This space collects engineering notes and playbooks from real projects. You can expect concise, production‑minded content with runnable ideas and trade‑offs explained.
What you'll find
Development Tutorials & Best Practices
In-depth tutorials covering modern software development practices, architecture patterns, and implementation strategies. From microservices to monoliths, from REST APIs to GraphQL, I share practical insights from real-world projects.
Topics covered:
- System architecture and design patterns
- Performance optimization techniques
- Database design and query optimization
- API design and development
- Testing strategies and quality assurance
- DevOps and deployment pipelines
- Code quality and maintainability
Artificial Intelligence & Machine Learning
Practical guides and case studies on building AI-powered applications. Learn from my experience deploying ML models in production, handling edge cases, and optimizing inference performance.
Topics covered:
- Model development and training workflows
- Feature engineering and data preprocessing
- Model evaluation and validation
- Production deployment strategies
- MLOps and model versioning
- Performance monitoring and drift detection
- Cost optimization for AI workloads
Deep Learning & Neural Networks
Deep dives into neural network architectures, optimization techniques, and advanced topics in deep learning. From CNNs to Transformers, from PyTorch to TensorFlow.
Topics covered:
- Neural network architectures and design
- Training optimization and hyperparameter tuning
- Transfer learning and fine-tuning
- Computer vision applications
- Natural language processing
- Generative models and GANs
- Model compression and quantization
Real-World Experiences & Lessons Learned
Honest retrospectives on projects, what worked, what didn't, and why. Learn from mistakes and celebrate wins. These are living documents that evolve as I gain more experience.
What you'll discover:
- Project post-mortems and retrospectives
- Technology stack decisions and trade-offs
- Scaling challenges and solutions
- Team collaboration and workflow improvements
- Performance bottlenecks and their resolutions
- Security considerations and implementations
- Cost analysis and budget management
How to navigate
Use the left doc navigation to jump between sections. Posts are living documents and will evolve as I refine approaches and learn from new deployments.
Finding content
By topic: Browse through the tags on each post to find content matching your interests.
By complexity:
- Beginner-friendly: Step-by-step tutorials with detailed explanations
- Intermediate: Real-world scenarios with code examples
- Advanced: Deep technical dives and architectural discussions
By technology: Posts are tagged with the specific technologies, frameworks, and tools used.
Content structure
Each post follows a consistent structure to make information easy to digest:
- Overview: What problem are we solving?
- Context: Why does this matter?
- Approach: How did I tackle this?
- Implementation: Code examples and technical details
- Results: What were the outcomes?
- Lessons learned: Key takeaways and recommendations
Start here
If you're new, browse recent posts, then deep‑dive into the areas that match your current challenges.
Recommended reading paths
For developers: Start with the development tutorials, then explore real-world experiences to see how concepts apply in practice.
For ML engineers: Begin with machine learning fundamentals, move to deep learning architectures, and learn from production deployment experiences.
For architects: Dive into system architecture posts, study the trade-offs in technology decisions, and review scaling case studies.
Contributing and feedback
I'm always open to suggestions, corrections, and discussions. If you find an error, have a better approach, or want to share your experience, feel free to reach out through the contact page.
Stay updated
New posts are added regularly as I complete projects and learn new things. Check back often or follow along to stay updated with the latest content.
Last updated: This blog is continuously evolving with new content and improvements.