I'm a creative technologist working in real-time 3D and generative AI. My practice combines Unreal Engine, MetaHumans, Vicon motion capture, Blender, Octane, and ComfyUI with image and video generation models.
Selected work spans film, fashion, music, and exhibition; shown internationally and featured in editorial press. I also teach on YouTube and Instagram.
Open to commissions, collaborations, and R&D partnerships.
Based between Tel Aviv and Paris. Specializing in 3D animation, modeling, rendering, immersive video, GenAI, and AR.
Name the success criteria.
Process illustration. A bounded loop with explicit evidence and a stopping rule.
A reusable quality workflow that makes an AI assistant define success, verify its work and iterate against explicit criteria.
The skill asks an assistant to define two to five fixed success criteria before starting, then follow a plan, do, verify and decide cycle. Each pass uses observable evidence rather than treating completion of an action as proof that the result is good.
When the work misses the bar, the next pass targets the weakest criterion. A six-pass cap prevents an open-ended loop, and the final report must distinguish what passed from what remains unresolved.
This project is a reusable instruction protocol for AI-assisted work. It provides a disciplined review structure that can be applied to different tasks; it does not ship a separate agent runtime.
Set two to five fixed criteria so the goal stays clear throughout the work.
Check the result against observable evidence before deciding that a pass succeeded.
Improve the weakest criterion rather than repeating the same work without a diagnosis.
Cap the loop at six passes and state any remaining limitations explicitly.