DSA
Part 13 — Sorting
A practical, interview-focused guide to sorting in Python: how sorting simplifies problems, how to exploit order, how to merge and partition data, and how to reason about sorting algorithms at Staff level.
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DSA
Part 14 — Binary Search Trees
A practical, interview-focused guide to Binary Search Trees in Python: BST invariants, search, insertion, deletion, successor and predecessor, range queries, lowest common ancestors, reconstruction, and Staff-level reasoning.
DSA
Part 15 — Recursion
A practical, interview-focused guide to recursion in Python: recursive decomposition, base cases, call-stack reasoning, backtracking, divide-and-conquer, memoization, invariants, complexity, and Staff-level interview patterns.
DSA
Part 9 — Binary Trees
A practical, interview-focused guide to binary trees in Python, covering tree structure, traversals, recursion, balance, symmetry, lowest common ancestors, path problems, reconstruction, and reusable algorithmic patterns.
Building LLM System
How LLMs Actually Work
Agentic AIPRO
Production-Grade Agentic AI Architecture
A premium preview demonstrating subscriber-only content.
LLMs
Reasoning Models: From Training to Inference
A practical deep dive into how reasoning models are trained and served.
DSA
Part 12 — Hash Tables
A practical, interview-focused guide to hash tables in Python, covering hashing, collisions, frequency counting, anagrams, caches, range and subarray problems, streaming patterns, memoization, and Staff-level trade-offs.
DSA
Part 11 — Searching
A practical, interview-focused guide to searching in Python, covering binary search, boundary conditions, cyclically sorted arrays, square roots, 2D search, min/max, QuickSelect, missing values, and reusable search invariants.
DSA
Part 10 — Heaps
A practical Python guide to heaps and priority queues for coding interviews, covering heap fundamentals, top-k problems, merging sorted streams, almost-sorted data, running medians, and heap-based algorithmic patterns.
DSA
Part 8 — Stacks & Queues
The LIFO and FIFO Patterns Every Python Coding Interviewer Expects You to Know.
DSA
Part 7 — Linked Lists
A Staff-Level Coding Interview Guide to Pointer Rewiring, Fast/Slow Pointers, Cycles, Overlap, Partitioning, and Linked-List Algorithms in Python.
DSA
Part 6 — Strings
A Staff-Level Guide to String Manipulation, Parsing, Backtracking, Run Scanning, and Rolling Hashes in Python.
DSA
Part 5 — Arrays
The Core Patterns Behind Staff-Level Coding Interviews. A Staff-level array problem is rarely about the array itself.
DSA
Part 4 — Primitive Types
The Hidden Power of Bits, Mathematics, and Representation. Staff-level coding interviews aren't about memorizing bit tricks.
DSA
Part 3 — Evidence
How Interviewers Actually Evaluate Staff Engineers. Stop thinking like a candidate, start thinking like the interviewer.
DSA
Part 2 — Strategy
How to Solve and Communicate in a Staff-Level Coding Interview. The interviewer evaluates how you think.
DSA
Part 1 — Getting Ready
Why senior engineering interviews are not really about solving LeetCode problems. A Staff-level preparation guide.
DSA
Part 20 — Design Problems
A Staff/Principal-level guide to approaching design-oriented interview problems through requirements, constraints, architecture, trade-offs, scale, reliability, and technical judgment.
DSA
Part 19 — Parallel Computing
A Staff-level coding interview guide to parallelism, concurrency, synchronization, parallel reduction, scalability, and Python reasoning.
DSA
Part 18 — Graphs
A Staff-level coding interview guide to graph modeling, traversal, BFS, DFS, connectivity, shortest paths, complexity, and practical Python problem-solving.
DSA
Part 17 — Greedy Algorithms
A practical, Staff-level interview guide to Greedy Algorithms in Python: local choices, global optimality, exchange arguments, invariants, counterexamples, complexity, and when greedy should—and should not—be used.
DSA
Part 16 — Dynamic Programming
A practical, interview-focused guide to Dynamic Programming in Python: state design, recurrence relations, memoization, tabulation, optimization, invariants, complexity, and Staff-level interview reasoning.