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Managing Information + Rewarding Contributors

Video: https://www.youtube.com/watch?v=-2PD3uk0Hz4 Slides: https://docs.google.com/presentation/d/1W4BpsRRx-fiG01ERTr5JaKyb_AqyjdfqK0dRDKlpXCM/edit#slide=id.p

0:00 - Introduction

  • Growth in project over last month
  • Working on preparing for next phase of growth
  • Focus on managing work distribution and communication

1:27 - Context: Hypergrowth Challenge

  • Messages increased from ~10k to 90k per day
  • Led to more Discord channels and information overload
  • Current tools like Rick bot require manual invocation

2:26 - Discord Limitations

  • Discord acts as "dark pool" unlike public forums
  • Information gets lost easily
  • Chat rooms move too fast for people to keep up

2:52 - Proposed Solution: LLM-Based Automation

  • Using LLMs to summarize daily chat logs per channel
  • Extracting insights about FAQs, helpers, action items
  • Goal: Remove human bias and add transparency

4:22 - Technical Implementation

  • Private GitHub repo storing implementation
  • Taking Discord chat from public/working group channels
  • Creating compact version of daily engagement and roles
  • Using Ollama with Langchain and PHI-3 (14B model)

6:20 - Key Features Being Extracted

  • Decisions and discussions
  • Major topics and themes
  • Milestones and achievements
  • Frequently asked questions (for docs updates)
  • Who helped who (with sentiment analysis)
  • Action items and tasks

9:02 - Airdrop Planning

  • Created spreadsheet tracking contributions
  • Point system for measuring engagement
  • Combines GitHub and Discord contributor data
  • Using tip bot for distribution

10:59 - Contributor Profile Page

  • Located in docs fork
  • Shows GitHub contribution data
  • Plans to combine with Discord activity
  • Aims to make open source feel like a video game

13:30 - Future Integration Ideas

  • Virtual show format with seasoned game devs
  • Dashboard showing AI agents, GitHub activity, Discord news
  • Museum-style expo view
  • Weekly summarization capabilities

15:06 - HATS Protocol Integration

  • Codifying roles and work groups
  • Training AI agents within work groups
  • Creating human-readable updates
  • Infrastructure for AI and human collaboration

15:54 - Technical Details

  • Running locally without cloud APIs
  • Private repo with plans to open source summarization tools
  • Potential integration with existing AI agents

17:27 - Questions & Answers

  • Discussion of consistency checking
  • Multiple agents for different summary types
  • Integration with notebookLM
  • Command line customization options
  • Work group specific filtering

24:28 - Future Vision

  • TLDraw implementation with HATS protocol
  • AI agents as "interns" following same agreements as humans
  • Goal of progressive automation while maintaining organization
  • Eventually leading to AI-assisted DAO management