Skip to content

hey, i’m

AYUSH.

↻ give the panel a tap

I buildAI SYSTEMSAGENTSWEB APPSDEV TOOLSpeople actually use

RAGEvalsPrompt EngineeringVector SearchOpenAILangGraphMCPTool CallingMulti-Agent OrchestrationNode.jsTypeScriptNext.jsReactTailwind CSSPostgreSQLDrizzleMongoDBPrismaGroqHugging FaceProvider Fallback ChainsSSE Streaming
01About

Engineering the part where a model becomes a product.

I am an engineer who cares about the seam between models and products — the part where a demo becomes something people rely on every day.

Most of my work sits between AI systems and the interfaces that make them legible: retrieval pipelines, agent graphs, evaluation harnesses, and the front ends that keep all of it honest.

When I am not shipping, I am usually reading distributed systems papers or grinding LeetCode with a coffee that went cold two hours ago.

Public repositories
70
Problems solved
388
Submissions this year
845
GitHub followers
25
Ayush Kumar, portrait

Ayush Kumar

AI Engineer

Capabilities

What I actually build with

No ratings out of ten. Every group below is something the work in this page runs on.

AI Engineering

01

Model-backed features that hold up outside the demo.

  • RAG
  • Evals
  • Prompt Engineering
  • Vector Search
  • OpenAI

› embed query · 6 chunks retrieved · grounded answer · 48ms

Agentic AI

02

Stateful agents with explicit handoffs and traceable runs.

  • LangGraph
  • MCP
  • Tool Calling
  • Multi-Agent Orchestration

› planner → tool: search_repo → verifier · 3 hops · trace saved

Backend

03

APIs designed to survive their second year.

  • Node.js
  • TypeScript
  • Next.js

› POST /api/run · validated · queued · 202 in 12ms

Frontend

04

Interfaces with motion that means something.

  • React
  • Next.js
  • Tailwind CSS

› stream tokens → UI · 60fps · first paint 0.9s

Database & ORM

05

Modelling data for the queries it will actually get.

  • PostgreSQL
  • Drizzle
  • MongoDB
  • Prisma

› SELECT … WHERE embedding <=> $1 · index hit · 4ms

Models & Serving

06

Running models in production, where the provider is the flaky part.

  • Groq
  • Hugging Face
  • Provider Fallback Chains
  • SSE Streaming
  • Model Routing

› groq timeout → hf fallback · SSE open · 41 tok/s

Experience

Where I have been responsible for the thing working

01 / 03
  1. 01In production

    SMVIT Debating Society

    Building and running the society's system · 2025 — present

    smvitdebsoc.com

    Took the society from a promotional page to the application the cabinet actually runs sessions on — public site, members' area and the AI practice features layered on the session data.

    • Role-separated access: members see their own record, the cabinet sees the society
    • Session conduct flow used live during club meetings
    • Prisma over PostgreSQL for member, session and attendance records
  2. 02In production

    Wedsy

    Freelance — frontend and backend · Freelance

    wedsy.in

    A live commercial product I maintained on both sides of the wire. Straight web engineering rather than an AI build — most of the weight sat in the backend and the API surface the front end runs on.

    • Maintained the frontend and the backend behind it, not one half of the stack
    • Complex backend: a wide API surface driving every screen on the site
    • Shipping against a client's production site, not a sandbox
  3. 03In production

    Priyelle

    Freelance — jewellery e-commerce · Freelance

    priyelle.shop

    Client work: an e-commerce site for a handcrafted, gold-plated jewellery brand — necklaces, earrings, rings and bracelets sold in small batches. Built and shipped as the store the brand actually sells through.

    • Client's live storefront on Next.js, not a sandbox
    • Catalogue, product pages and checkout flow for a small-batch jewellery brand
    • Shipped against real orders and real customers

FlagshipPathwayAI

I built the assistant that coaches me from evidence, instead of asking how confident I feel.

An open-source mentor backend that turns your real work into interview-readiness coaching — watching Telegram reflections, GitHub activity and LeetCode practice, then coaching you on a schedule instead of behaving like a stateless chatbot.

Morning · 1/4

Checks in

Opens the day over Telegram and asks what the plan is, before there is anything to rationalise.

