Hardcore Rust Ethereum System Engineering
Learn the architecture momentum behind Hyperliquid by implementing it in Rust. Across Reth, Revm, Alloy, and Foundry, you'll read real source, build real components, and turn understanding into working artifacts.
// The real ADD opcode that runs on every Ethereum block
pub fn add<IT: ITy, H: ?Sized>(
context: Ictx<'_, H, IT>,
) -> Result {
popn_top!([op1], op2, context.interpreter);
*op2 = op1.wrapping_add(*op2);
Ok(())
}
// Walked through line-by-line in lesson:
// → reth-advanced / revm-interpreterIntermediate/Expert are anchored in real production source.
Open This Lesson21 Courses Across Five Learning Paths
Through four tiers plus the Build Path (DIY Perp), you learn to read implementations in Reth/Revm/Alloy/Foundry, explain design decisions, and turn them into working artifacts.
Concept / Deep Dive
Foundation thinking plus source-reading tracks for unblock momentsBeginner
Three courses that ground you in the Rust EVM stack. Rust and Alloy setup, EVM concepts, byte-level dispatch loop. The foundation for the source-reading tier.
Intermediate
Three courses reading the production source of Alloy, Revm, and Reth line by line. Provider/Network/Signer, interpreter internals, Staged Sync and ExEx. Take in any order.
Advanced
Five courses on architecting an L1: BFT consensus, cross-chain bridges, sequencer & rollup architecture, P2P networking, validator operations. The design skills behind Hyperliquid- and Tempo-class chains.
Expert
Two courses for production and app engineering. Reth Expert covers performance, MDBX, Tokio, MEV, zkEVM, and Reth fork ops. Building with the Stack ships ten runnable apps — MEV searcher, indexer, wallet backend, order router, an HTTP 402 machine-payments endpoint, and more.
Build Path
The hands-on implementation-first primary pathMove from “I know it” to “I can build it.”
Don't stop at following the Hyperliquid wave — convert it into implementation skill.
RethLab combines concept, source reading, and build execution into a single engineer-first training flow.
Note: course implementations are inspired learning references and are not official Hyperliquid code.