# NNLang Adoption & Engagement Plan

## Executive Summary

NNLang (Neural Network Language) is a declarative language for defining neural network architectures that compiles to standalone, zero-dependency native binaries. This plan outlines strategies to drive adoption among programmers, focusing on embedded systems developers, ML engineers, and systems programmers.

---

## 1. Web Presence & Branding

### 1.1 Domain & Website
**Priority: HIGH**

| Element | Description |
|---------|-------------|
| Domain | `nnl-lang.org` or `nnl.ai` |
| Hosting | Cloudflare Pages, Vercel, or Netlify (free tier suitable) |
| SSL | Automatic via hosting provider |

**Website Structure:**

```
/
├── index.html              # Hero, quick start, features
├── download.html          # Install instructions for all platforms
├── docs/                  # Full documentation
│   ├── getting-started/
│   ├── language-reference/
│   ├── tutorials/
│   └── examples/
├── playground/            # Interactive online editor
├── blog/                  # Announcements, tutorials
├── community/             # Links to Discord, GitHub, etc.
└── playground.html        # Interactive REPL
```

**Homepage Content:**
- Hero: "Neural Networks without the bloat"
- Tagline: "Compile ML models to zero-dependency binaries"
- Quick demo: Before/after showing .nnl source → compiled binary
- 3 key selling points (no runtime, no heap, static binaries)
- "Get Started" CTA buttons for each platform
- Embedded YouTube demo video (30-60 seconds)

### 1.2 Branding Assets
- Logo (SVG, PNG at multiple resolutions)
- Color scheme: Define primary, secondary, accent colors
- Brand guidelines document
- Social media assets (OG images, Twitter card)

---

## 2. Packaging & Distribution

### 2.1 Primary: Cargo crates.io
**Priority: HIGH** — Already exists, but optimize for discovery

```toml
# Cargo.toml optimizations
[package]
name = "nnl"
description = "Neural Network Language - Declarative ML model compiler"
repository = "https://github.com/gdesouza/nnl"
homepage = "https://nnl-lang.org"
keywords = ["ml", "neural-network", "compiler", "embedded", "inference"]
categories = ["command-line-utilities", "machine-learning"]
```

**Actions:**
- [ ] Add comprehensive keywords for discoverability
- [ ] Add CI badge to crates.io page
- [ ] Publish to crates.io (verify it's published)

### 2.2 Secondary: Binary Installation
**Priority: HIGH**

| Method | Command | Target Users |
|--------|---------|---------------|
| **cargo install** | `cargo install nnl` | Rust developers |
| **cargo-binstall** | `cargo binstall nnl` | Fast installation, CI/CD |
| **Homebrew** | `brew install nnl` | macOS users |
| **Chocolatey** | `choco install nnl` | Windows users |
| **apt/yum** | `sudo apt install nnl` | Linux users |
| **Docker** | `docker run nnl-lang/nnl` | Container users |
| **Download** | Direct binary from GitHub Releases | All platforms |

### 2.3 Homebrew Tap
```ruby
# homebrew-nnl/formula/nnl.rb
class Nnl < Formula
  desc "Neural Network Language compiler"
  homepage "https://nnl-lang.org"
  url "https://github.com/gdesouza/nnl/releases/download/vX.Y.Z/nnl-X.Y.Z-x86_64-apple-darwin.tar.gz"
  sha256 "..."
  license "Apache-2.0"

  def install
    bin.install "nnc"
  end
end
```

### 2.4 Windows Package (Chocolatey)
```xml
<?xml version="1.0" encoding="utf-8"?>
<package xmlns="http://schemas.microsoft.com/packaging/2015/06/nuspec.xsd">
  <metadata>
    <id>nnl</id>
    <version>0.1.5</version>
    <title>NNL (Neural Network Language)</title>
    <description>Compile ML models to zero-dependency binaries</description>
    <projectUrl>https://nnl-lang.org</projectUrl>
    <licenseUrl>https://github.com/gdesouza/nnl/blob/main/LICENSE</licenseUrl>
  </metadata>
  <files>
    <file src="nnc.exe" target="tools" />
  </files>
</package>
```

### 2.5 apt Repository (Linux)
```bash
# Setup for Debian/Ubuntu
curl -fsSL https://apt.nnlang.org/gpg.key | sudo gpg --dearmor -o /etc/apt/trusted.gpg.d/nnl.gpg
echo "deb [arch=$(dpkg --print-architecture)] https://apt.nnlang.org stable main" | sudo tee /etc/apt/sources.list.d/nnl.list
sudo apt update && sudo apt install nnl
```

### 2.6 GitHub Releases Optimization
- [ ] Use GitHub Releases with auto-generated binaries for:
  - Linux (x86_64, ARM64, ARMv7)
  - macOS (x86_64, ARM64)
  - Windows (x86_64)
- [ ] Use [gh-mount](https://github.com/cloudflare/ghaction-github-release-attachments) or similar for CDN distribution
- [ ] Add SHA256 checksums
- [ ] Add installer scripts (install.sh for Linux/macOS)

