Getting Started with TimeCapsule-SLM: AI-Powered Research & Learning Platform

Research

Education

Open Source

Author

The Fire Hacker

Published

January 16, 2025

Introduction

TimeCapsule-SLM is an innovative AI-powered research and learning platform that’s revolutionizing how we discover knowledge and collaborate on research. Built with privacy-first principles and cutting-edge AI technology, it democratizes access to powerful research tools while keeping your data secure and local.

What is TimeCapsule-SLM?

TimeCapsule-SLM combines the power of Small Language Models with advanced research capabilities to create a comprehensive platform for:

Researchers seeking AI-assisted discovery and pattern recognition
Students looking for adaptive, personalized learning experiences
Teachers creating interactive educational content
Teams collaborating on knowledge discovery

The platform addresses critical challenges in modern education and research: - Research fragmentation across multiple sources - Inefficient learning workflows - Privacy concerns with cloud-based AI - Limited AI integration in educational settings - Resource constraints in low-bandwidth environments

Core Features

🧠 DeepResearch TimeCapsule

Transform your research workflow with multi-agent AI collaboration:

AI-Powered Discovery: Generate novel research ideas and hypotheses
Pattern Recognition: Uncover hidden connections in your data
Multi-Agent System: Leverage specialized AI agents for different research tasks
Collaborative Intelligence: Combine human expertise with AI insights

🎥 AI-Frames Interactive Learning

Create immersive, adaptive learning experiences:

Sequential Learning Paths: Build structured knowledge journeys
Multimodal Content: Integrate videos, documents, and interactive elements
AI-Guided Explanations: Get personalized help when you need it
Self-Paced Progress: Learn at your own speed with AI support

📚 In-Browser RAG (Retrieval-Augmented Generation)

Experience the power of semantic search without compromising privacy:

Local Vector Store: All processing happens in your browser
Offline Capability: Works without internet after initial model load
Semantic Understanding: Find information based on meaning, not just keywords
Privacy-First Design: Your documents never leave your device

Getting Started

Prerequisites

Before installing TimeCapsule-SLM, ensure you have:

# Node.js 18 or higher
node --version

# npm or yarn package manager
npm --version

# Git for cloning the repository
git --version

Installation

Clone the Repository:

git clone https://github.com/thefirehacker/TimeCapsule-SLM.git
cd TimeCapsule-SLM

Install Dependencies:

npm install
# or
yarn install

Configure Environment:

cp env.example .env.local

Edit .env.local to configure your AI providers:

# Optional: Add API keys for cloud models
OPENAI_API_KEY=your_key_here

# Local model configuration (Ollama)
OLLAMA_HOST=http://localhost:11434

Start the Development Server:

npm run dev
# or
yarn dev

Visit http://localhost:3000 to access TimeCapsule-SLM!

Setting Up Local AI Models

For the best privacy and offline experience, use local models with Ollama:

Install Ollama

# macOS/Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Windows - Download from ollama.ai

Pull Recommended Models

# For research and general tasks
ollama pull gemma:2b

# For code and technical content
ollama pull qwen2.5:3b

# For creative writing
ollama pull llama3.2:3b

Using TimeCapsule-SLM

Creating Your First Research Project

Initialize a Knowledge Base:
- Click “New Project”
- Choose your domain (Research, Education, Personal)
- Select your preferred AI model
Import Your Documents:
- Drag and drop PDFs, Word docs, or text files
- The system will automatically index and embed them
- All processing happens locally in your browser
Start Researching:
- Use natural language queries to explore your knowledge base
- The AI will surface relevant information and suggest connections
- Generate summaries, insights, and new research directions

Building AI-Frames for Learning

Create interactive learning experiences with AI-Frames:

// Example AI-Frame configuration
{
  "title": "Introduction to Quantum Computing",
  "modules": [
    {
      "type": "video",
      "content": "intro-video.mp4",
      "ai_notes": true
    },
    {
      "type": "interactive",
      "content": "qubit-simulator",
      "ai_guidance": "adaptive"
    },
    {
      "type": "quiz",
      "ai_generated": true,
      "difficulty": "progressive"
    }
  ]
}

Collaborative Features

TimeCapsule-SLM supports real-time collaboration:

Shared Workspaces: Invite team members to research projects
Live AI Sessions: Collaborate with AI assistance in real-time
Knowledge Graphs: Visualize connections discovered by your team
Version Control: Track changes and contributions

Architecture & Technology

TimeCapsule-SLM is built with modern, performant technologies:

Frontend: Next.js 15, React 19, TypeScript
AI Integration: Support for Ollama, OpenAI, and local models
Database: RxDB for offline-first data persistence
Vector Store: In-browser embeddings with WebAssembly
Authentication: NextAuth.js for secure access

Privacy & Security

Your privacy is our priority:

Local-First: All sensitive processing happens on your device
No Telemetry: We don’t track your usage or collect data
Open Source: Audit the code yourself (Apache 2.0 License)
Encryption: Local data is encrypted at rest
Control: You decide what stays local vs. what uses cloud services

Use Cases

For Researchers

Literature reviews with AI-powered synthesis
Pattern discovery in research data
Hypothesis generation and validation
Collaborative paper writing

For Students

Personalized study guides
AI tutoring for complex topics
Interactive learning paths
Exam preparation with adaptive quizzes

For Teachers

Create engaging course content
Build interactive lessons
Track student progress
Generate assessments automatically

For Teams

Knowledge management
Collaborative research
Training materials
Documentation with AI assistance

Performance Tips

Optimize TimeCapsule-SLM for your hardware:

Model Selection: Choose smaller models (2-3B parameters) for faster responses
Caching: Enable browser caching for repeated queries
Batch Processing: Process multiple documents simultaneously
GPU Acceleration: Use WebGPU when available for faster inference

Roadmap & Future Features

We’re constantly improving TimeCapsule-SLM:

Mobile Apps: iOS and Android applications (Q2 2025)
Voice Interface: Natural conversation with your knowledge base
Advanced Visualizations: 3D knowledge graphs and mind maps
Plugin System: Extend functionality with custom modules
Federated Learning: Collaborate without sharing raw data

Contributing

TimeCapsule-SLM is open source and welcomes contributions:

# Fork the repository
# Create a feature branch
git checkout -b feature/amazing-feature

# Make your changes
# Run tests
npm test

# Submit a pull request

Check our contribution guidelines for more details.

Community & Support

Join our growing community:

GitHub Discussions: Technical questions and feature requests
Discord Server: Real-time chat with developers and users
Documentation: Comprehensive guides at timecapsule.bubblspace.com
X/Twitter: Follow @thefirehacker for updates

Conclusion

TimeCapsule-SLM represents a new paradigm in AI-assisted research and learning. By combining powerful AI capabilities with privacy-first design and local-first architecture, we’re making advanced research tools accessible to everyone.

Whether you’re a researcher pushing the boundaries of knowledge, a student seeking personalized learning, or a teacher creating engaging content, TimeCapsule-SLM empowers you to work smarter, not harder.

Start your journey today and experience the future of AI-powered research and learning!

Ready to transform your research and learning workflow? Get started with TimeCapsule-SLM or visit timecapsule.bubblspace.com for more information.

Have questions or feedback? Reach out on X/Twitter or open an issue on GitHub.