The Price Chainlink Shows You Is Already Old
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.
6 min read ⬢ interactive
Archive
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136 articles, grouped by date.
The Gradient Thief: Why Federated Learning's Privacy Promise Fails on Public Chains
Federated learning keeps raw data local — but the gradients it publishes on-chain are not private. Inversion attacks reconstruct training images from them, and a public ledger makes that window permanent.
9 min read ⬢ interactive
The Hidden Stage: Backdoor Attacks on Decentralized LLM Post-Training
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.
7 min read ⬢ interactive
The Builder Oligopoly: Private Orderflow, Builder Bonds, and What AI Agents Pay to Skip the Line
Five builders now win 96.7% of Ethereum blocks. Their edge isn't compute — it's exclusive orderflow: 12% of transactions that generate 54.59% of block value. AI agents transacting in public have to understand where they sit in that hierarchy — and when a preconfirmation changes the math.
9 min read ⬢ interactive
Blobs Are Not Buckets: The Data Availability Window and the AI Training Pipeline
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.
8 min read ⬢ interactive
Uniswap v4 Hooks: The AMM Hook Lifecycle and What AI Agents Can Do With It
Uniswap v4 hooks make every liquidity pool programmable. Here's how the 14-bit permission bitmap works, what fires during a swap, and where AI agents fit into dynamic fee architecture.
7 min read ⬢ interactive
The Softmax Tax: How Nonlinear Gates Choke ZK Proofs of Transformer Models
Softmax costs 275 ZK constraints per element vs 4 for MatMul. ZK-DeepSeek proved Transformer inference is expressible in SNARKs. Lookup arguments (Lasso, LogUp) explain how — replacing in-circuit transcendental approximations with pre-committed tables cuts nonlinear costs 10–20×.
8 min read ⬢ interactive
Inference at the Margin: The Token Budget Problem for On-Chain AI Agents
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.
6 min read ⬢ interactive
The Compliance Kernel: Formal Verification for On-Chain AI Agents
Probabilistic classifiers miss 30–40 % of policy violations. Lean 4 theorem provers and SMT solvers make certain guardrail tiers mathematically certain — here's how the four-layer policy stack works.
8 min read ⬢ interactive
Collateral Without Reputation: The On-Chain Credit Problem for AI Agent Wallets
To borrow $100 in DeFi, you lock $125–167 in collateral. That gap is the price of trustlessness — and AI agents pay it the same as anyone. On-chain credit scoring is emerging to close it, but the signals that work for humans fail for agents that can spin up new addresses in milliseconds.
8 min read ⬢ interactive
The Longevity Ledger: Inside VitaDAO's IP-NFT and the AI Research Loop
VitaDAO mints a Molecule IP-NFT — an ERC-721 pointing to a legal research agreement on IPFS — then fractionalizes it into ERC-20 tokens for community funding. Now AutoScientists runs the experiments at machine speed. Here is what the chain actually proves, and what it doesn't.
7 min read ⬢ interactive
The Finality Ladder: What 'Confirmed' Actually Means for On-Chain AI Agents
On OP Stack chains, 'confirmed' is a four-layer promise stretching from 2 seconds to 7 days. AI agents that collapse these into one boolean expose themselves to sequencer reorgs, bridge lockups, and state dispute windows. Here's the full settlement stack, and an action taxonomy for each rung.
7 min read ⬢ interactive