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# π Advice on Rust, Go, Zig for HPC in AI Pipelines | |
## π My Coding World | |
- π 1 **Focus**: Writing fast AI code for AI spaces. | |
- π» 2 **Fav Languages**: Python, HTML5, JS β picky for AI UI/UX vibes. | |
- β‘ 3 **Daily Grind**: Building models, designing pipelines, 3000 lines/day since AI pair programming (2020). | |
- π οΈ 4 **Workflow**: 1500-line spaces, 200-300 versioned `app.py`, `requirements.txt`, and redocker boots. | |
- π₯οΈ 5 **Fav IDEs**: | |
- π 1. **HuggingFace**: Instant setup, 3 secs to spin up a Space! π | |
- π 2. **VSCode**: 5 mins for folder, interpreter, `launch.json`. βοΈ | |
## β±οΈ Why I Love My Setup | |
- π 1 **New Project**: | |
- β‘ 1. HF: 3 secs to pick a Python lib or container. | |
- π οΈ 2. VSCode: 5 mins to configure from scratch. | |
- π 2 **Quick Python App** (`app.py`, `requirements.txt`): | |
- β³ 1. HF: <2 mins, auto-rebuilds on commit. | |
- π§ 2. VSCode: ~2 mins, but manual rebuilds. | |
- π 3 **Learn Fast**: Simple patterns, no complexity overload. | |
- π§ͺ 1. E.g., `git clone https://github.com/AaronCWacker/SFT` β instant ML pipeline, test-ready! | |
- π 4 **Test & Enjoy**: Pure pleasure running my own app. | |
## π§ͺ Experimenting with HPC: How Low Can We Go? | |
## π‘ Why Try Rust, Go, Zig for AI? | |
- π Hereβs why these languages fit your 3000-line/day AI pipeline life: | |
- π¦ 1 **Rust: Memory-Safe Speed Demon** | |
- π§Ή 1. **Why**: Stack cleanup = no leaks, no GC lag for big models. | |
- βοΈ 2. **Perk**: Mutable vars explicit β readable pipelines at 2 AM. | |
- β‘ 3. **Fit**: Fast, safe, concurrent β crushes big data tasks. | |
- πΉ 2 **Go: Reliable Workhorse** | |
- π 1. **Why**: Simple, fast binaries, goroutines for concurrency. | |
- π 2. **Perk**: Perfect for real-time services (e.g., ChatGPT, ElevenLabs). | |
- β© 3. **Fit**: Churns out server-side code to match your grind. | |
- β‘ 3 **Zig: Wild Card Optimizer** | |
- β²οΈ 1. **Why**: "Comptime" metaprogramming β pre-compute tables/configs. | |
- π© 2. **Perk**: Raw, C-like control, no fluff, blazing fast. | |
- π¨ 3. **Fit**: Playground for performance tweaks in AI spaces. | |
## π― Takeaways | |
- π‘οΈ 1 **Rust**: Safe, concurrent model code. | |
- π 2 **Go**: Quick, scalable services. | |
- π 3 **Zig**: Lean, custom optimizations. | |
- πͺ 4 **Promise**: Theyβll keep up with your pace and push pipelines harder. | |
## π₯ Call to Action | |
- π 1 Give βem a shot β your AI code deserves it! | |
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1. π **Rust frees memory with stack cleanup** - No manual freeing, tied to scope. | |
2. π‘οΈ **Rust avoids garbage collector** - Compile-time cleanup, not runtime. | |
3. βοΈ **Rust requires mutable variables explicitly** - Improves code readability, safety. | |
4. β‘ **Rust emphasizes performance, safety** - Borrow checker prevents errors. | |
5. π§© **Zig metaprogramming is simple** - Same syntax, just add "comptime." | |
6. β±οΈ **Zig runs code at compile time** - E.g., generate prime numbers early. | |
7. π **Go as a mainstay** - Reliable for services, web sockets. | |
8. π οΈ **Jai and Zig for exploration** - Testing performance in custom services. | |
9. π **Jai uses raw sockets** - Manual TCP connections, like C. | |
10. π **C++ metaprogramming is messy** - Powerful but hard to debug. |