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Frontend

  • Framework: React (Vite) + TypeScript
  • Styling: Tailwind CSS + shadcn/ui
  • Linting / Formatting: Biome

Backend

  • Runtime: Node.js
  • API: Hono
    • Single codebase
    • Runs in web mode or desktop mode

Database (source of truth)

  • Web: PostgreSQL
  • Desktop: PGlite (embedded Postgres, local file)
  • ORM: Drizzle
    • Relational + app data
    • Embeddings stored via pgvector

Vector Store (derived data)

  • Embeddings (web + desktop): pgvector (Postgres extension)
    • Desktop: PGlite + pgvector extension
    • Web: Postgres + pgvector extension

Inference

  • Web: WebLLM (in-browser via @browser-ai/web-llm, Vercel AI SDK)
  • Desktop: llama.cpp (local runner / sidecar via @ai-sdk/openai)

Features:

all of these features should allow for "@"ing courses/documents/terms???

  • Dashboard (Upload Docs, Manage Context, View Courses)
  • Grounded Chat
  • Quiz Generation
  • Flashcard Deck Generation
  • Voice to Note (.md)

Performance Controls

  • Allow users to dial how much performance of there computer they want to use (CPU/GPU)
  • Find wattage Draw for the given thing they are doing. maybe a cool analytics page

Business Logic

  • Open Source
  • Point users to download the desktop app for more performance + full privacy
  • Explain Deeply what is going on when you run a LLM on your machine. Animation?

About

An open-source AI study assistant that runs locally on your machine. Generate flashcards, quizzes, and notes while tracking your environmental impact. Study smarter, drop by drop

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