DayBuddy — a daily companion I built for my friend Yashu

softGpt 9299 — Sun, 04 Oct 2026 10:48:51 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

DayBuddy is a daily companion I built for my friend Yashu. Her routine kept slipping — sleeping past her alarm, skipping meals without noticing, losing track of what she meant to get done, and nobody actually checking in on how her day went.

DayBuddy wakes her up by name and reads out a short plan built around her real wake time, food and exercise preferences, and whatever is on her list that day — out loud, in plain English or Hinglish, whichever she's comfortable in. She can talk to it instead of typing: add a task, do her evening check-in, or ask "why was I tired this week?" by voice. Each night's check-in quietly shapes tomorrow's plan — a missed workout becomes a lighter start, a skipped meal gets a gentle nudge, never a guilt trip.

It's built so a bad network day never breaks her morning: no AI reachable, no database reachable, no voice service reachable — the app still answers with sensible defaults, every time.

Demo

Live app: https://daybuddy-yashu.onrender.com
API: https://daybuddy-yashu-api.onrender.com

(Both run on Render's free tier, so the very first request after a quiet period can take 30–50s to wake up — give it a moment.)

Code

https://github.com/Gautam5514/DayBuddy_hack

How I Built It

Frontend — Next.js 16 (App Router), React 19, Tailwind CSS v4, shipped as a static export.
Backend — Node.js, Express 5, with Zod validating every request and every model response.
Open-source AI — Gemma 4 (Google's open-weight model), called through the free tier of the Gemini API. It writes the daily plan (greeting, schedule, meal and exercise suggestions, one specific tip) and answers questions about Yashu's own saved check-ins — only from what she's actually written down, never a guess. If Gemma is slow, unreachable, or returns something unusable, a deterministic rule-based planner answers instead, so the app never visibly breaks.
Voice — ElevenLabs — Scribe turns her spoken tasks, check-in notes and questions into text (it understands Hinglish with no extra setup), and the text-to-speech voice reads the daily plan, answers, and a brand-new daily wake-up alarm out loud. The alarm rings once a day at her saved wake time while the app is open, speaking her greeting and that day's pending tasks — with a browser-voice and a plain tone as fallbacks if the voice service is ever unreachable, so she's never met with silence.
Database — MongoDB Atlas, with an in-memory store as an automatic fallback and a background retry loop, so a flaky connection never takes the whole app down.
Observability — Sentry Agent Tracing, tracing every planning and "ask" call using the gen_ai.* semantic conventions — one span per agent run, one child span per model call, with latency and token usage. Nothing else: no prompts or answers are ever recorded, because they're someone's private check-ins.
Deployment — Render, a free Node web service for the API and a free static site for the frontend, defined as one render.yaml Blueprint.
GitHub Copilot, configured with a .github/copilot-instructions.md file so Copilot's coding agent always has the real project context — the stack, the "never break on a failed service" rule, and the "no implementation jargon in the UI" rule — plus GitHub Actions (.github/workflows/ci.yml) that lints and tests the backend and lints and builds the frontend on every push.

Why Does Open Innovation Matter?

Because Gemma is open-weight and free to call through the Gemini API's free tier, I could build this exactly the way a friend building for a friend actually would: no budget, no enterprise contract, no vendor lock-in, nothing stopping Yashu from running her own copy later if she ever wants to.

Being able to see the full shape of a prompt and its response, instead of a black box, is what let me build a rule-based fallback planner that mirrors the AI one closely enough that Yashu — or a judge — usually can't tell which one answered. That same visibility is what made the Sentry Agent Tracing genuinely useful here: it traces real call structure and real token counts, not a guess at what a closed API might be doing behind the scenes. Openness is the reason the whole app can promise "it never breaks" instead of just hoping it won't.

Prize Categories

Best Use of Gemma
Best Use of ElevenLabs
Best Use of MongoDB Atlas
Best Use of Sentry Agent Tracing
Best Use of Render
Best Use of GitHub Copilot

Thanks for reading — and happy Hacktoberfest!

SplitEase - I Built an AI-Powered Bill Splitter So My Roommates Would Stop Owing Me Money

softGpt 9299 — Sun, 04 Oct 2026 07:44:53 +0000

This is a submission for the MLH x DEV Writing Challenge

What I Built

SplitEase (live at split.elitecrew.online) is a zero-paywall expense-splitting platform for roommates, friend groups, and trips — built to kill the awkward "I'll pay you back later" conversation for good.

The idea came from a very real, very annoying problem: tracking hostel bills, flat rent, and trip expenses across a WhatsApp group and a mental spreadsheet that nobody actually updates. SplitEase replaces that chaos with one place where every group tracks who paid, who owes, and how to settle up with the fewest possible transfers.

Core features:

Groups for everything — unlimited groups and members, with roommate, trip, and one-off "Quick Split" modes
Flexible splitting — equal, percentage, shares, or exact-amount splits
QR, link & email invites — get a group started in seconds, no signup friction
Real-time group chat — payments and settlements are recorded directly inside the conversation, so there's no "wait, did you actually pay?"
Minimum-transfer settlement suggestions — the app does the math to simplify 18 messy bills into just 2 transfers
Receipt photos + a built-in AI assistant — attach a receipt and let the assistant help itemize it
Live balance dashboard & spending charts — see exactly where the group's money went, by category, by month
Secure OTP login, with TLS in transit and encryption at rest (security practices inspired by OWASP)

It's completely free — no per-member fees, no trial period, no feature gates — and it runs both as a web app and as a mobile app on Google Play.

Demo

🔗 Live app: https://split.elitecrew.online

📱 Also on Google Play (React Native app for Android & iOS)

Partner Technologies

SplitEase is a full-stack build end to end:

Frontend: Next.js, React, TypeScript, Tailwind CSS, Framer Motion, Recharts, Axios
Backend: Node.js + Express, MongoDB (via Mongoose), JWT authentication, Socket.io for real-time chat and live balance updates
Infrastructure: Firebase, Cloudinary for receipt/image storage, Nodemailer for email delivery
AI & OCR: OpenAI and Google Gemini power the in-app assistant, with Tesseract.js handling optical character recognition on receipt photos — together they turn a photographed receipt into itemized, split-ready expense data
Mobile: React Native (Android & iOS), Firebase
Quality: Jest test suites, CodeQL static analysis, and CI running fast-check property-based security tests on every push

Hackathon Experience

Building SplitEase at an MLH hackathon was less about chasing a trendy idea and more about finally fixing a problem I'd been living with for years — the awkward "who owes who" math that follows every trip, every shared flat, every group dinner. Hackathon weekends have a very specific energy: a hard deadline, a working demo as the only acceptable outcome, and no time to overthink the stack. That pressure is exactly what turned "I should build an expense splitter someday" into a real, deployed product with real-time chat, AI-assisted receipts, and a live settlement engine in one weekend.

The biggest takeaway was how much a tight hackathon timebox forces good scoping — deciding what's a "coming soon" feature (multi-currency, CSV export, UPI payouts) versus what has to work flawlessly on demo day (splitting, settling, chatting). That discipline is still shaping how I'm building out SplitEase today.

DEV Community: softGpt 9299

DayBuddy — a daily companion I built for my friend Yashu

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Prize Categories

SplitEase - I Built an AI-Powered Bill Splitter So My Roommates Would Stop Owing Me Money

What I Built

Demo

Partner Technologies

Hackathon Experience