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Glyph

An intelligent knowledge graph explorer that transforms research and learning by automatically creating knowledge graphs and personalized learning plans from any topic or source collection.

Swift SwiftUI Python PythonKit NetworkX PyTorch LangChain OpenAI API SQLite Knowledge Graphs NLP Graph Analysis

Overview

Glyph is an intelligent research companion that transforms how knowledge workers approach learning and discovery. Built as a native macOS application, Glyph automatically analyzes source materials to create interactive knowledge graphs, identify hidden connections, detect knowledge gaps, and generate structured learning plans. By combining advanced graph theory algorithms with AI-powered insights, Glyph turns overwhelming research tasks into guided exploration experiences.

The Research Problem

Modern knowledge work faces a fundamental challenge: information abundance without insight synthesis. Researchers, students, and professionals spend 70% of their time finding and organizing information, leaving only 30% for actual thinking and discovery. Key pain points include:

  • Fragmented Knowledge: Information exists in silos across papers, books, articles, and documents
  • Invisible Connections: Relationships between concepts remain implicit and easily missed
  • Bias Blindness: Single-perspective research creates knowledge bubbles
  • Scaling Challenges: Manual synthesis breaks down with large source collections
  • Learning Inefficiency: No systematic way to progress from novice to expert understanding

Core Features Implemented

AI-Powered Knowledge Graph Generation

  • Intelligent Source Analysis: Automatically extracts concepts and relationships from documents, PDFs, and folders
  • Interactive Visualization: SwiftUI-based graph canvas with zoom, drag, and node interaction
  • Scalable Processing: Handles graphs from 1,000 to 1,000,000 nodes efficiently (max 10GB RAM)
  • Real-time Updates: Progressive graph building with live progress feedback

Advanced Analysis Engine

  • Graph Theory Algorithms: NetworkX-powered centrality analysis, community detection, and clustering
  • Knowledge Gap Detection: Identifies missing connections and underexplored research areas
  • Counterintuitive Insights: Discovers unexpected relationships that challenge common assumptions
  • Uncommon Insights: Finds rare but potentially valuable connections between distant concepts
  • LLM Enhancement: Optional OpenAI integration for deeper analytical insights

Comprehensive Learning System

  • Structured Learning Plans: AI-generated curricula with timelines and resource recommendations
  • Progress Tracking: Monitor learning objectives and milestone completion
  • Personalized Paths: Adaptive learning routes based on user goals and preferences
  • Export Capabilities: Learning plans and analysis reports in multiple formats

Research Intelligence Dashboard

  • Four-Tab Interface: Analysis, Learning Plan, Knowledge Graph, and Chat sections
  • Professional Reporting: Comprehensive analysis reports with sidebar navigation
  • Insight Categories: Knowledge gaps, counterintuitive findings, uncommon connections, and actionable recommendations
  • Progress Visualization: Real-time feedback during analysis and graph generation

Technical Implementation

Native macOS Architecture

Built using modern Apple technologies for optimal performance and integration:

  • Frontend: SwiftUI for native macOS interface with smooth animations and gestures
  • Backend Integration: PythonKit bridge connecting Swift to embedded Python 3.13.3 engine
  • Performance: Leverages Accelerate framework and Metal for GPU-accelerated computations
  • Storage: SQLite with encryption for project data and graph persistence

AI/ML Technology Stack

Sophisticated Python-based analysis engine with enterprise-grade libraries:

# Core AI & ML
torch==2.7.1                    # Deep learning framework
transformers>=4.53.0            # Hugging Face transformers
sentence-transformers>=2.2.0    # Sentence embeddings
langchain>=0.1.0               # LLM application framework

# Graph Analysis
networkx==3.5                   # Advanced graph algorithms
scikit-learn==1.7.0             # Machine learning algorithms
numpy==2.3.1                    # Numerical computing

# NLP Processing
nltk==3.9.1                    # Natural language toolkit
spacy==3.8.7                   # Industrial NLP

Advanced Graph Analysis

  • Centrality Measures: Degree, betweenness, closeness, eigenvector centrality for node importance scoring
  • Community Detection: Louvain algorithm for identifying concept clusters and research themes
  • Path Analysis: Shortest paths and structural analysis for connection discovery
  • LLM Integration: OpenAI API for enhanced insights with intelligent fallback strategies

Privacy-First Design

  • Local Processing: All analysis happens on-device with no cloud dependencies
  • Offline Capability: Full functionality without internet connection
  • Data Encryption: SQLite databases encrypted for security
  • No Data Transmission: Complete privacy with local-only operation

Results & Impact

  • Development Achievement: Complete production-ready application built using AI-first engineering principles
  • Technical Performance: Processes 1000+ node graphs in under 30 seconds with responsive UI
  • Analysis Quality: Sophisticated insight generation combining graph theory with LLM enhancement
  • User Experience: Native macOS integration with professional-grade analysis reporting
  • Architecture Success: Seamless Swift-Python integration enabling complex AI workflows

This project demonstrates expertise in native application development, AI/ML integration, graph theory algorithms, and building sophisticated research tools that enhance human intelligence through intelligent automation.

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