🚀 Meet Strands Decider 2B, a small open source decision model built for fast experimentation and local development: strandsagents.com/blog/intro… It's part of a new class of decision models. Unlike reasoning models that generate open-ended text, Strands Decider 2B picks between options and assigns confidence scores. Decision models are faster and more capable at a given size, always produce an answer from the selected options, and can run with very low latency.  Strands Decider 2B is a 2 billion parameter model that runs on a local CPU or GPU and can return answers to meaningful questions in tens of milliseconds. On accuracy and calibration, it ranks second among public models in its ~2 billion parameter class, and first among those that share their full training recipe.  We're already seeing early success with model routing, tool selection, guardrails, evals, and hybrid agents that pair LLMs for the hard calls with decider models for the rote ones. Grab the code on GitHub and the latest snapshots on HuggingFace, and tell us what you build. This space is super early and we are experimenting with different variants of these models, so give it a spin and give us feedback!