---

## 3. Developer Experience

### 3.1 IDE Support
**Priority: MEDIUM**

| Tool | Status | Action |
|------|--------|--------|
| **VS Code** | Not exists | Create extension |
| **IntelliJ** | Not exists | Plugin (optional, lower priority) |
| **Vim/Neovim** | Not exists | Tree-sitter grammar |
| **Emacs** | Not exists | Major mode |

**VS Code Extension Structure:**
```
nnl-vscode/
├── package.json
├── syntaxes/
│   └── nnl.tmLanguage.json    # TextMate grammar
├── language-configuration.json
├── snippets/
│   └── nnl.json
└── src/
    └── extension.ts           # LSP client (if implemented)
```

**TextMate Grammar (example):**
```json
{
  "scopeName": "source.nnl",
  "patterns": [
    {"include": "#keywords"},
    {"include": "#layers"},
    {"include": "#numbers"},
    {"include": "#strings"}
  ],
  "repository": {
    "keywords": {
      "patterns": [
        {"match": "\\b(version|model|layer|config|input|output)\\b", "name": "keyword.nn1"}
      ]
    }
  }
}
```

### 3.2 Language Server (LSP)
**Priority: MEDIUM** — For IDE features

Implement LSP with:
- Diagnostics (errors, warnings)
- Auto-completion
- Go to definition
- Hover information
- Document symbols

**LSP Features to Implement:**
1. Parse on save, emit diagnostics
2. Provide completion for layer types, config keys
3. Find definitions for layer references

### 3.3 Tree-sitter Grammar
```javascript
// tree-sitter-nnl
module.exports = grammar({
  name: 'nnl',
  rules: {
    source_file: $ => $.model_definition,
    model_definition: $ => seq(
      'model',
      $.identifier,
      '{',
      optional($.config_block),
      repeat($.layer_declaration),
      '}'
    ),
    layer_declaration: $ => seq(
      'layer',
      $.identifier,
      '=',
      $.layer_type,
      $.layer_args
    ),
    // ... more rules
  }
});
```

---

## 4. Learning Resources

### 4.1 Documentation
**Priority: HIGH**

| Doc | Status | Location |
|-----|--------|----------|
| Language Reference | Exists | `docs/language-reference.md` |
| CLI Reference | Exists | `docs/cli.md` |
| Quick Start | Exists | README.md |
| Examples | Exists | `docs/examples.md` |
| Tutorial: First Model | Needs work | Create |
| Tutorial: Import from ONNX | Needs work | Create |
| Tutorial: Embedded Deployment | Needs work | Create |

**Documentation Site Generator:**
- Use [mdBook](https://rust-lang.github.io/mdBook/) (Rust-friendly, same tool Rust uses)
- Deploy to `docs.nnl-lang.org`

### 4.2 Interactive Playground
**Priority: MEDIUM**

Web-based editor at `playground.nnl-lang.org`:

```yaml
Features:
  - Code editor (Monaco Editor or CodeMirror)
  - Syntax highlighting for NNL
  - Compile button → show IR output
  - Error display with line numbers
  - Pre-loaded examples dropdown
  - Share via URL (base64 encode code in URL)
  - Download compiled artifacts
```

**Tech Stack:**
- Frontend: Vanilla JS + Monaco Editor
- Backend: Compile via WASM (compile nnc to WebAssembly)

### 4.3 Tutorial Series
**Priority: HIGH**

| # | Tutorial | Length | Target |
|---|----------|--------|--------|
| 1 | "Hello World" - Your First NNL Model | 5 min | Beginners |
| 2 | From ONNX to Binary in 5 Minutes | 10 min | ML engineers |
| 3 | Building a MNIST Classifier | 20 min | Intermediate |
| 4 | Deploying to a Raspberry Pi | 15 min | Embedded devs |
| 5 | Custom Layers and Extensions | 30 min | Advanced |
| 6 | Memory Optimization Techniques | 25 min | Performance engineers |

### 4.4 Video Content
**Priority: LOW-MEDIUM**

| Content | Platform | Length |
|---------|----------|--------|
| Introduction (what/why) | YouTube | 3-5 min |
| Quick Start Demo | YouTube | 5-10 min |
| Building a Model | YouTube | 15-20 min |
| Embedded Deployment | YouTube | 10-15 min |
| Deep Dive: Compiler Internals | YouTube | 30-45 min |

### 4.5 Blog
**Priority: LOW**

Post topics:
- Release announcements
- Performance benchmarks
- Use case spotlights
- Guest posts from community

