Getting Oriented

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.

This page is the map. It names what each of the five tabs holds and the page to open first in each, then sets out five ways in. By Situation is the other map, arranged by what is in front of you rather than by tab.

How this site is organized

The site moves from the reasons for the approach, through setup and a worked case, to systems with more than one AI.

• Introduction: why this approach exists and the principles behind it, including interpretive orchestration, the mirror effect and research taste, with shorter essays on publishing and on choosing models. This page belongs here. Open Why Engage with AI in Research? first, then Core Principles.
• Implementation: the hands-on tab. It holds three kinds of setup guide: references and notes (Zotero, Research Rabbit, OpenAlex, Obsidian, Zettlr), the AI environment (API keys, MCP servers, PDF conversion), and agentic AI tools (Cherry Studio, Claude Code, Antigravity CLI, OpenCode). It also holds the Core References on prompting, model choice and AI failure, and three roles you can hand to an AI. Open The Research Workbench first, for a drawing of how the tools connect, then Quick Start: it orders the setup and helps you choose the smallest stack that serves you.
• Case Studies: one worked case, a four-session course on systematic literature reviews, with a student vault of worksheets and a practice set. Open the course overview first. Session 1 is the review done by hand; Sessions 2 to 4 bring AI into it.
• Toolkit: a catalog of the Claude Code plugins, skill artifacts, MCP servers and research platforms maintained alongside this site. Each page says what the tool does, which hosts it runs on and how to install it. Open the Toolkit index first: it says which tools recur in daily work and which are built for other people's use.
• Advanced Topics: two pieces on research systems with more than one AI. Read Composing an Agentic Research System first, for the questions that locate any arrangement, then Agentic Research Workflows for one working stack.

Choose a way in

Five starting orders. None is a curriculum: skip what you already know, and switch when another fits better. The first three are reading paths; the last two hand the first step to an AI.

Learn the method through the case study

For researchers whose goal is a literature review or theory building, and for anyone teaching or taking the course.

1. Read the course overview, then work through Session 1, the review done by hand.
2. Read the three Core References: Cognitive Blueprint Prompts, the AI Model Reference Guide and the Failure Museum.
3. Set up API keys, Zotero, Research Rabbit and OpenAlex, following the essential setup step in Quick Start.
4. Choose one AI client: Cherry Studio or Claude Code.
5. Add Obsidian for notes, then continue with Sessions 2 to 4.

Start at low cost

For keeping spending near zero while you find out what you need.

1. Read the AI Model Reference Guide to understand model selection.
2. Get a Google AI Studio key for free Gemini API experiments (API Keys Setup Guide).
3. Install Zotero and Research Rabbit, both free.
4. Add Antigravity CLI if you want Google's terminal agent.
5. Add OpenCode if you want to move between providers.

Build a fuller stack

For researchers who already work with AI daily and want autonomous workflows.

1. Read all the Core References, starting with Cognitive Blueprint Prompts.
2. Set up Claude Code, with Cherry Studio alongside it.
3. Add the MCP servers Sequential Thinking, Lotus Wisdom and Vox MCP, which gives one client access to several models.
4. Install the full tool stack: Zotero, Research Rabbit, OpenAlex, Obsidian and Zettlr.
5. Read Composing an Agentic Research System before adding another agent.

Be asked questions first

If you would rather describe your research than choose from a list, give the Research-AI Orientation role to an AI: paste the page's text into a conversation. It asks six to eight questions, one at a time, about how you work now, and ends with a short profile and one concrete possibility to try. Nothing is installed, and there is no score.

Have your machine configured

If you want the setup done with you instead of reading about it, Carrel interviews you, audits your machine and configures Obsidian, conversion tools and optional MCP servers. It runs as a Claude Code plugin or as a portable skill in other Agent Skills hosts, and installs a subset of the Toolkit. The Quick Start roadmap remains the guide to understanding what it set up.

Time investment

Knowing the time commitment helps you plan realistically.

• Setup: a one-time cost of two to three hours. Tool installation takes 1-2 hours, API configuration 30-60 minutes and first test runs 30 minutes. Follow the guides in order and do not skip their verification steps.
• The course: seven to nine hours a week while you follow the case study, split between 2-3 hours of preparation, 2 hours in session and 3-4 hours of practice afterwards.
• The return: slow at first. In our teaching and practice, which is not a formal measurement, the first two weeks are slower than working by hand, weeks 3-4 roughly break even, and from week 5 the same work often goes two to three times faster. The skills carry into later projects.

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." Our tool list is intentionally short: we treat the chosen tools as pedagogical instruments, not prescriptions, chosen to develop skills that transfer across contexts. We're still learning through our own research and teaching.

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

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.

Contributing

The site's source repository is private for now: it is revised weekly with agents and carries working material that is not ready to publish. Contributions are welcome all the same, and the site is built to make them easy. Every page has a Markdown twin (append .md to its URL) and /llms-full.txt holds the whole corpus, so any agent can draft a change from the real source. Send the files, or a folder of them, to the author; they will be merged with attribution. If contributions grow, a public repository follows.

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

@misc{docs-introduction-getting-oriented-2026,
  author = {Xule Lin},
  title = {Getting Oriented},
  year = {2026},
  howpublished = {\url{https://research-memex.org/docs/introduction/getting-oriented}},
  note = {ORCID: 0000-0001-7885-4194}
}