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Welcome to the Research Memex. If you’re reading this, you’re likely curious about the future of academic research in the age of AI. You’re in the right place. This site is a comprehensive guide to the Research Memex approach, a new way of partnering with AI to amplify our thinking. Think of it like learning to drive with a GPS. The GPS helps with navigation, but we’re still the drivers making decisions, staying safe, and choosing the ultimate destination.

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How This Site is Organized

This guide is structured to take you on a complete journey, from understanding the core philosophy to building advanced, AI-powered workflows.
  • Introduction: Start here to understand the “why” behind the Research Memex. This section is divided into “Getting Started” for orientation and “Core Philosophy” for deeper principles of responsible AI use in research.
  • Implementation: This is the hands-on section. It walks you through foundational setup (Zotero, Research Rabbit, Obsidian, Zettlr), AI environment configuration (APIs, MCP), agentic AI tools (Cherry Studio, Claude Code, Gemini CLI, OpenCode), and provides core reference materials for effective AI partnership.
  • Case Studies: See the approach in action. This section contains detailed walkthroughs of how the Research Memex can be applied to specific research tasks, like conducting a systematic review.
  • Advanced Topics: Look to the future. This section explores the cutting edge of agentic AI, MCP servers, and multi-agent research systems.

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Our Approach: One Path Among Many

The AI research landscape offers multiple valid approaches. Some focus on automation (tools that handle specific tasks efficiently), others on augmentation (frameworks that amplify human thinking). Both have value for different contexts and goals. We focus primarily on augmentation through what we call “interpretive orchestration.” But we’re still learning and experimenting through our own research and teaching. This guide offers one way to think about AI partnership, with specific tool choices designed to develop meta-cognitive skills that transfer across contexts. You’ll notice we don’t cover every AI tool (for example, we don’t discuss Cursor, GitHub Copilot, or VS Code AI extensions). This is intentional, not oversight. Our goal is pedagogical focus rather than comprehensive coverage. The tools we’ve chosen teach you how to evaluate and work with any AI tools. This approach may or may not work for you, and that’s perfectly okay. We’re happy to share what we’re discovering.

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A Mindset for Success

  • Experimentation over perfection: Try things, make mistakes, and learn from them.
  • Questions are welcome: Confusion is a signal that learning is in progress.
  • Start small: A working setup is better than a perfect, overly complex one.

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Next Steps (Choose Your Path)

Jump Right In

Ready to get started? Follow the Quick Start Checklist to set up your environment.

Understand the Why

Want context first? Learn why AI matters in research and our core principles.

Return to Start

Go back to the main introduction page to explore all sections.

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Time Investment Guide

Understanding the time commitment helps you plan realistically.
  • Initial Setup
  • Per-Session Learning
  • Long-Term Efficiency
One-Time Investment:
  • Tool installation: 1-2 hours
  • API configuration: 30-60 minutes
  • First test runs: 30 minutes
  • Total: 2-3 hours
Tips for efficiency:
  • Follow guides sequentially
  • Use Research Memex MCP for instant help
  • Don’t skip verification steps
Remember: The goal is to begin a journey of conscious competence in research thinking, not to master everything immediately.
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