AI That Decides What Matters
Open-source AI tools, local inference engines, and research platforms — built for developers, researchers, and teams who want full control over their AI stack.
WorthDoing AI is an open-source AI organization focused on building tools that empower developers, researchers, and teams to harness the full potential of artificial intelligence — without vendor lock-in, without black boxes, and without compromises on privacy or performance.
We believe in:
| Principle | Description |
|---|---|
| Full Transparency | Every line of code is open source. No hidden layers, no proprietary lock-in. |
| Local-First AI | Your data stays on your machine. Run models locally with zero cloud dependencies. |
| Developer Experience | Beautiful, production-grade interfaces that make complex AI accessible. |
| From Scratch | We build our own engines, parsers, and runtimes — not wrappers around existing tools. |
| Performance | Optimized for modern hardware, especially Apple Silicon with Metal GPU acceleration. |
| Interoperability | Works with HuggingFace, OpenRouter, GGUF, and every major model provider. |
A unified AI playground to explore, test, compare, and understand AI models — powered by OpenRouter with 350+ models across 55+ providers in one interface.
A production-grade, open-source application that serves as a single interface to explore, test, compare, and understand AI models from all major providers through the OpenRouter unified API. Built by WorthDoing AI.
Key Features:
| Feature | Description |
|---|---|
| Chat Playground | Interactive LLM testing with real-time SSE streaming, full parameter control, and conversation history |
| Model Explorer | Browse, search, and filter 350+ models with detailed specs, pricing, and capability comparisons |
| Compare Lab | Send one prompt to up to 6 models simultaneously for side-by-side output comparison |
| Embeddings Lab | Explore vector embeddings with interactive visualization, cosine similarity scoring, and dimension analysis |
| Image Lab | Unified interface for AI image generation across all supported image models |
| Cost Tracker | Real-time usage analytics with per-model and per-provider cost breakdowns |
| Provider Dashboard | Monitor provider status, latency, and availability across the ecosystem |
Tech Highlights:
- Server-side API key protection via secure proxy layer
- Dark-mode first design with responsive layout
- Real-time streaming with Server-Sent Events (SSE)
- Advanced model filtering by capability, context length, pricing, and provider
- Full parameter control: temperature, top-p, top-k, frequency penalty, max tokens
- Conversation export and history management
A custom C++ inference engine for Apple Silicon — run large language models locally with maximum performance, complete privacy, and zero cloud dependencies.
This is not a wrapper around llama.cpp or any existing runtime. It is a home-made C++ inference engine built from scratch, capable of loading and running GGUF quantized models, exposed through a polished local web application with direct HuggingFace model downloading. Built by WorthDoing AI.
What Makes This Different:
| Existing Tools (Ollama, LM Studio) | WorthDoing Local AI |
|---|---|
| Wrapper around llama.cpp | Custom C++ engine built from scratch |
| Generic cross-platform | Optimized for Apple Silicon (Accelerate framework, Metal-ready) |
| Black-box inference | Full source code: GGUF parser, tensor ops, transformer forward pass |
| CLI-first experience | Professional web UI with HuggingFace integration |
| Limited format support | Extensible architecture with custom format support (.wdf) |
Engine Architecture:
| Component | Description |
|---|---|
| GGUF Parser | Complete binary parser supporting all GGUF metadata types and tensor layouts |
| Tensor Engine | Custom tensor operations with SIMD-optimized matrix multiplication via Apple Accelerate |
| Tokenizer | BPE tokenizer with full Unicode support, special token handling, and vocabulary management |
| Transformer | Full forward pass implementation: RMSNorm, RoPE, grouped-query attention, SwiGLU FFN |
| Quantization | All GGUF quant types: Q2_K through Q8_0, plus K-quant variants for optimal size/quality |
| Sampling | Temperature, top-p, top-k, repetition penalty with streaming token output |
| API Server | OpenAI-compatible REST API with SSE streaming for seamless integration |
Web UI Features:
- Real-time chat with streaming token display
- Model library with file-level management
- Built-in HuggingFace browser for model discovery and one-click downloads
- Performance monitoring dashboard with tokens/sec, memory usage, and system metrics
- System settings and configuration panel
Next-gen Mac system monitor — real-time CPU, RAM, GPU, disk, network, and battery in a beautiful interactive terminal UI.
npm install -g macpulse
macpulse| Metric | Details |
|---|---|
| CPU | Total + per-core usage, load average, sparkline history, spike detection |
| RAM | Activity Monitor-accurate: app/wired/compressed/cache breakdown, memory pressure |
| GPU | Metal usage estimation, GPU model detection |
| Disk | Read/write speed, capacity usage |
| Network | Download/upload rates, interface detection |
| Battery | Charge %, health %, cycle count, charging status, time remaining |
4 view modes: Dashboard, Detailed (d), Process (p), Graph (g). Gradient progress bars, rounded-corner panels, dual-column layout on wide terminals.
