Shape the Future of Agentic Science

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Bio x AI Hackathon: Build the Future of Science with AI

Subheadline: Be part of the open-source revolution at the convergence of biotechnology and artificial intelligence. Develop AI-driven tools to accelerate scientific discovery through knowledge graphs.

Why Participate?

• Shape the Future of Science: Contribute to the development of digitally native systems that reimagine how scientific knowledge is created, shared, and utilized.
• Solve Real-World Challenges: Focus on building tools that organize scientific information, generate meaningful connections between research, and produce novel hypotheses.
• Work with Cutting-Edge Technology: Leverage the power of AI, knowledge graphs, and potentially blockchain to transform basic and translational research.
• Collaborate with Experts: Connect with researchers, developers, and BioDAO community members. Receive mentorship from expert judges in MetaScience, AI, DeSci, and BioTech.
• Build Plugins for Eliza: Develop self-contained software pieces that solve specific scientific problems, connect to the Eliza AI framework, and work together in a larger ecosystem.
• Win Prizes: Compete for a share of the $125,000 prize pool across multiple challenge categories, including Scientific Outcomes and CoreAgent Development.
• Be at the Forefront of DeSci: Help build the infrastructure for a more open, transparent, and efficient research ecosystem.

Hackathon Focus:

The Bio x AI Hackathon focuses specifically on scientific outcomes derived from knowledge graph creation and utilization. We aim to empower developers to build tools that:

• Organize scientific information.
• Generate meaningful connections between disparate research.
• Produce novel hypotheses that can advance scientific discovery.

Projects should leverage the power of AI to extract, structure, analyze, and derive insights from scientific data, with a particular emphasis on basic and translational research.

Key Challenges:

• Transforming Scientific Systems: Envisioning and building digitally native systems for science that transcend legacy constraints.
• Addressing the Research Funding Challenge: Developing innovative solutions to maximize resources and accelerate discovery.
• Harnessing AI for Good: Building robust systems to track and verify scientific claims, preserve research context, and enhance reproducibility.

Expected Outcomes:

You'll be developing "plugins" for the Eliza AI framework, an intelligent scientific assistant. These plugins are self-contained pieces of software that:

• Solve a specific scientific problem or provide a particular capability.
• Connect to Eliza through standardized interfaces.
• Can be developed independently but work together in the larger ecosystem.

Open Source Commitment:

All submissions must be open source to foster collaboration and innovation:

• MIT License Requirement
• Public Repositories (e.g., GitHub)
• Clear Documentation
• Structure for Community Contribution

Important Dates:

• April 8-10: Hackathon Kickoff (In Person: https://lu.ma/BioAIHackathonApply & Virtual: https://lu.ma/bio-agents)
• April - June: Virtual Hacking Phase
• June 10-11: Hackathon Closeout

Prize Structure

The hackathon has three main prize pools:

• $75,000 - Scientific Outcomes: This track focuses on projects that generate meaningful scientific outputs, emphasizing information organization, knowledge graph generation, and hypothesis generation.
• $35,000 - CoreAgent Development: This prize is dedicated to projects that address specific challenges faced by bioDAOs. Hackers will be given scoped problem statements defined by the founders and operators of BioDAOs and asked to solve the problems.
• $15,000 - Discretionary Prizes: This portion of the prize pool will be allocated at the discretion of Bio.XYZ to recognize various valuable contributions.

Judging Criteria:

Purposeful Flexibility in Evaluation

We have intentionally designed the prize criteria with a degree of flexibility to allow judges to exercise their expert judgment. The scientific process is inherently creative and multifaceted, and we believe that prescriptive evaluation metrics might inadvertently constrain innovation or miss breakthrough approaches that don't fit neatly into predefined boxes.

Judges have the autonomy to identify and reward excellence as you see it in your domain of expertise. That said, we do provide the following general guidelines to help frame your project:

General Evaluation Guidelines (subject to change)

1. Scientific Impact
   • Potential to meaningfully advance scientific research or knowledge
   • Novelty and originality of the approach to knowledge graph creation or utilization
   • Quality of insights or hypotheses generated
   • Anticipation of future scientific research needs in the age of AI
2. Technical Implementation
   • Quality and robustness of the knowledge graph model or architecture
   • Appropriate use of AI techniques for knowledge extraction and connection
   • Technical sophistication and innovation
   • Data handling and processing methods
3. Usability and Accessibility
   • Ease of use for scientific researchers
   • Clear and intuitive visualization or presentation of complex data
   • Documentation quality and comprehensiveness
   • Potential for adoption by the broader scientific community
4. Sustainability and Scalability
   • Potential for continued development and improvement
   • Ability to scale to larger datasets or different scientific domains
   • Approach to maintaining data quality and accuracy over time
   • Open-source commitment and community engagement potential

Ready to Build the Future of Science? Learn More at:

• ai.bio.xyz
• About the Vision
• Docs
• Join the Telegram for updates
• Join the Discord to start building!