---

## 5. Community Building

### 5.1 Communication Channels

| Channel | Purpose | Priority |
|---------|---------|----------|
| **GitHub Discussions** | Q&A, feature requests | HIGH |
| **Discord** | Real-time chat, support | MEDIUM |
| **Twitter/X** | Announcements, engagement | MEDIUM |
| **Reddit** (r/MachineLearning, r/embedded) | Sharing, discussions | LOW |

### 5.2 GitHub Organization
- [ ] Transfer repo to `nnl-lang` organization
- [ ] Create related repos:
  - `nnl-lang/nnl` (main compiler)
  - `nnl-lang/nnl-vscode` (VS Code extension)
  - `nnl-lang/nnl-playground` (web playground)
  - `nnl-lang/awesome-nnl` (curated list of resources)

### 5.3 Events & Outreach

| Event | Type | Priority |
|-------|------|----------|
| RustConf | Conference talk | HIGH |
| Embedded Linux Conference | Conference talk | MEDIUM |
| ML Compiler Summit | Conference talk | MEDIUM |
| Local meetups | In-person | LOW |

### 5.4 Contributor Journey

```
1. First Issue
   ├── Good first issue label
   ├── Clear instructions
   └── Mentoring available

2. Documentation PRs
   ├── Lower barrier to entry
   └── Review feedback loop

3. Code Contributions
   ├── Contribution guide
   ├── Code of conduct
   └── Design docs for major features
```

---

## 6. Ecosystem Integration

### 6.1 Model Zoo
**Priority: MEDIUM**

Create `awesome-nnl` repository with:
- Pre-compiled models
- Community submissions
- Benchmark results

```
models/
├── vision/
│   ├── resnet50/
│   │   ├── model.nnl
│   │   ├── weights/
│   │   └── README.md
│   └── mobilenet/
├── nlp/
│   └── ...
└── audio/
    └── ...
```

### 6.2 ONNX Integration (Existing, but improve)
- [ ] Document all supported ops clearly
- [ ] Create converter status page
- [ ] Add unsupported ops request tracker

### 6.3 ML Framework Exporters
**Priority: LOW** — Future

- [ ] PyTorch → NNL exporter
- [ ] TensorFlow → NNL exporter
- [ ] JAX → NNL exporter

---

## 7. Marketing & Positioning

### 7.1 Key Messages

| Audience | Message |
|----------|---------|
| Embedded devs | "Deploy ML to microcontrollers without Python" |
| ML engineers | "Ship inference as a single binary, no runtime needed" |
| Systems programmers | "ML inference as a systems problem — zero allocations, full control" |
| Safety-critical | "DO-178C ready inference, auditable source" |

### 7.2 Competitive Differentiation

| Competitor | NNL Advantage |
|------------|----------------|
| PyTorch/TensorFlow | No Python dependency, static binary |
| ONNX Runtime | Smaller footprint, no runtime DLL |
| TensorFlow Lite | More control, C output |
| TVM | Simpler DSL, no auto-tuning overhead |

### 7.3 SEO Strategy

Target keywords:
- "neural network compiler"
- "embed ML model in C"
- "ML for microcontrollers"
- "static neural network binary"
- "no Python ML inference"

---

## 8. Metrics & Analytics

### 8.1 Success Metrics

| Metric | Target (Year 1) |
|--------|-----------------|
| GitHub Stars | 1,000 |
| crates.io downloads/month | 5,000 |
| Discord members | 500 |
| GitHub contributors | 50 |
| Models in zoo | 20 |
| Blog subscribers | 200 |

### 8.2 Tracking

- Google Analytics on website
- GitHub Insights for repo metrics
- crates.io stats page
- Discord server insights

---

## 9. Implementation Roadmap

### Phase 1: Foundation (Months 1-3)
- [ ] Set up website domain and hosting
- [ ] Deploy documentation with mdBook
- [ ] Optimize crates.io listing
- [ ] Set up GitHub organization
- [ ] Create VS Code extension (basic)

### Phase 2: Growth (Months 4-6)
- [ ] Launch playground (WASM compile)
- [ ] Publish to Homebrew, Chocolatey
- [ ] Write tutorial series
- [ ] Create Tree-sitter grammar
- [ ] Start blog publishing

### Phase 3: Scale (Months 7-12)
- [ ] LSP implementation
- [ ] Conference talks
- [ ] Model zoo launch
- [ ] Contributor mentorship program

---

## 10. Budget Estimate

| Item | Cost (Year 1) |
|------|---------------|
| Domain (.org) | ~$12/year |
| Hosting (Cloudflare Pages) | Free |
| VS Code extension development | $2,000-5,000 (contractor if needed) |
| Video production | $1,000-3,000 |
| Conference travel | $2,000-5,000 |
| **Total** | **~$5,000-15,000** |

---

## Appendix: Quick Wins

1. **Today**: Add "awesome" badge/links to README
2. **This week**: Set up GitHub Pages with docs
3. **This month**: Publish to Homebrew tap
4. **This quarter**: Launch playground
