Introduce Dual-Track Pattern Documentation with Intuition-First Learning Across Core Algorithmic Patterns #47
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- Add comprehensive core concepts section in _header.md - Expand _templates.md with detailed explanations for each template - Include usage scenarios, complexity analysis, and LeetCode problems - Add template variants (duplicates handling, constraints, etc.)
- Replace generic "variant" and "base" suffixes with problem-specific names - Align naming convention with sliding_window pattern - Update _config.toml to reference new filenames - Improve readability: filenames now indicate problem content at a glance
Create intuition.md as a complementary document to templates.md that focuses on building pattern-level understanding before code: - Introduce Explorer/Gatekeeper mental model for window boundaries - Frame invariants as "promises" the window must keep - Describe maximize/minimize modes with vivid metaphors - Add pattern recognition flowchart and problem mapping table - Defer code until after conceptual understanding is established
Improve intuition.md with practical visualizations and examples: - Add ASCII flowcharts for Maximize and Minimize window modes with color-coded R (Explorer) and L (Gatekeeper) movements - Convert "Visualizing the Dance" to structured table format with Step, R, State, L move, Window, and Result columns - Add concept-to-code comments linking Explorer/Gatekeeper roles to for-loop and while-loop in template - Add Fixed Window (K=3) example trace after Shape 3 demonstrating constant-size sliding behavior
…ion.md - Add 5 runnable solutions: LC 3, 340, 76, 438, 209 - Include detailed time/space complexity analysis per problem - Add verification code with test cases for each solution - Add definitive O(n) complexity analysis section - Use semantic, project-level variable naming throughout
Create comprehensive pattern intuition document with: - Six two-pointer shapes with vivid mental models - Invariant and irreversibility principles explained - Visual traces for opposite, same-direction, fast-slow, and Dutch flag - Pattern recognition flowchart for instant identification - Templates derived from intuition, not as starting points
- Add "How to Use This Documentation" section explaining learning paths - Update pattern table with direct links to intuition.md and templates.md - Apply to sliding_window, two_pointers, and backtracking_exploration
Change "Copy-paste ready code" to "Production-ready implementations" for a more professional tone.
- Add Intuition and Templates learning paths for each pattern - Update BacktrackingExploration from "coming soon" to available - Add LinkedListInPlaceReversal and MonotonicStack as upcoming patterns - Update README.md and README_zh-TW.md with new table format
lufftw
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Dec 14, 2025
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Thanks for the update! I went through the implementation and didn’t find any issues. The changes are clear and well-structured.
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This PR introduces a dual-track documentation system for algorithmic patterns, designed to support both intuition-first learning and production-ready implementation reference.
The objective is to help readers recognize patterns instantly before writing code, while still providing high-quality, reusable templates for real-world problem solving.
✨ Key Highlights
1. Dual-Track Learning Structure
Each pattern now provides two complementary learning paths:
Intuition Path (
intuition.md)Focuses on mental models, invariants, irreversibility, and pattern recognition before code.
Templates Path (
templates.md)Provides production-ready implementations with clear usage scenarios, variants, and complexity analysis.
Navigation between the two paths is clearly documented in each pattern README.
2. Backtracking Exploration Enhancements
_header.md_templates.mdwith:_config.tomlto align with the new naming scheme3. Sliding Window Intuition System
intuition.mdemphasizing pattern recognition over mechanics4. Two Pointers Intuition Guide
intuition.mdcovering six canonical two-pointer shapes5. Documentation Navigation & Tone Improvements
🎯 Why This Matters
This documentation structure reflects how strong problem solvers actually think:
It lowers the learning barrier for beginners while remaining valuable for experienced engineers who want clean, reusable implementations.