Kartikey Agrawal

ML Engineer, Full Stack Developer and more.

TLDR: I am an ML engineer and full-stack developer. I research how small AI models reason, and I build products end to end. I look for real gaps that people live with every day, and build whatever fills them.

I like owning the whole thing: the database, the API, the last button, and the tests that keep them honest. HostelMate, which I built alone, has over 300 automated tests (260+ Jest, 59 Playwright) and an 80% coverage floor. It's open source, with a live demo.

I keep learning on purpose and pick up whatever a problem needs. In my second year I co-authored a paper accepted at ICTCS, built Gearbox, a tool that lets expensive AI agents hand routine work to cheaper models safely, and finished in the top 5% of 35,000 teams in the Amazon ML Challenge.

I grew up in Kota, Rajasthan. In 2015 I won 1st place at the UCMAS All-Rajasthan Abacus Championship, and I've been curious about how numbers and things work ever since. My father trades grain at Bhamashah Mandi, and watching him check prices every morning is where my Mandi bot comes from.

I study B.E. Computer Science (Data Science) at BMS College of Engineering, Bengaluru. Since March 2026 I've been building HostelMate, a hostel management platform for Indian colleges, on my own. This summer I interned at Trinkerr as a software developer. Before that I ran social media for Veynqor, a US-based B2B startup, and built its website. I'm looking for SWE or ML internships.

Outside work, I'm a lifelong learner who likes finding a real gap and building something to fill it. Away from the screen, I play table tennis and snooker.

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Professional Experience

Your Team?

SWE / ML Intern

Next

I'm looking for SWE or ML internships. I build products end to end, from the database to the last button, and I research how small AI models reason.

  • Built HostelMate solo: open source, live demo, 300+ automated tests.
  • Co-authored a paper accepted at ICTCS 2026.
  • Top 5% of 35,000 teams in the Amazon ML Challenge 2026.

Trinkerr

Software Developer Intern

Jun 2026 - Aug 2026

Tech stack: Django, DRF, PostgreSQL, Celery, Redis, React, TypeScript.

  • Engineered a full-stack Investment Poll Platform: a Django REST Framework API (30+ endpoints, OpenAPI docs) and a React 18 + TypeScript front end with responsive UIs.
  • Designed and implemented the polling workflows for investment surveys, letting 1,000+ users take polls, submit responses and see real-time aggregated results in a client-ready web interface.
  • Implemented backend security: rotating JWT refresh tokens, token blacklisting, role-based access control, rate limiting, and revocable dashboard share links secured by 256-bit tokens.

Veynqor

Social Media Manager & Web Dev Intern

Apr 2026 - May 2026

Veynqor is a US-based B2B startup. I worked with it remotely, alongside my studies.

  • Designed and built Veynqor's company website.
  • Ran its social media: planned, wrote and posted content for a B2B audience.
  • Worked remotely with a US-based team, across time zones.

Tech Stack

Tech stack

What I build with, from the database to the last pixel.

Languages

  • Python
  • TypeScript
  • JavaScript
  • C++
  • Java
  • SQL

Frontend

  • React
  • Next.js
  • Tailwind CSS
  • Three.js
  • GSAP

Backend

  • Node.js
  • Express
  • Django
  • Django REST Framework
  • FastAPI
  • Flask
  • Celery
  • WebSockets
  • Socket.io

Databases

  • PostgreSQL
  • Redis
  • Supabase
  • pgvector
  • Prisma
  • SQLite

AI / ML

  • PyTorch
  • Hugging Face
  • LangChain
  • LightGBM
  • FinBERT
  • LiteLLM
  • MCP
  • Ollama
  • Groq
  • Gemini
  • AssemblyAI

DevOps & testing

  • Docker
  • GitHub Actions
  • Jest
  • Playwright
  • Git
  • Turborepo

3D (this site)

  • Blender (Python scripting)
  • Cycles light baking
  • GLSL shaders

Education

Bachelor of Engineering

Computer Science (Data Science)

Oct 2024 - May 2028

BMS College of Engineering, Bengaluru. CGPA 8.42.

  • Harvard CS50x certificate (weeks 0 to 9 in C), with FoodBridge as the final project.
  • 200+ DSA problems in C++, and Codeforces Specialist (1443).

Class 12 (CBSE)

Aklank Public School

Kota, Rajasthan. Scored 80%.

Class 10 (CBSE)

Disha Delphi Public School

Kota, Rajasthan. Scored 92%.

Projects

HostelMate

  • Next.js
  • Express
  • Supabase (PostgreSQL)
  • Redis
  • Socket.io
  • Groq
  • Razorpay
  • Docker
  • Turborepo
Status: Live demo online (still being polished); preparing for its first college

Indian college hostels still run on paper registers, WhatsApp complaint groups and handwritten leave forms.

