Merge pull request #17 from rgbkrk/insane-typing-golf

Refactor: Eliminate schema compilation, use direct TypeScript imports

Author: rgbkrk

.github/workflows/ci.yml

@@ -41,9 +41,6 @@ jobs:
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
-      - name: Build schema
-        run: pnpm build:schema
-
       - name: Run type checking and linting
         run: pnpm check
 
@@ -120,8 +117,5 @@ jobs:
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
-      - name: Build schema
-        run: pnpm build:schema
-
       - name: Run tests
         run: pnpm test

AGENTS.md

@@ -2,6 +2,8 @@
 
 This document provides essential context for AI assistants working on the Anode project.
 
+For current work state and immediate next steps, see `HANDOFF.md` - it focuses on where development was left off and what to work on next.
+
 ## Project Overview
 
 Anode is a real-time collaborative notebook system built on LiveStore, an event-sourcing based local-first data synchronization library. The project uses a monorepo structure with TypeScript and pnpm workspaces.
@@ -10,7 +12,7 @@ Anode is a real-time collaborative notebook system built on LiveStore, an event-
 
 ## Architecture
 
-- **Schema Package** (`@anode/schema`): LiveStore schema definitions (events, state, materializers)
+- **Shared Schema** (`shared/schema.ts`): LiveStore schema definitions (events, state, materializers) - TypeScript source file directly imported by all packages with full type inference
 - **Web Client** (`@anode/web-client`): React-based web interface
 - **Document Worker** (`@anode/docworker`): Cloudflare Worker for sync backend
 - **Kernel Client** (`@anode/dev-server-kernel-ls-client`): Python execution server (manual start per notebook)
@@ -22,111 +24,54 @@ Anode is a real-time collaborative notebook system built on LiveStore, an event-
 - **React**: UI framework
 - **TypeScript**: Primary language
 
-## Current Architecture - Fully Operational
+## Current Working State
+
+### What's Working ✅
+- ✅ **Instant Python execution** with zero polling delays
+- ✅ **Rich output rendering** - HTML tables, SVG plots, markdown, JSON
+- ✅ **Pandas DataFrames** with styled HTML table output
+- ✅ **Matplotlib plots** as crisp SVG vector graphics
+- ✅ **Real-time collaboration** across multiple users  
+- ✅ **AI cell integration** with mock responses and markdown rendering
+- ✅ **Offline-first operation** with sync when connected
+- ✅ **Comprehensive testing** (60 passing tests)
 
-### **Simplified Notebook/Store Relationship**
+### Core Architecture Features
 - `NOTEBOOK_ID = STORE_ID`: Each notebook gets its own LiveStore database
-- URL routing: Access notebooks via `?notebook=notebook-id`
-- Single notebook per store eliminates data boundary confusion
-- All events naturally scoped to one notebook
-
-### **Reactive Execution Queue System**
-- Flow: `executionRequested` → `executionAssigned` → `executionStarted` → `executionCompleted`
-- **Kernels use reactive `queryDb` subscriptions** for instant work detection (no polling)
-- **Zero-latency execution** - cells execute immediately when run
-- Session-based assignment with auth enforcement planned
-- **Fully operational** - working end-to-end with lightning-fast response
-
-### **Rich Output System**
-- **Multiple media types**: text/plain, text/markdown, text/html, image/svg+xml
-- **Pandas DataFrames**: Professional HTML table styling with borders and formatting
-- **Matplotlib plots**: SVG vector graphics with crisp rendering
-- **AI responses**: Rich markdown with syntax highlighting and code blocks
-- **Media type prioritization**: Best format automatically selected for display
-- **Fallback support**: Always includes plain text as backup
-
-### **Kernel Session Tracking**
-- Each kernel restart gets unique `sessionId`
-- 30-second heartbeat mechanism
-- Session IDs tracked in execution queue
-- Manual kernel management (start one per notebook)
-
-## Development Setup
-
-### Common Commands
+- **Reactive execution**: `executionRequested` → `executionAssigned` → `executionStarted` → `executionCompleted`
+- **Zero-latency**: Kernels use reactive `queryDb` subscriptions for instant work detection
+- **Session-based kernels**: Each kernel restart gets unique `sessionId`
+
+## Development Commands
+
 ```bash
 # Start core services (web + sync)
 pnpm dev
 
 # Start kernel for specific notebook (manual)
 NOTEBOOK_ID=notebook-123-abc pnpm dev:kernel
 
-# Individual services
-pnpm dev:web-only
-pnpm dev:sync-only
-
-# Development utilities
+# Utilities
 pnpm reset-storage  # Clear all local storage
-pnpm build:schema   # Required after schema changes
 ```
 
