A personal archive of algorithmic decision-making, heuristics, and strategy patterns. Built for clarity through code.
This project is a structured knowledge base that captures possible ways through algorithmic problems.
Rather than focusing on raw code or platform solutions, this vault documents:
- 🧩 Problem breakdowns
- 🚧 Initial approaches and where they fail
- ✅ Optimal strategies and key insights
- 🧠 Heuristics used in real-time decision-making
⚠️ Common traps and false starts- 🧭 Meta-patterns that reappear across problems
It's part of a broader effort to externalize reasoning, build reusable mental models, and eventually train intelligent agents to think with context.
Most tutorials focus on what to do.
This vault focuses on how and why certain decisions are made — especially when:
- Multiple solutions exist
- Tradeoffs between speed, memory, and simplicity emerge
- Failure paths reveal deeper understanding
- You want to teach AI how humans actually reason
Problems are organized by source:
/problems/
├── leetcode/
├── hackerrank/
├── geeksforgeeks/
├── advent-of-code/
Each file is a standalone markdown document using a consistent format with metadata, rephrased summaries, strategy notes, and general insights.
- No LeetCode, HackerRank, or GfG content has been copied or redistributed.
- All summaries, insights, and heuristics are written originally based on problem-solving experience or public domain understanding.
- This vault is about reasoning, not solution sharing.
This project may evolve into:
- A dataset for AI training or fine-tuning
- A searchable, taggable reference for engineers
- A framework for teaching decision-making over syntax
- An open-source mental model index for real-world problem solving
"This is how I externalize the invisible parts of thinking — and turn them into tools."
MIT License — see LICENSE.md
You are free to use, remix, or build upon this work. Attribution is appreciated.