One platform for students, wardens and parents: attendance, leave, complaints, mess, fees, visitors and lost-and-found.

HostelMate warden dashboard with attendance, leaves, complaints, room occupancy, mess rating and curfew violations
Warden
HostelMate student dashboard showing attendance rate, pending leaves, complaints and fees due
Student
HostelMate parent portal with the ward's monthly attendance calendar
Parent

Highlights:

  • Attendance you can't mark for a friend: a rotating HMAC-signed QR code, checked together with a 100 m GPS fence.
  • Face check in the browser (blink, motion, five angles), so verifying a student costs nothing per scan.
  • Complaints triaged by an LLM (Llama 3.1 on Groq): category, urgency and a summary for the warden; a 30-day view spots recurring problems.
  • Fees paid online through Razorpay, every payment's signature verified; visitors go through warden approval.
  • Live notifications over Redis and Socket.io instead of polling.
  • 260+ Jest tests in 23 suites and 59 Playwright end-to-end tests, run by a pre-commit hook and CI; an 80% line-coverage floor.

My Role:

  • Sole engineer (1,124 commits) (2026 – now)

Links:

StockSense

  • Next.js
  • Python
  • FastAPI
  • Supabase
  • pgvector
  • FinBERT
Status: In development

Retail investors in India see a P/E of 24 and can't tell if that's healthy, because what's normal differs by sector.

Stock research that scores every NSE company on six fundamentals against its own sector, and explains each score in plain language.

StockSense analysis of Infosys: score 70 of 100, P/E 24.2 inside the ideal zone, ROE 31% marked healthy, each with a plain-language explanation
Every number explained
StockSense screener listing eight stocks with score, P/E, ROE, debt-to-equity and a strong, moderate or weak signal
Screener

Highlights:

  • Live prices for 2,200+ NSE stocks, with no API calls while the market is closed.
  • Reads 400-page IPO prospectuses and pulls out the risks in under 30 seconds.
  • Screener, IPO scoring, a geopolitical risk map and paper trading.

My Role:

  • Built the product, data pipeline and interface (2026 – now)

Links:

https://www.youtube.com/watch?v=XnkwVpmIzhA

FoodBridge

  • Python
  • Flask
  • SQLite
Status: Finished

Restaurants throw away good food while NGOs nearby run short.

A marketplace where donors list surplus food and NGOs or volunteers claim it before it expires.

FoodBridge impact page: 11 kg of food rescued, 22 meals provided, 28.1 kg CO₂ prevented, with top donors and claimants
Impact page, from the demo video

Highlights:

  • Live countdowns and urgency colours on every listing.
  • Claims are atomic, so two NGOs can never take the same food.
  • Tracks food rescued, meals provided and CO₂ prevented.

My Role:

  • Solo, Harvard CS50x final project (2026)

Links:

AI Meeting Intelligence

  • Python
  • FastAPI
  • AssemblyAI
  • Groq
Status: Finished

Action items get lost after meetings.

Turns meeting audio into action items, decisions and open questions, each linked to the exact quote and speaker.

Highlights:

  • 6.67% word error rate, against 40% for Whisper Large-v3 on the same multi-speaker audio.

My Role:

  • Solo (2026)

Links:

AI hiring assistant

  • Next.js
  • Express
  • Prisma
  • Gemini
Status: Finished

Hiring managers drown in candidate PDFs.

Reads résumés, normalises skills and recommends matches. The final score is plain code, so the AI can't be talked into a result.

Highlights:

  • Every AI output is validated against a schema before it's saved.

My Role:

  • Solo, technical task for Zelosify (Sep 2026)

Links:

AshCam

  • Python
  • OpenCV
  • MediaPipe
  • ONNX Runtime
  • pyvirtualcam
Status: Working, open source

A playful one: leaving a video call is dull. What if you could vanish the way the Endgame snap does it?

A macOS virtual camera: snap your fingers and you turn to dust while the room behind you stays live; snap again to come back. Works in WhatsApp, FaceTime, Google Meet and Zoom through OBS.

Highlights:

  • A snap counts only when the camera sees the thumb and middle finger snap apart and the mic hears the click, so claps and typing don't set it off.
  • Fills the wall behind you from a saved empty room, or with the open-source LaMa inpainting model (about 1.5 s on a MacBook).
  • Mutes the Mac's microphone while you're gone and restores it when you're back; a go-away mode loops a clip of you at your desk.

My Role:

  • Solo (Oct 2026)

Links:

Mandi bot

  • Python
  • Scraping
  • Forecasting
Status: Building

My father trades grain at Bhamashah Mandi in Kota and checks prices on a website every morning.

