NeetCode Practice Framework

Solve. Forget. Repeat. Let's fix that.

"Algorithm mastery is not about memorizing 300 solutions — it's about internalizing 15 fundamental patterns."

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See the Big Picture

Our AI-generated mind maps reveal how patterns connect — relationships that take years to internalize on your own.

English · 繁體中文 · All Mind Maps →

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What Makes This Different

1. Pattern Learning with Two Paths

Each pattern comes with two complementary guides:

• 💡 Intuition — Understand the why through stories and visual metaphors
• 🛠️ Templates — Production-ready code for interviews and quick reference

Example from Sliding Window:

"The window is a moving lens of attention — it forgets the past to focus on what matters now."

Every sliding window algorithm is a dance between two forces: The Explorer (right pointer) discovers new territory, while The Gatekeeper (left pointer) enforces validity.

This isn't just another solution collection. It's a system for building transferable intuition.

2. Production-Grade Testing

Your solution passes LeetCode. But is it correct? Is it optimal?

Compare multiple approaches:

python runner/test_runner.py 0215_kth_largest --all --benchmark

╔════════════════════════════════════════════════════╗
║ 0215_kth_largest_element_in_an_array - Performance ║
╠════════════════════════════════════════════════════╣
║ default:     ████████████████████  114ms           ║
║ quickselect: ████████████████░░░░   96ms           ║
║ heap:        ██████████████████░░  107ms           ║
╚════════════════════════════════════════════════════╝

Method         Avg Time   Pass Rate  Complexity
-----------  ----------  ----------  --------------------
default        113.51ms         3/3  O(n) average time, O(1) space
quickselect     96.06ms         3/3  O(n) average time, O(1) space
heap           107.34ms         3/3  O(n log k) time, O(k) space

Why algorithm choice matters — real data from 100+ problems:

Benchmark Results (n = 5,000)
┌───────────────────────┬────────────────────────┬────────────────────────┬───────────┬─────────────────────┬──────────────────────┐
│ Problem               │ Fast                   │ Slow                   │ Speedup   │ Time Complexity     │ Space Complexity     │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0010 Regex Matching   │ Top-down Memo 0.08ms   │ Bottom-up DP 5.3s      │ 62,000×   │ O(mn) vs O(mn)      │ O(mn) vs O(mn)       │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0044 Wildcard Match   │ Greedy Backtrack 1.4ms │ 2D DP Table 10.0s      │ 7,000×    │ O(mn) vs O(mn)      │ O(1) vs O(mn)        │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0011 Container Water  │ Two Pointers 0.75ms    │ Nested Loops 4.9s      │ 7,000×    │ O(n) vs O(n²)       │ O(1) vs O(1)         │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0121 Buy Sell Stock   │ Running Min 2.0ms      │ Nested Loops 3.1s      │ 2,000×    │ O(n) vs O(n²)       │ O(1) vs O(1)         │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0416 Partition Sum    │ 2D DP Table 0.08ms     │ 1D DP Space-Opt 96.6ms │ 1,000×    │ O(n×target) vs same │ O(n×target) vs O(t)  │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0016 3Sum Closest     │ Two Ptr+Prune 1.1ms    │ Two Ptr Basic 1.4s     │ 1,000×    │ O(n²) vs O(n²)      │ O(1) vs O(1)         │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0435 Non-Overlap Intv │ Greedy Sort 5.0ms      │ DP Array 3.1s          │ 617×      │ O(n log n) vs O(n²) │ O(1) vs O(n)         │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0001 Two Sum          │ Hash Map 0.66ms        │ Nested Loops 70.1ms    │ 106×      │ O(n) vs O(n²)       │ O(n) vs O(1)         │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0494 Target Sum       │ DP Transform 0.04ms    │ Memoization 3.2ms      │ 73×       │ O(n×sum) vs same    │ O(sum) vs O(n×sum)   │
├───────────────────────┼────────────────────────┼────────────────────────┼───────────┼─────────────────────┼──────────────────────┤
│ 0875 Koko Bananas     │ Binary Search 14.6ms   │ Linear Search 1.1s     │ 72×       │ O(n log m) vs O(nm) │ O(1) vs O(1)         │
└───────────────────────┴────────────────────────┴────────────────────────┴───────────┴─────────────────────┴──────────────────────┘

