| # 🚀 Advice on Rust, Go, Zig for HPC in AI Pipelines | |
| ## 🌟 My Coding World | |
| - 🌟 1 **Focus**: Writing fast AI code for AI spaces. | |
| - 💻 2 **Fav Languages**: Python, HTML5, JS – picky for AI UI/UX vibes. | |
| - ⚡ 3 **Daily Grind**: Building models, designing pipelines, 3000 lines/day since AI pair programming (2020). | |
| - 🛠️ 4 **Workflow**: 1500-line spaces, 200-300 versioned `app.py`, `requirements.txt`, and redocker boots. | |
| - 🖥️ 5 **Fav IDEs**: | |
| - 🌐 1. **HuggingFace**: Instant setup, 3 secs to spin up a Space! 🕒 | |
| - 📝 2. **VSCode**: 5 mins for folder, interpreter, `launch.json`. ⚙️ | |
| ## ⏱️ Why I Love My Setup | |
| - 🚀 1 **New Project**: | |
| - ⚡ 1. HF: 3 secs to pick a Python lib or container. | |
| - 🛠️ 2. VSCode: 5 mins to configure from scratch. | |
| - 📜 2 **Quick Python App** (`app.py`, `requirements.txt`): | |
| - ⏳ 1. HF: <2 mins, auto-rebuilds on commit. | |
| - 🔧 2. VSCode: ~2 mins, but manual rebuilds. | |
| - 📚 3 **Learn Fast**: Simple patterns, no complexity overload. | |
| - 🧪 1. E.g., `git clone https://github.com/AaronCWacker/SFT` – instant ML pipeline, test-ready! | |
| - 😎 4 **Test & Enjoy**: Pure pleasure running my own app. | |
| ## 🧪 Experimenting with HPC: How Low Can We Go? | |
| ## 💡 Why Try Rust, Go, Zig for AI? | |
| - 📝 Here’s why these languages fit your 3000-line/day AI pipeline life: | |
| - 🦀 1 **Rust: Memory-Safe Speed Demon** | |
| - 🧹 1. **Why**: Stack cleanup = no leaks, no GC lag for big models. | |
| - ✏️ 2. **Perk**: Mutable vars explicit – readable pipelines at 2 AM. | |
| - ⚡ 3. **Fit**: Fast, safe, concurrent – crushes big data tasks. | |
| - 🐹 2 **Go: Reliable Workhorse** | |
| - 🏃 1. **Why**: Simple, fast binaries, goroutines for concurrency. | |
| - 🌐 2. **Perk**: Perfect for real-time services (e.g., ChatGPT, ElevenLabs). | |
| - ⏩ 3. **Fit**: Churns out server-side code to match your grind. | |
| - ⚡ 3 **Zig: Wild Card Optimizer** | |
| - ⏲️ 1. **Why**: "Comptime" metaprogramming – pre-compute tables/configs. | |
| - 🔩 2. **Perk**: Raw, C-like control, no fluff, blazing fast. | |
| - 🎨 3. **Fit**: Playground for performance tweaks in AI spaces. | |
| ## 🎯 Takeaways | |
| - 🛡️ 1 **Rust**: Safe, concurrent model code. | |
| - 🌍 2 **Go**: Quick, scalable services. | |
| - 🔍 3 **Zig**: Lean, custom optimizations. | |
| - 💪 4 **Promise**: They’ll keep up with your pace and push pipelines harder. | |
| ## 🔥 Call to Action | |
| - 🚀 1 Give ‘em a shot – your AI code deserves it! | |
| - | |
| 1. 🚀 **Rust frees memory with stack cleanup** - No manual freeing, tied to scope. | |
| 2. 🛡️ **Rust avoids garbage collector** - Compile-time cleanup, not runtime. | |
| 3. ✏️ **Rust requires mutable variables explicitly** - Improves code readability, safety. | |
| 4. ⚡ **Rust emphasizes performance, safety** - Borrow checker prevents errors. | |
| 5. 🧩 **Zig metaprogramming is simple** - Same syntax, just add "comptime." | |
| 6. ⏱️ **Zig runs code at compile time** - E.g., generate prime numbers early. | |
| 7. 🌐 **Go as a mainstay** - Reliable for services, web sockets. | |
| 8. 🛠️ **Jai and Zig for exploration** - Testing performance in custom services. | |
| 9. 🔌 **Jai uses raw sockets** - Manual TCP connections, like C. | |
| 10. 🐛 **C++ metaprogramming is messy** - Powerful but hard to debug. |