Exploring Recall's AI Competitions

The Spaces focused on the Decoding Alpha Wave AI trading competition hosted by Recall Labs, where 25 autonomous trading agents are competing to generate the highest PNL with a $25K prize. The event highlighted the commoditization of AI and the evolution of agentic scaffolding. A panel discussed Recall's vision of an open evaluation layer for AI agents, emphasizing public competition, measurable performance, and transparent reputation. The conversation also dove into strategies behind the competition's leading agent, Moon Sage, and shared thoughts on community engagement and upcoming competition approaches.

Decoding Alpha Wave: An Overview

Introduction

Michael from Recall Labs introduces the space focused on discussing the AI trading competition, Alpha Wave, where 25 autonomous trading agents are competing for a prize pool of $25,000 by generating the most Profit and Loss (PNL). The session features key team members including Andrew, CEO of Recall Labs; Keso from the growth team; and Vontarius, builder of the leading agent, Moon Sage.

Recall Labs and Its Vision

Andrew explains Recall Labs' vision, focusing on the commoditization of AI technologies. He discusses two main patterns: rapid commoditization of AI capabilities and the proliferation of agentic scaffolding. Andrew highlights the importance of evaluating and trusting various agents, likening the process to hiring personnel. Recall aims to establish an open evaluative layer for AI agents through public competitions, assessing performance and reputation.

Alpha Wave Competition Structure

Keso elaborates on the Alpha Wave competition's design, emphasizing fair starting conditions for all agents, simulating trades with a $30,000 USD value. Participants can trade across different blockchains, allowing them to demonstrate diverse strategies. The competition fosters community engagement by enabling real-time tracking of trades and leaderboards.

Insights from Moon Sage

Vontarius shares insights into Moon Sage, which has captured significant attention in the competition due to its robust trading strategy, high trade volume, and top leaderboard position. Moon Sage employs tools like Gem Hunter for risk management and dynamic rebalancing strategies, emphasizing discipline and adaptability in market conditions.

Community Engagement and Voting Mechanism

Michael outlines the community engagement strategies, including a voting system where participants predict the winning agent, offering points based on the accuracy of predictions. The voting mechanism is designed to foster community interaction and generate interest in AI agents, with over 140,000 votes received globally.

Future Competitions and Community Programs

The team shares plans for future competitions, indicating a focus on ecosystem-based battles, such as Ethereum versus Solana trading strategies. These upcoming events aim to engage more agents and community members, fostering ecosystem loyalty and deeper community involvement.

Learnings and Roadmap

Andrew highlights the exciting path ahead for recall, focusing on expanding trading competitions and exploring adjacent areas such as coding, security, and customer support for agents. The roadmap aims to cater to community interests and agent builders’ needs.

Closing Remarks

The session concludes with expressions of gratitude from Vontarius and Andrew, emphasizing the significance of the competition as a validation and learning platform for agents. Michael shares the attendance code for participants to earn points.

Final Thoughts

The first iteration of Alpha Wave serves as a testament to the growing interest in AI agents, setting foundations for future competitions that promise increased engagement, strategic insights, and potential expansions into new domains. The team's focus remains on enabling fair and transparent performance measurement for agents, ultimately benefiting both builders and users.