A daily crypto, finance, and AI vocabulary grouping puzzle covering reserves, execution, agent workflows, and retrieval systems.
Stablecoin Balance Sheets
These words describe how a stablecoin stays credible through reserves, issuance, and redemption.
Reserve CompositionAttestation ReportMint and BurnDepeg Risk
- Reserve Composition: Reserve composition is the mix of assets held to support a stablecoin or similar liability.
- Attestation Report: An attestation report summarizes what reserve assets were held at a point in time.
- Mint and Burn: Mint and burn is the issuance and destruction cycle used when tokens are created or redeemed.
- Depeg Risk: Depeg risk is the chance that a stablecoin moves away from its intended reference value.
Order Flow Execution
These words explain how orders meet liquidity and how fills move in real markets.
- Order Flow: Order flow is the stream of buy and sell instructions moving through a market.
- Market Maker: A market maker provides two-sided quotes to help other traders buy and sell.
- Limit Order: A limit order only executes at a chosen price or better.
- Slippage: Slippage is the gap between an expected fill price and the price a trade actually gets.
Agent Loop Control
These words cover the control loop an AI agent uses to plan, call tools, store context, and stay constrained.
- Planner: A planner is the part of an AI system that breaks a goal into steps.
- Tool Call: A tool call is a request from a model to use an external function or API.
- Memory: Memory is stored context an agent can reuse while it keeps working.
- Guardrail: A guardrail is a rule or filter that keeps a system within safe bounds.
Retrieval and Context Ops
These words describe the data plumbing that helps apps retrieve knowledge and stay within runtime limits.
Vector StoreEmbeddingContext BudgetLatency Budget
- Vector Store: A vector store is a database for saving and searching embeddings.
- Embedding: An embedding is a numeric representation that lets a model compare meaning across items.
- Context Budget: A context budget is the amount of context a model can keep in one run.
- Latency Budget: A latency budget is the maximum time a system can spend before it is too slow.