Archive
22 articles published in this period.
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.
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.
Every EIP-4844 blob is secured by a KZG polynomial commitment — 48 bytes committing to 128 KB, verifiable in 50,000 gas. That efficiency required 140,416 people to collectively bury a secret for 69 days. Here's the math, the ceremony, and why Ethereum chose a different scheme for state.
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.
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.
An SP1 proof costs 327k gas on Ethereum mainnet. At a 30M-gas block target, a naive AI inference market maxes out at 91 verifications per block. SnarkPack's O(log N) aggregation collapses 1,000 proofs to a single 880k-gas verification — here's the mechanism, the math, and the latency tradeoffs.
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.
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.
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.
Foundry forks freeze five properties of the Ethereum execution environment that are live, adversarial, and expensive in production: basefee, oracle prices, blockhash entropy, MEV competition, and TWAP accumulation. Each one silently misleads AI agents through testing.