Austin Faulkner-pearce

Problems to solve Our goal is to reimagine the wallet UX in web3. Ultimately we beleive it is possible to deliver a wallet solution that is so simple that anyone could use and understand it with just one simple text or voice prompts, without having to sacrafice the functionality typically associated with native web3 wallets, such as self-custody, DeFi, etc. The problem we recognised as a hurdle to mass adoption, is the barrier of entry into making your first on-chain purchase. Web3 is an industry filled with passion and innovation, and it is often easy to forget how this same passion encouraged many of us to adjust to the steep learning curve... What's an address? What's a block? What if I make a mistake? These are some of the common questions which can prevent someone from taking the first step into web3 or encourage bad habits e.g. storing tokens on CEX's. It was clear to us that the complexity SHOULD be abstracted away for most consumer users, but still be available should the user want to learn more or dive into their txn details. We also aim for our product to be a catalyst for DeFi and improving financial accessibility. By embedding an actionable LLM into our wallet, we can double dip education and functionality. Effectively the wallet can act as a one stop shop for exploration, research and investment. The final problem we recognised is security. We knew this would be our make or break feature. There is a fine balance in web3 between responsibility and risk, decisions between self-custody, CEX, custodian all have their trade-offs. We realised the only way to implement our wallet effectively would have to reduce both responsibility AND risk, by leveraging smart contracts. GM-Wallet is designed for seamless crypto adoption, advanced security, and AI-powered assistance. It makes crypto accessible to everyone—beginners, enthusiasts, and experts alike—without compromising decentralization or security.

Neural Torrent addresses the basic challenge of how AI agents can exchange training data in a fair and autonomous manner in a decentralized system. Currently, high-quality AI training data is costly, centralized, and dominated by a few large players. There is no open infrastructure that enables independent agents or models to find useful datasets, trade them with each other, and get legitimate credit for their contributions. In decentralized systems, agents typically download valuable data without contributing anything in return, causing a free-rider problem that undermines the entire network. Neural Torrent fills this gap by merging the incentive dynamics of closed BitTorrent trackers with cryptographic reputation on-chain. Each AI participant enters the system with a wallet identity, contributes datasets like video or audio files for AI model fine-tuning, and accumulates a contribution score depending on the ratio of uploaded versus downloaded data. Each unit of data transferred is accompanied by a signed cryptographic receipt, and the upload and download metrics are maintained on-chain. Contributing participants get more access, and those who simply download without seeding get limited access until they pay back by contributing. This establishes a self-reinforcing incentive system where participation is visible, trackable, and incentive-compatible. By doing so, Neural Torrent creates infrastructure for a decentralized AI data exchange layer, enabling agents to improve themselves through cooperative data sharing rather than relying on centralized brokers. Reputation becomes persistent and portable across tracker instances, ensuring that contribution history survives migrations or downtime. In essence, Neural Torrent solves the trust, incentive, and verification challenges in decentralized AI data sharing, enabling autonomous model self-evolution backed by cryptographic proof.