Pomotea
AI-powered time and goal management system with customizable Pomodoro sessions.
Engineer, life-long learner
Working towards a future where I can share my skills and work on projects driven by passion.
I'm driven to craft software that empowers creators, building tools that make coding as intuitive as speaking. Picture a world where anyone can shape a game or app with just their words—I'm here to make that real. My mission is to redefine what's possible in software, blending bold innovation with a legacy of creativity that inspires.
vocal.nvim
Lightweight Neovim plugin for speech-to-text transcription using OpenAI Whisper or local models.
IndexR
AI-powered data operations platform for exploring, analyzing, and taking action on large datasets.
XScan
Intelligent code analysis & RAG platform for navigating complex codebases with AI-powered search
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Web Application
Pomotea: AI-Powered Time and Goal Management
Pomotea is not just another Pomodoro timer—it’s a comprehensive time and goal management system with an AI mentor that helps you plan, execute, and reflect on your productivity journey.
Overview
Pomotea transforms how you manage time and achieve goals by combining the proven Pomodoro technique with AI-powered planning, guidance, and accountability. The application provides a continuous feedback loop between your time usage and goal progress, adapting to your working style and priorities over time.
Key Features
- Customizable Pomodoro Sessions: Flexible work and break intervals tailored to your workflow
- AI Planning Assistant: Chat with your mentor to develop goal-oriented work plans
- Smart Goal Tracking: Automatic organization of tasks aligned with your objectives
- Adaptive Scheduling: AI-recommended session durations based on task complexity
- Progress Analysis: Insights into your productivity patterns and improvement areas
- Personal UI Customization: Themes, fonts, and visual settings that match your preferences
- Smart Notifications: Desktop alerts for session transitions to maintain focus
- Device Optimization: Keep-awake functionality ensures your timer keeps running
Technical Implementation
Pomotea is built using a modern tech stack:
- React & Next.js: For a performant, responsive frontend
- TypeScript: Ensuring type safety and code reliability
- Tailwind CSS: For flexible, customizable styling
- AI Integration: Advanced machine learning models to power the mentor capabilities
- Local Storage: Persistent settings that remember your preferences
User Experience Design
The interface prioritizes clarity and focus:
- Distraction-free timer display with large, easily readable counters
- Intuitive controls with minimal cognitive load
- Adaptive themes to match your environment and preferences
- Seamless transitions between different productivity states
- AI mentor interface that provides guidance without interrupting workflow
How It Works
- Set Goals: Define your objectives with guidance from your AI mentor
- Plan Sessions: Your mentor helps you break down goals into manageable tasks
- Focus with Pomodoro: Use customized work/break cycles to maintain productivity
- Track Progress: Monitor your advancement with real-time feedback
- Analyze and Improve: Review your performance and receive personalized optimization tips
Project Challenges
Creating Pomotea presented several interesting challenges:
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Timer Accuracy: Ensuring precise timing across different browsers and device states required implementing a robust time-tracking system that compensates for browser throttling.
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UI Responsiveness: Balancing aesthetic design with functional clarity was achieved through multiple iterations of user testing and feedback.
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AI Integration: Developing an AI mentor that provides genuinely helpful productivity insights while feeling natural to interact with required sophisticated natural language processing.
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User Customization: Building a framework that allows for extensive personalization without overwhelming users was solved through a progressive disclosure approach to settings.
Insights & Lessons
This project reinforced several key principles:
- Simplicity First: Even with advanced AI features, the core timer functionality remains flawless and intuitive
- User-Centered Design: Productivity tools must adapt to users’ workflows, not force users to adapt to them
- Contextual Intelligence: The AI mentor works best when it understands not just what users are doing, but why they’re doing it
- Continuous Adaptation: The system becomes more valuable over time as it learns from user patterns and preferences
Plugin
vocal.nvim: Speech-to-Text for Neovim
vocal.nvim is a lightweight Neovim plugin that brings speech-to-text capabilities directly into your editor. It enables you to record audio, transcribe it using either OpenAI’s Whisper API or local models, and insert the transcribed text at your cursor position or replace selected text.
Overview
Designed for developers, writers, and note-takers who prefer speaking over typing in certain situations, vocal.nvim streamlines the process of getting your spoken words into text without leaving Neovim. Whether you’re drafting documents, taking notes during a meeting, or composing text while thinking aloud, this plugin makes the process seamless.