-## Current Working State
-## What's Working ✅
-
-- ✅ **Instant Python execution** with zero polling delays
-- ✅ **Rich output rendering** - HTML tables, SVG plots, markdown, JSON
-- ✅ **Pandas DataFrames** with styled HTML table output
-- ✅ **Matplotlib plots** as crisp SVG vector graphics
-- ✅ **Real-time collaboration** across multiple users  
-- ✅ **Reactive architecture** using LiveStore's `queryDb` subscriptions
-- ✅ **Multiple isolated notebooks** with separate kernels
-- ✅ **AI cell integration** with mock responses and markdown rendering
-- ✅ **Offline-first operation** with sync when connected
-- ✅ **Event sourcing** for complete history and debugging
-- ✅ **Session management** with kernel isolation
-- ✅ **Comprehensive testing** (68 passing tests)
-
-## Next Phase: Real AI Integration & Automation 🤖
+## Next Phase: Real AI Integration
 
-**Priority Focus**: AI ↔ Python ↔ User interactions with enterprise-grade features
+**Priority Focus**: Replace mock AI responses with real API integration
 
 ### Immediate Goals
-- **Real AI API Integration** - Replace mock responses with OpenAI, Anthropic, local model calls
-- **Automatic Kernel Management** - One-click notebook startup with auto-kernel lifecycle
+- **Real AI API Integration** - OpenAI, Anthropic, local model calls
+- **Automatic Kernel Management** - One-click notebook startup
 - **Authentication System** - Google OAuth with proper session management
-- **Code Completions** - LSP + kernel-based suggestions with Pyodide integration
-- **SQL Cell Implementation** - Real database connections and query execution
-
-### Rich Output System ✅ COMPLETED
-- **Multiple Media Types** - text/plain, text/markdown, text/html, image/svg+xml
-- **Pandas DataFrames** - Professional HTML table styling with proper formatting
-- **Matplotlib Integration** - SVG vector graphics with interactive rendering
-- **AI Markdown Responses** - Rich formatted responses with syntax highlighting
-- **Media Type Detection** - Automatic selection of best display format
-
-### Medium-term Roadmap
-- **Interactive Outputs** - Widgets, 3D plots, and dynamic visualizations
-- **Real-time Collaboration** - Live cursors and presence indicators
-- **Advanced Cell Operations** - Multi-select, drag-and-drop reordering
-- **Performance Optimization** - Handle large notebooks and datasets efficiently
-
-### Recent Major Achievements ✅
-- ✅ **Rich Output System** - HTML tables, SVG plots, markdown rendering
-- ✅ **Pandas DataFrame Support** - Professional table styling matching Jupyter quality
-- ✅ **Matplotlib Integration** - Crisp vector graphics with proper rendering
-- ✅ **Fluid notebook navigation** with arrow keys
-- ✅ **Always-on textareas** replacing click-to-edit model
-- ✅ **Clean, focus-driven interface** design
-- ✅ **Standard keyboard shortcuts** (Shift+Enter, Ctrl+Enter)
-- ✅ **Consistent behavior** across all cell types
+- **Code Completions** - LSP + kernel-based suggestions
+- **SQL Cell Implementation** - Real database connections
 
 ## Important Considerations
 
 ### Schema Design
-- Schema package must be built before dependent packages can consume changes
-- Single `notebook` table per store (not `notebooks`)
-- `kernelSessions` and `executionQueue` tables for lifecycle management
-- **No timestamp fields** - eliminated for simplicity and stability (LiveStore handles timing automatically)
+- **Direct TypeScript imports**: `shared/schema.ts` provides zero-build-step imports with full type inference across all packages
+- **Single source of truth**: No compiled artifacts needed - TypeScript handles type checking from source
+- **No timestamp fields** - LiveStore handles timing automatically
 
 ### Local-First Architecture
 - All data operations happen locally first
@@ -143,73 +88,37 @@ pnpm build:schema   # Required after schema changes
 ## File Structure
 ```
 anode/
+├── shared/
+│   └── schema.ts         # LiveStore schema - TypeScript source directly imported by all packages
 ├── packages/
-│   ├── schema/           # LiveStore schema definitions
 │   ├── web-client/       # React web application
 │   ├── docworker/        # Cloudflare Worker sync backend
 │   └── dev-server-kernel-ls-client/  # Python kernel server
-├── start-dev.sh          # Development startup script
-├── reset-local-storage.cjs  # Clean development state
 ├── package.json          # Root workspace configuration
 └── pnpm-workspace.yaml   # Dependency catalog
 ```
 
