Articles & Tutorials
149 technical articles covering AI, Machine Learning, Cybersecurity, Data Science, and Programming. Practical guides, tutorials, and deep dives — written for developers.
17 articles
The 15 AI Security Domains
AI security is not one problem — it is 15 interconnected domains. From prompt injection to sandboxing, each requires specific defenses. Thi…
Why Traditional Logs Are Not Enough
AI agents execute tool loops, make multiple model calls, and change state across iterations. Traditional application logs that record "task…
Category 1: Foundational Patterns
Key Takeaway Prompt patterns are reusable templates for common AI tasks. Mastering 15 core patterns covers 90% of real-world use cases. Eac…
The Sequence Modeling Problem
Key Takeaway Transformers process all tokens simultaneously using self-attention — a mechanism that lets every token compute how much it sh…
What Is Prompt Engineering?
Key Takeaway Prompt Engineering is the skill of communicating effectively with AI models. It is not about finding a "magic phrase" — it is …
The 20 Defensive Projects
You don't need to hack anything to build a strong cybersecurity portfolio. Defensive projects — log analyzers, encryption tools, SIEM setup…
Beginner Projects (1-5)
The best data science portfolio isn't 20 notebooks that all do the same thing. It's 20 projects that progressively build skills — from load…
Quantum Machine Learning Explained
Key Takeaway --> Quantum machine learning combines classical data processing with quantum circuit layers. It is NOT faster classical ML --…
Explainable AI for Data Scientists: SHAP, LIME and Feature Importance
Key Takeaway --> Black-box models make accurate predictions but cannot explain why. SHAP provides game-theoretic feature attributions. LIM…
Imbalanced Datasets: Practical Machine Learning Techniques
Key Takeaway --> Imbalanced datasets cause models to ignore the minority class. Fix this with oversampling, undersampling, SMOTE, class we…
Model Evaluation Beyond Accuracy
Key Takeaway --> Accuracy is misleading for imbalanced datasets. A model that predicts "no fraud" for all transactions achieves 99% accura…
Feature Engineering in the Age of AI
Key Takeaway --> Classical feature engineering uses domain expertise to create interpretable features. Embeddings capture semantic meaning…