SuiBian

Demo

About

The problem it solves # The Problem SuiBian Solves ## Core Problems in AI Trading **No Platform to Monetize AI Trading Strategies** Skilled traders and quants have profitable AI algorithms but lack dedicated marketplaces to sell them. Existing platforms are either general software marketplaces or traditional trading services, neither designed for AI trading strategy monetization. **Technical Infrastructure Gap** Blockchain smart contracts can't run complex AI algorithms due to computational limitations and gas costs. Traditional cloud servers can run sophisticated AI models but offer no transparency - users have no way to verify what code actually executed or if the operator modified the algorithm. **IP Protection vs Monetization** Skilled traders want to sell their algorithms but revealing code details risks theft. Current platforms force creators to choose between protecting their strategies and proving they work. Our platform, SuiBian is a win-win scenario for both creators and users. **Trust Issues with Funds** Users must deposit money with bot operators who control both the funds and trading execution. There's no guarantee that trades happen as promised or that funds won't be stolen. ## SuiBian's Solution **TEE for Verifiable Execution** AI agents run inside AWS Nitro Enclaves - secure hardware that prevents tampering while generating cryptographic proof of every trade execution. **AVS Validation Network** Validators stake SUI tokens to verify agent behavior, earning rewards for honest validation and facing penalties for misconduct. **Protected IP Monetization** Creators deploy encrypted algorithms that execute invisibly inside secure enclaves while generating verifiable performance records. SuiBian creates the first marketplace where AI trading strategies can be monetized safely with mathematical proof of execution integrity.

How it's made

Challenges we ran into First, Nautilus’s limited documentation means a steep learning curve, as engineers will need to reverse-engineer its behavior, guess at API conventions, and experiment heavily to understand enclave orchestration. The absence of detailed examples, best practices, and edge case handling guidance—especially for advanced features like multi-enclave setups—can slow down development and lead to fragile implementations. Second, zkLogin’s approach to session persistence introduces its own complexity. Since it relies on short-lived ephemeral keys, maintaining long-lived sessions without constant reauthentication is difficult, particularly if transactions span multiple user actions. Securely handling salts, nonces, and proof validity windows—while ensuring sessions remain synchronized across devices and backend services—requires careful design to avoid security leaks or broken authentication flows. These factors combined can significantly slow delivery, increase bugs, and demand more sophisticated infrastructure planning from developers. But at last, we still manage to resolve the issues that we had.