Macaw

🦜 Macaw

The assistant your Mac should have shipped with.

Ask for something in plain English and Macaw does it — sends the email, finds the file, reads the PDF, tells you what's eating your battery. It runs entirely on your Mac. Nothing is uploaded, nothing is logged, and there is no account to make.

Available today. Not a waitlist.

👉 badtheorylabs.com/macaw

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What you can say to it

"email ada the q3 numbers and tell her i approved"
"why is my mac slow?"
"read ~/Documents/contract.pdf and summarise it in two sentences"
"take a screenshot, make a folder called Shots, and move it there"
"what's on my screen?"
"am i free tomorrow afternoon?"
"find my biggest files"

It chains steps on its own. "Take a screenshot, make a folder, move it there" is three actions from one sentence, and it checks each one worked before moving to the next.

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What it can reach

97 tools, each one hand-written and tested — not a scrape of every command macOS exposes.

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Private by construction

Not a policy — an architecture. The model weighs 1.5 GB and lives on your machine. There is no server to send anything to.

• No cloud, no API keys, no account
• No telemetry, no analytics, no crash reporting
• Works on a plane
• Your mail, files and calendar never leave the device

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Specs

Two builds:

• Macaw-4bit-MLX — 1.5 GB, what the app runs. Start here.
• Macaw (this repo) — 5.4 GB BF16, for fine-tuning or serving on a GPU.

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Run it yourself

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("badtheorylabs/Macaw")
model = AutoModelForCausalLM.from_pretrained("badtheorylabs/Macaw",
                                             dtype=torch.bfloat16)

messages = [
    {"role": "system", "content": "You are Macaw, an on-device assistant running on this Mac."},
    {"role": "user", "content": "what's my battery at?"},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_dict=True, return_tensors="pt")
out = model.generate(inputs["input_ids"], max_new_tokens=128)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Pass your tools with tools=[...] and it replies with a call:

<|tool_call_start|>[battery_status()]<|tool_call_end|>

The app in github.com/Badtheorylabs/Macaw handles the rest — parsing, running, and asking before anything destructive.

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Measured

On an Apple M2, 4-bit build:

Method and harness are in the GitHub repo.

model.safetensors SHA-256 966d206651b96446f6a4c415d118049f478a8df79be3fa45bfd04959a4d68be0

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License

Weights derive from LFM2.5-2.6B under the LFM Open License v1.0. Redistribution keeps that license and Liquid AI's attribution. Commercial use is free below $10M annual revenue. The app and tooling are MIT.

© 2026 Bad Theory Labs