Project Info

Braillience

Devpost

About Braillience 🧠 What It Does Braillience is an accessible flashcard learning platform for blind and visually impaired college students. It enables fully voice-driven studying with AI-generated flashcards, speech navigation, and screen reader support. Users can upload PDFs, generate flashcards, and learn hands-free through spoken commands. πŸ› οΈ How We Built It The frontend is built with React and TypeScript, and the backend uses Node.js + Express. We integrated VAPI for seamless voice processing and Letta File + Agent to manage intelligent flashcard generation and conversational learning. Accessibility was designed in from the start β€” with ARIA markup, keyboard-only navigation, and high-contrast visual themes. 🚧 Challenges We Ran Into Integrating Letta agents with dynamic voice feedback loops. Handling speech-to-text accuracy in noisy environments. Achieving full screen reader and browser compatibility (NVDA, VoiceOver). πŸ† Accomplishments Built a voice-first learning experience end-to-end. Successfully linked Letta AI agents with VAPI voice pipelines. Created a fully accessible workflow from upload β†’ generate β†’ learn β†’ test. πŸ’‘ What We Learned We explored the intersection of AI agents, voice UX, and accessibility engineering. We learned to design for inclusivity and discovered the complexity behind combining speech systems with real-time learning interfaces. πŸš€ What’s Next Expand Gemini AI integration for deeper content understanding. Add multi-language voice support. Launch a mobile PWA for offline study. Partner with universities to pilot Braillience in accessibility programs.

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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
  • ExpressIn code
  • HTMLIn code
  • JavaScriptIn code
  • OpenAIIn code
  • ReactIn code
  • Google GeminiClaimed
  • Node.jsClaimed
  • TypeScriptClaimed

6 of 9 appear in the indexed code. 3 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

  • Claude CodeCommits
  • CursorConfig

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

Codebase size

Source size

417 KB

Source files

97

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

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