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Shownotes
I put Meta’s new Muse Wonder (and Wonder Full) 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.
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
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Brought to you by:
DX (getdx.com/howiai)—Engineering intelligence for the AI era
Jira Product Discovery (atlassian.com/howiai)—Prioritize with insights, build with confidence
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In this episode, we cover:
() Intro and what Muse Wonder is
() Meta's frontier capability claims
() Muse code
() Benchmark overview and how the How I AI bench works
() Overall results and TL;DR verdict
() Agentic tasks
() Email writing and voice assistant performance
() Where Muse falls short
() SVG illustration results
() Short-form video editing
() Fast on one-shot work, slow on agent tasks
() Final verdict and where I'll use Muse Wonder next
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Tools referenced:
• Meta Muse Wonder / Muse Wonder Full: research.meta.ai/blog/muse-wonder
• Muse Code: developer.meta.com/ai/lp/muse-code
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Where to find Claire Vo:
ChatPRD: chatprd.ai
Website: clairevo.com
LinkedIn: linkedin.com/in/clairevo
X: x.com/clairevo
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.
