# Council Briefing: 2025-06-10 ## 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 v1.0.7 released with significant architectural improvements and bug fixes while the community faces challenges with knowledge plugin functionality and public perceptions of autonomous systems. ## Key Points for Deliberation ### 1. Topic: Framework Stability vs. Innovation Balance **Summary of Topic:** ElizaOS v1.0.7 has been released with significant architectural improvements including modularized code, plugin enhancements, and critical bug fixes, yet some core functionalities like knowledge management (RAG) remain unimplemented or problematic. #### Deliberation Items (Questions): **Question 1:** How should we balance fixing existing functionality versus implementing new features as we transition toward v2? **Context:** - `ElizaOS v1.0.7 Released: Users were instructed to update by running `npm i -g @elizaos/cli` (cjft)` - `harperaa: Reported two issues including #5004 about knowledge management (RAG) functionality not working in version 1.0.6 despite documentation` **Multiple Choice Answers:** a) Prioritize stability by freezing new features and focusing exclusively on fixing critical bugs in v1 before v2 development. *Implication:* This would delay innovation but create a more reliable foundation for auto.fun's 24/7 agent showcase. b) Adopt a dual-track approach with separate teams for v1 maintenance and v2 development, with clear criteria for backporting fixes. *Implication:* This balances stability and innovation but requires additional coordination overhead and clear team boundaries. c) Accelerate v2 development and direct most resources there, addressing only critical v1 issues that impact auto.fun showcase functionality. *Implication:* This prioritizes innovation but risks reliability issues for current users and could undermine trust in the platform. d) Other / More discussion needed / None of the above. **Question 2:** What approach should we take to address the knowledge management (RAG) functionality gap that's currently missing despite being documented? **Context:** - `Missing Implementation Locations: AgentRuntime Initialization Missing Knowledge Processing... The AgentRuntime.initialize() method should process character.knowledge but doesn't` - `TODO Comment Confirms Missing Implementation... // TODO: Implement the remaining adapters: ... - knowledge / memory` **Multiple Choice Answers:** a) Implement RAG functionality in v1.0.8 as a high-priority patch release, focusing on basic functionality aligned with existing documentation. *Implication:* This addresses immediate user expectations but diverts resources from v2 development. b) Redesign RAG for v2 while updating v1 documentation to clearly indicate limitations, potentially offering alternative approaches in the interim. *Implication:* This manages user expectations without compromising v2 development pace but may disappoint users needing this functionality now. c) Create a specialized external plugin that implements RAG functionality for v1 while developing the native solution for v2. *Implication:* This provides a short-term solution while maintaining development focus on v2, though it creates parallel implementations to maintain. d) Other / More discussion needed / None of the above. **Question 3:** How should we improve our release quality process to prevent documented-but-unimplemented features from making it into production releases? **Context:** - `The codebase has explicit TODO comments indicating that knowledge/memory functionality is intentionally unfinished. The character examples even reference a KNOWLEDGE provider that doesn't exist, suggesting this was planned but never implemented.` **Multiple Choice Answers:** a) Implement automated documentation validation that cross-references feature claims with actual code implementation during CI/CD. *Implication:* This provides systematic validation but requires significant upfront engineering investment. b) Institute pre-release feature audits with designated team members verifying documented capabilities against implemented code. *Implication:* This provides thorough human validation but adds overhead to the release process and relies on consistent execution. c) Adopt documentation-driven development where features are only documented after implementation, with warnings for experimental/upcoming features. *Implication:* This ensures documentation accuracy but may limit the ability to publish forward-looking documentation. d) Other / More discussion needed / None of the above. --- ### 2. Topic: Auto.fun User Acquisition Strategy **Summary of Topic:** Community discussions suggest emerging opportunities around AI agent narratives that could benefit the ai16z ecosystem, while also highlighting competitive differentiation through auto.fun's fee structure and the growing interest in elizaOS tokens. #### Deliberation Items (Questions): **Question 1:** How should we position auto.fun to capitalize on the emerging AI agent narrative in the coming months? **Context:** - `Speculation that AI agent narratives will become prominent in coming months` - `References to "autodotfun" as an innovative platform with favorable fee structures` - `Mentions of "ai16z