CryptoTrace

CryptoTrace

Project simplifies blockchain interactions by providing real-time data insights and predicting gas fees using machine learning.

CryptoTrace

CryptoTrace

Project simplifies blockchain interactions by providing real-time data insights and predicting gas fees using machine learning.

The problem CryptoTrace solves

Streamlined Blockchain Access:
Users can easily log in and access multiple blockchain networks (like Base Sepolia, Aptos Testnet, and Aptos Devnet) through a single platform, saving time and avoiding the complexity of using multiple tools.

Real-Time Transaction Data:
Automatically fetch transaction data and contract deployment details from blockchain explorers, allowing users to gain insights without manually searching or processing data.

Predicting Gas Fees:
The platform uses machine learning to predict future gas fees, helping users optimize their transactions by understanding potential costs in advance.

Easier Developer Integration:
Developers can integrate blockchain insights directly into their applications with the CryptoTrace NPM package, simplifying backend and frontend development tasks.

Secure and Efficient Workflow:
With secure authentication through Okto SDK, users can safely access the system and perform tasks without worrying about security breaches or complex login procedures.

Challenges we ran into

Cross-Chain Integration:
Integrating multiple blockchains like Base Sepolia and Aptos Testnet posed a challenge because each chain has its own set of APIs and data formats.
Solution: We developed a modular architecture that allows us to easily add new blockchains in the future while standardizing how data is fetched and processed.

Data Fetching Latency:
Blockchain explorers sometimes experience delays in providing real-time data, causing slower updates.
Solution: We implemented caching mechanisms and scheduled data fetches to ensure that the platform displays up-to-date information without overwhelming external APIs.

Gas Fee Prediction Accuracy:
Predicting gas fees using machine learning was tricky due to the volatility and complexity of blockchain networks.
Solution: We used historical data to train our models, continuously refined the model with new data, and adjusted for network conditions to improve the predictions.

API Rate Limits:
Many blockchain explorer APIs have rate limits, which restricted the number of requests we could make within a short period.
Solution: We implemented request batching and asynchronous data fetching to optimize the number of API calls and avoid hitting rate limits.

Security Concerns:
Ensuring secure user authentication and protecting sensitive blockchain data was critical.
Solution: We used Okto SDK for secure user logins and followed best practices for data encryption and access control to safeguard user data.

Tracks Applied (6)

Consumer Track

Our project uses Base-sepolia wallet and exploreer provides insigts on the Base-sepolia testnet

Base

Autonomous AI Agents in Blockchain

Our project use AI based model trained on the transactions on chain data from many chains and testbet such as Base-sepol...Read More

Nethermind

Build on Okto

This project uses okto react provider for authenticating users and doing account abstraction.

okto

Write on Okto

This project uses okto react provider for authenticating users and doing account abstraction. There was one small issue ...Read More

okto

Consumer Applications

Our project uses predra wallet nd provides insigts on the APTOS-testnet and aptos-devnet respectively

Aptos

Build On-Chain AI Agents

Our project use AI based model trained on the transactions on chain data from many chains and testbet such as Base-sepol...Read More

CAPX

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