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jncln/README.md
Typing hero

ML for decentralized systems - architecture-first, verification-aware, and built to ship.

About Me

I design verifiable ML systems for decentralized applications: model inference pipelines that can be attested on-chain, data layers that resist manipulation, and zk-proof-ready components that turn "trust me" into cryptographic evidence. From Solidity + Hardhat to Python-based training/inference stacks, I build end-to-end architectures where intelligent signals become first-class primitives in the decentralized web.

Tech Stack

Blockchain & Web3

Solidity Hardhat Ethers.js IPFS

Frontend

React Next.js Tailwind CSS TypeScript

Machine Learning & Python

PyTorch TensorFlow Python

Core Tools

Git Linux Docker

Featured Projects

ZK-Rollup ML Oracle (Placeholder)

Tech stack: Solidity · Hardhat · Ethers.js · IPFS · PyTorch · Python · zk-proof-friendly commitments
Impact: Provide provable, verifiable ML inference outputs for on-chain execution, reducing trust and improving oracle integrity under adversarial conditions.

Decentralized DeFi Risk & Anomaly Engine (Placeholder)

Tech stack: Next.js · React · TypeScript · Tailwind CSS · Python · TensorFlow/PyTorch · Linux · Docker
Impact: Real-time risk scoring and anomaly detection with reproducible pipelines and on-chain-readable outputs for transparent liquidation/guardrail decisions.

Privacy-Preserving On-Chain Recommender (Placeholder)

Tech stack: Solidity · Hardhat · Ethers.js · IPFS · Python · PyTorch · Federated learning (concept)
Impact: Deliver user-specific recommendations through privacy-preserving training/inference while maintaining auditable commitments that can be verified by smart contracts.

GitHub Stats

Top Languages GitHub Streak Total Commits

Contact & Socials

LinkedIn  |  Twitter / X  |  Email

Built for the decentralized future: verifiable intelligence, minimal trust, shippable systems.

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