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Turning complex problems into scalable solutions.
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Turning complex problems into scalable solutions.
9 followers · 5 following

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Achievement: Pair ExtraordinaireAchievement: Pull Sharkx2Achievement: QuickdrawAchievement: YOLO

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SpaceWalkerr/README.md

Suraj Nandan — Full-Stack & AI Engineer


Portfolio LinkedIn LeetCode Email Instagram

Reliable web products and grounded AI systems, built end to end.

I'm Suraj Nandan, a full-stack and AI engineer from Kishanganj, India, studying Computer Science at SRM IST, Delhi NCR. I like to own a problem from end to end — the data model, the API, the interface — and I care most about software people can trust: it behaves the same way every time, explains itself, and fails honestly when it has to.

Honours: Campus Winner, ACG Pack A Pitch 2.0 · NPTEL Gold Medal, IIT Delhi · First Place, Innovate 2024 · Lead Coordinator, Viksit Bharat 2047 · Campus Mantri, GeeksforGeeks

Section A · The Front Page — selected work, in clippings

Knot.ai — a spoken AI interview panel whose report can only quote the candidate Hospitality.ai — insurance-aware hospital navigation with source-verified citations CardBridge — a marketplace whose escrow rules are enforced by PostgreSQL Job Scheduler — distributed job scheduling on PostgreSQL with SKIP LOCKED GigShield — parametric income protection for delivery riders The Nandan Review — my portfolio, set like a newspaper

The moving clippings are live captures of each site. Every card opens a full case study — the brief, the architecture, the decisions, and what broke along the way.

Section B · By the Numbers — the practice behind the portfolio

Scoreboard: LeetCode problems solved and GitHub contributions in the last year

Languages across my repositories, by bytes of code

A snake eating the last year of contributions

Scoreboard, type specimen and snake are redrawn every morning from live data.

Section C · The Op-Ed — latest from the writing desk

Updated daily from the RSS feed.

Section D · How I Work — three rules, set in type

How I work: keep the model’s job small — let the LLM judge and phrase, put the guarantees in code; the database is the referee — transactions, state machines and row-level security; measure, then ship — code-split, cached, tested, and written up after.

Section E · The Toolbox — filed by department

Toolbox — Front end: React, Next.js, TypeScript, Tailwind, Three.js. Back end: Node.js, Express, Fastify, REST, WebSockets. Data: PostgreSQL, Supabase, Prisma, SQLite, pgvector. AI and more: Claude API, RAG, Python, Java, Vercel.


Set in the style of The Nandan Review — my portfolio, printed like a newspaper. Try ↑ ↑ ↓ ↓ ← → ← → B A on the front page.

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  1. Whiteboard-ai Whiteboard-ai Public

    TypeScript

  2. GigShield GigShield Public

    Parametric income protection for delivery riders — automatic, weather-triggered payouts. Team build.

    JavaScript

  3. Hospitality.ai Hospitality.ai Public

    Insurance-aware hospital and treatment navigator: policy extraction with source-verified citations, deterministic cost ranking.

    TypeScript

  4. Job-Scheduler Job-Scheduler Public

    Distributed job scheduler on PostgreSQL — SKIP LOCKED claiming, DAG workflows, retries, leader election, live dashboard.

    TypeScript

  5. Knot.ai Knot.ai Public

    Spoken AI interview panel that adapts to your answers — and a report that can only quote you. Agora voice + Claude, interview control in a deterministic proxy.

    TypeScript

  6. CardBridge CardBridge Public

    Card-discount marketplace with a PostgreSQL-enforced escrow state machine, KYC tiers and Row Level Security.

    TypeScript