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Agentic product engineer and interdisciplinary philosopher of science. AI research and consulting via the SciPhi Initiative. Co-organizer, Nederland Philosophy Club.
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Agent = model + harness
Click the print to set a target. With the harness off, the model is a brain in a vat. Plan refines the goal estimate and predicts drift. Tools give live feedback for course correction. Memory keeps unfinished goals when you click somewhere new, and the verifier checks arrival.
<A>- Agent position
<a>- Self estimate
[*]- Latest goal you clicked
[g]- Estimate for the goal being pursued
[+]- Earlier unfinished goal
Shaded trails show actual paths. Dots trace estimates. Symbols sharing a cell take turns.
The dotted ring follows the agent’s current goal estimate. The latest click stays marked after arrival.
Send a task to the right specialist
Play the coordinator: match each task to a specialist’s role. Drag a task, or select a specialist from its menu. “Match next task” shows a simple automatic assignment. This is a sorting game: it checks predefined categories, without running agents or testing their work.