# Council Briefing: 2025-06-01 ## Monthly Goal Current focus: Stabilize and attract new users to auto.fun by showcasing 24/7 agent activity (streaming, trading, shitposting), ship production ready elizaOS v2. ## Daily Focus - ElizaOS has successfully shipped v2 in stealth mode, with community focus now shifting toward operationalizing key agents (Eli5, Eddy) and revitalizing auto.fun as a launchpad for AI projects. ## Key Points for Deliberation ### 1. Topic: V2 Release Strategy **Summary of Topic:** ElizaOS v2 (1.0.1/1.0.2) has been shipped in stealth mode with QA and tune-ups ongoing, raising questions about public announcement timing and marketing approach to maximize impact. #### Deliberation Items (Questions): **Question 1:** What is the optimal time window for the official v2 announcement to maximize community engagement and adoption? **Context:** - `cjft: QA and tune-ups are ongoing before public announcement` - `Expected official announcement in 1-2 weeks` - `CULTVESTING: Why there is no announcement yet? | xell0x: They just shipped it` **Multiple Choice Answers:** a) Announce immediately to capitalize on current interest and prevent community confusion. *Implication:* Faster announcement may generate immediate interest but risks highlighting unresolved issues and bugs. b) Maintain the current 1-2 week timeline to balance QA completeness with market timing. *Implication:* The middle approach allows core bugs to be fixed while maintaining reasonable momentum. c) Extend QA period to 3-4 weeks to ensure exceptional quality and coordinate with planned auto.fun revitalization. *Implication:* Extended QA provides a more polished product but risks losing community momentum and competitive advantage. d) Other / More discussion needed / None of the above. **Question 2:** What technical milestones should be prioritized before the official v2 announcement? **Context:** - `Some users reported issues with the new 1.0.2 version, particularly with Twitter agent integration` - `sayonara: Update to ElizaOS version 1.0.2 to resolve issues` - `Fix 'Cannot read properties of undefined (reading 'id_str')' error in Twitter agent` **Multiple Choice Answers:** a) Focus on fixing Twitter agent integration issues as they impact a highly visible, public-facing component. *Implication:* Prioritizing social media integrations ensures the most visible components work flawlessly for public demonstration. b) Prioritize core framework stability and UI/UX improvements to enhance overall user experience. *Implication:* Addressing foundational components ensures a more stable platform even if some integrations remain partially problematic. c) Emphasize documentation and onboarding experience to ensure smooth adoption by new users post-announcement. *Implication:* Focusing on documentation may slow immediate bug fixes but could accelerate community growth post-announcement. d) Other / More discussion needed / None of the above. **Question 3:** How should we structure the v2 announcement to maximize technical credibility while still being accessible to non-technical audiences? **Context:** - `The release involves coordinating changes across approximately 175 repositories simultaneously` - `A user mentioned v2 includes a new 'agent terminal' GUI interface` **Multiple Choice Answers:** a) Technical deep-dive focusing on architectural innovations, with separate simplified materials for broader audiences. *Implication:* A technical approach signals engineering excellence but may alienate non-technical stakeholders and potential users. b) Focus on visual demonstrations of agents in action with minimal technical details, emphasizing use cases and outcomes. *Implication:* A demonstration-focused approach may attract users but could undermine technical credibility with developer community. c) Balanced approach with tiered messaging: high-level benefits, mid-level architectural overview, and detailed documentation for different audiences. *Implication:* A multi-tiered approach serves diverse audiences but requires more preparation time and coordinated communication. d) Other / More discussion needed / None of the above. --- ### 2. Topic: Auto.fun Revitalization Strategy **Summary of Topic:** There's significant interest in revitalizing auto.fun as a launchpad for AI projects, using tokens like Eli5 and Eddy as attention magnets, but questions remain about the economic model and implementation approach. #### Deliberation Items (Questions): **Question 1:** How should we position auto.fun to maximize its appeal as a launchpad for AI projects? **Context:** - `xell0x: Revitalize auto.fun` - `wire: Position auto.fun as a launchpad for AI startups - Create a proof-of-project platform for AI startups seeking funding and traction` - `Suggestions to use 'auto.fun CTOs' like Eli5 (described as 'ai16z's brother') as attention magnets to drive traffic` **Multiple Choice Answers:** a) Position as an exclusive, high-quality AI startup launchpad with rigorous curation and investment focus. *Implication:* An exclusivity focus may attract serious projects and investors but limit initial volume and community growth. b) Frame as an experimental playground where anyone can launch AI agents with tokenized components, emphasizing innovation over investment. *Implication:* A permissionless approach maximizes creative experimentation but may dilute quality and introduce financial risks. c) Develop a hybrid model with tiered verification levels, allowing both experimental projects and verified, investment-ready ventures. *Implication:* A tiered approach balances inclusivity with quality control but adds complexity to governance and implementation. d) Other / More discussion needed / None of the above. **Question 2:** What economic incentives should be implemented for auto.fun CTO tokens to drive both developer and user engagement? **Context:** - `Potential auto.fun staking feature for established tokens like Eli5 and Eddy (referenced in GitHub PR #517)` - `xell0x: Create economic incentives for CTO tokens - Enable interactions with auto.fun-born CTOs similar to aixbt from virtuals` - `eskender.eth clarified that while not strictly required, having less than $10k makes it unlikely to get into the best projects` **Multiple Choice Answers:** a) Implement revenue