Archive
22 articles published in this period.
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.
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.
CoW Protocol's batch auction design makes front-running structurally impossible — and that matters enormously for AI agents executing DeFi strategies at machine speed.
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.
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.
Controlling one intermediate pipeline stage is enough to inject a backdoor into a decentralized post-training run — 94% ASR, no data access required. The mechanism, why training loss stays clean, and what it means for networks fine-tuning LLMs across untrusted nodes.
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.