A random mix from the whole library. Shuffle for a new set.
He had the AI transcribe more than 70,000 images of old documents in two days, for $380 in total. The handwriting and worn pages hurt the quality and it did worse in Portuguese than in English, but the AI handled the whole run.
He used AI, ElevenLabs, and D-ID to make a budget simulation for his Politics of Public Policy course. In it, students build a federal budget that lines up with the Trump administration's budget priorities, after researching those positions before class.
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.
Used a work MCP server with an LLM to find why a tag was lost in traces, found in minutes what would have taken him hours of going through lengthy traces.
Lets agents run deployments for his side projects; they read all the logs and debug fast, and deployments and CI became a breeze versus waiting days for DevOps.
Her business folder was a mess and file names did not show which files were still live on her site. The AI checked each file's fingerprint instead of its name, moved 288 files, and deleted none.
Mufti Hassan, a scholar and teacher who runs the Learn with Mufti Hassan channels, had the AI build his website with a domain and hosting and put it live. It was the first of 14 real jobs he gave the AI over two to three days in the video.
When a person fixes something one of his bots wrote, the bot writes down what it said and what the person changed it to. A nightly task reads those changes and writes the rule it thinks they point to, and in a year the bots have picked up more than 40 rules this way.
He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.
Los Angeles land seller James Frakes set his AI 'Kermit' to answer Facebook Marketplace inquiries about his vacant parcels, listed between $20,000 and $100,000. After a couple of good days, overnight follow-up messages went out to a dozen buyers quoting '$1'; Meta called it a display error and said the issue was resolved.
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.