This repository serves as a centralized storage of markdown and text files gathered from various sources, organized to be easily ingested into Retrieval-Augmented Generation (RAG) systems for AI agents. The primary purpose is to maintain an up-to-date collection of knowledge that can be quickly referenced and utilized by AI systems.

The repository is organized into several main directories, each containing information from different sources:

.
├── archive/            # Archived discussions (legacy, to be deprecated)
├── docs/               # Documentation from elizaOS (from elizaOS/eliza repo)
├──── news/             # Daily AI news summaries
└──── partners/         # Information about elizaOS partners and integrations
├──── static/packages/  # Documentation from elizaOS plugin ecosystem
├── github/             # GitHub activity logs organized by day/week/month
├── daily-silk/         # Daily AI news from Discord channel using SILK
├── ai-news/            # AI News summaries from M3-org/ai-news
├──── elizaos/          # Summaries related to ElizaOS
└──── hyperfy/          # Summaries related to Hyperfy

Archive of Discord discussions from various channels related to AI development and communities. This section is being deprecated in favor of more structured data sources.

Technical documentation from the ElizaOS/eliza repository, specifically from its packages/docs folder. These files contain guides, tutorials, API references, and technical specifications for the ElizaOS system.

Activity logs from ElizaOS/elizaos.github.io (_data branch), organized by day, week, and month. This provides a chronological view of development activities and changes.

Tip: here's a command to turn the JSON stats files into a single text file:

Stats

jq -r '                     
  "\n=== \(.interval.intervalStart) ===",
  .overview,
  "\nTop Issues:",
  (.topIssues[]? | "- #\(.number) [\(.state)] \(.title) by \(.author) (\(.commentCount) comments)"),
  "\nTop PRs:",
  (.topPRs[]? | "- #\(.number) \(.title) by \(.author) (\(.additions) +, \(.deletions) -)"),
  "\nCompleted Items:",
  (.completedItems[]? | "- \(.type): \(.title) (#\(.prNumber))"),
  "\nTop Contributors:",
  ([.topContributors[]? | "\(.username) (score: \(.totalScore | floor))"] | .[:3] | .[])
' github/stats/month/stats_2025-04*.json > monthly-github-stats.txt

User summaries

jq -r '                       
  map(select(.date | startswith("2025"))) |
  group_by(.date)[] |
  ("=== " + (.[0].date) + " ==="),
  (.[] | .summary, "---"),
  ""
' user_summaries.json > user_summaries.txt

Daily AI news summaries generated by M3-org/ai-news, an AI-powered news aggregation platform that collects, analyzes, and summarizes information from multiple sources in real-time. The news data is stored in the gh-pages branch of the original repository.

Daily AI news collected from a Discord channel using SILK and discord.py. The data is automatically fetched, processed, and stored in markdown format, with each file representing a day's worth of AI news and updates. The content is organized chronologically and includes timestamps for each entry. The data is collected daily and provides a comprehensive view of AI developments and announcements.

Daily summaries and discussions related to AI, specifically from the ElizaOS and Hyperfy communities, sourced from the M3-org/ai-news repository (gh-pages branch). This includes:

• ai-news/elizaos/: Summaries and logs from ElizaOS related channels.
• ai-news/hyperfy/: Summaries and logs from Hyperfy related channels.

Documentation from the ElizaOS package ecosystem, which includes a collection of adapters, clients, and plugins that extend the functionality of the ElizaOS platform. This directory contains detailed information about each package's features, configuration, and integration methods.

Information about ElizaOS partners and integrations, including details about official partnerships, supported platforms, and integration capabilities. This documentation helps users understand the broader ecosystem of services and platforms that work with ElizaOS.

This repository is designed to be used as a knowledge source for RAG systems. The markdown files can be ingested into vector databases or other retrieval systems to provide context for AI agents.

1. Clone this repository to your local machine or server
2. Use the files as a corpus for training or fine-tuning AI models
3. Index the content for retrieval in RAG systems
4. Reference specific sections in your AI prompts for domain-specific knowledge

To add a new source to the knowledge repository:

1. Create a dedicated directory for the source
2. Ensure all files are in markdown (.md) format when possible
3. Update this README with information about the new source
4. Create a GitHub action to keep the source updated (see below)

This repository uses GitHub Actions to automatically update content from various sources. To contribute a new action:

1. Create a new workflow file in .github/workflows/
2. Configure the action to fetch and format data from the source
3. Set an appropriate schedule for updates
4. Test the action to ensure it correctly updates the repository

These summaries encapsulate the core mission, vision, and key initiatives, derived from project blog posts.

Auto.fun is an AI-native, creator-first token launchpad designed for sustainable AI/crypto projects. It aims to balance fair community access with project funding needs through mechanisms like bonding curves and liquidity NFTs. Key features include a no-code agent builder, AI-generated marketing tools, and integration with the elizaOS ecosystem. It serves as a core product driving value back to the native token ($ai16z) through buybacks and liquidity pairing.

The elizaOS mission is to build an extensible, modular, open-source AI agent framework for Web2/Web3, seeing agents as steps toward AGI. Core values are Autonomy, Modularity, and Decentralization. Key products include the framework itself, DegenSpartanAI (trading agent), Autonomous Investor/Trust Marketplace (social trading intelligence), and the Agent Marketplace/auto.fun (launchpad).

elizaOS is an open-source "operating system for AI agents" aimed at decentralizing AI development away from corporate control. It's built on three pillars: 1) The Eliza Framework (TypeScript toolkit for persistent, interoperable agents), 2) AI-Enhanced Governance (building autonomous DAOs), and 3) Eliza Labs (R&D for future capabilities like v2, Trust Marketplace, auto.fun, DegenSpartanAI, Eliza Studios). The native Solana token coordinates the ecosystem and captures value. The vision is an intelligent internet built on open protocols and collaboration.

Addresses the challenge of information scattered across platforms (Discord, GitHub, X). Proposes using AI agents as "bridges" to collect, wrangle (summarize/tag), and distribute information in various formats (JSON, MD, RSS, dashboards, 3D shows). Showcases an AI News system and AI Assistants for tech support as examples. Emphasizes treating documentation as a first-class citizen to empower AI assistants and streamline community operations.