Software Engineering

API design fundamentals for contracts, compatibility, and reliability, so integrations stay predictable as systems evolve.

Understand why naming matters in software: how clear names improve readability, reduce bugs, and make code easier to understand, maintain, and modify over time.

Understand why software maintainability matters: how code quality, technical debt, refactoring, and design patterns create systems that are easier to modify, debug, and extend over time.

Software caching explained: why caches speed up systems, where they fail, and how to think about freshness, consistency, and cache misses.

CI/CD and release engineering fundamentals: automation, feedback loops, deployment strategies, and why continuous integration prevents integration hell.

Software availability explained: uptime metrics, redundancy patterns, health checks, and graceful degradation for keeping systems accessible.

Algorithmic patterns reference: two pointers, sliding window, dynamic programming, and 30+ more. Learn to spot patterns and solve problems faster.

Algorithmic patterns explained: why patterns work, how patterns combine, and how to practice recognition without memorizing solutions.

CPU vs GPU vs TPU: learn how processing architectures differ, when to use each, and how latency, throughput, and data movement shape performance.

Data structures guide: arrays, hash maps, sets, stacks, queues, trees, graphs. Types, properties, when to use in Python, JavaScript, Java, C++, Go, Rust.

Master data structure fundamentals: arrays, hash maps, trees, and graphs. Learn how structure choices shape algorithm performance, reliability, and developer sanity.

Master algorithm fundamentals: data structures, Big O notation, and runtime complexity. Learn how algorithmic thinking helps build fast, reliable software and prevent production incidents.