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Shimmy Roadmap 🚀

Vision: The privacy-first local AI infrastructure that replaces cloud dependencies

Shimmy is a zero-config, OpenAI-compatible inference server with a native WebGPU GPU engine. Its mission is invisible infrastructure: drop it in, it works.

🆓 Forever Free Commitment

Shimmy Core will always remain completely free and open-source. This is not a "free tier" or "community edition" - it's a permanent commitment to the developer community.

  • ✅ No feature limitations - Full functionality, forever
  • ✅ No usage limits - Use it commercially, personally, anywhere
  • ✅ No forced upgrades - Current version will always work
  • ✅ Community first - Built for developers, by developers

Premium offerings (Console/Cloud) are separate products that extend Shimmy's capabilities but never replace or limit the core experience.

📊 Market Position

  • Target Market: 127M+ developers worldwide running AI workloads
  • Problem: Cloud AI costs $0.002-0.06/token, vendor lock-in, privacy concerns
  • Solution: 100% local, 100% private, 100% free, drop-in OpenAI replacement

Current Milestones

  • ✅ Basic server skeleton with OpenAI-compatible endpoints
  • ✅ Initial /v1/chat/completions support
  • ✅ Native Ollama model discovery (~/.ollama/models/)
  • ✅ Auto port allocation with conflict avoidance
  • ✅ GGUF model auto-discovery from HuggingFace cache
  • ✅ VS Code extension integration
  • ✅ WebSocket streaming support
  • ✅ LoRA adapter foundation (llama.cpp path)
  • ✅ Airframe engine — pure-Rust WGSL GPU inference, shipped in v2.0.0
    • Deterministic GPU output, GGUF-native spec, YaRN RoPE extended context
    • No CUDA toolkit or Vulkan SDK required; wgpu handles adapter selection

🎯 Q2–Q3 2026 Milestones

  • Stop tokens from GGUF metadata — read tokenizer.ggml.eos_token_id natively
  • Quantization in Airframe — Q4_K_M and Q8_0 inference on the WebGPU pipeline
  • SafeTensors support — ingest .safetensors model checkpoints directly
  • Multi-model serving — load balancing across multiple active models
  • Enterprise Embeddings — /v1/embeddings endpoint targeting RAG workloads
  • Sub-50ms startup — benchmarking and initialization optimization

🧠 Airframe MoE Support (Planned)

Mixture of Experts model support in the Airframe WebGPU engine — enabling Mixtral, DeepSeek, Qwen MoE and other sparse transformer architectures without falling back to --legacy.

Engineering estimate: 21 story points across 7 work items (GGUF loader, router shaders, top-K selection, per-expert dispatch, output combine, buffer management).

MoE models currently supported via --legacy (llama.cpp). Native Airframe MoE is post-quantization.

→ See docs/AIRFRAME_MOE_ROADMAP.md for full engineering breakdown.

🚀 2026 Strategic Initiatives

  • Shimmy Console — terminal UI frontend with retro aesthetics and advanced controls
  • Developer Experience Suite — integrated development environment for AI workflows
  • Multi-Model Orchestration — load balancing across multiple models
  • Shimmy Cloud — enterprise cloud deployment and management platform

🌟 Long-Term Vision (2027+)

Technical Excellence

  • 100% OpenAI API Parity - Complete feature compatibility
  • Universal Deployment - Zero configuration, runs anywhere
  • Hardware Optimization - WebGPU/wgpu acceleration, MoE sparse dispatch
  • Enterprise Reliability - 99.99% uptime, consumer simplicity

Market Expansion

  • 1M+ Active Developers - Become the standard for local AI
  • Product Suite Leadership - Shimmy Console and Cloud ecosystem dominance
  • Enterprise Standard - Default choice for privacy-conscious organizations
  • Ecosystem Platform - Hub for local AI development tools
  • Global Infrastructure - Enable offline AI development worldwide
  • Revenue Diversification - Free core + premium products (not freemium limitations)

Industry Impact

  • Privacy Leadership - Set standards for local-first AI development
  • Cost Reduction - Save developers billions in cloud AI costs
  • Innovation Catalyst - Enable new categories of privacy-first AI applications
  • Trust Building - Demonstrate sustainable open-source without bait-and-switch tactics

Non-Goals

  • UI/dashboard (invisible infrastructure philosophy)
  • Model training (inference only)
  • Complex configuration (zero-config principle)
  • Feature bloat (lightweight focus)

Governance

  • Lead Maintainer: Michael A. Kuykendall
  • Contributions are welcome via Pull Requests
  • The roadmap is set by the lead maintainer to preserve project vision
  • All changes must align with Shimmy's core philosophy: lightweight, zero-config, invisible infrastructure