I put Meta’s new Muse Wonder (and Wonderful) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored everything with an LLM-as-a-judge pass and my own voice notes, then pulled together the first-look results you see here.
Listen or watch on YouTube, Spotify, or Apple Podcasts
What you’ll learn:
Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t
What active focus mode actually does on long-running tasks, and whether it’s real
The three agentic tasks where Muse ranked first, and why those categories matter
What Muse’s design output actually looks like (it’s not good)
How it compares to Sonnet on voice assistant tasks
Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable
Whether I’m actually putting Muse into any of my agent workflows
Brought to you by:
DX—Engineering intelligence for the AI era
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In this episode, we cover:
(00:00) Intro and what Muse Wonder is
(01:19) Meta’s frontier capability claims
(04:36) Muse code
(06:06) Benchmark overview and how the How I AI bench works
(07:51) Overall results and TL;DR verdict
(11:53) Agentic tasks
(13:36) Email writing and voice assistant performance
(14:35) Where Muse falls short
(18:28) SVG illustration results
(19:33) Short-form video editing
(21:18) Fast on one-shot work, slow on agent tasks
(22:15) Final verdict and where I’ll use Muse Wonder next
Tools referenced:
• Meta Muse Wonder / Muse Wonderful: https://research.meta.ai/blog/muse-wonder
• Muse Code: https://developer.meta.com/ai/lp/muse-code
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