-## Troubleshooting
-
-### Common Issues
-- **Build failures**: Run `pnpm build:schema` first
-- **Sync issues**: Check document worker deployment
-- **Execution not working**: Start kernel manually with `NOTEBOOK_ID=your-notebook-id pnpm dev:kernel`
-- **Stale state**: Run `pnpm reset-storage` to clear everything
-
-### Debugging
-- Browser console for client-side issues
-- Wrangler logs for worker debugging
-- Terminal output for kernel server issues
-- Comprehensive logging in kernel for execution flow
-
 ## Notes for AI Assistants
 
-### Current State - Fully Operational + Rich Outputs
-- **Zero-latency execution** with reactive architecture breakthrough
-- **Rich output rendering** ✅ COMPLETED - HTML tables, SVG plots, markdown
-- **AI cell integration** ✅ COMPLETED - Unified execution queue system
-- **Pandas DataFrame support** ✅ COMPLETED - Professional HTML table styling
-- **Matplotlib integration** ✅ COMPLETED - SVG vector graphics rendering
-- **Mock AI responses** ✅ WORKING - Rich markdown formatting
-- Manual kernel management (automation planned)
-- Simplified schemas for reliability and rapid development
-- Each notebook = separate LiveStore database for clean isolation
-- **Stable reactive architecture** leveraging LiveStore's capabilities
-- Ready for real AI API integration and advanced features
-
-### Communication Style
-- Use authentic developer voice - uncertainty is fine, just be explicit
-- Focus on AI ↔ Python ↔ User interaction goals as primary differentiator
-- Acknowledge both technical, UX, and rich output achievements completed
-- Emphasize production readiness and Jupyter-quality output rendering
-- Balance current capabilities with enterprise collaboration roadmap
+### Current State - Ready for AI Integration
+- **Zero-latency execution** with reactive architecture
+- **Rich output rendering** completed - HTML tables, SVG plots, markdown
+- **Mock AI responses** working - ready for real API integration
+- **Zero-build schema architecture** - Direct TypeScript imports eliminate build complexity
+- **Production-ready foundation** for AI-native collaborative notebooks
 
-### Key Insights for Development
+### Key Development Insights
 - **Reactive architecture breakthrough** - Zero-latency execution achieved
-- **Rich output system completion** - HTML tables, SVG plots, markdown rendering
-- **Pandas DataFrame integration** - Professional styling matching Jupyter quality
-- **Matplotlib SVG rendering** - Crisp vector graphics with proper display
-- **Fluid UX transformation** - Jupyter-like navigation and interaction completed
 - **Unified execution system** - AI cells work exactly like code cells through execution queue
-- Simple schemas enable rapid prototyping and reliable operation
-- Event sourcing provides excellent debugging and audit capabilities  
-- Local-first design enables offline work and instant responsiveness
-- **Proper event deferral** resolves LiveStore execution segment conflicts effectively
-- Session-based kernel management enables clean isolation and scaling
+- **Event sourcing** provides excellent debugging and audit capabilities  
+- **Proper event deferral** resolves LiveStore execution segment conflicts
 - **Focus-based UI patterns** create clean, keyboard-driven workflows
-- **Consistent cross-cell behavior** enables predictable user experience
-- **AI integration architecture** - Mock responses working, ready for real API integration
-
-**Current Development Cycle**: Major UX improvements and rich output system completed in June 2025, creating a fluid notebook experience with Jupyter-quality output rendering while maintaining real-time collaboration advantages.
 
-The system provides a **production-ready foundation** for AI-native collaborative notebooks with modern UX, professional-quality output rendering, and is positioned for advanced enterprise features.
+### Communication Style
+- Use authentic developer voice - uncertainty is fine, just be explicit
+- Focus on AI ↔ Python ↔ User interaction goals as primary differentiator
+- Emphasize production readiness and Jupyter-quality output rendering
 
 ## Important Note on Timestamps
 
-**Do NOT use manual timestamps in code or events.** LiveStore automatically handles all timing through its event sourcing system. Focus development on features and architecture rather than timestamp management - this was a key lesson learned that improved system stability significantly.
+**Do NOT use manual timestamps in code or events.** LiveStore automatically handles all timing through its event sourcing system. Focus development on features and architecture rather than timestamp management.