🧪 open-concept-lab

A long-term, exploratory lab of concept projects focused on the foundations of AI, Security, Systems, and High-Performance Computing — and how they intersect in real-world tools, protocols, and research.

This lab serves as a place to:

• Build from first principles and implement key ideas from scratch
• Document thought processes, tradeoffs, and experiments
• Reflect on challenges, gaps in understanding, and future possibilities
• Stay in sync with what’s being researched and built across DeepMind, OpenAI, Stanford, ETH Zurich, and other leading institutions
• Maintain tooling to track and stay in touch with research updates, conferences, and scholarly progress

🎯 Purpose

• 🔍 Explore complex topics through minimal, focused implementations
• ✍️ Develop conceptual intuition via code, reflection, and iteration
• 🧠 Think out loud through README files, dev notes, and extension plans
• 🔗 Bridge theory and tooling across AI, security, and systems
• 🔭 Track and amplify research aligned with world-class labs

This is meant to be a public notebook of ideas — part playground, part documentation hub, part future research garden.

📁 Repository Structure

.
├── ai/                # Neural networks, transformers, LLM agents, evaluation
├── hpc/               # Parallelism, matrix ops, performance tuning
├── security/          # TLS, sandboxes, analysis tools, crypto experiments
├── systems/           # OS components, allocators, consensus algorithms
├── reproducibility/   # Benchmarks, paper replications, repeatable results
├── tooling/           # CLI experiments, test kits, automation utilities
├── rank-nsf-linker/   # Track researchers, venues, NSF grants, recent trends
├── templates/         # Reusable README + reflection + extension templates
└── meta/              # Personal or meta information I track privately

Each folder includes a README.md for high-level structure and status.

📘 Project Structure

Each project typically contains:

• README.md — What was built, why it matters, and how it connects
• reflection.md — What worked, what didn’t, what surprised me
• extension.md — How I’d scale this further as a tool or research direction

Templates for all of these live in /templates/.

🧠 What You'll Find Here

Some sample ideas explored or in progress:

Domain	Sample Ideas
AI	Transformers from scratch, multilingual eval agents
Security	TLS handshake tracer, syscall sandboxing, static analyzers
Systems	Memory allocators, OS schedulers, Paxos/Raft from scratch
HPC	Parallel sorting, matrix ops with OpenMP/CUDA
Tooling	LLM testbeds, CLI research dashboards, intelligent scrapers
Reproducibility	Recreating results from papers, metrics, and plots

🛡️ Why This Lab Exists

I’ve always believed real understanding begins when we build things ourselves.

This lab is my way of diving into the internal mechanics of research — not just reading papers, but recreating and rethinking them. Not just building tools, but making sure they align with where the field is heading.

In the end, I want to:

• 🎯 Prove the kind of research engineer I am becoming
• 🧠 Think and build like the people I admire in labs across the world
• ↺ Keep pace with papers, researchers, and meaningful tools
• ✍️ Document the struggle, the learning curve, and the breakthroughs
• 🌱 Grow this as a living, open-source intellectual garden