Breaking out of the Web Sandbox
For years, PHP has been my "home" language. It's great for the web, it's reliable, and it pays the bills. But there's a catch: PHP was designed to live within the Request/Response cycle. PHP comes alive when you receive a request, processes it, and then "dies". It's a comfortable C-style language, but it can feel limiting when you want to experiment with systems that live outside the web server environment.
The Search for a "Desktop Home" Language
I wanted to find a language that could become my "desktop" tool. A language where I could control every byte and build complex systems without the constraints of a web server. Then I found Rust.
It promised to resolve one of the most painful experiences in my PHP career: memory management. Most web devs know exactly what I'm talking about. Imagine you're maintaining a legacy OOP monolith. There's a recursive function multiple layers deep, and somewhere in the middle, it's making database calls or triggering a 3rd-party library.
Suddenly, the process hits the memory_limit and dies silently. Finding the leak in a sea of implicit references and 'magic' objects is a nightmare. In PHP, you often don't care about memory until it’s already gone.
Instead of just memorizing syntax, I decided to learn Rust by implementing something that has always appealed to me: Neural Networks. Rust promised speed and memory efficiency, making it the perfect test for a new language involving complex math and matrix operations.
Step 1: The Adaline Experiment
My first milestone was implementing Adaline (Adaptive Filters from Widrow). It's a simple single-layer network, but it was my first real "clash" with Rust's ownership model.
I started with raw Vec<f64> to keep things simple. No math libraries, no shortcuts. Just me and the borrow checker.
What I learned in this phase:
- Ownership is no joke: Passing training data around forced me to think about references and lifetimes in a way PHP never did.
- Explicit over Implicit: In PHP, you often don't care about memory. In Rust, you have to keep in mind exactly where your data is at every single step.
This isn't just about AI; it's about the journey of changing your engineering mindset.
What's next?
Next, I will be implementing a Multi-Layer Perceptron (MLP) for the MNIST dataset.
- The code is here: Nutscracker87/adaline
- Original LinkedIn discussion: View Post
Feel free to connect with me on LinkedIn to follow my progress!
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