A daily crypto, finance, and AI vocabulary puzzle about backing, economic releases, context, and model efficiency.
Stablecoin Guardrails
These terms cover backing, permissioning, redemption flow, and small drift from the peg.
Reserve AssetsRedemption QueueMint AuthorizationPeg Drift
- Reserve Assets: Reserve assets are the liquid holdings that support a stablecoin's redeemability and target value.
- Redemption Queue: A redemption queue is the order in which withdrawal or redemption requests are processed.
- Mint Authorization: Mint authorization is the control gate that determines who may create new stablecoin supply.
- Peg Drift: Peg drift is the gap between a token's market price and the value it is designed to track.
Macro Data
These terms connect inflation prints, labor data, bond pricing, and small rate increments.
Consumer Price IndexNonfarm PayrollsYield CurveBasis Point
- Consumer Price Index: The consumer price index tracks how prices change for a basket of goods and services over time.
- Nonfarm Payrolls: Nonfarm payrolls is a monthly U.S. labor market release that tracks payroll job changes.
- Yield Curve: A yield curve shows how interest rates vary across different maturities.
- Basis Point: A basis point is one-hundredth of a percentage point, often used to quote rate changes.
Agent Memory
These terms describe how models represent meaning, search prior context, and extend working memory.
Context WindowEmbeddingVector DatabaseRetrieval-Augmented Generation
- Context Window: A context window is the amount of input and prior output a model can consider at once.
- Embedding: An embedding turns text or other data into a numerical vector that captures meaning.
- Vector Database: A vector database stores embeddings so similar items can be retrieved efficiently.
- Retrieval-Augmented Generation: Retrieval-augmented generation combines search over external content with model generation.
Inference Ops
These terms describe the main levers for lowering latency and using GPU memory more efficiently.
- 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 draft model to propose tokens before verification.