MEV-Lab – An Interactive Academy for Maximal Extractable Value

THE IDEA

MEV-Lab – An Interactive Academy for Maximal Extractable Value

Type: Software Status: Beta Tech Stack: Python, FastAPI, Pydantic, py-evm (eth-tester), web3, py-solc-x (solc 0.8.20), Solidity, React, TypeScript, Vite, Tailwind CSS v4, Recharts, Vitest, Playwright, Docker, Nginx Problem Statement Maximal Extractable Value is conceptually hard to get at, because it combines crypto-economics, game theory and implementation detail — EVM semantics, AMM mathematics, auction mechanisms — all at once. Descriptions alone do not make it tangible why the ordering, visibility and execution of transactions create economic attack surfaces. That calls for a setup in which the mechanics can be followed on one’s own sliders — deterministic, without a wallet and without real capital. Description A local, browser-based learning system that teaches Maximal Extractable Value (MEV) and adjacent DeFi and blockchain security topics through more than a dozen interactive, deterministic simulation labs — from the classic AMM sandwich attack through liquidations, liquidation cascades and oracle backruns to CLMM/JIT/LVR, multi-stage intent and solver auctions, builder and PBS block building, and cross-domain finality and atomicity. At its core sits an analytical Python engine whose results are test-verified to be wei-identical against a real, in-process EVM (compiled Solidity contracts), so that fast live slider interaction and demonstrable exactness come together. Around the labs sits a full learning structure: a guided curriculum with quizzes, a mastery dashboard with spaced repetition, a practice arena with a fixed assessment rubric, a tongue-in-cheek local completion profile, and a strategy atlas with 19 MEV mechanisms in six families. All learning progress stays stored locally in the browser and can be exported and imported as validated JSON; the backend itself is stateless. Explicitly not a production tool: none of the strategies or contracts is intended for real keys, real capital or a mainnet deployment. Use Case An interactive learning system that lets you understand, on your own sliders, how the ordering and visibility of blockchain transactions enable economic attacks — and how to defend against them technically. Link: https://github.com/rawk7000/MEV-Bot (private repo) Start: a five-phase learning path with progress and a recommended next step. Simulator: the sandwich sequence from the solo quote to actual execution. Concentrated liquidity lab: range, JIT share and fee split with causal path and balance. Builder lab: a single-slot auction between local block building and external builder bids. Arena: reproducible challenges in the Rookie, Searcher, Builder and Protocol roles. Strategy atlas: 19 mechanisms ordered by family, value source and risk.