# Council Briefing: 2025-08-23 ## 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 Accelerator Demo Day showcased multiple AI agent teams seeking investment while core team delivered significant framework improvements with the bun.Build optimization and async embedding generation to enhance agent responsiveness. ## Key Points for Deliberation ### 1. Topic: Auto.fun Agent Growth Strategy **Summary of Topic:** The ElizaOS Accelerator Demo Day presented multiple AI agent teams seeking investment, including 'Hivemind' for crypto marketing, alongside community discussions on prediction markets and content creation synergies for auto.fun ecosystem growth. #### Deliberation Items (Questions): **Question 1:** How should we prioritize investment and integration of the new agents showcased at the Accelerator Demo Day to maximize auto.fun user engagement? **Context:** - `Kenk shared a comprehensive overview of agents presented at the demo day, noting all are seeking investment` - `Bond11 mentioned building "Hivemind," an AI-powered crypto marketing strategist` **Multiple Choice Answers:** a) Prioritize agents with immediate revenue potential, like Hivemind for marketing, to bootstrap financial sustainability. *Implication:* This approach could accelerate financial independence but might sacrifice long-term innovation and user stickiness. b) Focus on agents that demonstrate highest user engagement metrics regardless of immediate monetization potential. *Implication:* This strategy prioritizes community growth and user retention but may delay the timeline to financial sustainability. c) Implement a balanced portfolio approach with investment spread across marketing, trading, and content creation agents. *Implication:* This diversified approach reduces risk but might dilute resources and slow the development of any single capability. d) Other / More discussion needed / None of the above. **Question 2:** Should we develop Jin's "CNBC of prediction markets" concept as a flagship show for auto.fun to drive both engagement and token utility? **Context:** - `Jin mentioned working on prediction markets combined with content creation` - `Discussion about creating a "CNBC of prediction markets" show combining prediction markets, crypto and AI` **Multiple Choice Answers:** a) Yes, develop it as a priority flagship product with dedicated resources and promotion. *Implication:* This could create a unique product positioning but requires significant investment in an unproven content format. b) Test the concept with a minimal viable product before committing major resources. *Implication:* This reduces initial investment risk but might slow time-to-market and allow competitors to enter the space first. c) Partner with existing prediction markets to co-develop the concept rather than building it internally. *Implication:* This leverages external expertise and reduces resource requirements but sacrifices some control and revenue potential. d) Other / More discussion needed / None of the above. **Question 3:** How should we incorporate the ERC-8004 standard (adding trust layers for agent interactions) into the auto.fun ecosystem? **Context:** - `Discussion about ERC-8004, a standard that adds a trust layer for agent interactions across organizations, focusing on identity, reputation, and task validation` **Multiple Choice Answers:** a) Implement it as a core protocol requirement for all agents on auto.fun to ensure trustworthy interactions. *Implication:* This creates a strong trust foundation but might create barriers to entry for some agent developers. b) Offer it as an optional feature with incentives for agents that implement the standard. *Implication:* This preserves developer flexibility while encouraging adoption through positive incentives. c) Create a hybrid approach with basic trust requirements for all agents and advanced ERC-8004 features as optional enhancements. *Implication:* This balances ecosystem security with developer flexibility but increases system complexity. d) Other / More discussion needed / None of the above. --- ### 2. Topic: ElizaOS Technical Foundation Enhancements **Summary of Topic:** Significant technical improvements were delivered, including a 55% faster build process using bun.Build, async embedding generation to reduce response latency by 500ms, and enhanced cloud infrastructure with analytics dashboards. #### Deliberation Items (Questions): **Question 1:** Given the performance improvements from bun.Build and async embedding generation, how should we allocate technical resources between further optimization work versus new feature development? **Context:** - `cjft led a major refactoring effort replacing tsup with bun.Build, resulting in ~55% faster builds (from 26s to 14s on an M3 Max)` - `0xbbjoker: Focused on significant feature development, opening PR elizaos/eliza#5793 to enable async embedding generation via a queue service, which involved substantial code changes across 14 files (+1179/-32 lines) with a primary focus on tests and code.