Maxler — Sports Nutrition E-commerce Platform (MVP)

A self-initiated e-commerce MVP built solo end-to-end for a real local business I noticed was still selling only through Instagram DMs.

⚠️ Problem

A real local sports-nutrition business was selling only through Instagram DMs — no actual online ordering, no order tracking, no admin tooling to run daily operations.

🔒 Constraint

No client, no spec, no budget — self-initiated, meant to be pitched cold to the business afterward, so it had to look and work like something a real operator could run on day one, not a demo.

🛠️ Solution

Customer side: product catalog, cart, checkout with both delivery and in-store pickup flows, phone-based OTP login, order tracking. Admin side: a full order status machine (unpaid → paid → assembled → in delivery → completed/cancelled), product and category management, branch management, and a customer database.

Designed two separate design systems from scratch (storefront and admin panel) with a full motion spec — exact easing curves and durations for every hover/active/entrance/exit state — because the first visual pass didn't hold together once compared against real references.

📈 Result

A working MVP covering both the customer and operator sides, built solo, ready to pitch directly to the business it was designed for.

This is a strong MVP story because the operator workflow is treated as a first-class product, not an afterthought. The delivery-to-completed state machine and pickup flow feel especially useful for a real local business.

I built ProofFace a visual provenance & content verification engine in Rust I have been building ProofFace an end-to-end visual provenance and content verification engine built in Rust.

The idea is simple:

Finding an image online doesn’t prove that it’s authentic, original, or unchanged.

So I built a verification pipeline that goes from:

📸 Image input → 🧠 Neural face detection → 🔎 Web visual discovery → 🧬 Biometric similarity → 🔐 Deterministic SHA-256 fingerprinting → ⛓️ Polygon Amoy blockchain proof → ✅ Verification result

Some of the things I worked on:

• ONNX-based face detection • 128-dimensional biometric embeddings • Presentation attack detection • Parallel visual candidate evaluation • Cosine similarity matching • RFC 8785 JSON canonicalization • SHA-256 content fingerprinting • Polygon smart-contract integration • Automated unit & integration testing

The project currently has 30 passing tests.

What I enjoyed most was bringing several different areas together computer vision, Rust systems programming, cryptography, and blockchain into one reproducible verification workflow.

GitHub: https://github.com/nikhilkumarpanigrahi/proofFace

Would love to hear what you’d improve or add to a system like this.