# Getting Oriented

URL: https://research-memex.org/docs/introduction/getting-oriented
Description: Start here when you need the map before choosing a workflow or tool.



*Before you choose a tool or workflow, you need a map of the territory.*

Welcome to the Research Memex, a guide to partnering with AI to amplify your thinking, not replace it. You're the driver. AI is the GPS.

## How this site is organized [#how-this-site-is-organized]

This guide moves from the core philosophy to advanced workflows that coordinate AI agents.

* 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 engagement with AI in research.
* [Implementation](/docs/implementation/foundational-setup/quick-start-checklist): This is the hands-on section. It walks you through foundational setup (Zotero, Research Rabbit, Obsidian, Zettlr), AI environment configuration (APIs, MCP), and agentic AI tools (Cherry Studio, Claude Code, Antigravity CLI, OpenCode). It also includes core reference materials for effective AI partnership.
* [Case Studies](/docs/case-studies/systematic-reviews/systematic-review-syllabus): 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.
* [Toolkit](/docs/toolkit/carrel): Reference pages for the Claude Code plugins, MCP servers, and research platforms maintained alongside this site, including environment bootstrapping ([Carrel](/docs/toolkit/carrel)), persistent memory ([Memex Plugin](/docs/toolkit/memex-plugin)), and multi-model access ([Vox](/docs/toolkit/vox-mcp), [Kimi Plugin](/docs/toolkit/kimi-plugin-cc)).
* [Advanced Topics](/docs/advanced-topics/agentic-workflows): Look to the future. This section explores agentic AI, multi-agent research systems, and future-of-research essays.

## Our approach: one path among many [#our-approach-one-path-among-many]

AI research methods include multiple valid approaches. Some focus on automation, with tools that handle specific tasks efficiently. Others focus on augmentation, with frameworks that amplify human thinking. Both have value for different contexts and goals.

We focus primarily on augmentation through what we call "[interpretive orchestration](/docs/introduction/core-principles#1-interpretive-orchestration)." This guide offers one way to think about AI partnership, with specific tool choices designed to develop meta-cognitive skills that transfer across contexts. We're still learning through our own research and teaching.

Our tool list is intentionally short: we treat the chosen tools as [pedagogical instruments, not prescriptions](/docs/introduction/core-principles#anti-templating-implementation-flexibility).

This approach may not work for you. That's okay.

## A mindset for success [#a-mindset-for-success]

Treat the setup as an experiment: start small, expect mistakes, and use confusion as a signal that learning is in progress. A working setup is better than a perfect, overly complex one.

## Next steps (choose your path) [#next-steps-choose-your-path]

<IndexRows>
  <IndexRow title="Jump Right In" href="/docs/implementation/foundational-setup/quick-start-checklist">
    Follow the Quick Start Checklist to set up your environment.
  </IndexRow>

  <IndexRow title="Understand the Why" href="/docs/introduction/why-ai-in-research">
    Learn why AI matters in research and our core principles.
  </IndexRow>
</IndexRows>

## Time investment guide [#time-investment-guide]

Understanding the time commitment helps you plan realistically.

<Tabs>
  <Tab title="Initial Setup">
    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
    * Skim the [llms.txt index](/llms.txt) for navigation
    * Don't skip verification steps
  </Tab>

  <Tab title="Per-Session Learning">
    Weekly time commitment:

    * Pre-session preparation: 2-3 hours
    * Session attendance: 2 hours
    * Post-session practice: 3-4 hours
    * Total: 7-9 hours per week

    What you gain:

    * Systematic research skills
    * AI orchestration capabilities
    * Quality control expertise
  </Tab>

  <Tab title="Long-Term Efficiency">
    ROI timeline (anecdotal, based on Research Memex teaching and practice rather than a formal benchmark):

    * Weeks 1-2: Slower than manual (learning curve)
    * Weeks 3-4: Breaking even with manual methods
    * Weeks 5+: 2-3x faster than traditional approaches
    * Months 3+: 5-10x productivity gains

    Lifetime value: Skills transfer to all future research
  </Tab>
</Tabs>

***

The goal is to begin a journey of conscious competence in research thinking, not to master everything immediately.