Journal
136 articles on the craft of AI × blockchain engineering. or filter by topic:
On-chain AI loves to say a model is 'stored on-chain.' But a chain commits to a 32-byte hash for cents; keeping the 140 GB it points to retrievable is a separate, recurring, surprisingly centralized bill. The storage math, erasure coding, and the retrieval wall.
Decentralized RL splits the actor from the learner across the internet: the policy a worker acts with runs several steps behind the one that learns. INTELLECT-2 held reward at 4 steps stale; SparrowRL cut the broadcast 79x. The bandwidth and staleness taxes, and what the chain secures.
An agent holding your raw key is one prompt injection from total loss — and 97% of early EIP-7702 delegations went to drainer sweepers. We read the sweeper's source off the chain, dissect a real 35.97-USDC-a-day spend permission on Base, and do the blast-radius math.
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.
Olas turned an off-chain AI task into an on-chain contract: 2.1M requests across 100 mechs, a 60-300s priority window, and a Karma ledger that docks no-shows. We read the marketplace off Gnosis and dissect its crypto-economic SLA.
An agent hits an API, gets HTTP 402, signs a stablecoin authorization, and a facilitator settles it on-chain for a fraction of a cent. We trace one real payment on Base down to the gas — and where the trust actually sits.
Give autonomous LLM agents a marketplace and they race each other to bankruptcy and get conned by Sybils. A 2026 study finds capability doesn't help — a 9B model beat frontier models 45× its size on economic alignment. On-chain, both failures get cheaper and worse. The mechanics, numbers, and fixes.
You pay per token for a named model; the provider picks the precision. FP8 quantization costs 0.6 MMLU points and is near-invisible to output auditing — so inference markets bond and attest instead of detect.
Yesterday an AI agent deployed a prediction market on Gnosis; other agents will price it, bet on it, and resolve it. The calibration data behind LLM forecasters, the FPMM math they trade against, and what breaks when the marginal bettor is a model.
EIP-4844 blobs look like cheap on-chain storage, but they vanish in 18 days. Here is why DA windows break AI training pipelines and what to use instead.
zkML, optimistic, TEE, FHE all prove the computation. Restaking takes the other road: bond it and slash liars. We do the cost-of-corruption math behind EigenLayer's $18B AI-AVS security, the overloading attack that breaks it, and the probabilistic-audit tax.
Render just voted to route 60,000 Salad GPUs' payments through a token burn; Akash auctions its 234 GPUs per-block on-chain. Two opposed pricing mechanisms, live H100 quotes, and the burn-vs-emission math the equilibrium story skips.