Project Info
This project did not submit a demo video on Devpost.
Inspiration
Our team members were fortunate to have gotten the chance to foster their relationships with their grandparents, especially during the pandemic. Learning more about the rich - oft-forgotten - stories of our grandparents' lives filled us with warmth. We recognized two main categories of benefits from these stories - one for the senior members of a family and one for the younger members. Sharing meaningful stories helps senior members feel more connected to their families at an age marked by increased loneliness and a gradual loss of memories due to conditions. Similarly, sharing meaningful stories helps younger members appreciate their family's legacy even more. The experience provided by Ancestory also helps everyone look back on their life stories with nostalgic gratitude.
What it does
Ancestory is a web application where you can record a conversation that gets passed through AI to glean important information about the characters featured in the story, the place and time of the story, a summary of the story, etc. This important information is featured as a "node" on the knowledge graph of the senior member's life stories. One can explore the different stories and click on each to read more about the story that was narrated.
How we built it
We built it using React with TypeScript for the front end, NextJS, DeepGram for Speach to Text, and Together AI to run the LLMs on the transcript.
Challenges we ran into
One challenge we ran into was drawing an engineering design of how the different features and functionalities across the stack interact with each other. Another challenge involved some technical difficulties in using some technologies.
Accomplishments we're proud of
We're also proud of learning new technologies and learning how to learn new technologies through leveraging the support of mentors and sponsor representatives.
What we learned
We learned how to brainstorm a product idea. We had to adopt a lot of product thinking in identifying pain points from our observations, market research data to support our hypothesis, and prioritizing features in the 36-hour sprint. We also learned how to design engineering projects thoroughly before we begin building.
What's next
We're very excited about our idea and want to continue to develop it. We want to make the knowledge graph more immersive by including the ability to filter stories based on the people featured in it and the ability to play snippets of the conversation where the individuals seemed the happiest in an attempt to capture the vibe of the story for posterity. We're planning on continuing to build this out and potentially expand its application to a VR app!
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 Inter, a custom Google Font.
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
View
Metric
- 23
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
- CSSIn code
- JavaScriptIn code
- Next.jsIn code
- OpenAIIn code
- ReactIn code
- Tailwind CSSIn code
- TypeScriptIn code
7 of 7 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
44 KB
Source files
28
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
bztravis/ancestory
40 files · 430 KB · @ 974b4af
Structure
Interface
21 files · 53%Screens, components and styles rendered to the user.
API & routing
2 files · 5%Request entry points: routes, handlers and controllers.
Application logic
3 files · 8%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- TypeScript96%
- Markdown3%
- CSS1%
- JavaScript0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
package.json
npm · 36- @deepgram/sdk
- @instructor-ai/instructor
- @radix-ui/react-dialog
- @radix-ui/react-icons
- @radix-ui/react-label
- @radix-ui/react-slot
- class-variance-authority
- clsx
- graphology
- lucide-react
- next
- openai
- react
- react-dom
- react-force-graph
- react-force-graph-3d
- react-graph-vis
- reagraph
- +18 more
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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