Tools

Tools I Use

The tools, languages, frameworks, and hardware I rely on daily, from ML experimentation to production deployment.

🐍 Languages

  • Python: primary language for ML, data, and automation
  • SQL: PostgreSQL, SQLite, query optimization
  • PHP: Laravel full-stack web applications
  • JavaScript: frontend when needed (Livewire/Alpine)
  • Bash/PowerShell: scripting and automation

🧠 ML & Data Science

  • PyTorch: deep learning, custom architectures
  • XGBoost / LightGBM: structured data, time series
  • Scikit-learn: classical ML, preprocessing, evaluation
  • OpenCV: computer vision, image processing
  • YOLO (Ultralytics): object detection
  • Pandas / Polars: data manipulation
  • NumPy: numerical computing

⚙️ MLOps & Infrastructure

  • MLflow: experiment tracking, model registry
  • Docker and Docker Compose: containerization
  • Grafana: monitoring dashboards, drift detection
  • ONNX: model export and optimization
  • Git: version control (GitHub)
  • GitHub Actions: CI/CD
  • PostgreSQL: primary database

🤖 LLM & RAG Stack

  • LangChain: LLM orchestration
  • FAISS: vector search
  • OpenAI API: GPT-4o, embeddings
  • RAGAS: RAG evaluation
  • Streamlit: quick ML demos and UIs
  • Chroma: lightweight vector DB alternative

🌐 Web Development

  • Laravel: PHP framework, full-stack apps
  • Livewire: reactive components without a JS framework
  • Alpine.js: lightweight interactivity
  • Tailwind CSS: utility-first styling
  • FastAPI: Python APIs, model serving
  • NGINX: reverse proxy, static serving

🕷️ Web Scraping & Automation

  • Playwright: browser automation with stealth
  • Puppeteer: alternative browser automation
  • Anti-bot evasion: fingerprint spoofing, stealth scripts, CAPTCHA handling
  • GitHub Actions: scheduled scraping workflows

💻 Hardware & Workspace

  • Laptop: Windows + WSL2 (Ubuntu)
  • VPS: Linux server for MLflow, Grafana, APIs
  • Editor: VS Code with Vim keybindings
  • GPU: NVIDIA (cloud/colab when needed)
  • Terminal: Windows Terminal + PowerShell
  • Notes: Markdown and Git

📚 Learning & Staying Current

  • Papers With Code: tracking SOTA
  • Hacker News: tech ecosystem awareness
  • arXiv: ML/cs.CV/cs.CL papers
  • Twitter/X: ML engineering community
  • Documentation-first: I read the source when docs fail
  • Build to learn: every concept becomes a mini-project

🎨 Design & Communication

  • Mermaid.js: architecture diagrams as code
  • Excalidraw: quick sketches, system designs
  • Markdown: everything is documented in .md
  • GitHub Issues/Projects: task tracking
  • ScreenToGif: quick demos and bug reports