Session #2

Mar 21, 2025

 Key Points Covered:

  • Experiences and feedback on researching AI agent crypto projects.
  • Initial presentations and brief reviews of selected projects.
  • Discussion on project analysis methods, criteria, and overall course direction.

Participant Experiences and Reflections:

John:

  • Found the research more challenging and time-consuming than expected.
  • Struggled with balancing risk and market cap size.
  • Recognized many smaller projects lack liquidity and long-term clarity.
  • Chose to focus on mid to higher market-cap projects to ensure stability.
  • Recommended projects: DeBridge, Hive, Orbit, Fraser

Dan:

  • Focused on tangible solutions and clear value propositions.
  • Explored no-code solutions (Alchemist) and institutional-grade security (Polymesh).
  • Preferred larger, safer market-cap projects rather than microcaps.
  • Highlighted difficulty evaluating extremely new projects with no track record.

Ewald:

  • Experienced confusion due to vast numbers of projects and unclear differentiation.
  • Emphasized the importance of identifying a target audience (greed-driven vs. deeper educational interest).
  • Recommended projects related to user-friendly DeFi solutions: Griffain, Reploy, Fetch.ai (for platform stability).

Ali:

  • Found complexity in identifying trustworthy projects, highlighting challenges verifying claims made in whitepapers.
  • Preferred projects backed by reputable investors or teams.
  • Recommended careful, skeptical analysis, emphasizing substance over hype.

Oskar:

  • Suggested embracing curiosity during the initial prospecting stage.
  • Highlighted the importance of skepticism in evaluating projects and identifying genuine innovation versus clones.

Project Reviews:

The team began reviewing projects presented by participants, briefly covering these:

1. Beeper (Dan):

  • Microcap on BNB chain, using NLP to facilitate crypto transactions via X (Twitter).
  • Simplifies wallet and account setup with voice commands.
  • Concern: Similarity with existing projects (e.g., BankerBot).

2. Hive AI (John):

  • Multi-agent platform making crypto easier through specialized AI agents.
  • Enables seamless crypto transactions across platforms.
  • Strong community backing despite market decline.

3. Orbit (John):

  • AI-driven cross-chain transaction platform ("army" of specialized agents).
  • Optimizes liquidity positions, cross-chain interoperability.
  • Notable leadership team, better liquidity management compared to Hive.

4. Alchemist (Dan):

  • No-code development platform combining blockchain tech and gaming/app creation.
  • Strong market growth and adoption potential.

5. DeBridge (John):

  • Cross-chain bridge solving interoperability issues for AI-driven liquidity.
  • Solid revenue generation, institutional backing, benefiting from AI agent integration.

6. YourNews (Oskar & Lars):

  • Personalized newsletter with AI-generated updates for user-specific crypto portfolios.
  • Revenue shared with token holders, creating strong community incentives.
  • (Revealed as a hypothetical test project created by Oskar & Lars) 

7. Virtuals (Oskar):

  • Major platform for AI agent creation, enabling cross-platform agent continuity.
  • Massive growth potential if next AI boom reignites.
  • Past significant market valuation due to easy agent launching and token utility.

8. Polymesh (Dan):

  • Institutional-grade blockchain addressing regulatory compliance and KYC for tokenized real-world assets (RWAs).
  • Strong leadership, established credibility, good market cap stability.

9. Fraser (John):

  • AI adversarial gaming project, using gamification and prize incentives.
  • Highly engaging community, innovative concept of players competing directly against AI.
  • Strong storytelling and effective marketing creating a highly engaged community.

Main Discussion Points:

  • Participants grappled with identifying ideal target audiences (high-risk traders vs. broader educational value).
  • Agreement emerged around the value of clear and repeatable analysis processes, rather than solely providing a static list of recommended projects.
  • The importance of acknowledging the short shelf-life of crypto investments due to market cycles was highlighted.
  • Challenges in distinguishing genuine innovation from superficial or copied projects were widely recognized.

Next Steps:

  • Complete the remaining project presentations in the next session (scheduled for Monday).
  • Continue refining criteria for deeper analysis and project selection.
  • Share presentation slides of all projects for participants to review ahead of the next meeting.

Conclusion:

The session provided valuable insights into both project evaluation methodologies and potential strategic directions for the course content. The introduction of diverse participant perspectives enriched the discussion, guiding the ongoing development of a robust analysis framework for evaluating AI agent projects.

Participants were encouraged to continue individual project research ahead of the next session and utilize the WhatsApp group and community platform for interim questions and discussion.

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