A daily crypto, finance, and AI vocabulary puzzle about token supply, derivatives, agent workflows, and model serving.
Stablecoin Lifecycle
These terms describe how a stablecoin is issued, redeemed, and kept in circulation.
Reserve BalanceRedemption QueueMint FunctionBurn Function
- Reserve Balance: Reserve balance is the amount of assets held to support a token's issuance or redemption.
- Redemption Queue: A redemption queue is the order in which holders' requests are processed.
- Mint Function: A mint function creates new tokens according to protocol or issuer rules.
- Burn Function: A burn function permanently takes tokens out of circulation.
Funding and Carry
These terms explain common futures metrics used to describe market positioning and carry.
BasisFunding RateOpen InterestLeverage
- Basis: Basis is the price difference between related markets, such as spot and futures.
- Funding Rate: Funding rate is a periodic payment that helps keep perpetual futures near the spot price.
- Open Interest: Open interest is the total number of derivatives contracts that are still active.
- Leverage: Leverage increases market exposure by using borrowed capital or notional exposure.
AI Agent Stack
These terms cover the building blocks an agent uses to plan actions and gather context.
- Planner: A planner breaks a task into steps and decides what the agent should do next.
- Tool Calling: Tool calling lets an AI model trigger external functions or APIs.
- Retrieval: Retrieval brings in documents or data that help the model answer more accurately.
- Memory: Memory stores useful context from earlier steps or sessions for later use.
Inference Efficiency
These terms describe common techniques for lowering latency and compute during model inference.
- Batching: Batching combines multiple requests so a model can process them more efficiently.
- 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.
- Speculative Decoding: Speculative decoding uses a smaller model to suggest tokens before a larger model checks them.