His team's test framework drives the product, has an LLM judge it, and files the issues
His team runs a fuzz framework that drives a Migration Assistant with Playwright, has an LLM judge results, and files issues back into their system.
A random mix from the whole library. Shuffle for a new set.
His team runs a fuzz framework that drives a Migration Assistant with Playwright, has an LLM judge results, and files issues back into their system.
Jacob runs four AI products alone. He built Tracetify, a tool that rebuilds how a competitor got its first users from twelve public data sources, in about three days, and gave the AI one instruction for the whole interface: you decide. Users later asked whether he had hired a designer.
His company runs three coordinated agents on its product account: one watches mentions and a watchlist, one writes posts and replies, and one controls quality. Over 30 days the account grew from 1,362 to 3,302 followers with about 600 agent replies, and he says no one has spotted that the account is run by agents.
Gave the AI a classification task he had been doing by hand and it raised accuracy from 81.3/89.9/92.1 to 90.9/99.3/99.6 in a little over 2 hours.
His order was doubled, and the company support bot closed his ticket. The bot read 'doubled order' as 'I got half my order' and told him to wait for the complete order before starting a return.
A peer reviewer put a manuscript's reference list into AI and asked it to find a web link for each one. Two references were fictitious and 13 more looked suspect, so the reviewer rejected the paper.
The AI made a new OBS launcher in about a minute that opens OBS and refreshes and checks all of his sources.
A home services business was losing leads nobody followed up. The AI scores each new lead A, B or C against the owner's 4 rules, writes a short summary on the contact, and drafts personal WhatsApp follow-ups that a person checks and sends each morning. The poster says it brings in 5 extra contracts a month at about 9,500 euros each.
He is not a developer. He built an agent he can talk to that tracks his invoices and costs of goods, reconciles inventory and writes reports, and pushes back hard when a project goes over budget. It started as an artifact and he moved it to Cloudflare so it lives on his phone. He is now thinking about making it a tool for his whole industry.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
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.