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In the Loop Development Roadmap

Vision: A real-time collaborative notebook system enabling seamless AI ↔ Python ↔ User interactions through local-first architecture.

Current Status: Production system deployed at https://app.runt.run with real-time collaboration, AI integration, and Python execution. Focus is on alpha release preparation with improved runtime management.

Production Foundation Complete ✅

What's Working Today

  • Real-time collaborative notebooks at https://app.runt.run
  • Python execution via Pyodide with rich outputs (matplotlib SVG, pandas HTML, IPython.display)
  • AI integration with full notebook context awareness and tool calling
  • Event-sourced architecture with LiveStore providing reliable state management
  • Artifact service for large outputs with R2 storage
  • Authentication system with OIDC OAuth
  • Mobile support with responsive design and keyboard optimizations
  • Offline-first operation that syncs when connected

Core Capabilities

Users can create notebooks, execute Python code, collaborate in real-time, use AI assistants that see code and outputs, and work offline. The system is stable and handles production workloads.

Alpha Release Priorities (Q4 2025)

1. Browser-Based Runtime Agent (🔥 Critical)

Goal: Eliminate manual runtime setup friction for alpha users

Current State: Users must manually run NOTEBOOK_ID=xyz pnpm dev:runtime Target: One-click runtime startup directly in browser

Implementation (#447):

  • Browser-based Pyodide runtime agent
  • Embedded runtime management in web client
  • Auto-connect when opening notebooks
  • Owner-only runtime launch permissions

Benefits:

  • Zero-friction notebook experience
  • Scalable for alpha user onboarding
  • No infrastructure management needed

2. Runtime Management Improvements

Stale Runtime Cleanup (#465):

  • Button to evict disappeared runtimes
  • Runtime health monitoring
  • Automatic cleanup of stale connections

RuntHQ Integration (#335):

  • Autoconnect runtime on notebook open
  • Seamless runtime orchestration
  • Owner permission controls

3. Product Naming & Alpha Preparation

Finalize Branding (#448):

  • Complete transition to "In the Loop" branding
  • Domain strategy for alpha launch

4. Critical Bug Fixes

LiveStore Output Reactivity (#415 - Critical):

  • Fix UI becoming unresponsive when outputs update
  • React Compiler compatibility issues
  • Ensure reliable real-time collaboration

Short-term Goals (Next 3-6 Months)

Runtime Ecosystem Expansion

Local CPython Support (#87, #99):

  • Connect to external Python environments
  • Ephemeral and persistent environment options

Enhanced User Experience

Improved Code Editing (#154):

  • Tab completion (Jupyter-style)
  • Better autocomplete behavior
  • LSP integration foundations

Output Management (#173):

  • Granular output collapsing

Medium-term Vision (6+ Months)

Advanced Runtime Capabilities

Multi-Runtime Support:

  • ZeroMQ integration for external kernels (#61)
  • Jupyter kernel protocol compatibility
  • Container and remote runtime management

Enhanced Python Experience:

  • Package management system (#63)
  • Variable inspection and debugging (#175)
  • SQL cell implementation with DuckDB (#62)

Rich Media Support

Enhanced Outputs:

  • Vega/Vega-Lite visualization (#267)
  • GeoJSON mapping support (#266)
  • Interactive widgets (#183)
  • Advanced 3D visualizations

Content Management:

  • Jupyter notebook compatibility (#184)
  • Import/export capabilities

Success Metrics

Alpha Release Goals

  • Runtime startup: < 10 seconds (one-click browser launch)
  • User onboarding: < 2 minutes from signup to first execution
  • System reliability: > 99% uptime for production notebooks
  • Collaboration latency: < 100ms for real-time updates

Technical Performance

  • Test suite: < 30 seconds execution time
  • Build time: < 5 minutes for full deployment
  • Memory efficiency: Support 100+ cell notebooks
  • Cross-browser support: Chrome, Firefox, Safari compatibility