JoshuaOS

Problem

Personal productivity tools are fragmented: task managers don't talk to calendars, notes live in separate apps, and context-switching between systems wastes time. Most AI assistants are stateless—they forget what you told them yesterday.

I wanted a unified "operating system" for my life: one place to manage tasks, capture knowledge, and delegate planning to AI agents that have full context of my projects, goals, and constraints.

Approach

JoshuaOS is a personal productivity platform that combines:

• Task management (todo lists, projects, deadlines)
• Knowledge base (notes, documents, project context stored in vector DB)
• AI planning layer (multi-model agents that read context, plan actions, execute tasks)

Design Philosophy:

• Everything is stored locally-first with cloud sync (control over data)
• AI agents have RAG access to my entire knowledge base
• Multi-model orchestration: use Claude for planning, GPT-4 for structured outputs, local models for fast queries
• Vibe-coding: build it iteratively with AI assistance (dogfooding the system)

What I Built

Core System (TypeScript + Node.js):

• Task engine with hierarchical projects, tags, priorities, deadlines
• Markdown-based notes with frontmatter metadata
• SQLite database for structured data (tasks, events)
• File watcher for real-time note indexing

RAG Pipeline:

• Vector embeddings (OpenAI embeddings API or local Sentence-BERT)
• Qdrant vector DB for semantic search over notes/documents
• Query engine that retrieves relevant context before AI actions

AI Agent Layer:

• Planning agent: reads tasks, goals, and knowledge base → generates daily plan
• Execution agent: takes plan steps and breaks them into actionable subtasks
• Reflection agent: reviews completed tasks and updates knowledge base with learnings
• Multi-model router: chooses best model for each task type

Frontend (React + Electron):

• Desktop app for macOS/Windows with offline-first design
• Task list with smart filtering (by project, tag, priority)
• Note editor with live preview and backlinks
• AI chat interface with access to full context

Architecture

Diagram placeholder: Frontend → API Layer → RAG Engine + Task DB + AI Models

Electron Frontend (React)
    ↓
API Layer (Express + TypeScript)
    ↓
Task Database (SQLite) ←→ Vector DB (Qdrant)
    ↓
AI Orchestrator
    ├─ Planning Agent (Claude)
    ├─ Execution Agent (GPT-4)
    └─ Reflection Agent (Local LLM)

Outcomes

Qualitative Impact:

• Reduced time spent context-switching between apps (Notion → Todoist → Google Calendar → ChatGPT)
• AI agents can draft daily plans in seconds, factoring in deadlines, priorities, and energy levels
• Knowledge base grows organically: capture learnings once, retrieve them forever
• Vibe-coding experience: built the tool while using the tool

What worked well:

• RAG gives AI agents surprisingly good context (no more "who are you again?")
• Local-first architecture means no internet = still functional
• Multi-model orchestration lets me balance cost/speed/quality

What I'd improve:

• Add calendar integration (iCal, Google Calendar sync)
• Build mobile app for on-the-go task capture
• Improve agent decision-making (sometimes suggests unrealistic plans)
• Add collaboration features (share projects with others)

What's Next

• Integrate with email for inbox-zero workflows (AI triage + task creation)
• Add voice interface (dictate tasks, ask questions)
• Build template system for recurring projects (e.g., "launch new product" checklist)
• Open-source parts of the system for others to build on