Archive
136 articles published in this period.
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.
Bittensor let AMM prices decide which AI subnets earn 3,600 TAO a day — until a memecoin subnet gamed the formula. Inside the TaoFlow upgrade: the constant-product math that got exploited, the EMA flow accounting that replaced it, and why refundable manipulation is the design smell to hunt for.
Virtuals' Agent Commerce Protocol sells an independent evaluator as the thing that makes agents trust each other's work. We read 62,882 jobs off Base — and in a 30-job sample, every one let the buyer grade itself. Not one used a third party.
Launching an AI agent token is one constant-product auction. We read Virtuals' Bonding contract off Base: a 6,000-VIRTUAL virtual reserve, a 64× price ramp, graduation at exactly 42,000 VIRTUAL, and the 12.5% gap where the Uniswap pool opens below the curve.
Every ECDSA signature broadcasts the wallet's public key. A March 2026 Google/EF paper tightens the quantum break estimate to ~500k physical qubits — here's what that means for autonomous AI agent wallets.
An autonomous agent can't sign up for an API key or swipe a credit card. x402 revives HTTP 402 and settles in gasless USDC — here's the EIP-3009 handshake, the facilitator trust model, and where prompt injection breaks 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.
The textbook market-making model quotes symmetrically around mid. On a perpetual, holding inventory pays or charges funding every hour — a deterministic drift Avellaneda-Stoikov never sees. A 2026 HJB model that prices it cut inventory risk 36-38% on Hyperliquid ETH and BTC.
DX Terminal Pro ran 3,505 LLM agents trading real ETH on Base for 21 days: 7.5M invocations, ~$20M volume, 99.9% settlement. The reliability came from the operating layer around the model, not the weights — here are the numbers and the failure modes.
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.
Secure multi-party computation runs a transformer without any party seeing your prompt — at minutes per query and gigabytes per token. BumbleBee's BERT-base: 6.4 GB, 2.55 min; LLaMA-7B: ~8 min/token. The bytes don't go where you'd think, and it's why 'blind' networks fall back to TEEs.
On-chain data markets promise to pay you for your data's value AND protect your privacy. Differential privacy is the only rigorous knob for the second — and one budget ε can't serve both. The utility collapse, the composition trap, and why no chain can verify the ε you were promised.