the five-year mile
Five years of engineering experience, distilled.
Master the HLD, LLD, and CS fundamentals that make or break interviews. Learn through first-principles reasoning and complete, production-ready code.
The Experience
Understand the why. Don’t just memorize the what.
Dive into full, file-by-file implementations using our integrated IDE viewer. Because the best way to learn is by reading real code.
// SYSTEM DESIGN// Q: Design an API rate limiter —// N requests per window, per client,// O(1) to check and thread-safe. From year one to year five
Every article moves you one marker further down the road.
- 1
Year 1
Ships features that work
- 2
Year 2
Reads big codebases fast
- 3
Year 3
Owns a service end to end
- 4
Year 4
Designs for scale & failure
Year 5
Reasons from first principles
Courses
Prefer a guided week? Take a course
All-in-one interview prep — the right topics in the right order, with tested code and hand-drawn diagrams. Day 1 is free.
The 15-Day AI/ML Interview Bootcamp
Machine learning from first principles to the interview room — one lesson and one assignment a day.
A 15-day, hands-on AI and machine learning interview course — linear and logistic regression, decision trees, clustering, neural networks, CNNs, transformers, LLMs and RAG, and ML system design — every algorithm built from scratch in tested code, with a graded assignment every day.
The 7-Day Interview Prep Cheatsheet
One focused week from rusty to interview-ready.
A 7-day, all-in-one Java backend interview crash course — data structures, algorithms, core Java, streams, multithreading, Spring Boot, JPA & Hibernate, clean-code principles, design patterns, and machine-coding system design (LRU, rate limiter, tic-tac-toe, snake & ladder).
Latest
Fresh from the notebook
Arrays and Dynamic Arrays: Random Access and Amortized O(1) Append
A diagram-first guide to the array and dynamic array data structure: O(1) random access, why append is amortized O(1) via doubling, time complexity, and interview questions.
Balanced Search Trees: How AVL and Red-Black Trees Stay Fast
Balanced search trees explained: AVL and red-black, the self-balancing binary search tree data structure that guarantees O(log n). Rotations, time complexity, and interview questions.
Big-O and Time Complexity: Reading an Algorithm’s Cost Straight Off the Code
A diagram-first guide to Big-O notation and time complexity analysis: the growth classes from O(1) to O(n!), reading complexity off code, space complexity, amortized analysis, and interview prep.
Tracks
Start with the two that interviews lead with