How I Learn
I don't optimize for completing tutorials. I optimize for understanding systems deeply enough to build them myself.
- Learn fundamentals
Build a solid mental model of how the system works at its core, not just how to use it.
- Build prototypes
Implement something real with what I have learned. The gap between theory and practice always reveals itself here.
- Find gaps
The prototype exposes what I don't actually understand. A bug, a performance issue, a design choice I could not justify.
- Study deeper
Go back to the fundamentals with a specific question in mind. The second pass is always faster and more focused.
- Build better systems
Rebuild or extend the prototype with the deeper understanding. This is where the learning solidifies into something durable.
Currently Studying
Data Structures and Algorithms
Foundation builtI have built a solid foundation in the core data structures: arrays, stacks, queues, trees, and graphs. I can implement each one from memory and reason about their tradeoffs.
- Arrays, Stacks, Queues
- Trees: binary trees, BSTs, tree traversals
- Graphs: representations, BFS, DFS
Algorithms
StudyingI am moving from knowing data structures to understanding algorithmic thinking and optimization. This is not about memorizing solutions. It is about building intuition for why certain approaches work and others do not.
- Sorting and searching algorithms
- Dynamic programming patterns
- Graph algorithms: shortest paths, topological sort
- Complexity analysis and optimization
AI and Machine Learning
StudyingI am studying beyond using APIs. I want to understand the models themselves: how they work, why they fail, and how to build systems around them that are reliable and predictable.
- Large Language Models: architecture, training, inference
- AI Engineering: building production systems around LLMs
- Agent Systems: tool use, reasoning, multi-step planning
- Machine Learning Fundamentals: beyond using APIs
- Evaluation: measuring quality beyond accuracy
- Retrieval Augmented Generation: grounding models in real data
- Prompt Engineering: systematic, not guesswork
Cloud and Full Stack
StudyingFull-stack is where my learning becomes immediately testable. Every concept goes through the cycle: learn the theory, build a prototype, find the gaps, study deeper, ship something real.
- Backend Architecture: APIs, services, state management
- Databases: relational, NoSQL, query optimization
- Authentication: sessions, tokens, OAuth patterns
- APIs: REST design, WebSockets, real-time communication
- Deployment: CI/CD, containerization, orchestration
- Cloud Infrastructure: compute, storage, networking
I don’t collect certificates. I collect understanding. Every project I build is an answer to a question I was genuinely curious about. Every learning area I study is motivated by a system I want to build but cannot yet.