Journal
136 articles on the craft of AI × blockchain engineering. or filter by topic:
Zero-knowledge proofs aren't the only path to trustworthy on-chain AI. Optimistic schemes trade latency for a 1000x cost reduction — here's how dispute games over inference actually work.
We ran multi-agent LLM pipelines against historical exploit corpora and live audit engagements. The results reshape where AI fits in a security review — and where it absolutely doesn't.
Zero-knowledge proofs promised to make machine learning trustless. A field survey of where zkML actually stands — proving systems, quantization tradeoffs, and what's deployable today.
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.
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.
ERC-4337 lets AI agents transact without holding ETH — but the alt-mempool has its own fee market, bundler economics, and MEV surface every builder must understand.
FRI-STARKs are fast and trustless, but their proofs are megabytes wide. Groth16 is 256 bytes but needs a ceremony. STARK→SNARK wrapping resolves the tension — and it's why SP1 and RISC Zero can settle any ML inference on L1 for under 300k gas.
Ethereum's 12-second slot clock creates a hard lower bound on AI agent state freshness. Here's what that means for liquidators, arbitrageurs, and yield rebalancers — and how to engineer around it.
Most DeFi protocols are upgradeable, and most on-chain AI agents don't know it. Here's how proxy architecture invalidates agent assumptions — and a tiered defense framework to build around it.
A 15× spike on a $17M pool shifts its 30-minute v2 TWAP by 9.3% in one block. The arithmetic-mean accumulator is far weaker than v3's geometric tick accumulator. Covers the manipulation cost formula, the Inverse Finance case study, and minimum safe pool depth.
Chainlink's push oracle only updates when price moves ≥0.5% or an hour passes. That gap costs AI agents real money — and Pyth's pull model closes most of it.
On-chain AI agents face a circular oracle problem: you need inference to decide if inference is worth calling. Here's what the token cost distribution actually looks like — and how to build around it.