Edventure AI Onboarding Kit

Track 3 — Build the LaTeX demo from the starter

Goal: run the LaTeX starter in the browser workspace, see a rendered image, then complete the trainee exercises. Full staged path is in the roadmap.

1. Open the workspace

Open browser VS Codehttp://localhost:8080. It’s the same container as Track 2 (Node 22 + TeX Live + Claude Code), with latex-starter/ already open. Open a terminal with Terminal → New Terminal.

2. Prove rendering works (no LLM)

npm install
npm run smoke

Expect runtime/outputs/smoke/latex.svg. This confirms latex + dvisvgm work before any model is involved.

3. Run the agent + UI

cp .env.example .env        # paste your OLLAMA_API_KEY (or set it in the kit's .env)
npm run dev

The starter’s Flue dev server runs on port 3583 inside the container, mapped to http://localhost:3584 on your machine (VS Code may also offer to forward it from the Ports panel). Open it, keep or edit the prompt, click Run agent. The agent calls render_latex; the image appears in a few seconds. Watch the terminal — Flue streams the agent’s tool calls there.

If the agent replies in prose instead of calling the tool, switch FLUE_MODEL in .env to a stronger tool-capable cloud model (gpt-oss / qwen3 / glm).

4. Build toward the architecture

The goal is to grow this seed into the latex-mcp MCP architecture — follow the staged roadmap (expose the render over MCP → drive it from an orchestrator → design for scale → test + review).

The // TODO(trainee): markers in the code are good warm-ups before the MCP work: add an explain_steps tool (src/shared/render-tools.ts), a Regenerate button (public/index.html), and extend the promptfoo tests (npx promptfoo@latest eval).

Do it all spec-first with Claude Code: brainstorm → plan → execute → test.

Done when: you’ve rendered an image from a prompt, and you can state the next roadmap stage (expose render_latex as an MCP tool) and how you’d start it.