When AI learns "blockchain": How MIT engineers are building the Web3 world with Cursor "Cursor and Claude can handle Web2's React, but when it comes to Web3, they are like blind men touching an elephant." When Luke said this, the hackathon participants in the audience chuckled knowingly — they are all too familiar with the pain of such "stuck" moments. Writing smart contracts is never as simple as "just piecing together a few functions." A tiny deviation in a state variable can directly expose a security vulnerability worth millions of dollars; a line of code that doesn't consider gas costs can render the entire application immobile on the chain. Ironically, AI has already turned Web2 programmers into "full-stack" developers overnight, while Web3 developers are still switching back and forth between Remix, Hardhat, and Foundry, repeatedly checking test reports — afraid of stepping into those "invisible pits" on the chain. So Luke decided to take matters into his own hands: to create an AI that truly "understands blockchain semantics." An AI that can write contracts, test security, and manage the entire on-chain process. This is the starting point for Nora. @mynoraai #MyNoraAI #BuiltWithNora #NoraAgent #CodeWithNora #NoraAI

1. From MIT to On-Chain: AI Researchers Falling into the "Context Trap" of Web3 Before diving into Web3, Luke was an AI researcher at the MIT Media Lab; later, he became one of the few technical experts deeply involved in the underlying development of blockchain, having personally designed the HotStuff consensus mechanism and the BlockSTM parallel execution scheme. This experience made him realize a key issue: the bottleneck of Web3 is not the code itself, but the "on-chain context" behind the code. The world of smart contracts has never been just about logical operations, but rather a complex "state machine ecology": every transaction is influenced by previous and subsequent blocks, every line of code must be executed under the rules of "on-chain consensus," and even minor optimizations in the compiler can change the final execution result. He has seen too many young developers stumble over these "invisible complexities"—clearly, the syntax is correct, yet the contract crashes on-chain; clearly, the functionality is implemented, but due to high gas fees, no one uses it. It was also at this time that a thought took shape in his mind: "Perhaps, AI should not only understand code syntax but also the 'language logic' of blockchain."

2. The Blind Spots of AI Tools: Why Web2's Cursor Struggles with On-Chain Development? To understand the value of Nora, one must first grasp the "Web3 blind spots" of traditional AI coding tools. Today's LLM coding assistants—whether it's Cursor, Claude Code, or Copilot—are adept at generating React components and writing API interfaces, even capable of building entire site logic. But asking them to write a Solidity smart contract? Problems are almost guaranteed. Where does the problem lie? The "semantic understanding" of these models is entirely based on the Web2 paradigm: front-end rendering, back-end interfaces, HTTP calls, function inputs and outputs... They cannot perceive the unique state flow changes on-chain, the execution logic of virtual machines, or the calculation of gas costs, and they are completely unaware of security boundaries (such as reentrancy attacks and access control). "They understand the world of JavaScript but cannot comprehend the 'dialect' of blockchain." Luke's summary hits the pain points of countless Web3 developers. And this is precisely where Nora comes in.

3. The Epiphany: Let AI Understand the "Temperature of Bytecode" At the end of 2024, Luke encountered a tricky problem while debugging a Move contract: the code generated by AI was syntactically correct, but it threw an error once deployed on-chain — the reason was that after compiler optimization, the execution logic was completely different from the original code's expectations. At that moment, he suddenly realized: to enable AI to write secure contracts, it must first understand the "low-level language" of compilers and virtual machines. This became the core design point of Nora. Unlike traditional AI agents, Nora's model architecture directly embeds **"Compiler-Aware" and "VM-Level Context"**. It not only understands the syntactical differences between Solidity, Move, Cairo, and Rust, but can also trace the execution path of the compiled bytecode and analyze the flow logic of each instruction. This means: Nora does not just "write code"; it can also automatically verify contract logic, detect security vulnerabilities, and even optimize gas consumption — more like an "all-around engineer" who understands compilation principles, consensus mechanisms, and security audits.