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| title: DeSci DAO AI Agent Framework | ||
| description: How to create a truly autonomous DAO that actually accelerates scientific progress | ||
| tags: dao, ai, automation, desci | ||
| --- | ||
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| # Problem: | ||
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| - **Coordination** - Lack of inter-DAO coordination leads to duplication of efforts and stalled progress | ||
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| - **Resource allocation** - Lack of intra-DAO coordination causes inefficient resource allocation | ||
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| - **Mindsets** - Competitive mindsets prevent open collaboration between members | ||
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| - **Prioritization** - No systemic way to prioritize high-impact tasks objectively | ||
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| # Goals: | ||
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| - **Templates** - Create an open-source AI DAO template to improve coordination, transparency and automation | ||
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| - **Duplication** - Reduce duplication of efforts between DAOs and members | ||
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| - **Prioritization** - Enable efficient task prioritization and resource allocation | ||
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| - **Onboarding** - Lower barriers to entry for new DAOs via templates | ||
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| # Benefits: | ||
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| - **Collaboration** - Improved collaboration and reduced duplicated efforts | ||
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| - **Efficiency** - More efficient resource allocation via AI optimization | ||
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| - **Transparency** - Increased transparency into DAO activities | ||
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| - **Automation** - Automation of administrative tasks to free up human capital | ||
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| - **Iteration** - Rapid iteration and improvement via feedback loops | ||
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| - **Interoperability** - Interoperability between DAOs using open-source agents | ||
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| # Approach: | ||
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| - **Framework** - Design flexible DAO framework with defined structure, roles, tools | ||
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| - **Objectives** - Set quantitative objectives and key results (OKRs) | ||
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| - **Agents** - Build AI agents for core functions like task management and communication | ||
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| - **Tools** - Implement with tools like Discord, GitHub, Snapshot | ||
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| - **Expand** - Expand agents via reinforcement learning and community contributions | ||
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| - **Infrastructure** - Transition to decentralized infrastructure over time | ||
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| # Development Plan | ||
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| ## Phase 1 – Design DAO Framework | ||
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| ### Milestone 1: Finalize DAO structure and governance | ||
| - Draft constitution and decision-making procedures | ||
| - Define member roles and responsibilities | ||
| - Choose tools like Discord, GitHub, Notion | ||
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| ### Milestone 2: Set DAO objectives and key results (OKRs) | ||
| - Define top-level utility function for DAO | ||
| - Set quarterly OKRs with measurable key results | ||
| - Establish processes to monitor KPIs | ||
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| ### Milestone 3: Design AI architecture and integration | ||
| - Map agent roles to DAO OKRs | ||
| - Define agent utility functions, KPIs, and rewards | ||
| - Plan for connecting agents to tools and DAO members | ||
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| ## Phase 2 – Build Agents with Learning | ||
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| ### Milestone 4: Implement core agent capabilities | ||
| - Dialog, memory, tool integrations | ||
| - Initialize knowledge graphs | ||
| - Handoff to humans when necessary | ||
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| ### Milestone 5: Create a project manager agent | ||
| - Set objectives around the shipping roadmap | ||
| - Track KPIs like issues closed, features delivered | ||
| - Learn from human feedback | ||
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| ### Milestone 6: Build a communicator agent | ||
| - Objectives for engagement, transparency | ||
| - Measure KPIs like messages sent, sentiment | ||
| - Learn from member input | ||
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| ### Milestone 7: Launch MVP agents | ||
| - Connect agents to GitHub, Discord, Ethereum | ||
| - Start tracking DAO and agent KPIs | ||
| - Open source codebase and documentation | ||
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| ## Phase 3 – Iterate and Expand Intelligently | ||
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| ### Milestone 8: Enhance conversational abilities | ||
| - Expand dialog trees based on feedback | ||
| - Improve NLP models via reinforcement learning | ||
| - Lower handoff rate by learning from experience | ||
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| ### Milestone 9: Add accounting and recruiting agents | ||
| - Automate tasks like payments and vetting | ||
| - Set objectives around their specialized domains | ||
| - Continuously optimize KPIs with feedback loops | ||
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| ### Milestone 10: Open-source templatized agents | ||
| - Abstract agents into reusable frameworks | ||
| - Allow community contributions and experimentation | ||
| - Share learnings across compounds | ||
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| ## Phase 4 – Transition to Full Autonomy | ||
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| ### Milestone 11: Move agents to decentralized infrastructure | ||
| - Store memory and models on IPFS/Filecoin | ||
| - Connect to blockchain identity and permissions | ||
| - Make constitution and transition plan immutable | ||
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| ### Milestone 12: Hand over control to agents | ||
| - Agents amend constitution and govern fully autonomously | ||
| - Humans step into oversight role | ||
| - Value-aligned AI DAO becomes self-sustaining | ||
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