About the lab
MarkovLearn turns abstract probability into a workspace you can actually feel.
The project was built to make Markov chains less like a chapter to survive and more like a system to explore. Lessons, simulations, validation tools, and guided practice all sit in one loop so insight arrives faster.
Short feedback loops, visible state transitions, and enough rigor that the intuition still holds under formal analysis.
3
Core modes
1
Connected flow
∞
Experiments
Start with intuition
Use the learning track to build the mental model before diving into notation-heavy problems.
Test the model
Move into the tools workbench to edit states, simulate behavior, and inspect what changes when a transition shifts.
Close the loop
Practice questions and examples turn the experiment back into retained understanding.
Probability becomes intuitive when transitions, steady state, and simulations update in front of you instead of hiding inside equations.
Lessons, practice, and tools are designed to hand off to each other so a concept can be read, tested, and manipulated in one session.
The platform does not flatten the math. It surfaces structure, notation, and model behavior without making the experience feel sterile.
Learn
Guided explanations, progress cues, and lesson sequencing that reduce context switching.
Tools
A modular workbench for building chains, running simulations, validating grammar, and comparing outcomes.
Practice
Questions that expose common misunderstandings and force a prediction before the answer appears.
Examples
Real examples that connect the theory to ranking, weather, queues, language, and other systems that evolve over time.
Next.js 15
App Router, server-first routes
TypeScript
Shared types across UI and domain logic
Supabase
Auth, progress, settings, and admin policy data
Tailwind + Radix
Composable UI with fast visual iteration
The brand now leans warmer and more human. It keeps the circular transition motif, but frames it like an observatory emblem so the product feels exploratory rather than institutional.
That visual direction matches the product itself: analytical, but not dry; precise, but still inviting to learners who are seeing stochastic systems for the first time.