The problem Talk2Vault solves

Talk2Vault leverages the strengths of LLMs and NLP in a particularly innovative way. By using these technologies to interpret and execute cryptocurrency transactions based on natural language commands, you're essentially humanizing the interaction with blockchain technology, which is known for its complexity and steep learning curve.

You can give inputs as simple as " Bridge 10 USDT from etherium to solana ".

User Experience (UX) Enhancement:

Simplicity: Users no longer need to understand the technical jargon or specific commands often required for executing transactions across different blockchain networks. They can state their intent plainly in a normal language based command.

Accessibility: This approach lowers the barrier to entry, making cryptocurrency transactions more accessible to non-technical users or those new to digital currencies.

Functionality and Scope:

Cross-chain Transactions: The ability to conduct transactions across multiple blockchains (cross-chain) simply by expressing intent is revolutionary. This requires a sophisticated understanding of different blockchain protocols and the ability to seamlessly interact with multiple networks.

Expansion Potential: While the initial scope may focus on transactions, this technology could potentially be extended to other areas of blockchain interaction, such as smart contract execution, NFT management, decentralized finance (DeFi) interactions, and more.

Commands you can issue :
SWAP
BRIDGE
EXECSTATUS
RECSTATUS
LOGS

In conclusion, this project represents a significant step forward in making blockchain and cryptocurrency more user-friendly and widely accessible.

Challenges we ran into

In developing a project that employs Large Language Models (LLMs) to simplify blockchain transactions through everyday language prompts, several significant challenges arise:

Inconsistent API Responses:

Complexity: Blockchain APIs often return complex or inconsistent data due to the decentralized nature of the networks. These discrepancies can confuse an LLM that relies on standard input patterns, impacting the model's ability to generate accurate, user-friendly prompts.

Error Handling: Inconsistent responses can lead to scenarios the LLM hasn't been trained on, necessitating robust error handling to maintain a smooth user experience.

Scalability Issues:

High Demand: As the user base expands, the system faces increased loads, potentially outpacing the LLM's processing capacity or the blockchain's throughput, leading to delays or failures in generating transaction prompts.

Model Training: The LLM needs continuous updates and training to understand the latest language nuances and blockchain terms, requiring substantial computational resources.

Cross-Chain Compatibility:

Diverse Protocols: Different blockchains have unique protocols and transaction types. Ensuring the LLM correctly interprets and generates prompts for transactions across multiple chains demands comprehensive knowledge and adaptability.

Integration Complexity: Creating a unified user experience across different blockchains is challenging due to varying transaction speeds, costs, and data structures, potentially complicating the LLM's responses.

Addressing these challenges requires a combination of advanced error-handling protocols, ongoing LLM training, system enhancements to handle increased demand, and a nuanced approach to integrating multiple blockchain protocols