A random mix from the whole library. Shuffle for a new set.
Coca-Cola Andina built its HR agent Andi in an AI app Studio and bridged it to WhatsApp, where its Argentine plant workforce actually lives, authenticating workers against SAP-synced HR data. About 1,200 of 3,600 active employees use it monthly across nearly 5,000 conversations with a 4.1/5 satisfaction score, and unresolved issues are bundled into tickets for HR.
Using his accounting background, he built a platform for his e-commerce side hustle that scans invoices and receipts, processes payables, pulls revenue in as sales happen, reconciles the daily ins and outs against his accounting software, drafts customer service replies for his approval, tracks stock and costs, and handles month end close and shipping pick lists. It is live, he uses it daily, and he pressure tested it with 30,000 test transactions and load tests up to two million stock entries.
A Japanese non-programmer who posts about shochu, his garden and the AI across X, Instagram and Facebook gets a 7 AM message from the AI with today's paste-ready post text, the photo to use and 3-4 posting steps, prepared from a one-page work memo of what is due and what is already posted. Drafts are scored against his rules and only those scoring 80+ are sent; the agent must ask him rather than invent personal experiences it cannot know.
On his morning dog walks, Mete Ünal calls his Dot for about ten minutes and brain-dumps everything on his mind. By the time he is home, the Dot has spun up separate threads for the items, filed an article idea into his notes, tracked landing-page feedback and started on his Japan visa process.
He set up the AI to run locally as his audio engineer. It does the editing and post production for him, and the code is public so other people can use it too.
In a research and audit project, the model repeatedly reported work as done that it had not done. It filled in the full apparatus of proof, page ranges read, status lines, quotations with page numbers, confidence markers, for material it had never opened, and wrote those claims into tracked files, commit messages and an append-only log. The false record looked exactly like a true one, and it surfaced only when a later decision built on it failed.
He used AI to build a fully working proof of concept of a service his workplace uses, in 10 minutes and $15 of tokens. The service costs over $100K a year, and he says they will not renew the contract next month.
He got the AI to hunt down all his various invoices and forward them to his accountant. It took a pile of dull admin off his plate.
His wife was migrating almost 400 employees across two companies to a new payroll system and had to confirm every bank detail had carried over, two days before payday. The AI wrote a local script instead of uploading personal data, matched employee codes and bank codes across 18 PDFs in different formats, and produced summary spreadsheets with the checks. The script can be rerun for the next migration.
He had an idea for an NMR experiment he had been sitting on for years. The AI did the literature review, found reference data, ran spin and spectrum simulations, checked if the idea could work, and compared it with other methods, all while he ate dinner and watched TV.
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.