Try it in your browser

This is the recommended first step — no local installs, nothing to configure. The repository ships a dev container, so GitHub Codespaces (or any devcontainer-compatible tool: VS Code Dev Containers, DevPod, Gitpod) gives you a fully built workspace with the Rust toolchain, the Python SDK, the example packs signed, and Studio one command away. These docs follow a codelab format: every guide is a numbered, copy-paste sequence you can run top-to-bottom in the workspace.

1. 1

   Open a Codespace

   On the GitHub repo page: Code → Codespaces → Create codespace on main. First boot builds the workspace and runs the test suite (~2–3 minutes).

2. 2

   Launch Studio

   In the Codespace terminal. This signs the example packs into packs/dist (first run mints a local dev key) and starts Studio with them trusted. Add --with-llm to also download a tiny real model (Qwen2.5-0.5B) and attach it so answers are generative:

   ./scripts/try.sh              # built-in deterministic tier
   ./scripts/try.sh --with-llm   # + a real tiny LLM (Qwen 0.5B)

3. 3

   Open the forwarded port

   Codespaces forwards port 4242 and pops an “Open in Browser” toast. Studio prints a URL containing a ?token=… in the terminal — open the forwarded port and append that same ?token=… (the token gates the API). Studio automatically allows the Codespaces forwarded domain.

4. 4

   Attach your own model (optional)

   In Studio's “Local model (BYO)” panel, click Download to fetch a GGUF (defaults to Qwen 0.5B) or paste a local .gguf path, then Attach — new turns immediately use that model. No restart needed.

5. 5

   Things to try

   what does the manual say about the pump seal
   capture this transcript asset_id: PUMP-7 condition: leaking seal
   create a task for calibrating the flow sensor
   help me plan the launch: write docs then record a demo

Option B — Google Colab (Python notebook)

Prefer notebooks? notebooks/lyboai_quickstart.ipynb is a ready-to-run Google Colab notebook: one setup cell bootstraps the runtime inside the Colab VM, then you build an agent cell by cell — local knowledge with sourced answers, a human-approval workflow (you click approve), routing trained from labelled examples with train_triggers, a pack built and signed with PackBuilder, an eval gate, and the tamper-evident audit check. No API keys, no local installs.

1. 1

   Open the notebook in Colab

   Click the *Open in Colab* badge in the repo README, or open the URL directly:

   https://colab.research.google.com/github/rajandua20/lyboai-mobile-edge-runtime/blob/main/notebooks/lyboai_quickstart.ipynb

2. 2

   Run the setup cell (~4 minutes)

   Clones the repo, installs Rust, builds the runtime binary, installs the lyboai Python SDK — all inside the Colab VM.

3. 3

   Run the rest top-to-bottom

   Each cell is one concept: knowledge → RAG → approval workflow → training → pack signing → evaluation → observability.