Aug 16, 2026

Stablecoin Lifecycle, Macro Rates, Agent Planning, and Model Serving

An educational crypto, finance, and AI vocabulary puzzle about token issuance, rate signals, agent workflows, and production inference.

Stablecoin Lifecycle

These terms cover issuance, removal, backing assets, and cash-out flows.

  • Mint: Minting is the process of creating new tokens according to protocol rules.
  • Burn: Burning permanently removes tokens from circulation.
  • Reserve: A reserve is the asset pool held to support a stablecoin or similar claim.
  • Redemption: Redemption is the process of exchanging a token or claim for the underlying asset or value.

Macro Rates

These terms cover short-term government debt, the shape of yields, and rate sensitivity.

Treasury BillYield CurveBasis PointDuration
  • Treasury Bill: A treasury bill is a short-term U.S. government security used as a low-risk cash-like instrument.
  • Yield Curve: The yield curve shows how interest rates vary across different bond maturities.
  • Basis Point: A basis point is one-hundredth of a percentage point.
  • Duration: Duration measures how sensitive a bond's price is to changes in interest rates.

Agent Planning

These terms cover step selection, fetching references, external actions, and temporary working notes.

  • Planner: A planner is the part of an AI system that decides what action to take next.
  • Retrieval: Retrieval is the act of fetching useful external information for a model or agent.
  • Tool Calling: Tool calling is when a model or agent invokes a software tool to complete a task.
  • Scratchpad: A scratchpad is temporary working space for intermediate reasoning or notes.

Model Serving

These terms cover delay, work rate, grouped processing, and lower-precision math.

  • Latency: Latency is the delay between a request and the start or completion of a response.
  • Throughput: Throughput is the amount of work a system can process in a given time period.
  • Batching: Batching groups multiple inputs or requests together to improve efficiency.
  • Quantization: Quantization reduces numerical precision to lower memory use and speed up inference.