class AIArchitect:
    def __init__(self):
        self.name = "Sachin"
        self.role = "AI Architect"
        self.modes = ["Research", "Engineer", "Shipper"]
        self.state = "evolving"  # change is constant, embrace it

    def current_stack(self):
        return {
            "research": ["Membrane", "AdsAI", "LAKER", "ANBR"],
            "philosophy": "change is constant, embrace it"
        }

    def say_hi(self):
        print("Building the future, one model at a time.")

me = AIArchitect()
me.say_hi()

I architect AI systems that live at the intersection of cutting-edge research and real-world impact. I spend my mornings reading papers and my afternoons shipping code — sometimes the same afternoon.

• 🏗️ Designing agentic systems, reasoning pipelines, and intelligent interfaces
• 🔬 Pushing boundaries on AGI research and multi-agent orchestration
• ⚡ Turning "what if?" into "it works on my machine... and yours too"

Agentic & Generative AI

• membrane — Global Contextual Memory Fabric. The connective tissue between agents, models, and long-term state.
• ads-ai — A multi-agent AI framework that automates the entire advertising lifecycle. Agents doing marketing so humans can do strategy.

Deep Learning & Graph Research

• rmr — Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion.
• anbr — Adaptive Norm-Based Regularization for Neural Networks.
• igasgd — Information-Geometric Adaptive Sampling for Graph Diffusion.
• laker — Learning-based Attention Kernel Regression.
• tsn-affinity — Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning.

Algorithms & High-Performance Systems

• prspnsd — Parallel Reachability and Shortest Paths on Non-sparse Digraphs.
• fdmm — Fully Dynamic Maximal Matching.
• factorise — High-performance prime factorisation in pure Python.
• tnss — Integer factorization via tensor-network Schnorr's sieving.

Full repo list: github.com/sachn-cs?tab=repositories

Change is constant. Embrace it.

The field shifts daily. Models evolve, paradigms break, and yesterday's best practice is today's legacy code. I don't just keep up — I lean in. Every refactor is a lesson, every broken pipeline is a story, and every shipped feature is a hypothesis validated.

• 📫 Reach me here on GitHub
• 💡 Always happy to collaborate on anything ambitious, weird, or world-changing

"The best time to build the future was yesterday. The second best time is now." — but also, feel free to say hi first! 😄