PathwayAI · live runloops
MorningAll dayEveningWeekly

    In focusCodeBuddy

    I built the project brain that lets an agent remember, reason, and edit with context.

    Durable memory and prepared context for Claude Code and Codex: knowledge lives as readable Markdown in your repo, and the agent gets the smallest useful slice of it before every edit instead of re-reading the project each turn.

    Before edit · 1/4

    Prepares the context

    Finds the relevant files, rules, risks and architecture neighbours, then returns a bounded context pack instead of a repo dump.

    CodeBuddy · live runloops
    Before editDuring workAfter turnEvery project
      Portfolio

      Selected work

      Five systems, each one still running. Open any of them for the decisions behind it.

      SMVIT DebSoc

      The website for my college's debating club — a rich front end backed by a real system that took it from a promotional page to the tool the club actually runs sessions on, with AI features that help members upskill their debating.

      sessions
      conduct flow run live during club meetings
      members
      roster, participation and history in one place
      access
      role-separated — members see their own record, the cabinet sees the society
      stack
      Next.js App Router, Prisma/Postgres, Tailwind
      100+
      concurrent users
      0
      failed requests
      role-gated
      cabinet vs member

      PathwayAI

      An open-source mentor backend that turns your real work into interview-readiness coaching — watching Telegram reflections, GitHub activity and LeetCode practice, then coaching you on a schedule instead of behaving like a stateless chatbot.

      workflows
      morning check-in, evening reflection, weekly review
      evidence
      Telegram reflections, GitHub commits, LeetCode submissions
      scoring
      deterministic, computed in code rather than by the model
      stack
      FastAPI, LangGraph, PostgreSQL, Groq
      3
      scheduled workflows
      deterministic
      readiness score
      groq + hf
      model lanes

      RepoPilot

      Paste a public GitHub URL, say why you are here, and get a tour of the codebase shaped by that intent — with every factual claim tied to a source span and re-checked by a verifier before you see it.

      intent
      persona or free text, captured before any analysis runs
      retrieval
      dense pgvector plus BM25, cross-encoder rerank, MMR
      claims
      every one cites a line range and is verified before display
      stack
      FastAPI, Next.js, LangGraph, pgvector, tree-sitter
      file:line
      every claim
      verified
      before display
      SSE
      streamed while indexing

      CodeBuddy

      Durable memory and prepared context for Claude Code and Codex: knowledge lives as readable Markdown in your repo, and the agent gets the smallest useful slice of it before every edit instead of re-reading the project each turn.

      memory
      Markdown under .codebuddy/, committable and reviewable
      surfaces
      CLI, MCP stdio server, TypeScript SDK, LangGraph helpers
      index
      PostgreSQL + pgvector, rebuildable from the Markdown
      stack
      TypeScript, Node.js, MCP, pgvector
      npm
      published
      local-first
      nothing leaves the machine
      4
      surfaces, one core

      ClawCode

      A Telegram-controlled autonomous coding agent: one trusted operator sends a task, the agent inspects the repo, edits code and runs tests in an E2B sandbox, and opens a pull request only after explicit approval.

      control
      Telegram commands, free text and voice notes
      sandbox
      E2B for isolated dependency install and pytest runs
      gate
      human approval required before any push or PR
      stack
      Python, LangGraph, E2B, GitHub API
      phone
      approval gate
      checkpointed
      every dispatched tool
      sandboxed
      tests before proposal

      Writing

      My
      Writing

      Swipe → · Tap a card

      05Signals

      The public record.

      Problem solving and open source, pulled live where the APIs allow it.

      LeetCode

      Live

      388

      Problems solved

      Rating
      1651
      Rank
      343,847

      159

      Easy

      201

      Medium

      28

      Hard

      Submissions845 in the last year
      LessMore
      06Open source

      Work that shipped in someone else's repo.

      Every pull request I have authored and had merged outside my own account, pulled live from GitHub.

      Merged

      Live

      36

      Pull requests merged

      zvec-ai/zvec-grep#226 fix(rust): treat daemon lock records naming the current PID as staleopen
      Projects
      7
      Latest
      Oct 2026
      06Contact

      Let’s build something worth shipping.

      Open to AI engineering and full stack roles, freelance builds and genuinely interesting problems. I reply to everything.