⛓️ CHAIN 0hold to expand
⛓️ Mechanical Blockchain

How This Blockchain Works

This is a live simulation of a blockchain running inside a D3.js force‑directed physics graph. Blocks are not mined on a scheduleβ€Šβ€”β€Šthey are minted by emergent physical events in the scene.

  • Sentry turret fires 3‑round bursts at random skill nodes. Target selection and bullet trajectories are governed by live node positions, which themselves drift under many‑body gravitational forces, collision repulsion, and card‑area obstacle fields.
  • Minting trigger: when a bullet strikes a blockchain‑category node (Cryptography, ZK Proofs, Smart Contracts, Ouroboros…), a new block is appended to this chain.
  • Randomness sources: D3 force simulation (charge, collision, link tension), Lissajous drone flight path pulling the graph’s center of mass, KUKA robot pick‑and‑place repositioning nodes, sentry cooldown jitter (3β€œβ€“7.7β€œ), barrel tracking lag, and bullet travel time through moving targets.
  • Block data: each block records the struck node’s label, a timestamp, a hash derived from label + index + epoch, and a pointer to the previous block’s hashβ€Šβ€”β€Šmirroring a real chain’s linked‑hash structure.
  • Node respawn: destroyed nodes teleport to a random open‑space coordinate, rejoining their parent links. This reshuffles the graph’s topology, altering future bullet paths and mint timing unpredictably.

Every visit produces a unique chain. No two sessions will ever generate the same sequence of blocks.

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Mohab Metwally - CTO, AI Engineer & Blockchain Expert

Mohab β€” CTO, Founder, Engineer

I am an engineer and leader with over a decade of experience in software engineering, spanning AI, blockchain, and robotics. Currently, I serve as CTO at Funhi, a borderless crypto marketplace, and I am the founder of FinAI, where we build intelligent financial tools powered by AI and blockchain.

I led the first implementation of the Ouroboros Crypsinous protocol for DarkFi, advancing privacy in blockchain through applied cryptography and decentralized design. My work extends to AI and robotics, with contributions in deep learning, natural language processing, reinforcement learning, computer vision, and control systems. I am also the author of a machine learning book and a mission planning library that bridges research with real-world engineering.

Beyond technology, I am a bodybuilder and powerlifter, dedicated to discipline, precision, and strength inside and outside the gym. I bring the same mindset to engineering: building systems that are resilient, efficient, and enduring.

A University of London alumnus, I combine academic rigor with entrepreneurial drive. My passion lies in taking emerging technologies from concept to impact β€” transforming complex challenges into systems that last.

Progression of total powerlifting strength (sum of deadlift, squat, bench) over time.
LIVE SIMULATION
drone · robot · turret · physics graph
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