Founder of Devanshu Studios: two production apps on Google Play and a third in closed testing. On-device LLMs with LiteRT-LM and GGUF, Flutter and Kotlin in production, RevenueCat + AdMob monetization, full Play Console lifecycle — plus ML research, two patents, and a measured hackathon study below. I care about things people can install today.

Two production releases. One in Google Play closed testing. Each one: architecture, monetization, CI/CD, Crashlytics, and publishing — owned end to end.
Scan to PDF on the phone. OCR, merge, compress. No account. On-device OCR with Google ML Kit — zero API cost, zero cloud. Local document vault with on-device search, PDF tools (split, merge, compress to target size, password), ID-card mode, QR scan, and signing. Keepary Pro via RevenueCat: searchable PDFs, encryption, high compression. Ads engineered with caps and cooldowns — interstitials only after saves, never on first launch.

Learn Python on the phone. 1,200+ exercises across gamified levels with an AI tutor, offline-first sync, and a clean-architecture Flutter codebase past 50k lines — built solo. RevenueCat subscriptions + AdMob native ads in production; the monetization postmortem is on the blog.

Privacy-first on-device AI chat — with a dual runtime. Runs modern LLMs entirely on the phone: Gemma 4, Qwen3.5, Phi 3.5, and SmolLM — switchable between LiteRT and GGUF engines with per-model accelerator profiles and dual sampler modes for thinking vs. instruct. No cloud inference, no accounts. Jetpack Compose UI, Hilt, CameraX vision skills, and agent skills with per-call permissions.
Four hackathons this season: WeMakeDevs × Modiqo, the Zerops Challenge, Bright Data's Into the Scrape-Verse, and the Agent Harness Hackathon. One of them turned into a full research study.
I shipped a 38-Play arsenal of zero-key agent procedures, then asked the question every builder should ask: does crystallized procedure memory actually beat model effort? The answer became a controlled study — same agent, same model, same task, with-Play versus re-derivation. Frozen ground truth, deterministic grader, six tasks, thirty-one graded trials, a leak audit that caught two baselines reading the experiment's own answer key (reported, not hidden), and raw telemetry for everything.
| transfer task | w/ Play | baseline |
|---|---|---|
| side-effect audit | 10, 10 | 7, 7 |
| instruction burial | 9, 9 | 2, 2 |
| commit hygiene | 10, 10 | 8, 8 |
| JSON integrity | 10, 10 | 10, 9 |
| event deadline join | 10, 10 | 10, 10 |
baseline @ 572–2,180 s vs with-Play @ ~32 s on instruction burial
Your URL under fire, live: four challenge packs (heartbeat, HTTPS/uptime, JSON validation, capped concurrent load) with a real scoreboard and letter grades. Fastify + React 19 + PostgreSQL with an async probe worker as the product — deployed on Zerops, SSRF-hardened, pre-seeded scorecards so it's never empty for judges.
github.com/bhanu-dev82/Crucible ↗"The jar got smaller. The price didn't." Custom Scraper Studio collectors read ALDI, Sprouts, and a self-hosted fixture shelf; when package size drops and price holds, HYDRA flags it — and collectors self-heal from a plain-English field list when the HTML moves. Live on Vercel with a 1:38 demo video.
Live demo ↗ · repo ↗A quota-aware multi-tier Gemini model router, an agent-harness client with turn running, local + remote MCP tool configs, and automated Qodo PR-review workflows — packaged with offline docs, project starters, and a Kaggle benchmark harness so any agent (or human) can execute the build cold.
github.com/bhanu-dev82/agentscope ↗Shipped PyMaster (50k+ LOC, live) — Clean Architecture, Riverpod, Flavors, Firebase, RevenueCat, Gemini AI tutor. Shipped Chakuli — on-device Gemma/Phi/SmolLM via LiteRT-LM, Compose, Hilt, CameraX. Leading Keepary through closed testing: architecture, monetization (AdMob + IAP), CI/CD, Crashlytics, Play Console publishing.
Tablet keyboard layouts in Kotlin for 7+ languages; UI fixes and a KeyHandler refactor for maintainability.
95%-accuracy ideology classifier (BERT/Transformers) plus a researcher web UI for validation.
Sole developer on a cross-platform Flutter field-trip app (iOS/Android/Web) with Firebase and OpenStreetMaps; presented at the TRESL Lab Symposium.
TMJ screening app with MediaPipe FaceMesh + ML Kit — 60% accuracy gain, 30% camera speedup. Contributed to a granted patent (Pub. No. 202321022476).
Led a medical-device prototype for xerostomia — 80% faster assessments; commended at IIT Bombay.

Women's safety wearable + Flutter app: panic button triggers live GPS sharing via Firebase. 98% uptime. Published by IGI Global.
Flutter · Firebase · GCP · IoT — IGI Global 2024
Android app using MediaPipe FaceMesh for OSMF diagnosis — 40% efficiency gain. National awards + patent contribution.
Kotlin · MediaPipe · Firebase — Pub. No. 202321022476
YOLOv5-powered robot for floating waste detection and retrieval — 60% less manual effort. ACM ICACS 2024, Hong Kong.
Python · YOLOv5 · OpenCV — ACM 2024Mobile is a pillar — not the whole house. Production stack across product, systems, and ML.
Third Place — Avishkar 2024 · Runner-up — DIPEX 2023 · Runner-up — SHRISTI 2023 · Grant — BIREC E-YUVA (₹10 lakh) · Winner — MEDHA & MEDIC-2022
"V-Safe-Anywhere: Wearable AI & IoT" — IGI Global, 2024. "Autonomous Floating Garbage Collection" — ACM ICACS 2024. Patent (granted): Smart Chairside TMJ Examination Tool. Patent (pending): AI Portable Device for Dental Disease Detection.
"Mobile Field Trip Application" — TRESL Lab Symposium, Athabasca, Canada, 2024. "Autonomous Garbage Collection" — 8th ICACS, Hong Kong, 2024.
Same engineer, five lenses — generated from one shared data source, so the facts never drift. LaTeX and generator sources live in the repo.
Open to software engineering roles, mobile/AI product work, collaborations, and interesting problems. Prefer shipping over meetings.
bhanunagpure453@gmail.com · +91 982-210-7989 · Nagpur, MH, India