Curio

A voice-first AI study companion that turns talking out loud into a living whiteboard.

Curio is built around the Feynman technique: the best way to learn something is to explain it. You talk through a topic out loud, and Curio listens like an attentive tutor — encouraging you to keep going, gently asking a question when you skip over something — while a separate brain quietly turns your explanation into a live tldraw board of structured notes, diagrams, flowcharts, and mind maps in real time.

Voice is the input. The board is the output. You walk away with a study artifact you built yourself, just by thinking out loud.

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How it works

Curio runs as a dual-channel system — two brains working off the same live transcript:

                                   ┌─ Speaking agent ──→ talks back to you (TTS)
your voice ──→ STT + turn-taking ──┤
                                   └─ Caller channel ──→ structures the board

• Speaking agent — holds a natural conversation: encourages you, asks clarifying questions, never lectures. It doesn't touch the board.
• Caller channel — watches the transcript, detects where one topic ends and the next begins (a recursive topic tree), and hands each finished topic to a Structuring Agent that decides how to draw it. It talks to the board through a tool vocabulary (addFlowchart, addMindMap, connectNodes, …) instead of dumping raw text.

The board updates over a live stream, so what you say becomes structure on screen within seconds.

Tech stack

Layer	Tech
Web app	Next.js (App Router) · React 19 · TypeScript · Tailwind
Whiteboard	tldraw · TipTap · ELK / d3-force (layout) · Framer Motion
Voice agent	Python · Pipecat (WebRTC)
Speech-to-text	Deepgram Flux (STT + semantic turn-taking)
Text-to-speech	Cartesia Sonic-3
LLM	OpenAI-compatible, via Vercel AI Gateway → NVIDIA NIM → Z.ai/GLM
Data	Supabase (Postgres + Row-Level Security) · Redis (optional board state)
Observability	Sentry (errors, tracing, agent spans + token usage)

Repository layout

Path	What's in it
app/, components/	Next.js web app + the tldraw whiteboard UI
app/api/board/	The live bridge that streams agent commands to the board
agent/	The Pipecat voice agent (see agent/README.md)
idea.md	The why — product vision and the Feynman bet
prd.md	The what — the capture→structure loop, data model, decisions
implementation.md	The how — agent topology, tech stack, integration contract

Getting started

Curio runs two processes: the web app (:3000) and the voice agent (:7860).

1. Configure environment

cp .env.example .env.local

Fill in DEEPGRAM_API_KEY, CARTESIA_API_KEY, and one LLM key (AI_GATEWAY_API_KEY, NVIDIA_API_KEY, or ZAI_API_KEY). .env.local is read by both the web app and the agent. Supabase and Sentry are optional.

2. Run the web app

npm install
npm run dev          # → http://localhost:3000

3. Run the voice agent

cd agent
uv sync              # installs pinned deps
uv run bot.py        # → http://localhost:7860

Open the app, click the orb, and start talking.

Tuning the conversation

Everything that makes the agent feel human — interruption tolerance, backchannel handling (a quick "mm-hmm" shouldn't cut it off), end-of-turn sensitivity, voice and speed — lives in agent/tuning.py, each knob documented inline.

Privacy by design

Voice and transcripts are sensitive, so storage is optional and off by default. When enabled, every write goes through Supabase Row-Level Security using the user's own access token — there is no backend key that can read everyone's notes — and error monitoring is configured to never collect personal data. Curio is also deliberately anti-cheating: it never hands you answers to copy, it just helps you explain things better yourself.