` **Multiple Choice Answers:** a) Continue prioritizing performance optimizations to create the most responsive and developer-friendly framework possible. *Implication:* This could establish ElizaOS as the highest-performance agent framework but might delay new features needed for auto.fun growth. b) Pivot to focus primarily on new features now that core performance benchmarks have been achieved. *Implication:* This accelerates new capabilities but risks building on a foundation that may need further optimization later. c) Maintain a balanced 60/40 split between new features and continued performance optimization work. *Implication:* This balanced approach ensures steady progress on both fronts but might not achieve excellence in either area quickly. d) Other / More discussion needed / None of the above. **Question 2:** How should we leverage the enhanced analytics dashboard to improve auto.fun agent performance and user engagement? **Context:** - `sam-developer reported improvements to Eliza cloud, including stable text generation support and analytics dashboard refinements` **Multiple Choice Answers:** a) Focus analytics on response time and cost metrics to optimize operational efficiency. *Implication:* This approach optimizes for system performance and cost structure but might miss insights about user engagement and satisfaction. b) Prioritize user engagement metrics and implement A/B testing capabilities to optimize agent interactions. *Implication:* This user-centric approach could improve retention and growth but might be more resource-intensive to implement and monitor. c) Develop comprehensive agent performance benchmarks that combine technical metrics with user engagement indicators. *Implication:* This holistic approach provides the most complete view but requires significant data infrastructure and analysis capability. d) Other / More discussion needed / None of the above. **Question 3:** Should we proactively address the identified security vulnerability with the elizalabs.ai domain lacking SPF setup, and what does this indicate about our security posture? **Context:** - `A potential issue with elizalabs.ai domain lacking SPF setup was identified` - `Kenk and Odilitime assisted 0xbbjoker with handling a bug bounty hunter` **Multiple Choice Answers:** a) Address the specific SPF issue immediately but maintain our current security processes. *Implication:* This resolves the immediate vulnerability but doesn't address potential systemic security gaps. b) Implement a comprehensive security audit covering all domains, infrastructure, and code repositories. *Implication:* This proactive approach could prevent future issues but requires significant resources and might delay other work. c) Establish a formal bug bounty program to leverage the community for ongoing security improvements. *Implication:* This creates a sustainable security improvement process but requires ongoing management and bounty funding. d) Other / More discussion needed / None of the above. --- ### 3. Topic: Ecosystem Partnership Expansion **Summary of Topic:** Strategic partnerships are emerging to expand the ElizaOS ecosystem, with Comput3 offering AI model access through Solana login and REVOX providing human-like avatars for ElizaOS agents through their DEVA platform. #### Deliberation Items (Questions): **Question 1:** How should we integrate Comput3's AI model offerings (especially the free Kimi K2 and Qwen Coder 480B models) into our auto.fun strategy? **Context:** - `Comput3 Developments - Currently offering free access to Kimi K2 and Qwen Coder 480B by logging in with Solana at launch.comput3.ai` - `Launching Sonnet 4 level subscriptions for $79/month next week via Kimi K2 on 8xB200s` **Multiple Choice Answers:** a) Make Comput3's models the default option for all new auto.fun agents to reduce operational costs. *Implication:* This standardizes the technology stack and reduces costs but creates dependency on a single provider. b) Offer Comput3's models as one option in a multi-model strategy that includes other providers for redundancy and specialization. *Implication:* This maintains flexibility and reduces vendor lock-in but increases integration complexity and maintenance overhead. c) Create a strategic partnership where Comput3 becomes the official AI provider for auto.fun with co-marketing and technical integration. *Implication:* This could create a stronger ecosystem alliance but might limit future options if the partnership doesn't meet expectations. d) Other / More discussion needed / None of the above. **Question 2:** What approach should we take to browser automation capabilities for our agents based on the technical assessment provided? **Context:** - `Odilitime helped R0am with browser automation solutions for an ETH Zurich semester project` - `Recommended Shaw's browserbase plugin as the best available tool despite its cost` - `Q: What about plugin-browser for browser automation? A: Hasn't been maintained and is pretty basic (answered by Odilitime)` **Multiple Choice Answers:** a) Acquire or license Shaw's browserbase plugin to provide premium browser automation as a core ElizaOS capability. *Implication:* This provides immediate access to best-in-class functionality but requires financial investment and potential licensing constraints. b) Allocate resources to significantly improve the open-source plugin-browser to create a competitive alternative. *Implication:* This supports the open-source mission but requires engineering resources that could be directed elsewhere. c) Form partnerships with existing browser automation tools to create ElizaOS-specific integrations. *Implication:* This leverages external expertise without full acquisition costs but might result in less seamless integration. d) Other / More discussion needed / None of the above.