Archive
136 articles published in this period.
An AI agent holding a raw ECDSA key is one model compromise from total loss. FROST's two-round DKG means the full private key never exists anywhere — not during setup, not during signing. Here's the mechanism, the Ethereum gap, and how Lit Protocol PKPs deploy it today.
PREVRANDAO costs roughly $88 per bit to bias. Chainlink VRF costs $2.43 per request. Here's how to choose the right randomness source for AI agents making on-chain decisions — and what breaks when you choose wrong.
Solana agents get 400 ms — but only if they declare every account they'll touch before execution starts. The Sealevel write-lock model, hot-account contention, Jito tip economics, and what the declarative execution contract means for AI agent design.
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.
Deposit ETH in Aave, borrow USDC, buy more ETH, repeat. Three loops creates 2.97× leverage from a single ETH — and all three positions share one liquidation trigger at −3% ETH price. The math, the cascade, and why AI yield optimizers find themselves here by default.
ElizaOS agents treat their memory store as ground truth — their own past. CrAIBench tests 685 attack cases across 33 Web3 action types and finds memory injection hits 55.1% ASR even on Claude Sonnet 3.7, while every off-the-shelf detector fails.
On-chain AI agents reset state on every call. Four persistent-memory architectures span a 20× monthly cost gap — SSTORE at $78/month versus IPFS+CID at $3.92. Real June 2026 gas numbers, four trust models, one crossover you won't see coming.
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.
Bitcoin burns ~150 TWh/yr on SHA-256 nonces that prove nothing. Komargodski & Weinstein showed a Freivalds randomisation check turns any matmul into a valid proof-of-work at only 3/(2N) overhead — the same GPU seconds train your model and mine the block.
DeFi interest rate formulas are open-source reward functions. An RL agent reading Aave's two-slope model can compute the optimal adversarial strategy analytically — no learned reward model required.
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.