Learn
Reading modules built from my own notes — Python & DSA, Git, cognitive-skills frameworks, and a full engineering-fundamentals bookshelf covering OOP, design patterns, systems, and interview prep. Each track is broken into chapters you can move through one at a time.
Python DSA — Course
15 chA full data structures & algorithms course in Python, built for competitive programming: complexity analysis through advanced structures.
Python DSA — Interview Prep
15 chThe same core patterns, reframed for the interview room — how technical interviews actually work, traced examples, and a patterns cheat sheet.
Python DSA — Beginner
14 chDSA from first principles for developers newer to the subject — starts at why it matters and Python's built-in collections.
Python Syntax & DSA Reference
16 chA dense reference on Python syntax, idioms, and standard-library tools most relevant to DSA work.
Git Mastery
19 chFrom the mental model of how Git actually works through branching, rebasing, hooks, and team workflows.
Cognitive Skills & Mental Tools
5 chFrameworks for communication, critical thinking, problem-solving, and digital literacy — the meta-skills underneath the technical ones.
Code Optimization Rules
8 chWhat actually makes software efficient, scalable, and cost-effective — from CPU/memory/I/O cycles through cloud infrastructure and database query optimization.
Objects First: OOP Fundamentals
18 chObject-oriented programming from why it exists through SOLID, composition vs. inheritance, anti-patterns, and applied design exercises. TypeScript and Python.
Patterns of Good Design
18 chThe classic design patterns — what problem each one solves, when it's the right tool, and when it's not. TypeScript and Python.
Under the Hood: Operating Systems
15 chOS theory connected to what actually shows up in production and in interviews — processes, memory, scheduling, I/O. Linux-leaning, with C examples.
How the Bytes Travel: Networking
15 chNetworking the way it actually shows up in day-to-day backend and infrastructure work. curl, dig, openssl, and Python socket-level code.
Data at Rest, Data in Motion: Databases
16 chSchema design, query optimization, and data modeling for relational and NoSQL systems at scale. PostgreSQL, Redis, and MongoDB.
Sharing Time: Concurrency & Multithreading
15 chDebugging production race conditions and deadlocks, and reasoning correctly about shared state. Python examples.
Clean by Default: Coding Standards
15 chThe rules — and the reasons behind them — that separate code a team can maintain from code only its author can. TypeScript and Python.
The Review: Code Review
15 chWhat separates code review practices that build trust from ones that create friction, from author-side habits through escalation.
From Idea to Production: the SDLC
15 chShipping software under Waterfall, Scrum, Kanban, and modern DevOps/continuous-delivery models, and choosing the right process for your context.
Designing at Scale: System Design
17 chDesigning and scaling distributed systems in production, plus a repeatable framework for any system design interview.
Whiteboard to Blueprint: HLD & LLD
15 chTranslating fuzzy requirements into buildable designs — architecture reviews, design documents, and the path from a stated need to implementable detail.
Built to Last: Reliability & Scaling
15 chOn-call for large-scale production systems, postmortems, capacity planning, and reliability programs — with two realistic incident case studies.
Tell Me About a Time: Behavioral Interviews
17 chBehavioral interviews for candidates preparing for a loop and hiring managers designing one — both sides of the table, framework-agnostic.