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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. 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 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/ Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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