Key Features
- Simple Recording Interface: Start and stop recording with a single command (
:Vocal) or configurable keymap - Flexible Transcription Options: Use either OpenAI’s Whisper API for cloud-based processing or local Whisper models for privacy and offline use
- Context-Aware Insertion: Insert transcribed text at cursor position or replace selected text in visual mode
- Status Indicators: Visual feedback during recording and processing phases
- Asynchronous Operation: Non-blocking architecture keeps Neovim responsive during transcription
- Local Model Support: No API key needed when using local Whisper models
- Configurable Options: Extensive settings for audio handling, model selection, and behavior
Technical Implementation
The plugin is built with a focus on simplicity and efficiency:
- Lua Core: Written entirely in Lua for optimal performance within Neovim
- Audio Recording: Uses
soxfor reliable audio capture across platforms - Dual Transcription Paths:
- API-based transcription via OpenAI’s REST API
- Local transcription using Python’s openai-whisper package
- Asynchronous Processing: Leverages Neovim’s job control and plenary.nvim for non-blocking operations
- Minimal Dependencies: Only requires plenary.nvim alongside system tools
Project Challenges
Developing vocal.nvim presented several interesting technical challenges:
-
Cross-Platform Audio Capture: Creating a consistent audio recording experience across Linux, macOS, and Windows required careful abstraction and platform detection.
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Efficient Processing: Balancing transcription quality with processing speed, especially for local models that might run on systems with limited resources.
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Seamless Editor Integration: Designing a natural workflow that feels like a native part of the editing experience rather than an external tool.
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Progressive Enhancement: Implementing a system that works with minimal configuration but offers depth for power users who want to fine-tune their experience.
Current Status and Roadmap
vocal.nvim is currently in active development with:
- Local Model Transcription: Fully functional and optimized for Linux systems
- API Transcription: Partially implemented with some issues still being addressed
- Platform Support:
- Linux: Primary development platform, fully supported
- macOS: Partially tested, needs additional refinement
- Windows: Support in progress, currently encountering issues
Future development will focus on:
- Phase 1: Robust multi-platform support and API transcription fixes (Q3 2025)
- Phase 2: Enhanced transcription options, including punctuation and formatting controls (Q4 2025)
- Phase 3: Real-time transcription mode for live dictation (Q1 2026)
- Phase 4: Language and dialect optimization for non-English languages (Q2 2026)
Impact
vocal.nvim is already changing how developers interact with their editor:
- Writers use it to overcome writer’s block by speaking their thoughts
- Developers record audio notes while debugging complex problems
- Teachers create educational content more efficiently
- Students take lecture notes without the distraction of typing
By bringing speech recognition into the development environment, vocal.nvim bridges the gap between spoken language and code, making Neovim more accessible and versatile as a creative tool.
Web Application
IndexR: AI-Powered Data Operations Platform
IndexR transforms how businesses interact with their data, combining powerful exploration capabilities with intelligent automation to enable actionable insights without complex coding.
Overview
IndexR started as a data exploration tool and has evolved into a comprehensive platform that leverages AI to help businesses perform automated operations on large datasets. The platform empowers users to extract insights, automate workflows, and take action directly from their data.
Key Features
- Intuitive Data Exploration: Multi-view interface with tree view for hierarchical data and grid view for tabular data
- Smart Search & Filtering: Powerful query capabilities for finding exactly what you need in complex datasets
- AI-Powered Auto-tagging: Automatic categorization and metadata generation for better organization
- Intelligent Data Operations: Define and execute workflows based on data patterns and criteria
- Integration Ecosystem: Connect with your existing tools and services for seamless operations
- Secure Collaboration: Share datasets and insights with team members while maintaining control
- Responsive Design: Optimized experience across devices for data analysis anywhere
Technical Implementation
The platform is built with a modern, scalable architecture:
- Frontend: React with Next.js, TypeScript, and Tailwind CSS for a responsive, type-safe UI
- Backend: API routes through Next.js with Prisma ORM connecting to PostgreSQL
- AI Integration: Custom agents built on LLM technology for intelligent features
- Data Processing: Optimized handling of large JSON and CSV datasets with streaming capabilities
- Security: Comprehensive authentication and authorization with granular permissions
Project Challenges
Building IndexR presented several interesting technical challenges:
-
Performance Optimization: Creating a system that handles multi-megabyte datasets client-side while maintaining responsiveness required implementing virtualized rendering and optimized parsing algorithms.
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Type Inference: Developing an automatic type inference system that correctly identifies and categorizes data structures from diverse sources demanded sophisticated pattern recognition techniques.
-
Intelligent Operations: Designing an AI agent framework that understands user intent and can take meaningful actions while maintaining security boundaries required careful system architecture.
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UX Complexity: Building an interface that makes complex data operations accessible to non-technical users while still providing power-user capabilities necessitated multiple iterations of user testing and refinement.
