Project Info

StudyBuddy

This project did not submit a demo video on Devpost.

Inspiration

Accountability during studying is hard when you do it alone, finding people to help you study with is such an important aspect of learning. If there was a way to connect with people anywhere who are learning what you are, studying could be easier than ever.

What it does

Using OpenAi, matchmaking is computed for users anywhere around the world by class, major, and special interest. Join StudyBuddy through your email, enter the information you need to learn, and you'll be paired to a video chat with another student interested in studyng too.

How we built it

We used next.js to code and build the website, OpenAi was used to queue matchmaking based on course material, and Agora was used to create two way video/audio communication.

Challenges we ran into

Creating an AI matchmaking system using FetchAI was our original plan, however impelenting this idea proved more difficult than anticipated. We instead pivoted to using OpenAI to do the matchmaking instead. This was not desired, as we wanted to use our sponsor's products as much as possible, but we didn't find it detrimental to the end product. Additionally, we created a chatbox for users who cannot speak or communicate through the video, which was difficult to implement without putting a lot of strain on the server side but was eventually solved using Supabase's realtime database functions.

Accomplishments we're proud of

We successfully implemented two way video communication, profile login with google, a chatbox, as well as an embedded music player for users to listen to while they study together. We also were able to make a complete website with home, about section, and profile section.

What we learned

We learned a lot about Supabase's data base functions. One of our teammates had little to no experience coding in js, but was able to learn a substantial amount and completed a majority of front end development while the other members worked on implementing the matchmaking and video chat.

What's next

We'd like to finish polishing a lot of the front end and finishing formatting, although the video and text chat is fully functional.

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.

Learn More

To learn more about Next.js, take a look at the following resources:

Next.js Documentation - learn about Next.js features and API.
Learn Next.js - an interactive Next.js tutorial.

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.

Analysis

Compare with all teams

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

CSSIn code
JavaScriptIn code
Next.jsIn code
OpenAIIn code
ReactIn code
SupabaseIn code
Tailwind CSSIn code
TypeScriptIn code

8 of 8 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

50 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

Feature verification

7 Verified1 Code-supported1 Claimed only

AI matchmaking by class, major, and interestVerified
Using OpenAI, matchmaking is computed for users by class, major, and special interest
Claimed on Devposthigh confidence
- src/app/dashboard/actions.ts:123 — openai.chat.completions.create is called with a structured PairedMatch schema over queued users' classes/majors/interests
- src/app/dashboard/Form.tsx:14 — Form collects classes, majors, interests and submits to startSession
- src/app/dashboard/actions.ts:38 — addToQueue inserts user data into waiting_queue and triggers fetchMatches
Complete website with home, about, and profile sectionsVerified
We also were able to make a complete website with home, about section, and profile section
Claimed on Devposthigh confidence
- src/app/page.tsx:1 — Home page route exists
- src/components/AboutSection/About.tsx:9 — About section component renders product information, music/AI sections and a Get Started CTA
- src/app/profile/page.tsx:9 — Profile page fetches the user's Supabase profile and renders avatar, name, email and account actions
Email/Google account loginVerified
Join StudyBuddy through your email; profile login with Google
Claimed on Devposthigh confidence
- src/app/login/actions.ts:8 — LoginWithGoogle calls supabase.auth.signInWithOAuth with provider 'google' and redirects
- src/app/login/SignInButton.tsx:6 — Form submits to LoginWithGoogle server action
- src/app/auth/callback/route.ts:1 — OAuth callback route present to complete the login flow
Match users into a two-way video chatVerified
you'll be paired to a video chat with another student
Claimed on Devposthigh confidence
- src/app/dashboard/actions.ts:82 — After a match is found, a channel name is built from the two user ids and the user is redirected to /channel/[channel]
- src/app/channel/[channelName]/page.tsx:20 — Channel page renders the Dashboard component with the Agora appId and channel name
- src/components/Dashboard.tsx:120 — useJoin/usePublish from agora-rtc-react join the Agora RTC channel and publish local mic/camera tracks; RemoteUser renders the paired student's video
Next.js, Tailwind, TypeScript, Supabase, Agora tech stackVerified
Built with agora, google-cloud, nextjs, supabase, tailwind, typescript
Claimed on Devposthigh confidence
- package.json — Dependencies include next, @supabase/supabase-js, agora-rtc-react, tailwindcss, typescript
- src/utils/supabase/client.ts:1 — Supabase client utility present and used throughout the app
Text chatbox for users who cannot use video/audioVerified
created a chatbox for users who cannot speak or communicate through the video, using Supabase realtime
Claimed on Devposthigh confidence
- src/components/Chat/Chat.tsx:47 — Subscribes to a Supabase realtime broadcast channel scoped to the room_id and appends incoming messages
- src/components/Chat/Chat.tsx:103 — Form submit handler sends a broadcast message payload with text, user id, avatar and name
- src/components/Dashboard.tsx:67 — Chat component is rendered alongside the video call in Dashboard
Two-way video/audio communication via AgoraVerified
Agora was used to create two way video/audio communication
Claimed on readmehigh confidence
- src/components/Dashboard.tsx:4 — Imports AgoraRTC, AgoraRTCProvider, useJoin, usePublish, useLocalCameraTrack/useLocalMicrophoneTrack, RemoteUser and wires up a live call with mute/hide camera controls and a participant cap of 2
Embedded music player for studying togetherCode-supported
an embedded music player for users to listen to while they study together
Claimed on Devpostmedium confidence
- src/components/Music/Music.tsx:4 — A full Music component exists with Supabase-backed playlist fetch, realtime song inserts, play/pause and volume controls
- src/components/Dashboard.tsx:73 — The Music component import/usage is commented out in Dashboard.tsx and replaced with a static muted YouTube iframe, so the built player is not actually wired into the live call UI
FetchAI-based matchmaking (original plan)Claimed only
Creating an AI matchmaking system using FetchAI was our original plan, however implementing this idea proved more difficult than anticipated; pivoted to OpenAI
Claimed on Devposthigh confidence

An AI agent derived these features from the project’s Devpost page and readme, then searched the code for each one. Verified features are backed by cited code; claimed-only features had no supporting code, which is not by itself proof a feature is missing.