About AlgoGrinder Team
Joe Chang
True mastery
Turn unfamiliar problems into familiar patterns
Coding interview problems become much more manageable when you can recognize the structure underneath the story. AlgoGrinder organizes problems around reusable patterns—hashing, two pointers, sliding windows, binary search, backtracking, dynamic programming, trees, graphs, stacks, heaps, and more. Each solution follows the reasoning from structural clues through decomposition, invariants, optimization, complexity, edge cases, and implementation. The goal is to build the pattern recognition and algorithmic judgment that transfers to unfamiliar interview questions, university coursework, programming contests, and harder problems of your own.
Experience behind the lessons
AlgoGrinder Team
Joe Chang
Joe Chang brings more than 20 years of software engineering experience across startups, research, international banking and FinTech, entrepreneurship, and production systems. His career has included building software, leading engineering work, hiring and interviewing engineers, and teaching more than 1,000 students. AlgoGrinder brings that engineering and teaching perspective to the reasoning skills behind coding interviews and algorithmic problem solving.
- Recognize the pattern from the clues. Learn how constraints, sortedness, contiguity, monotonicity, repeated work, graph structure, and state dependencies point toward useful algorithm families.
- Break hard problems into smaller obligations. Decompose a large prompt into conditions, states, subproblems, invariants, and implementation responsibilities you can solve one at a time.
- Test whether you can reproduce the reasoning. Use self-assessment questions to check pattern recognition, complexity, invariants, edge cases, and algorithm derivation after each problem.
- See the algorithm unfold. Use clear diagrams and step-by-step states to follow pointers, windows, recursion trees, dynamic-programming tables, traversals, and structural changes.
- Explain your solution like an interview candidate. Practice communicating the baseline, pattern, tradeoffs, correctness, complexity, and edge cases in a way an interviewer can follow.
- Master time and space complexity. Connect implementation choices to their computational costs and learn how interview constraints guide the search for a stronger solution.
- Make dynamic programming systematic. Derive the state, recurrence, base cases, evaluation order, and memory optimization so DP becomes a repeatable reasoning process.
- Make graph problems manageable. Turn relationships into nodes and edges, then choose traversal, ordering, connectivity, or shortest-path techniques from the structure of the problem.
- Turn reasoning into clean implementation. Translate the invariant and algorithm into readable code where each variable, branch, loop, and data structure has a clear responsibility.
- Think from both sides of the interview table. Understand what a problem is testing, what strong reasoning sounds like, and how an interviewer can evaluate a candidate's approach.