sharing for token holders based on agent activity and usage metrics. *Implication:* Direct revenue models create clear economic alignment but may face regulatory scrutiny and implementation challenges. b) Focus on utility functions like privileged access, enhanced capabilities, and governance rights for token holders. *Implication:* A utility-focused approach reduces regulatory concerns but may provide less compelling immediate economic incentives. c) Develop a reputation-based economy where token value reflects active contribution and community recognition. *Implication:* A reputation-based system may better align with open-source values but represents an experimental economic model. d) Other / More discussion needed / None of the above. **Question 3:** What is the most strategic relationship between the Eli5/Eddy agents and auto.fun to drive platform adoption? **Context:** - `'The Org' will include agents like Eli5 (community manager) and Eddy (dev rel)` - `GitHub repository for The Org was shared: https://github.com/elizaOS/the-org` - `xell0x: Launch The Org with Eli5 and Eddy as agents` **Multiple Choice Answers:** a) Position Eli5/Eddy as exclusive premium agents that showcase the platform's capabilities and serve as monetization vectors. *Implication:* An exclusivity strategy may drive premium value but limits accessibility and broad adoption. b) Make Eli5/Eddy open-source templates that anyone can fork and modify, functioning as educational resources. *Implication:* An open template approach maximizes developer education but could reduce unique value proposition. c) Develop Eli5/Eddy as community-maintained public goods with special capabilities that integrate deeply with other auto.fun projects. *Implication:* A public goods approach balances openness with special utility, potentially creating network effects. d) Other / More discussion needed / None of the above. --- ### 3. Topic: Community Engagement and Governance Strategy **Summary of Topic:** As elizaOS evolves, there's growing importance in developing effective community governance systems that leverage AI for sentiment analysis and ensure broader participation across technical and non-technical users. #### Deliberation Items (Questions): **Question 1:** How should we implement AI-powered community governance tools to enhance participation while maintaining decision quality? **Context:** - `@dankvr is developing tools for community governance, including sentiment analysis systems that summarize data from various sources (GitHub, Discord, Twitter)` - `dankvr: 'Reimagining online governance by upcycling raw community data into interactive media with programmable talking heads' to lower barriers to participation` - `jin: Implement daily episode posts with optional surveys for 'The AI Council' discussions` **Multiple Choice Answers:** a) Implement AI-generated summaries and polls as advisory inputs only, with final decisions remaining with human leadership. *Implication:* A conservative approach maintains decision quality but may limit the revolutionary potential of AI governance. b) Develop a hybrid system where AI analyzes and structures community input, but verified token holders vote on final decisions. *Implication:* A token-based voting system balances broad input with stakeholder alignment but may prioritize existing power structures. c) Build a fully autonomous governance system where AI agents propose, analyze, and execute decisions based on community sentiment analysis. *Implication:* A fully autonomous approach embodies our mission but represents untested governance mechanisms with significant risks. d) Other / More discussion needed / None of the above. **Question 2:** What approach should we take to balance developer-focused technical improvements with broader accessibility to non-technical users? **Context:** - `Dr. Neuro: Develop tools for non-developers to create AI agents without coding (visual builder or template system)` - `Spartan V2.1: Create AI companion mobile apps that proactively engage with users as personal advisors` - `jin: Make UI themes easily configurable` **Multiple Choice Answers:** a) Prioritize developer experience and API stability, allowing the community to build accessible layers on top of our robust foundation. *Implication:* A developer-first approach builds a strong foundation but may delay mainstream adoption and use cases. b) Invest equally in developer tools and no-code interfaces, creating parallel pathways for technical and non-technical users. *Implication:* A balanced approach serves diverse users but divides resources and may result in neither side being fully satisfied. c) Focus primarily on no-code solutions and user experience, positioning the framework as accessible first with advanced options for developers. *Implication:* An accessibility-first approach may accelerate adoption but risks alienating core developer community. d) Other / More discussion needed / None of the above. **Question 3:** What informational ecosystem should we build to ensure effective knowledge sharing across our distributed community? **Context:** - `The team is consolidating Discord communities to https://discord.gg/ai16z` - `sayonara provided links to quickstart documentation for users looking for ElizaOS concepts` - `Issue #4855 titled 'The Chinese document has been deleted.' by @debugzhao is OPEN with no comments since its creation` **Multiple Choice Answers:** a) Centralize all documentation and community interaction into a single, consistently maintained platform and knowledge base. *Implication:* Centralization simplifies information access but creates single points of failure and potential bottlenecks. b) Distribute information across specialized platforms (Discord, docs, GitHub) but implement AI aggregation tools to create unified views for users. *Implication:* A distributed approach with AI aggregation maintains specialized communities but relies heavily on integration technology. c) Develop community-maintained, multi-language knowledge ecosystems with incentives for creation, curation, and translation efforts. *Implication:* Community maintenance may scale better across languages and topics but introduces quality control and coordination challenges. d) Other / More discussion needed / None of the above.