Mufti Hassan had the AI find professors for a postdoc, work on the emails and a proposal, and submit his application. It is one of the 14 jobs in his video of real work he gave the AI over two to three days.
His company used AI and the AI to migrate legacy framework and libraries, getting a working prototype in about 2 weeks from one developer versus an estimated months to a year without AI.
This only partly worked. Worth knowing before you try it.
GitHub Classroom shut down on August 28, and his Cloud Code Academy ran on it. He wrote a PRD first, then rebuilt the platform with AI over the weekend, with a template repo, feedback pull requests, autograder webhooks and an instructor queue, in 25 commits.
Micro.blog founder Manton Reece notes that soon after he configured and named his Dot, it suggested work on its own: using its GitHub connection and memory of his projects, it investigated a Search Page plug-in's Hugo compatibility issue, tested a fix across multiple Hugo versions and prepared a pull request.
Nokia built an ecosystem of specialized the AI Enterprise agents that automate troubleshooting. Issue resolution time has been cut by up to 80 percent.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
This one went wrong. See what happened before you try it.
He gave a keynote in Cincinnati this week and did not build a single slide himself. He told his AI assistant the talk, it made the deck, and he fixed it by text.