Markov Chain Lab
Interactive learning platform for Markov chains, automata, simulation, and grammar workflows.
Problem
Students and practitioners need one place to learn theory, build automata, run simulations, and validate behavior; most tools are fragmented across static notes and disconnected utilities.
Solution
Built a modern Next.js platform with an interactive builder, simulation and analysis tabs, grammar conversion tooling, and persisted user workspaces.
Overview
Markov Chain Lab is a full learning-and-practice environment that combines lessons, interactive tools, and experimentation workflows in one platform.
Technical Approach
The core toolset is organized around building state machines, running simulations, and analyzing behavior in real time. Users can switch between Markov Chain, DFA, and NFA modes while preserving a consistent editing workflow.
Conversion utilities bridge formal grammars and automata, while analysis views expose transition behavior and convergence-related properties for iterative experimentation.
Implementation Notes
The platform is implemented with a Next.js + React + TypeScript frontend and uses Supabase-backed persistence for settings and saved workspace state.