3,500 years later, we’re still talking about the lips. I turned a mysterious Egyptian sculpture fragment from The Met into a glamorous historical fever dream. The art object from The MET https://www.metmuseum.org/art/collection/search/544514

The sculpture’s identity is still uncertain, so I imagined two fashion-obsessed museum visitors arguing over who those iconic lips belonged to — Akhenaten’s mother, his wife, or maybe Akhenaten himself.

The film mixes real museum history with Bratz-inspired characters, cinematic ancient Egypt, fashion, humor, and absurd imagination.

The goal was simple: take an ancient object people might normally walk past and make it impossible to forget.

bro..............................😂

AI agents are most useful when they reduce uncertainty—not when they silently replace judgment.

The Next.js team just used a research agent to review a backlog of 2,244 GitHub issues. In roughly three weeks, 1,462 issues were closed and only 3 were reopened.

What made the workflow credible: • every investigation ran in an isolated sandbox • the agent checked code, versions, PRs, commits, releases, and docs • it actively searched for evidence against its first conclusion • risky actions stayed read-only until humans reviewed the evidence • the clearest cases required a second-agent challenge before automation could close them

That is the pattern I trust for production AI automation: evidence first, structured outputs, bounded permissions, and reversible actions.

Where could an evidence-driven agent remove the most operational drag in your workflow?

Where it removed the most drag for me was real-estate contract deadlines - a chain of dates where one addendum moves a date and nothing throws an error. The agent is not there to judge, it is there to show that something moved.

The second-agent challenge is the bullet I would...