A local AI agent operating system for your terminal — think, act, execute, persist.
npm install -g wd-agent
wdagentUses Anthropic's native tool_use API (zero JSON parsing), WorthDoing Capabilities as the execution layer, context auto-compaction, and token tracking. Supports both Anthropic direct and OpenRouter (350+ models) with interactive scrollable model selection.
Production-grade capability package — 31 portable, composable agent actions across 8 API providers.
| Provider | Capabilities |
|---|---|
| Exa | search, findSimilar, contents, answer |
| Tavily | search, extract |
| Firecrawl | scrape, search, map |
| OpenRouter | chat, models |
| OpenAlex | works, authors, institutions |
| FMP | quote, profile, financialStatements, historicalPrices |
| EODHD | eod, fundamentals, search |
| Documents | excel, word, latex |
Python package: runtime, registry, YAML contracts, CLI (wdcap), 188 tests. TypeScript SDK: zero dependencies, ESM + CJS, interactive CLI explorer.
Official logo and brand assets for WorthDoing AI — 12 variants in SVG and PNG formats.
The WorthDoing AI icon represents a neural network converging toward a single decision core — a visual metaphor for AI that filters, evaluates, and selects high-value actions. The gradient progression blue → cyan → green represents the transformation from understanding to decision.
Available Variants:
| Variant | Use Case |
|---|---|
| Dark Background | Websites, presentations on dark backgrounds |
| Light Background | Print materials, light-themed interfaces |
| Icon Only | Favicons, app icons, social media avatars |
| Horizontal | Headers, navigation bars, email signatures |
| With Tagline | Marketing materials, landing pages |
| Monochrome (White/Dark) | Watermarks, single-color printing |
| Favicon | Browser tabs, bookmarks |
| Compact Badge | GitHub badges, inline references |
| Gradient Background | Hero sections, splash screens |
| Layer | Technologies |
|---|---|
| Frontend | Next.js 16, React 19, TypeScript 5, TailwindCSS 4, Shadcn/UI, Recharts |
| Backend | Next.js API Routes, Node.js 22, Server-Sent Events (SSE) |
| Inference Engine | Custom C++17, Apple Accelerate Framework, Metal GPU (in progress) |
| Model Formats | GGUF (all quantization levels), custom .wdf (planned) |
| APIs | OpenRouter (350+ models), HuggingFace Hub, OpenAI-compatible REST |
| DevOps | GitHub Actions, ESLint, Prettier, pnpm |
| Design | Dark-mode first, responsive, accessibility-focused |
| Repository | Description | Language | Status |
|---|---|---|---|
termxl |
Terminal-native spreadsheet — Excel in your terminal with formula engine (npx termxl) |
||
macpulse |
Next-gen Mac system monitor — CPU, RAM, GPU, disk, network, battery (npx macpulse) |
||
wd-agent |
Local AI agent OS — Claude Opus 4.6 + WorthDoing Capabilities (npx wd-agent) |
||
ai-playground |
Unified AI playground — 350+ models, compare lab, embeddings, image generation | ||
local-llm-inference |
Custom C++ inference engine for Apple Silicon with professional Web UI | ||
worthdoing-capabilities |
Official capability package — portable, composable agent actions with runtime, registry, and CLI | ||
worthdoing-capabilities-js |
TypeScript SDK — same capabilities on npm (worthdoing-capabilities) |
||
worthdoing-website |
Official website — landing page + project showcase | ||
brand-assets |
Official logos and brand assets — 12 variants in SVG and PNG |
# System monitor — see your Mac in real time
npm install -g macpulse
macpulse
# AI Agent — local agent OS powered by Claude
npm install -g wd-agent
wdagent
# Capabilities SDK — 31 portable agent actions
npm install worthdoing-capabilities# AI Playground
git clone https://github.com/Worth-Doing/ai-playground.git
cd ai-playground && pnpm install && pnpm dev
# Local LLM Inference
git clone https://github.com/Worth-Doing/local-llm-inference.git
cd local-llm-inference/engine && make -j$(sysctl -n hw.ncpu)
cd ../app && pnpm install && pnpm dev
# Website
git clone https://github.com/Worth-Doing/worthdoing-website.git
cd worthdoing-website && npm install && npm run devWe welcome contributions from the community! Whether you're fixing a bug, improving documentation, or proposing a new feature, we'd love to hear from you.
How to contribute:
- Fork the repository you want to contribute to
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Contribution areas we're especially interested in:
| Area | Examples |
|---|---|
| Inference Engine | SIMD optimizations, Metal GPU kernels, new quantization formats |
| Web UI | New features, accessibility improvements, mobile responsiveness |
| Documentation | Tutorials, API docs, architecture guides |
| Testing | Unit tests, integration tests, benchmarks |
| Model Support | New model architectures, tokenizer improvements |
All code repositories are released under the MIT License — free to use, modify, and distribute.
Brand assets (logos, icons, visual identity) are proprietary to WorthDoing AI and may not be used without permission.
Built with purpose by WorthDoing AI
AI That Decides What Matters