A bot that fetches today's prices for what he holds and predicts profit from incoming crop arrivals and the weather.

My Role:

  • Solo (Building)

BMSCE events bot

  • Python
Status: Early

Our college runs events all the time, and the tech ones get lost in WhatsApp groups.

A bot that reads the groups and tells me only about hackathons and tech events.

My Role:

  • Solo (Early)

Research

Do small models talk themselves out of right answers?

  • PyTorch
  • Hugging Face
  • GSM8K
  • MATH-500
Status: Accepted at ICTCS 2026, presenting mid-December

A common test truncates a model's reasoning and forces an answer. If an early forced answer is right and the final one wrong, people call it 'overthinking'. But forcing the answer is itself an intervention.

A two-tier protocol that separates what the probe forces from what the model actually commits to, run on DeepSeek-R1-Distill-Qwen-1.5B, Qwen2.5-3B and Llama-3.2-3B over GSM8K and MATH-500.

Highlights:

  • 210 uninterrupted traces; 11 flagged as degradation (5.2%).
  • Two annotators, the second blind, agreed on all 11 (Cohen's κ = 1.00).
  • Only 1 of the 11 is genuine: the probe measures itself as much as the model.

My Role:

  • Co-author, with Mayanka Gupta, Kiran A Gupta, Utkarsh Mandal, Abhigyan Shekhar and Parth Bhat (2026)

Gearbox

  • Python
  • LiteLLM
  • MCP
  • Ollama
Status: Almost complete, paper in progress

Expensive AI agents spend most of their tokens on small, routine subtasks a cheaper model could do, if anyone could trust the cheaper model's 'done'.

An automatic transmission for AI agents: the agent hands subtasks to cheaper models, keeps working, and accepts an answer only after it passes a real test. Failures move up to a stronger model with exactly what failed.

Highlights:

  • Cheap-first with checking: 95.7% on HumanEval+, against 92.7% for always using the 27B model, at about a third of the compute.
  • The expensive model wrote about half as many tokens.
  • Tested on 542 problems and 12 open models on a lab GPU; our grader matches published scores within about 1 point.
  • Plugs into Claude Code, Cursor and Antigravity as an MCP tool.

My Role:

  • Built the tool and ran the benchmarks (2026)

Links:

Punarvaani (पुनर्वाणी)

  • Python
  • PyTorch
  • AV-HuBERT
  • LoRA
  • MediaPipe
  • IndicF5
Status: Code complete and tested on CPU; GPU training next

People who lose their voice to throat or oral cancer surgery, a stroke or ALS can still move their lips, but no sound comes out. India has one of the highest rates of oral and throat cancer.

Reads a person's silent lip movements through a phone camera and speaks for them in Hindi, in their own voice rebuilt from old voice notes and videos: lips to text (an LLM fixes look-alike sounds) to cloned speech, or lips straight to speech units.

Highlights:

  • Every stage is written and tested on CPU (80+ tests): lip crop, a Hindi audio-video data engine, an AV-HuBERT encoder with LoRA, both speech paths, voice rebuilding, real-time streaming and evaluation.
  • On a synthetic lip-reading world, per-user personalisation cut the character error rate from 92% to 15.5% with 64 clips.
  • Next: mine Hindi video, record volunteers mouthing silently, and train on GPUs.

My Role:

  • Built the codebase (2026 – now)

Links:

MetaGuard

  • Learning science
  • LLMs
Status: In progress

AI tutors can help so much that people stop learning.

Psychology × AI research on how people learn with AI tutors.

Highlights:

  • Details after the study is published.

My Role:

  • Researcher (2026 – now)

Awards & Recognitions

Top 5% - Amazon ML Challenge 2026

Match the same business across three messy sources with about 12.5 million records: typos, swapped legal forms, missing addresses, and names written in Devanagari, Tamil, Telugu or Gurmukhi.

A three-stage pipeline: cheap candidate search on rare name and address keys, a LightGBM matcher with about 70 features, and a decision layer that enforces one-to-one matches. A transliteration dictionary learned only from the training pairs, so Indic-script names meet their English versions. Score 0.965 out of 1, rank about 1,800 out of 35,000 teams. Built with team DayTrippers.

1st Place - UCMAS All-Rajasthan Abacus Championship

Won 1st place in the state-wide UCMAS abacus championship in 2015.

Harvard CS50x

Completed Harvard's CS50x, weeks 0 to 9 in C, with FoodBridge as the final project.

Links:

Codeforces Specialist

Rated 1443 on Codeforces, plus 200+ DSA problems solved in C++.

Links: