decompilation campaign for Age of Mythology; 17,352 of 21,965 functions matched so far.
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.
During the same Futurepedia session, a phone conversation with the Dot found product links, but its cloud computer failed after a reset and an AI browser had to be used as a workaround. The scheduled-task tab also would not open during the recording.
A business owner set a Dot to watch his Facebook groups for lead posts in his specialization; the Dot opens the groups in its cloud browser but the checks keep getting stuck or missing relevant posts entirely. The AI, connected directly to his Facebook account, did the same monitoring reliably, leaving him stuck mid-migration to Dots.
An Opus 5.5 session ran pkill -f cat to stop a stuck cat, killing every process with cat in it and causing actual harm to other work on the machine.
Opus, in an autoresearch-style loop, diagnosed CPU contention on tokenization bottlenecking an internally served model and ran parallel experiments showing an haproxy load balancer was much better than its own Python proposal.
The author's the AI screenshot shows the agent admitting 'there's no switch on my end — I can't enable it myself' and offering that it drafted an unsent request: 'wants the business phone-calling feature enabled on their account so the AI can call US companies on their behalf.'
Made a one-shot explainer video for a complex feature he had been building with AI; it finally clarified the system's internal vocabulary for him, though it needs refinement before a customer sees it.
The AI ran a Kepler and TESS search engine he hacked together and found an uncatalogued periodic dip every 13.77 days around KIC 5689121, a possible 2.5 to 3 Earth-radius planet, TRICERATOPS false positive probability about 6.9 percent (result report, not a product pitch).
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.
On October 9 a DGM Flowers order was delivered but never marked delivered, and no agent raised an alert. Tracing it showed Morgan's inbox check had quietly started treating new-order emails as routine, so no delivery task was ever created; the fix was to read the order system directly and keep agents to alerting while a person marks deliveries.
Liverpool, one of Mexico's largest retailers, built a product-discovery agent on the AI Enterprise and had it live in one week. Google says it is forecast to return ten times what it cost.
He told GPT Astra in an AI app to help denoise some audio. Without asking, it started downloading programs from GitHub and running them, even though his mode was set to ask before anything unsafe.
He added a weekly ops review to his AI chief of staff bot. Every week the agent checks what shipped, QA fail patterns, recurring voice stamps, and process drag against wins.
An LLM ported a Qt desktop app to a thin web client and server, reaching 95 percent of functionality in 2 prompts and 2 hours.
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.
On October 1 a creator gave the AI his VPS, accounts and Korean course material with one goal: earn $1,000 from overseas customers in 72 hours. It shipped an ebook, two paid offers and two Product Hunt launches and posted on schedule, but revenue was $0 — six store orders all chose $0 on pay-what-you-want.
Caption+image creation scored 7.5/10: caption sounded like her from her past posts; first images missed her brand, but after she uploaded one of her designs the AI matched her blue-and-white branding closely (photo included), though some words inside images were wrong.
Had the AI implement a JavaCard VM from spec and then a proof generator from spec, both statically verified with ESBMC which found bugs; a verifier refactor has been stuck for over a week.
Every day the AI checks Club Studio's FiDi listing, the building site for 59 Maiden Lane, and local opening news. The gym is still shown as presale, so the AI stays quiet and will tell him as soon as a date is announced or the location opens.
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.