Show Notes
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 — Brought to you by: DX—Engineering intelligence for the AI era Jira Product Discovery—Prioritize with insights, build with confidence — 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 Wonder Full: 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/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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