A clearer path to your next interview
Maîtrisez les Patterns d'Entretiens de Programmation.
Préparez vos entretiens de programmation avec des problèmes soigneusement sélectionnés, organisés par patterns, entreprises et difficulté.
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- Problèmes Sélectionnés
- 321+
- Patterns d'Entretien
- 21
- Entreprises Incluses
- 21+
- Gratuit pour Toujours
- 100%
Comment Ça Marche
Un système qui s'imprime vraiment.
01
Choisir un pattern
Commencez par Two Pointers ou les tableaux — les patterns les plus simples. Apprenez le template, quand l'utiliser et les pièges courants.
02
Résoudre des problèmes sélectionnés
Chaque pattern a une liste curatée — triée par difficulté. Filtrez par entreprise, marquez les problèmes comme terminés.
03
Suivez votre progression
Votre état d'avancement est sauvegardé localement. Reprenez chaque session exactement là où vous l'avez laissée.
Patterns d'Entretien
Apprenez par pattern, pas par problème.
Sliding Window
Two Pointers
Binary Search
Prefix Sum
Hash Map / Set
Stack
Queue / BFS
Heap / Priority Queue
Linked List
Trees / DFS
Graphs
Dynamic Programming
Par Entreprise
Pratiquez pour votre entreprise cible.
Complete preparation
Everything you need for your interview
From algorithms to system design, SQL to behavioral — every topic is free and in one place.
Référence Rapide
Fiches mémo pour le jour de l'entretien.
Pattern Cards
12 recognition cards — when to use each pattern and what the complexity tradeoffs are.
Complexity Table
O(1) through O(n!) at a glance with canonical examples for each complexity class.
Decision Tree
Keyword to pattern mapping. See a problem constraint and know which algorithm to reach for.
Code Templates
Every pattern page includes a copyable code template so you write faster on day one.
Developer Tools
Free Online Tools
JSON Formatter & Beautifier
Beautify, minify, validate, and convert JSON. Supports diff, tree view, TypeScript interfaces, CSV, YAML, JSON Schema, and JSONPath — all in your browser.
Invisible Text Remover
Detect and remove hidden Unicode characters like zero-width spaces, em dashes, and smart quotes from AI-generated or copied text — all in your browser.
Why it works
Why pattern-based coding interview prep outperforms grinding random problems
By Syed Peera Saheb · Updated
The engineers who ace FAANG and MAANG interviews don't memorize 500 solutions — they recognize which algorithm pattern applies within the first 60 seconds of reading a problem. That recognition is a learnable skill, and this guide is built around it.
What pattern-based prep builds
When you study the Sliding Window pattern — its template, trigger conditions, and complexity tradeoffs — you can solve any sliding window problem you've never seen before. Patterns scale to novel problems. Memorized solutions don't.
How this guide is structured
Every pattern page gives you: a core template in Python, the recognition keywords to spot in problem statements, a complexity table, and a curated problem list sorted Easy to Hard. Learn the pattern once, reinforce it across 5–15 targeted problems.
Company-specific targeting
Google favors graph traversal and dynamic programming. Amazon leans on Trees and BFS/DFS. Meta asks a lot of Two Pointers and Sliding Window. Filter problems by company to practice exactly what your target asks.
75–150 problems beats 500+
Quality beats quantity. Solving 75–150 well-chosen problems across all core patterns is more effective than grinding 500+ random ones. This guide is curated so every problem teaches a transferable technique, not just a one-off trick.
"The engineers who consistently pass technical interviews share one trait: they see through the surface of a problem to the underlying pattern. That meta-skill — pattern recognition — is what separates a 6-week focused prep from a 6-month grind."
— Consistent finding across FAANG interview prep research, including studies by interviewing.io and Glassdoor survey data
The 12 most important coding interview patterns to master in 2026
1. Sliding Window — contiguous subarray/substring problems with a size or sum constraint. Reduces O(n²) brute force to O(n).
2. Two Pointers — paired traversal from both ends or at variable speeds. Essential for sorted array and linked list problems.
3. Binary Search — not just sorted arrays; applies whenever you can define a monotonic decision function (search on answer).
4. Prefix Sum — precompute cumulative sums to answer range queries in O(1). Extremely common in array and subarray problems.
5. Hash Map / Set — trade space for O(1) lookup. The most frequently used pattern across all difficulty levels.
6. Stack (Monotonic) — maintain a monotonically increasing or decreasing stack for next-greater-element class problems.
7. BFS / Queue — level-order traversal, shortest path in unweighted graphs, minimum steps problems.
8. Heap / Priority Queue — efficient top-K, k-th largest, and merge k-sorted-lists problems. Python's heapq is the tool.
9. Linked List (Fast/Slow) — cycle detection, finding the middle, k-th from end. Elegant O(1) space solutions.
10. Trees / DFS — recursive depth-first search covers the majority of binary tree problems. Master pre/in/post-order traversal.
11. Graphs — BFS/DFS on adjacency lists, Union-Find for connectivity, Dijkstra for weighted shortest path.
12. Dynamic Programming — overlapping subproblems with optimal substructure. Covers 1D, 2D, knapsack, and interval DP variants.
| Pattern-Based Prep | Random Grinding | |
|---|---|---|
| Problems needed | 75–150 curated | 300–500+ random |
| Time to interview-ready | 6–10 weeks | 4–6 months |
| Handles novel problems | Yes — pattern recognition transfers | Rarely — memorization doesn't scale |
| Company targeting | Precise — filter by company | None |
| Retention after 2 weeks | High — mental models stick | Low — solutions fade fast |
Written and maintained by Syed Peera Saheb — Software Engineer II at ServiceNow, previously PayPal India and NCR Atleos. B.Tech in Computer Science, NIT Puducherry. Problem sets and pattern guides are reviewed for accuracy and updated regularly. An independent educational resource — not affiliated with LeetCode, Google, Amazon, Meta, or any other company mentioned. See our Privacy Policy and Terms of Use.
Key Takeaways
- Pattern-based prep requires 75–150 problems vs 300–500 for random grinding
- Most engineers are interview-ready in 6–10 weeks with consistent daily practice
- The 7 highest-yield patterns cover the majority of FAANG/MAANG interview problems
- Company-specific filtering lets you target exactly what Google, Meta, or Amazon asks
- All patterns, problems, and guides are free — no account or payment required
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