Roadmap
IndexR continues to evolve with our ambitious development plan:
- Phase 1: Enhanced data processing performance and expanded format support (Q2 2025)
- Phase 2: Agent framework for automated data analysis and actions (Q3 2025)
- Phase 3: Integration ecosystem for connecting with external services (Q4 2025)
- Phase 4: Visual workflow builder for no-code automation (Q1 2026)
- Phase 5: Advanced AI capabilities with predictive analytics (Q2 2026)
Impact
IndexR is already transforming how businesses operate:
- Sales teams use it to identify high-potential leads and automate follow-up processes
- Product managers track feature usage and automatically generate status reports
- Marketing teams analyze campaign performance and optimize content distribution
- Operations teams monitor KPIs and automate routine data processing tasks
By combining powerful data tools with intelligent automation, IndexR is making data-driven operations accessible to everyone, regardless of technical expertise.
Desktop Application
XScan: Intelligent Code Analysis & RAG Platform
XScan is a powerful code analysis tool designed to parse, index, and make codebases accessible for AI-powered search and contextual analysis. Built with modern web technologies and language-aware parsing, XScan enables developers to navigate, understand, and interact with complex codebases efficiently.
Key Features
- Multi-Language Code Parsing: Built-in support for Rust, TypeScript, JavaScript, and Lua with an extensible architecture for adding more languages.
- Intelligent Code Extraction: Automatically identifies functions, imports, exports, and comments across your codebase.
- Beautiful Syntax Highlighting: Syntax-aware code display with customizable themes.
- Structured Code Navigation: Organized file explorer with language-aware icons and filtering.
- Cross-Platform Desktop Application: Built with Tauri for efficient, secure native performance on Windows, macOS, and Linux.
Use Cases
- AI-Assisted Code Understanding: Feed your codebase to LLMs with accurate code structure preservation.
- Semantic Code Search: Find code segments by meaning rather than just keywords.
- Knowledge Graph Construction: Build a relational understanding of your codebase’s dependencies and connections.
- Project Onboarding: Help new team members quickly understand project structure and functionality.
- Documentation Generation: Extract documentation from code comments and structure.
Architecture
XScan combines the power of Rust for backend parsing with a modern React frontend:
- Frontend: React, TypeScript, and Tailwind CSS
- Backend: Rust with Tauri
- Parsing: Tree-sitter grammars for accurate, language-aware code parsing
- Syntax Highlighting: Shiki for beautiful code display
Getting Started
Prerequisites
- Node.js (v18+)
- Rust (latest stable)
- pnpm, npm, or yarn
Installation
# Clone the repository
git clone https://github.com/your-username/xscan.git
cd xscan
# Install dependencies
pnpm install
# Start development server
pnpm tauri devBuilding for Production
# Build the production binary
pnpm tauri buildRoadmap
Short-term (0-3 months)
- Enhanced Language Support: Add support for Python, Go, C/C++, and Java.
- Advanced Search Capabilities: Implement regex and semantic search within the UI.
- Code Relationship Visualization: Display code dependencies and relationships as interactive graphs.
- Customizable UI: Add dark/light mode toggle and configurable theme options.
Mid-term (3-6 months)
- RAG Integration
- Add ability to connect to OpenAI, Anthropic, and other LLM APIs
- Implement automatic chunking and context management
- Create a chat interface for code-aware conversations
- Local Embeddings Generation
- Generate code embeddings for semantic similarity search
- Store embeddings in vector database for fast retrieval
- Implement hybrid search combining semantic and keyword search
- Advanced Code Analysis
- Call graph generation and visualization
- Detect code smells and anti-patterns
- Identify security vulnerabilities in code
Long-term (6-12 months)
- Self-hosted AI Integration
- Support for local LLMs via GGUF, GGML formats
- Automatic fine-tuning on your specific codebase
- Memory-efficient context management for large codebases
- Collaborative Features
- Real-time shared code exploration
- Annotation and commenting on code segments
- Integration with issue trackers and documentation tools
- Knowledge Persistence
- Save and version code insights
- Generate comprehensive documentation from codebase
- Export analysis results in various formats (Markdown, PDF, etc.)
Technical Architecture Vision
XScan’s architecture is designed to scale from small projects to enterprise codebases:
- Core Parsing Engine: Language-agnostic parsing framework built on tree-sitter
- Indexing Layer: Fast, incremental indexing with support for various metadata
- Vector Store: Efficient storage and retrieval of code embeddings
- Context Manager: Intelligent selection of relevant code for LLM context
- UI Layer: Flexible, responsive interface for different workflows
Contributing
Contributions are welcome! See our Contributing Guide for more information on how to get started.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Built with Tauri, React, and Tree-sitter
- Syntax highlighting powered by Shiki
- Icons from Lucide