📊 Full benchmark: 100+ problems, 300+ solutions →

Try it yourself:

python runner/test_runner.py 0011_container --all --estimate

Auto-save failing cases for debugging:

gen_3: ❌ FAIL [generated]
   ┌─ Input ─────────────────────────────────
   │ [1,3,5,7]
   │ [2,4,6,8]
   ├─ Actual ────────────────────────────────
   │ 4.5
   └─────────────────────────────────────────
   💾 Saved to: tests/0004_failed_1.in

More capabilities:

• Seeded random generation for reproducible stress testing
• Custom judge functions for multiple valid answers
• Memory profiling with sparkline visualization

3. AI-Powered Knowledge Graph

We built an interconnected ontology: API Kernels → Patterns → Problem Families.

AI analyzes this structure to generate insights humans miss — synthesizing perspectives from Architect, Professor, Engineer, and Competitor viewpoints.

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Quick Start

1. Setup

# Clone and setup
git clone https://github.com/lufftw/neetcode.git
cd neetcode

# Create virtual environment (Python 3.11)
python -m venv leetcode
leetcode\Scripts\activate  # Windows
source leetcode/bin/activate  # Linux/macOS

# Install dependencies
pip install -r requirements.txt

2. Create a Problem

scripts\new_problem.bat 1 --with-tests  # Windows
./scripts/new_problem.sh 1 --with-tests  # Linux/macOS

3. Run Tests

python runner/test_runner.py 0001_two_sum

That's it. Press F5 in VS Code to debug, or Ctrl+Shift+B to run all tests.

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Pattern Guides

Each pattern provides Intuition + Templates. Start with intuition, use templates for interviews.

Pattern	Intuition	Templates	Example Problems
Sliding Window	💡	🛠️	LC 3, 76, 209, 239, 340, 438, 567
Two Pointers	💡	🛠️	LC 11, 15, 16, 26, 27, 75, 80, 88, 125, 141, 142, 167, 202, 283, 287, 876, 977
Binary Search	💡	🛠️	LC 4, 33, 34, 35, 81, 162, 875, 1011
Backtracking	💡	🛠️	LC 39, 40, 46, 47, 51, 52, 77, 78, 79, 90, 93, 131, 216
Monotonic Stack	💡	🛠️	LC 42, 84, 85, 316, 402, 496, 503, 739, 901, 907
Dynamic Programming	💡	🛠️	LC 70, 72, 121, 198, 213, 322, 416, 494, 516, 518, 746
Graph Traversal	💡	🛠️	LC 133, 200, 207, 210, 286, 417, 542, 547, 743, 785, 787, 802, 841, 994, 1631
Tree Traversal	💡	🛠️	LC 94, 102, 104, 110, 124, 337, 543, 968

View All 25+ Patterns →

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Features at a Glance

Category	Capabilities
Testing	Random generation, custom judges, stress testing, complexity estimation
Learning	Pattern intuition guides, reusable templates, dual-path documentation
Visualization	AI mind maps, pattern hierarchy, problem relationships
Tooling	VS Code integration, one-command scaffolding, benchmarking

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Documentation

Getting Started

Document	Description
Create New Problem	How to scaffold a new problem
Solution Contract	Solution file format and requirements
Test File Format	How to write .in/.out test files

Learning

Document	Description
Pattern Guides	All 25+ patterns with Intuition + Templates
Test Runner	Testing, benchmarking, and validation
Benchmarks	100+ problems compared: speedups up to 65,000x

Advanced

Document	Description
Generator Contract	Random test generation for stress testing

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Contributing

We welcome contributions! See our Contributor Guide.

# Run unit tests
python -m pytest .dev/tests -v

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License

MIT License — Free for personal learning and educational use.

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Built for learners who want to understand, not just memorize.

📚 Docs · 🧠 Mind Maps · 📐 Patterns · 🧪 Testing