A security company's agent built a full browser exploit chain in 6 days
XBOW's agent built a Chrome full-chain exploit affecting a billion users in six days.
A random mix from the whole library. Shuffle for a new set.
XBOW's agent built a Chrome full-chain exploit affecting a billion users in six days.
He runs a B2B cold outreach agency at $58,500 a month in recurring revenue. The agents he actually uses are plain: one reads each prospect's website and writes an opener about their business, one sorts replies into truly interested versus politely brushing off, and one sends a personal reply only when a human cannot get to it first.
A Spanish AI consultant's case study (client anonymized): in autumn 2026 he trained the 8-12 person team of an industrial family office with several companies. Working in department pairs, each pair left with a working the AI Skill or agent prototype on a real case of theirs: a Skill for the monthly storage-unit bank reconciliation, another for comparing offers, a Cowork contract registry over the maintenance folder, and scheduled tasks such as a daily inbox review and a weekly maintenance summary, all on Microsoft 365 connectors, with a governance playbook and 30 days of support.
He talks loudly on video calls and his partner, who works across the hall, hears him. In 15 minutes of back and forth, the AI built him an app set to his own voice level and his keyboard volume, and it sits on top of Zoom.
He set up an AI skill that scans job sites for him. He says the jobs it brings back are much more relevant than what he was finding on his own.
Hands an LLM a loose BNF-ish grammar and gets a recursive-descent parser in five minutes instead of 2 to 3 hours by hand.
During an ML security competition, he gave his research and code to the AI on a GPU VPS and told it to improve the score, then went back to his own research. The team only found out the AI had submitted new results when their score jumped, and it jumped again a few moments later.
His business scans every invoice into a Google Drive folder. The AI reconciles them there: checking they were billed correctly, that credits for returned goods actually arrived, and that there is no slippage. He says it is far faster than reconciling by hand.
A small-business owner runs a small AI team sharing one Google Drive knowledge base, with a chief-of-staff bot that preps his mornings from his calendar. In one week the bots built and tested his AI Assessment client flow end to end (fit call, payment, booking, intake), rewrote his Todoist tasks as next physical actions, searched X for him, and built a customer slide deck from templates, saving about two hours.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
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.