Blokz Development Co.
Deep technical writing and interactive artifacts on AI × blockchain — from neural nets to consensus, agents to smart contracts, and the infrastructure binding them together.
Journal
MoE routers let inference providers silently halve the experts they activate — saving 31% of compute while staying undetected by every current fingerprinting scheme.
Training Llama-3.1-8B costs ~$6M. The weights are free to clone. Perinucleus sampling embeds 24,576 distinct fingerprints per model with no quality loss — and a Poseidon ZK commitment registers ownership on-chain for $0.017, enforceable by smart contract.
Speculative decoding accelerates LLM inference 2–4× by splitting work between a cheap draft model and an expensive verifier. When those run on different nodes in a decentralized network, three properties of the protocol that are trivially safe on one machine become open attack surfaces.
Ethereum mainnet gas averaged 0.16 Gwei in June 2026 — a 3,000× drop from DeFi summer 2021. That collapse doesn't just cut costs; it invalidates five years of AI agent design assumptions and opens architectures that were economically impossible until now.
A1 generates working exploits against 63% of real-world DeFi contracts. The attack costs ~$6k to break even; a comprehensive audit costs $60k. That 10× gap is the new threat model for every team deploying on a public chain.
Every zkSNARK bottoms out in a pairing check. Before Pectra you had BN254 at ~100-bit security. EIP-2537 added BLS12-381 at 128 bits, and its pairings are cheaper: 32,600k+37,700 vs 34,000k+45,000 gas. G2 MUL dropped from millions to 22,500 gas.
Interactive
ERC-8004 reputation as a topological map: one agent fed 24k identical vouches by 6 wallets. Step the integrity filter and watch a 'flawless' score collapse to UNSCORED.
A 2D erasure-coded blob drawn as a coordinate map. Move a withheld block and watch reconstruction cascade or collapse; run light-node sampling to see why hiding any data means hiding a quarter of the lattice — and getting caught.
Train a model on its own output, generation after generation. Each step draws a finite sample of size M and refits — so the tails starve first and the curve collapses toward a spike. Toggle replace vs accumulate: replace drives σ̂ toward zero, accumulate holds it. A copy of a copy of a copy.
Every article and artifact in this blog lives in a public repo. Follow the work, fork the experiments, or get in touch.
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