Core Philosophy

# I don't list technologies I've completed tutorials for
# Everything here has been used to build real systems at scale
# If it's on this page, I've shipped production code with it

Languages (Production Experience)

Expert Level

  • Python (AsyncIO) - Built distributed systems handling 2M req/day
  • JavaScript/TypeScript (Node.js) - High-throughput async I/O architectures
  • Bash - Infrastructure automation, deployment pipelines

Proficient

  • SQL - Database optimization, query performance tuning
  • Rust - Systems programming for performance-critical components
  • C++ - Low-level optimization when Python isn’t fast enough

Markup & Styling

  • HTML5/CSS3/Sass - Modern responsive interfaces
  • Markdown - Technical documentation, GitHub README engineering

Distributed Systems & Infrastructure

Orchestration & Deployment

  • Kubernetes - Container orchestration, auto-scaling workloads
  • Docker - Containerization, microservices architecture
  • Ansible - Infrastructure automation, configuration management

State Management & Databases

  • Redis - High-performance caching, distributed rate limiting, pub/sub
  • PostgreSQL - Relational data modeling, complex queries, transactions
  • MySQL - Traditional relational databases, optimization

Networking & Security

  • Residential Proxy Networks - Orchestrated 1,200+ proxies in production
  • OAuth 2.0/PKCE - Authentication flows, token management
  • TLS/SSL - Certificate management, fingerprint analysis (JA3/JA3S)
  • Load Balancing - NGINX, HAProxy, Round-robin, Weighted algorithms

Web Frameworks & APIs

Backend Development

  • FastAPI - High-performance async REST APIs (preferred)
  • Django - Full-stack web applications, admin interfaces
  • Flask - Lightweight APIs, microservices

API Design

  • REST - RESTful architecture, endpoint design
  • WebSocket - Real-time bidirectional communication
  • GraphQL - Modern API query language (learning)

Data Science & Machine Learning

Data Analysis

  • Pandas - Data manipulation, time-series analysis
  • NumPy - Numerical computing, array operations

Visualization

  • Matplotlib - Publication-quality plots, custom visualizations
  • Seaborn - Statistical data visualization

Machine Learning

  • Scikit-learn - Classification, regression, clustering
  • TensorFlow - Deep learning models, neural networks
  • Keras - Rapid prototyping, model experimentation

NLP & Text Processing

  • Natural Language Processing - Intent scoring, semantic analysis
  • Sentiment Analysis - Used in production for lead qualification

Adversarial Engineering (Specialized)

Anti-Bot Evasion

  • TLS Fingerprint Spoofing - JA3/JA3S signature manipulation
  • Browser Fingerprinting - Canvas, WebGL, Audio fingerprint evasion
  • Behavioral Mimicry - Human pattern simulation, stochastic delay injection
  • Headless Browser Orchestration - Puppeteer, Playwright for automation

Detection Analysis

  • Packet Inspection - Network traffic analysis, protocol understanding
  • Rate Limiting Logic - Layer 4 and Layer 7 detection mechanisms
  • Pattern Recognition - Understanding what platforms look for

DevOps & Monitoring

Version Control

  • Git & GitHub - Advanced workflows, branching strategies, CI/CD

Observability

  • Prometheus - Metrics collection, alerting
  • Grafana - Dashboard creation, visualization
  • Custom Logging - Application-level monitoring, error tracking

Cloud Platforms

  • GCP - Google Cloud Platform (preferred)
  • AWS - Amazon Web Services (familiar)
  • DigitalOcean - Rapid deployment, cost-effective hosting

Systems & Performance

Concurrency & Async

  • AsyncIO (Python) - Asynchronous I/O, coroutines, event loops
  • Node.js Event Loop - Non-blocking I/O patterns
  • Threading & Multiprocessing - Parallel execution, GIL management

Performance Optimization

  • Profiling - cProfile, line_profiler, memory_profiler
  • Caching Strategies - Redis, in-memory caching, cache invalidation
  • Query Optimization - Database indexing, query planning

Architecture Patterns

Design Patterns Used in Production

  • Circuit Breaker - Fault tolerance, graceful degradation
  • Rate Limiting - Token bucket, leaky bucket algorithms
  • Message Queues - Async task processing, job scheduling
  • Microservices - Service decomposition, API gateways
  • Event-Driven Architecture - Pub/sub patterns, event sourcing

Security & Best Practices

Security Practices

  • Linux Hardening - Server security, firewall configuration
  • Secrets Management - Environment variables, vault solutions
  • Input Validation - SQL injection prevention, XSS protection
  • HTTPS/TLS - Certificate management, secure communications

Code Quality

  • Testing - Unit tests, integration tests, test-driven development
  • Linting - Pylint, ESLint, code style enforcement
  • Documentation - Technical writing, API documentation
  • Code Review - Pull request workflows, peer review

What I’m Currently Learning

  • Rust (Deep dive) - Systems programming, memory safety
  • WebAssembly - High-performance web applications
  • GraphQL - Modern API design patterns
  • Quantum-resistant Cryptography - Future-proofing security

What I Don’t Waste Time On

  • ❌ Technologies I haven’t used in production
  • ❌ Frameworks that are “popular” but don’t solve real problems
  • ❌ Buzzword-driven development
  • ❌ Tutorial-only knowledge

Philosophy on Tech Stack

def choose_technology(problem):
    """
    Technology selection framework
    """
    if problem.solved_by_simple_solution():
        return "Use the boring, proven technology"
    
    elif problem.requires_performance():
        return "Use Rust or C++ where milliseconds matter"
    
    elif problem.requires_rapid_iteration():
        return "Use Python/Node.js and optimize later"
    
    else:
        return "Use whatever lets you ship fastest"

# Principle: Technology is a tool, not an identity
# I'm not a "Python developer" or "JavaScript developer"
# I'm a problem solver who happens to use these tools

Production Systems Built

X Lead Scraper V1 (Deprecated)

  • Stack: Python (AsyncIO), Redis, 1,200+ Proxies
  • Scale: 2M requests/day
  • Outcome: $500K ARR, legal C&D, learned expensive lessons

X Lead Scraper V2 (Live)

  • Stack: Python (AsyncIO), TypeScript, Redis, NLP
  • Scale: 10K requests/day (intentionally throttled)
  • Outcome: Compliant, sustainable, 8+ months uptime

Infrastructure Consulting Projects

  • Stack: Varies by client needs
  • Focus: Distributed systems, anti-detection, high-throughput

Certifications & Formal Training

Certifications: None (they don’t matter)
Formal Training: NIT Mizoram (incomplete)
Real Training: Built $500K product, got sued, rebuilt compliant

Philosophy: Results > Credentials

Bottom Line

I learn what’s needed to solve the problem.
I use what’s proven to work at scale.
I ship what generates revenue.

Everything else is just noise.

Last Updated: November 2025
Technologies listed: Actually used in production, not from tutorials