The problem Ai-Avatar solves

Life's been getting crazier, right? Especially for us Web3 geeks—juggling trades, projects, and sleep? Forget about it. Imagine if you had someone who’s literally you but handles all that hustle. Yep, your Personal AI Avatar that thinks, acts, and trades like you while you kick back and recharge.

Missed the last Bitcoin dip ‘cause you were snoozing? Or busy with some other grind? No worries. Your AI twin’s got you. It’s trained on your on-chain vibes, your money moves, your risk appetite—basically, it’s your digital shadow, making moves just like you would. Got some funds? Allocate 'em, and your AI twin will trade like a boss, even when you're offline. Oh, and it’ll ping you for the big stuff or sketchy situations—like a guardian angel for your portfolio.

But this isn’t just about trading. Think big picture. This avatar can handle all the mind-numbing, repetitive stuff that eats your day—DAO votes, reviews, emails—anything that you’d otherwise have to grind through. It's like having a clone for the boring bits, so you’ve got more time to vibe, chill, or crush it in other areas.

Basically, it’s you, amplified. Smarter decisions, more free time, and a better shot at that work-life-health balance. Your AI twin’s here to save the day, and your sanity.

Challenges we ran into

1. No Available Options for Testnet DEX Trading
   Challenge: We struggled to find reliable testnet DEXs, lacking liquidation pools and essential trading features for effective testing.
   Solution: We explored various testnets, leveraging simulated environments and community insights to discover experimental DEXs for testing.

2. Mitigating Outliers in Human Behavior
   Challenge: Outliers in human behavior risked skewing our agent's decisions.
   Solution: We created a swarm of meta agents to monitor behavior, ensuring that anomalies wouldn’t compromise the model by averaging inputs from multiple agents.

3. Lack of Out-of-the-Box Solutions for Multiagent Interaction.
   Challenge: The new CDP agentkit didn’t offer out-of-the-box solutions for multiagent interactions.
   Solution: We dug into the libraries’ documentation and collaborated with contributors to develop a custom framework for agent communication.

4. Scraping Wallet Transactions for Training
   Challenge: Collecting accurate wallet transaction data for model training proved difficult.
   Solution: We used blockchain APIs and custom scripts to streamline data collection and implemented filters to ensure data quality.