DEV Community: Kethan Dosapati

How many apps do you open before lunch? I put all of them into one.

Kethan Dosapati — Wed, 17 Jun 2026 19:02:22 +0000

Think about a normal day.

You open one app to log your breakfast. Another to check your steps and heart rate. One to drink enough water. A workout app at the gym. A sleep app at night. Maybe an AI thing for meal ideas, and a separate planner to keep your day straight.

That is your morning, scattered across half a dozen apps that do not talk to each other.

I got tired of the juggling, so I built Arogyamandiram. One app that quietly takes care of all of it.

🔗 Live demo: https://arogyamandiram.vercel.app
⭐ Repo: https://github.com/utsaaham/arogyamandiram

One app for the whole day

Track everything
Food, water, workouts, sleep, and weight, all in one place. It pulls in your wearable data too, so steps, heart rate, active calories, and distance live right next to everything else. Every meal breaks down into protein, carbs, fat, fiber, sugar, and sodium, not just calories.

Log by just talking
This is my favorite part. You type "chicken sandwich for lunch" and the AI figures out what you mean, fills in the nutrition, and asks you to confirm before saving. No scrolling through endless food lists.

Stay motivated
There is an XP and level system, milestone badges for things like your first meal or your fiftieth workout, and streaks across eight different categories. It also pokes you with friendly email reminders, so you actually remember to drink water or log your day instead of forgetting until midnight. It sounds small but it genuinely makes me want to keep going.

Plan your day
A Today's Plan view that pulls your whole day onto one screen. You get an overview of your workouts, AI powered workout plans, and AI powered meal plans built around your goals. It also has todos, which you set up in your settings, so your daily tasks show up right alongside everything else.

See the trends
Charts across 7, 14, 30, and 90 days for every metric, a burn rate calculator, and a sleep quality score. Plus AI insights pulled from your actual data, not generic advice.

Make it yours
Set your own daily targets for calories, protein, water, sleep, and steps. Tell it your activity level. Connect your own API keys for external integrations.

And your data stays with you

Here is the part I care about most. With seven different apps, your most personal information, your weight, your meals, your sleep, sits on seven servers you will never see.

With this, it does not.

If you are a developer, you can clone it and run the whole thing on your own machine. Your data lives in your own database, on your own computer, and never leaves unless you want it to.

If you are not a developer, just open the live demo and start using it in your browser. No phone number, no subscription, no tricks.

Even the AI respects this. It never sends your name or email anywhere, only anonymized numbers. Your API key is encrypted before it ever touches the database, passwords are hashed properly, and responses are cleaned server side so nothing private leaks into your browser.

The stack, for the curious

Next.js 15 with the App Router, TypeScript, Tailwind, MongoDB, and NextAuth for login. Charts done with Recharts.

Where it is now

It is open source and free. I built it for myself, but a few friends use it now too, which honestly feels great. If it sounds useful, the repo is up there and a star helps more than you would think. And if something breaks, tell me, I want to fix it.

I was sick of bad health apps, so I built my own with Next.js

Kethan Dosapati — Fri, 15 May 2026 01:56:01 +0000

Not going to lie I built this app mostly to scratch my own itch.

Every health tracker I tried had the same problem. Great at one thing, terrible at everything else. Or just ugly. Or both. I wanted food logging, water tracking, workouts, weight trends, sleep, and actual AI insights all under one roof, without it looking like a hospital form.

So I spent the last few months building Arogyamandiram. Here's what I actually learned.

The stack

Next.js 15 with the App Router for the full-stack setup. TypeScript in strict mode non-negotiable when you're dealing with health data and math. MongoDB because a daily log is genuinely document-shaped and fighting a relational schema for every new field felt pointless. Tailwind, NextAuth, Recharts, and GPT 4o-mini for the AI layer.

Nothing exotic. Just tools that fit.

The thing I underestimated

The food catalog. I thought it'd take a weekend. It took way longer not because the code was hard, but because getting nutrition data right is actually hard. I ended up with 150+ foods built-in and a USDA FoodData Central API fallback for everything else. Fuzzy search so typos don't ruin the experience. Auto meal-type detection by time of day.

It sounds like a boring feature. It is the feature.

Security stuff I'm proud of

User API keys (for the AI features) are encrypted with AES-256-GCM before they touch the database. Passwords hashed with bcrypt at 12 salt rounds. All API responses filtered server-side so nothing leaks into the browser network tab. Route guards at both the layout level and the API level.

Not glamorous, but it's the kind of thing you only notice when it's missing.

The AI layer and why I was careful

When you ask for AI insights, only anonymized health metrics go to OpenAI. No name. No email. Nothing that identifies you. Just numbers.

