A daily crypto, finance, and AI vocabulary puzzle about token controls, trading mechanics, agent flow, and inference speed.
Stablecoin Guardrails
These terms describe backing, reporting, issuance, and the edge cases that can move a stablecoin off target.
- Reserve Ratio: The reserve ratio compares backing assets with outstanding stablecoin supply.
- Attestation: An attestation is a statement that reported reserves or balances were checked at a point in time.
- Mint Authorization: Mint authorization is the permission required to create new token supply.
- Redemption Window: A redemption window is the period when holders can exchange tokens for backing assets.
Market Structure
These terms describe how prices, liquidity, and execution quality are shaped in exchange markets.
Order Book DepthBid-Ask SpreadTick SizeSlippage - Order Book Depth: Order book depth is the amount of buy or sell interest available at different price levels.
- Bid-Ask Spread: The bid-ask spread is the gap between the highest bid and lowest ask.
- Tick Size: Tick size is the smallest allowed increment for a quoted price.
- Slippage: Slippage is the difference between an expected execution price and the price actually filled.
Agent Routing
These terms describe how an agent plans work, picks tools, and manages short-lived reasoning context.
- Planner: A planner breaks a task into steps and chooses what the agent should do next.
- Router: A router selects which tool, model, or workflow should handle a request.
- Tool Calling: Tool calling lets a model trigger an external function or API.
- Scratchpad: A scratchpad is temporary working space an agent uses while reasoning or planning.
Serving Efficiency
These terms describe common ways to make model serving faster and more memory efficient.
- Batching: Batching combines multiple requests so a model can process them more efficiently.
- Prefill: Prefill is the inference stage that processes the input prompt before generation starts.
- KV Cache: A KV cache stores past attention keys and values so generation can continue faster.
- Quantization: Quantization reduces numerical precision so models use less memory and often run faster.