Curated Learning Journeys
Structured, multi-part series designed to guide you step-by-step from foundational concepts to advanced production-ready engineering architectures.
Bridges to Functions: Imperative vs Kotlin FP
"Bridges to Functions" is a hands-on series for Kotlin developers who want to move from imperative patterns to functional thinking. Each post compares an imperative solution with a functional alternative, explains the ‘why’ through everyday analogies (recipes, conveyor belts, and blueprints), and introduces just-enough category theory (functors, monads, natural transformations) to make the abstractions meaningful. Expect concise Kotlin examples, visual pipelines, exercises, and practical patterns you can apply in production code.
✨ Math Behind AI: A Linear Algebra Journey
A step-by-step exploration of the essential linear algebra concepts that power modern AI and machine learning. From vectors and matrices to eigenvalues, SVD, and optimization, this series is both my learning path and a shared guide for anyone who wants to understand the math that makes AI work.
Patterns in Code: DSA Mastery Journey
A step-by-step journey through data structures and algorithms, exploring one pattern at a time — from simple problems to complex challenges. Learn, code, and master the art of problem-solving in a structured way.
From Trust to Threats: Security Engineering Journey
A practical, mindset-first journey into cybersecurity and security algorithms. This series guides developers from understanding trust, threats, and attack surfaces to mastering cryptography, authentication, and secure system design — learning not just how security works, but why systems fail when it is ignored.
CLI Mastery: Building the Foundation of Modern Engineering
Behind every cloud platform, deployment pipeline, AI system, and production server lies a command line. This series explores the concepts, tools, and automation techniques that transform simple terminal commands into powerful engineering workflows.
AI Application Engineering Journey: From Prompts to Production Systems
This series documents the journey of building real-world AI applications using local LLMs, prompt engineering, structured outputs, memory, RAG, tool calling, workflows, and agents. Instead of focusing on training foundation models, we explore how modern AI products are engineered, integrated, and deployed to solve real-world problems. From basic LLM interaction to production-ready AI systems, each article builds a practical understanding of the AI application layer.