About Me

01. About Me

I'm a Software Engineer with over 18 years of experience working on complex and large systems in Gaming, Backend Services, Networking, and Automotive. I take pride in the code I write and products I develop, always striving for quality and time-efficiency.

As a true C++ expert, I'm passionate about low-level programming and enjoy working with performance-critical code that considers CPU specifics like branch prediction, cache optimization, and memory efficiency to achieve ultra-low latency. I love template meta-programming and stay current with the latest standards like C++20 and C++23.

At the same time, I'm a clean-code enthusiast who writes maintainable and testable code, following Test-Driven Development practices whenever possible.

Recently, I've been focusing on developing an online learning platform where I serve as CTO while building most of the backend. This project has deepened my expertise in GoLang, NoSQL databases like MongoDB, GraphQL, AWS, and distributed systems.

My newest passion is cryptocurrency and blockchain technology. I've been exploring Ethereum, developing smart contracts in Solidity, and examining various open-source implementations in GoLang and Rust.

Leadership Experience

I have around 5 years of experience leading development teams, which includes:

Building a team of 8 developers through careful recruitment and interviewing
Documenting architectural decisions and establishing development workflows
Mentoring developers individually and conducting company-wide workshops
Holding 1-on-1 meetings for performance evaluation and career development

I'm constantly learning new technologies and tools, and I can adapt quickly to unfamiliar development environments while still making valuable contributions. My approach is to be pragmatic and never hesitate to ask questions, because seemingly complex problems often have simple solutions.

C++ Go Rust Blockchain MongoDB AWS GraphQL Solidity TDD Team Leadership

Technical Expertise

C++ Expertise

As a C++ expert, I focus on writing high-performance code that takes advantage of modern C++ features. I'm proficient with the latest standards (C++17, C++20) and advanced techniques like template meta-programming, SIMD optimization, and efficient memory management.

Modern C++20 with Parallel Algorithms

#include <iostream>
#include <vector>
#include <algorithm>
#include <execution>

// Example of modern C++20 with parallel algorithms
template<typename T>
std::vector<T> parallel_transform(const std::vector<T>& input, auto&& transform_fn) {
    std::vector<T> result(input.size());
    
    std::transform(
        std::execution::par_unseq,  // Parallel execution policy
        input.begin(), input.end(),
        result.begin(),
        transform_fn
    );
    
    return result;
}

int main() {
    std::vector<int> data(1000000);
    std::iota(data.begin(), data.end(), 0);  // Fill with 0, 1, 2, ...
    
    // Compute squares in parallel
    auto squares = parallel_transform(data, [](int x) { 
        return x * x; 
    });
    
    std::cout << "Computed " << squares.size() << " squares in parallel" << std::endl;
    
    return 0;
}

Go Development

My experience with Go includes building microservices, APIs, and distributed systems. I leverage Go's concurrency model with goroutines and channels to create efficient, scalable backend services that can handle high loads.

RESTful API Server in Go with Graceful Shutdown

package main

import (
	"context"
	"log"
	"net/http"
	"os"
	"os/signal"
	"time"

	"github.com/gorilla/mux"
)

func main() {
	// Create a new router
	r := mux.NewRouter()
	
	// Define routes
	r.HandleFunc("/api/data", getDataHandler).Methods("GET")
	r.HandleFunc("/api/data", createDataHandler).Methods("POST")
	
	// Configure server with timeouts
	srv := &http.Server{
		Addr:         ":8080",
		WriteTimeout: time.Second * 15,
		ReadTimeout:  time.Second * 15,
		IdleTimeout:  time.Second * 60,
		Handler:      r,
	}
	
	// Start server in a goroutine
	go func() {
		log.Println("Starting server on :8080")
		if err := srv.ListenAndServe(); err != nil {
			log.Println(err)
		}
	}()
	
	// Graceful shutdown
	c := make(chan os.Signal, 1)
	signal.Notify(c, os.Interrupt)
	
	<-c
	
	ctx, cancel := context.WithTimeout(context.Background(), time.Second*15)
	defer cancel()
	
	srv.Shutdown(ctx)
	log.Println("Server gracefully stopped")
}

func getDataHandler(w http.ResponseWriter, r *http.Request) {
	// Handler implementation
}

func createDataHandler(w http.ResponseWriter, r *http.Request) {
	// Handler implementation
}

Rust Programming

I've embraced Rust for projects requiring memory safety without sacrificing performance. With Rust's ownership model and zero-cost abstractions, I build robust systems that are both fast and reliable.

Thread-Safe Counter in Rust

use std::sync::{Arc, Mutex};
use std::thread;

// Thread-safe counter using Arc and Mutex
struct ThreadSafeCounter {
    count: Mutex<u64>,
}

impl ThreadSafeCounter {
    fn new() -> Self {
        ThreadSafeCounter {
            count: Mutex::new(0),
        }
    }
    
    fn increment(&self) {
        let mut count = self.count.lock().unwrap();
        *count += 1;
    }
    
    fn get_count(&self) -> u64 {
        let count = self.count.lock().unwrap();
        *count
    }
}

fn main() {
    // Create a thread-safe counter
    let counter = Arc::new(ThreadSafeCounter::new());
    let mut handles = vec![];
    
    // Spawn 10 threads, each incrementing the counter 1000 times
    for _ in 0..10 {
        let counter_clone = Arc::clone(&counter);
        let handle = thread::spawn(move || {
            for _ in 0..1000 {
                counter_clone.increment();
            }
        });
        handles.push(handle);
    }
    
    // Wait for all threads to complete
    for handle in handles {
        handle.join().unwrap();
    }
    
    println!("Final count: {}", counter.get_count());
}

My Approach

My development philosophy centers around these core principles:

Performance Optimization: I analyze algorithms and data structures to ensure optimal performance, especially for critical paths.
Clean Code: I write maintainable, readable code with comprehensive documentation and follow consistent coding standards.
Test-Driven Development: I create robust test suites to ensure code quality and prevent regressions.
Continuous Learning: I stay updated with the latest technologies and best practices in my fields of expertise.
Pragmatic Problem-Solving: I prefer simple, effective solutions over unnecessary complexity.