| 
 
  var expr = MathExpression
      .Parse("x^2 + 2*x + 1");
  var derivative = expr
      .Differentiate("x");
  var result = expr
      .Evaluate(new { x = 5 });
  let expr = MathCore::parse(
      "x^2 + 2*x + 1"
  )?;
  let derivative = MathCore::differentiate(
      &expr, "x"
  )?;
  let result = math.calculate(
      &expr, &[("x", 5.0)]
  )?;
 
 β‘ Performance Results I was shocked by these numbers: | Operation        | C# (MathFlow) | Rust (MathCore) | Difference     ||------------------|---------------|-----------------|----------------|
 | Parse Expression | 245 Β΅s        | 89 Β΅s           | 2.7x faster β¨  |
 | Differentiate    | 1,230 Β΅s      | 456 Β΅s          | 2.7x faster β¨  |
 | Solve Equation   | 890 Β΅s        | 234 Β΅s          | 3.8x faster π |
 | Memory Usage     | 847 MB        | 124 MB          | 6.8x less π―   |
 
 π‘ Key Differences I Discovered 1οΈβ£ Error Handling
   C# Way (Exceptions):
  try {
      var result = MathExpression.Parse("invalid");
  } catch (ParseException e) {
      Console.WriteLine($"Oops: {e.Message}");
  }
  Rust Way (Result Type):
  match MathCore::parse("invalid") {
      Ok(expr) => println!("Success!"),
      Err(e) => println!("Oops: {}", e),
  }
 π Insight: Rust forces you to handle errors. No more surprise crashes in production! 
 2οΈβ£ Memory Management C#: 
β
 Garbage collector handles everythingβ Random GC pausesβ Unpredictable performance Rust: 
β
 Predictable performanceβ
 No GC pausesβ Must think about ownership 
 3οΈβ£ Real-World Example Let's solve a physics problem - projectile motion with air resistance:
 
  // Calculate trajectory
  var equation = @"
      y = v0*sin(ΞΈ)*t - 
          0.5*g*t^2
  ";
  var solver = new MathEngine();
  var trajectory = solver
      .Parse(equation)
      .Substitute(new {
          v0 = 100,
          ΞΈ = Math.PI/4,
          g = 9.81
      });
  // Calculate trajectory
  let equation = "
      y = v0*sin(ΞΈ)*t - 
          0.5*g*t^2
  ";
  let math = MathCore::new();
  let trajectory = math
      .calculate(equation, &[
          ("v0", 100.0),
          ("ΞΈ", PI/4.0),
          ("g", 9.81),
      ])?;
 
 π¨ Architecture Evolution From OOP to Functional
   C# (Object-Oriented):
  public abstract class Expression {
      public abstract double Evaluate();
  }
  public class AddExpr : Expression {
      private Expression left, right;
      public override double Evaluate() {
          return left.Evaluate() + right.Evaluate();
      }
  }
 
  Rust (Algebraic Data Types):
  enum Expr {
      Number(f64),
      Add(Box<Expr>, Box<Expr>),
  }
  fn evaluate(expr: &Expr) -> f64 {
      match expr {
          Expr::Number(n) => *n,
          Expr::Add(l, r) => evaluate(l) + evaluate(r),
      }
  }
 π― Why this matters: Pattern matching eliminated entire class hierarchies! 
 π Cool Features in Both Features Comparison | Feature                  | MathFlow (C#) | MathCore (Rust) ||--------------------------|---------------|-----------------|
 | Symbolic Differentiation | β
             | β
               |
 | Equation Solving         | β
             | β
               |
 | Matrix Operations        | β
             | β
               |
 | Complex Numbers          | β
             | β
               |
 | Arbitrary Precision      | β
             | β
               |
 | Parallel Computation     | β
 Tasks       | β
 Rayon         |
 | WASM Support             | β³ Coming      | β
 Ready         |
 | FFT                      | β
             | β
               |
 
 π¦ Try Them Out! For .NET Developers: dotnet add package MathFlow
   using MathFlow;
  var math = new MathEngine();
  var derivative = math.Differentiate("sin(x^2)", "x");
  Console.WriteLine(derivative); // Output: 2*x*cos(x^2)
 For Rust Developers: cargo add mathcore
   use mathcore::MathCore;
  fn main() {
      let derivative = MathCore::differentiate("sin(x^2)", "x").unwrap();
      println!("{}", derivative); // Output: 2*x*cos(x^2)
  }
 
 π€ Which Should You Use? Choose MathFlow (C#) if you: 
π· Are building .NET applicationsπ· Use Unity for game developmentπ· Want fastest development timeπ· Prefer familiar OOP patterns Choose MathCore (Rust) if you: 
π¦ Need maximum performanceπ¦ Want to compile to WASMπ¦ Build system-level toolsπ¦ Care about memory efficiency 
 π What I Learned 
Rust's ownership model prevents entire categories of bugsPattern matching can replace complex OOP hierarchiesZero-cost abstractions are real - high-level Rust code is FASTBoth languages have their sweet spots 
 π Links & Resources MathFlow (C#) https://github.com/Nonanti/MathFlowhttps://nuget.org/packages/MathFlow
 MathCore (Rust) https://github.com/Nonanti/mathcorehttps://crates.io/crates/mathcore
 https://docs.rs/mathcore
 
 π¬ Let's Discuss! Have you ported a project between languages? What was your experience? Drop a comment below! π 
 If you found this helpful, consider giving the repos a β! Happy coding! π | 
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