And it won't make stuff up. If you haven't logged enough data to generate a meaningful insight, it tells you that. I think an AI that says "I don't have enough yet" is more trustworthy than one that confidently fills your screen with generic advice.

What I'd do differently

Build the onboarding flow first. I built it near the end and had to retrofit the data model around it annoying but fixable. And I'd write more tests for the AI layer specifically. Prompt consistency is harder to test than regular code and I learned that the slow way.

Try It
Live - https://arogyamandiram.vercel.app
Code - https://github.com/utsaaham/arogyamandiram - MIT licensed, and the setup actually works if you follow the README.
If you find a bug, want to contribute, or just tried it - I'd love to hear from you.

Turning AI Ideas into MVPs in Weeks, Not Months

Kethan Dosapati — Fri, 05 Sep 2025 14:42:50 +0000

Ever had an AI idea you were really excited about…
only to see someone else launch it the very next day?

👉 I’ve been there. That’s why I’m starting my journey as a founder.

I’m building a platform that helps turn AI ideas into real, production-ready MVPs in weeks not months. The goal is simple: make it faster and easier for founders, investors, and builders to bring their vision to life.

⚡ What’s inside the platform

Pre-built AI infrastructure (no need to reinvent the wheel)
Multi-agent orchestration
Real-time streaming chat
Secure, locally hosted LLMs (your data stays safe)
Document Q&A (PDF, DOCX, TXT, HTML)
Web scraping + search integration
Persistent conversation memory ...and more 🚀

🎥 Platform Walkthrough

👉 Watch the walkthrough

🌐 Portfolio

🔗 Don't click me 😉

💬 Let’s Connect

I’d love to connect with other founders, investors, and AI-curious builders.
If you’re working on something in this space—or just want to brainstorm—let’s chat.

Drop a comment or DM me. Always happy to swap ideas.

AI MVP Development Made Simple: Delivered in Weeks

Kethan Dosapati — Tue, 26 Aug 2025 16:29:41 +0000

I’m working on a base AI platform to help founders and devs launch faster—without rebuilding the same boilerplate every time.

What’s inside

Editable landing page – tweak without writing code
Complete authentication system – login, signup, forgot password, JWT + API key security
ChatGPT-style interface – streaming responses, context memory (internal RAG pipelines), chat history, search, file uploads
Extensible AI agents + MCP integration – plug-and-play new agents/tools
Smart left + right sidebars – manage conversations, docs, and recent tool history
API key management – generate and use keys internally (no data leakage)
Document intelligence – upload PDFs, DOCX, TXT, HTML and query contextually
Locally hosted AI models – privacy-first architecture (your data stays with you)

👉 Working demo

Real-world test: Teacher’s AI Assistant

To test the platform, I built a teacher’s AI assistant in just weeks. It can:

Read and answer from uploaded docs
Extract key concepts + break down topics
Create structured study plans
Generate flashcards for quick revision
Build flowcharts from raw ideas
Create quizzes and grade answers
Provide last-minute exam prep with contextual Q&A

👉 Demo here

Tech stack

Backend: FastAPI + MongoDB
Auth: JWT + API Key system
Frontend: React (Material-UI, responsive + dark theme)
Integrations: OpenAI + MCP (Model Context Protocol)
Infra: WebSockets for real-time streaming, locally hosted models

While You’re Still Planning… Your Competitors Are Already Launching

Kethan Dosapati — Fri, 15 Aug 2025 00:25:07 +0000

Every week, I meet founders and investors who are sitting on brilliant ideas. They have the funding. They have the vision. But their product? Still stuck in planning mode.

Meanwhile, competitors are moving fast — launching MVPs, talking to real users, and attracting more funding.

The difference isn’t always the idea. It’s speed to execution.

What We Do

Me and my team specialize in turning ideas into production-ready MVPs in weeks, not months. Our builds are scalable, investor-ready, and tailored to your exact vision.

We Deliver

Generative AI & RAG – Vector search, semantic search, real-time document Q&A, MCP integration
Custom AI Agents – Multi-model (GPT-4o, Claude, Gemini), agentic workflows
Web & Document Intelligence – Scraping, data extraction, insight generation
Scalable SaaS Infrastructure – MongoDB, WebSockets, enterprise-grade security

My Work

Portfolio: https://dkethan.github.io/kethandosapati/
GitHub: https://github.com/DKethan

Let’s Build It

If you’re a startup founder, VC-backed entrepreneur, or investor with an idea that’s ready for execution — let’s talk. We’ll get your product live before your next investor call.

Tags: startups mvp ai generative-ai rag ai-agents venture-capital saas llm machine-learning deep-learning langchain kubernetes docker product-launch founders