#Conducting Deep Research with Aino LifeOS

When you need to deeply research a topic — like "evaluate whether to migrate our project to React Server Components" — Aino LifeOS's features chain together into a complete research workflow.

#Step 1: Set Up a Research Space in PARA

Create a dedicated folder in PARA's Resources category:

1. Open the sidebar, navigate to 3-Resources/
2. Right-click → New Folder → React-Server-Components
3. Create a main note RSC Research.md in the folder

# React Server Components Research

## Research Goals

- Understand RSC core concepts and advantages
- Evaluate migration costs and risks
- Collect community case studies

## References

(Add as you go)

## Conclusions

(Fill in at the end)

#Step 2: Collect Materials with Built-in Browser

Open the built-in browser and start searching:

1. Type search keywords in the quick switcher to Google them
2. Open relevant articles one by one
3. Find valuable content → select text → click the clip button → save to daily note

You might clip 10 items to your daily note in a morning. Each automatically includes the source URL.

#Step 3: Let AI Organize for You

After collecting lots of raw material, use AI Chat to organize:

1. Open the AI chat panel
2. Use # to reference today's daily note (containing all clipped content)
3. Type your prompt:

Please organize these React Server Components materials:
1. Categorize by theme (concepts, advantages, disadvantages, migration plans, community cases)
2. Extract core points from each article
3. Flag conflicting viewpoints between sources

4. AI returns structured analysis results
5. Copy into the relevant sections of RSC Research.md

#Step 4: Deep Processing in the Editor

Open the editor for in-depth refinement:

1. Use [[wikilinks]] to connect to related project notes
2. Insert comparison tables:

## Approach Comparison

| Dimension      | Current (CSR)               | Migrate to RSC                    |
| -------------- | --------------------------- | --------------------------------- |
| First paint    | Must load full JS bundle    | Server-rendered, fast first paint |
| Dev complexity | Team is familiar            | New mental model to learn         |
| Migration cost | -                           | ~2-3 weeks                        |
| SEO            | Needs separate SSR solution | Native support                    |

3. Use code blocks for technical details
4. Add tags in Frontmatter:

---
tags: [tech-research, React, frontend-architecture]
status: in-progress
---

#Step 5: Deeper Analysis with AI Agent

If you've configured MCP servers (like filesystem access), use the AI Code Agent for deeper analysis:

1. Open the AI Code Agent
2. Type: "Analyze my current project's code structure and assess the RSC migration effort"
3. The AI agent reads project code via MCP, analyzes component structure
4. Returns a detailed migration assessment report

#Step 6: Track Progress in Periodic Notes

#Daily Notes

Record research progress in your daily note each day:

## Notes

- RSC research: Read 3 core articles today, understood Server/Client Component boundary rules
- Key finding: Next.js App Router uses RSC by default

#Weekly Summary

Summarize this week's research in your weekly note:

## Week Review

- RSC research mostly complete, conclusion is worth migrating but needs phased approach
- Migration plan doc ready, discuss with team next week

#Step 7: Archive When Done

After reaching conclusions:

1. If the conclusion is to start a migration project → move the research folder from 3-Resources/ to 1-Projects/
2. If it's just knowledge storage → keep in 3-Resources/
3. Add final tags and status to research notes

Use full-text search to find this research anytime.

#Feature Flow

Built-in Browser (collect)
    ↓ clip
Daily Note (staging)
    ↓ AI organize
Research Note (PARA - Resources)
    ↓ deep processing
Editor (wikilinks, tables, code blocks)
    ↓ AI agent analysis
Migration Assessment Report
    ↓ conclusion
PARA classification (Projects or Resources)