Show HN: Recursive Reasoning with Tiny Networks in the Browser (claude.ai)

A Show HN post demos “Recursive Reasoning with Tiny Networks in the Browser,” an experiment that runs compact neural models client-side and applies them recursively to perform multi-step reasoning. Instead of one huge transformer, the demo repeatedly applies a small network (run in-browser via WebGL/WebAssembly/TensorFlow.js-style tooling) so that output from one pass becomes input for the next, effectively chaining simple computations into deeper, iterative reasoning. The interface emphasizes interactivity, low latency, and full client-side execution, avoiding server-side inference and large model footprints. This approach is significant because it demonstrates a practical middle ground between rule-based recursion and heavyweight LLM inference: tiny nets can approximate iterative thought processes for structured or toy tasks, enable privacy-preserving local inference, and make reasoning pipelines easy to visualize and tweak. Technical implications include favorable compute and memory tradeoffs, benefits for education and prototyping, and potential use in edge/embedded environments; limitations remain for scale and generalization compared to large pretrained models. The project suggests recursive application of small, specialized networks is a viable pattern for lightweight, interpretable reasoning workflows in the browser.