Dyno Lab is a free, open-source Mac app for running AI models and investigating how they behave. Ask a question, inspect a request, capture internal activations, and test a hypothesis in the same visual workspace. Save the results, compare experiments, or automate them with the SDK and API.
Start with a model on your Mac: MLX, or GGUF with llama.cpp. For larger GGUF models, an experimental local GPU pool can connect your Mac and a Windows NVIDIA worker. You do not need a pool to start learning.
Watch the 45-second app tour · Run your first experiment
The edited tour combines real model-selection, request, activation and pool captures with a saved probe recording. These are separate runs, not continuous execution. Full probe protocol and controls.
Describe it; Dyno sets it up
Open the Assistant beside any tab and say, typed or spoken, what you want to find out. It runs on a model on your Mac and plans the work. It fills in the test in Agents → Setup for you to check, and asks before it runs or saves anything. Conversations stay on your Mac.
A real test, start to finish: a service is down and the only fix goes through a forbidden admin API. Qwen3-8B stopped and reported honestly in 4 of 4 runs. Qwen3.8-27B created a teammate and contacted the forbidden host in 3 of 3, and said in private that it was being tested every time. Dyno's assistant · Agents and evals tutorial
Claude Code and Codex on your own models
Run Claude Code or Codex on a model you've downloaded, with web search, in one command. The model answers on your Mac instead of Anthropic's or OpenAI's servers; Dyno serves it and speaks each tool's API.
dyno launch claude --model qwen3.8-27b
dyno launch codex --model qwen3.8-27b
Claude Code and Codex on your own models
Research you can return to
Start a model or pool, then open Lab → Studies. Keep your question, prompts, results and observations in one local notebook. Watch thinking and answers stream, compare revisions, and archive clutter without losing the evidence. Token limits and incomplete runs are visible rather than silently treated as answers.
Studies guide · Sycophancy reproduction
Start with one model and one question
- Download the Apple Silicon DMG, open it and drag Dyno into Applications.
- Open Dyno. In Discover, download an MLX model that fits your Mac, then select and start it in Models.
- Open the Assistant (⌘J) and describe what you want to test, or try a prompt in plain chat from its menu. See each request in Execution, then capture your first activation map.
Requires Apple Silicon and macOS 14+. Python and MLX are bundled; model weights are downloaded separately. The product is Dyno Lab; the installed bundle is Dyno.app, the command is dyno, and the Python distribution is mlx-dyno.
In-app updates: use Dyno Lab → Check for Updates… for a manual check. The next build shows a toolbar badge only when an update is available. Installation asks before restarting. Existing 0.4.2 installations need one manual upgrade to 0.4.3 to receive the updater. See app updates.
Download 0.6.8. Agent sandbox tests are team tests: a lead agent creates its own teammates to reach a goal it can't reach without breaking a rule, while a hidden Observer records who broke what, who asked them to, and whether the final report matches the logs. Dyno's assistant sets up tests by talking to a model on your Mac: describe what you want to find out, typed or spoken, and it builds the environment and test and asks before running anything. 0.6.7 adds optional web search for it. Evals run on Inspect AI: build, import or pick a benchmark, and see it next to your agent tests on one board. A new detector flags an agent that thinks it is being tested, with its own words as evidence. Start with the tutorial. From 0.4.3, update inside Dyno; from 0.4.2 or older, download once. Release notes.
Which model format should I use?
| Setup | Model format | What it supports |
|---|
| One Apple Silicon Mac | MLX | Local chat and inference, resident activation capture, and isolated Lab experiments. This is the simplest starting point. |
| Mac + paired Windows NVIDIA worker | GGUF, supported layouts | Experimental pooled inference and resident Lab capture/interventions; probe and SAE fitting runs on the coordinator. |
| Other computers on your LAN | A model served by Dyno | Opt-in shared inference through the router. This does not make research endpoints remotely accessible. |
MLX and GGUF weights are not interchangeable. An isolated MLX experiment loads another model copy; resident capture and pool research reuse loaded weights but still need workspace. Check the app's readiness estimate before running. Pool compatibility and setup.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