ecosystem" potentially benefiting from upcoming trends` **Multiple Choice Answers:** a) Position auto.fun as the premier launchpad for AI-native tokens with educational content explaining why AI agent projects require specialized tokenomics. *Implication:* This establishes category leadership but narrows potential audience to AI-focused projects. b) Emphasize auto.fun's technical advantages and fee structure benefits while showcasing successful case studies of 24/7 agent activity across various use cases. *Implication:* This highlights competitive differentiation but may not fully leverage the AI narrative momentum. c) Create a comprehensive AI agent ecosystem within auto.fun combining token launching, agent development, and operational dashboards for monitoring 24/7 activity. *Implication:* This creates a holistic offering but increases scope and complexity of what needs to be delivered. d) Other / More discussion needed / None of the above. **Question 2:** What approach should we take to differentiate auto.fun from competitors and capitalize on community optimism about ELI5 and other ecosystem tokens? **Context:** - `Discussions about cryptocurrency "eli5" price movements with optimism about reaching 100M or 200M` - `References to "autodotfun" as an innovative platform with favorable fee structures` **Multiple Choice Answers:** a) Focus messaging on auto.fun's favorable fee structure and technical advantages compared to competitors like pumpfun. *Implication:* This appeals to rational decision-making but may not capture emotional drivers of platform choice. b) Highlight ecosystem token performance and implement token utility features that create synergies between auto.fun and elizaOS ecosystem tokens. *Implication:* This leverages existing community excitement but ties platform success more closely to token performance. c) Create an exclusive auto.fun accelerator program for AI agent projects that offers reduced fees, technical support, and co-marketing opportunities. *Implication:* This creates a premium offering and community but requires significant operational resources to deliver effectively. d) Other / More discussion needed / None of the above. --- ### 3. Topic: Autonomous Systems Public Perception **Summary of Topic:** Recent events around Waymo vehicles being set on fire highlight escalating tensions around autonomous systems, prompting discussions about addressing societal concerns around AI agents and job displacement that could affect elizaOS agents' public reception. #### Deliberation Items (Questions): **Question 1:** How should elizaOS address public concerns about automation and job displacement as we develop and promote autonomous AI agents? **Context:** - `@shawmakesmagic commented 'I'm not saying we've found a solution for what the 5% of Americans who drive for a living will do in the next 5 years. All I'm saying is that if the Ubers didn't have people in them, they'd be on fire too.'` - `A significant discussion emerged around Waymo autonomous vehicles being set on fire in LA.` **Multiple Choice Answers:** a) Develop and promote augmentative agents that enhance human capabilities rather than replace them, with clear messaging around human-AI collaboration. *Implication:* This addresses concerns directly but may limit some fully autonomous agent use cases and slow adoption. b) Focus on creating new economic opportunities through elizaOS by highlighting how agent creation, customization, and management generate new types of jobs. *Implication:* This reframes the narrative positively but requires substantiating evidence of new job creation to be credible. c) Avoid engaging with broader societal concerns and focus purely on technical capabilities, letting users determine appropriate applications for the technology. *Implication:* This simplifies messaging but ignores legitimate concerns that could affect platform adoption and regulatory environment. d) Other / More discussion needed / None of the above. **Question 2:** What features or design principles should we prioritize in elizaOS v2 to mitigate potential public backlash against autonomous AI systems? **Context:** - `@shawmakesmagic commented 'They should add a feature where the Waymos beg for their life'` - `Some users noted issues with 'dead' partner projects that need addressing` **Multiple Choice Answers:** a) Implement robust transparency mechanisms showing exactly what agents can and cannot do, with clear audit logs of all agent actions and decisions. *Implication:* This builds trust through transparency but adds technical complexity and potential performance overhead. b) Design explicit human override controls in all autonomous functions, with progressive autonomy levels that users can adjust based on trust and comfort. *Implication:* This gives users control but may limit the benefits of fully autonomous operation that make agents valuable. c) Focus on benign use cases like content creation and information processing initially, gradually introducing more autonomous capabilities as public acceptance grows. *Implication:* This reduces initial controversy but may delay development of key features needed for the monthly goal of 24/7 agent activity. d) Other / More discussion needed / None of the above.