Analytics and ML at Google by day. Building things in the evenings. Ten years across data engineering, ML, and production systems. Two live apps and a homelab that runs itself.
Fact-checking and AI-generated image detection in a single tap. Built solo, shipped on Android first, then iOS. Reached 14K+ downloads organically (no ad spend, no influencer coverage) through word of mouth and YouTube. Backend connects multiple LLM APIs into a lightweight, fast verification pipeline.
Upload a photo of any room and get AI-generated design alternatives: different styles, furniture configurations, colour palettes. Early stage, exploring consumer generative AI in interior design. Built to see how quickly a solo dev can take an LLM-powered idea to production.
Built in 24 hours during a buildathon, Stashly turns Instagram Reel links into searchable cards with categories, notes, tags, and AI summaries. A small fix for the chaos of saved content.
WireGuard on Raspberry Pi with Pi-hole for network-wide DNS blocking. Secure remote access to the home network from anywhere in the world.
Self-hosted cloud storage on Ubuntu Server via Docker. Multi-device sync, backups, and sharing. No cloud subscription or vendor lock-in.
Plex on a mini-PC with hardware transcoding. Centralised media library for all devices on the home network, no buffering.
Webmin for system administration: services, packages, users, and backups from a single web UI. Observable infrastructure without SSH depth.
Ollama + OpenWebUI on a 4070 Ti Super rig. Running Llama, Mistral, and multimodal models locally for privacy-first AI experimentation.
YouTube + Instagram @techwithvatsal. Short-form tech explainers in Hinglish for the Indian tech-curious audience. AI, homelab, and data tools, explained plainly.
At Google Dublin, I work on production ML and AI systems for Trust & Safety in Ads. That covers feature pipelines, data engineering, LLM governance, and post-launch monitoring at scale.
The side-project stack runs just as deep. I've been running a homelab for years: Raspberry Pis, Ubuntu mini-PCs, Docker Compose stacks. Controlling your own infrastructure teaches you things that managed cloud services actively obscure.
FakeOut came out of genuine frustration with how hard it is to quickly fact-check a screenshot or determine if an image is AI-generated. Built it in spare evenings, shipped Android first, crossed 14K downloads without any ad spend. Now live on iOS too.
Working on something interesting? Questions about FakeOut? Want to talk AI, homelab, or data? Drop a message. I read everything.