Preliminary indexed-web scan: “Muse AI” is ambiguous. The public social results I found are mainly YouTube videos about Meta’s Muse AI agent, not a clear sample of everyday user conversation. I wouldn’t assume those videos describe the Muse product you mean.

In those videos, creators are talking about:

• What the agent can do: calendar tasks, research, shopping, and other multi-step errands. One hands-on walkthrough praises the interface but reports a shopping-task failure; another week-long test describes both useful errands and limits on precise creative work. (youtube.com)
• Trust and permissions: whether to connect personal data or allow an agent to handle payments. A hands-on review focuses on security, data use, and when the agent needs approval. (youtube.com)
• Whether it is genuinely useful or hype: explainers compare its features and cost with other assistants. These are creator/professional signals, and some videos include sponsorships or affiliate links—not independent measures of consumer sentiment. (youtube.com)

Coverage gap: This quick scan surfaced useful YouTube commentary but no Reddit discussion I could confidently attribute to the same product. Indexed results cannot tell us how common any opinion is, and “Muse AI” also appears to refer to more than one tool. No structured social records were added to the workspace.

Do you mean Meta’s Muse agent, muse.ai, or another Muse AI? If you confirm, would you like a deeper structured collection of public posts and comments? For broader evidence beyond social media, Meaningful’s separate Deep Research feature is a better fit.

What people are talking about

I collected public Reddit posts and comments and YouTube videos and audience comments. The key distinction: most recent results concern Meta’s Muse personal AI agent, while older results using “muse.ai” concern video hosting. I’ve kept those conversations separate; the name or domain alone is not enough to identify the product.

For Meta’s Muse agent, the conversation has two competing strands:

• Useful automation. Some people describe Muse as an accessible way to run browser-based or business tasks. A Reddit user comparing it with other agents praises its polished setup and background browsing; another reports using it to build business websites. These are individual accounts, not verified performance tests.
• Privacy and control. In a thread about a reported address disclosure, a commenter says they would not trust Meta with personal data. Another questions what an “Allow Always” permission means in practice. These links establish what commenters are saying, not that the underlying reported incidents have been independently confirmed here.
• Hype versus everyday value. A long Reddit critique argues consumer agents are not ready for mainstream tasks, while a hands-on enthusiast thinks Muse could displace other apps. A separate user’s $100-in-30-days experiment shows interest in testing whether it can produce a concrete outcome.

YouTube audience conversation adds practical detail. Under a positive Muse walkthrough, one commenter says cloud-based tasks continue after their Mac is off, while another reports saving time generating and filling PDF forms; other commenters in that same thread question Meta’s trustworthiness and automated phone calls. Under a business-use review, several viewers ask for access codes, and one offers a tip for organizing tasks in side chats. These links go to the video threads, not individual-comment permalinks.

Separate, older video-hosting conversation: In comments under a 2023 muse.ai hosting tutorial, viewers discuss WordPress embeds, playback quality, thumbnails, pricing, and whether hosted videos can be protected. That should not be counted as feedback on Meta’s agent.

Creator and owned signals, not community sentiment: A sponsored creator video emphasizes shopping and other agent features; a Muse-channel product tour describes intended uses. I have not blended either with independent audience comments.

Workspace update: Three collections were imported: 100 capped Reddit records, 24 YouTube video records, and 48 YouTube comment rows. These are coverage diagnostics, not measures of opinion prevalence. Several Reddit threads repeat the same reported privacy story, and the YouTube comment importer did not preserve comment text correctly in its normalized view; I checked the already-imported raw rows, but cannot offer exact comment links. The strongest next step is to choose Meta’s agent or the older video-hosting product and a time period, then expand that entity’s public conversation without mixing the two.

On X/Twitter, the posts I found are mostly about Meta’s Muse personal AI agent, not muse.ai’s video-hosting product. The useful distinction is between people describing their own experience and posts promoting an invite or repeating a news story.

First-hand conversation

• A user testing Muse against Grok Bot says Muse gives them more free usage, while Bot has an edge on browser tasks. They like both.
• One user planning a birthday evening says Muse initially proposed an impractical dinner location, then booked dinner and show tickets after more specific instructions and their permission.
• A reply about personal agents says the author has not yet found a good use case.
• A post criticizing “Muse AI” image editing reports unwanted added objects. Entity caution: that post does not clearly establish whether it means the personal agent or another Muse-branded image tool.

Creator, media, and promotional signals—not interchangeable with user reviews

• A creator shares a calorie-tracker build made with Meta Muse. It is a use-case demonstration that also promotes their video.
• A user questions what “Allow Always” should permit, responding to a reported address-sharing incident. TNW summarizes a separate disputed Messages-access claim and Meta’s denial. These show what is being discussed; this collection does not verify either incident.
• One invite-code post and another account’s invite promotion illustrate a substantial source of noise. The collection contains repeated, similar code pitches from the same accounts; I would not count each as an independent endorsement.

Bottom line: the direct X accounts here echo the Reddit discussion: enthusiasm for getting tasks done alongside questions about reliability, useful everyday applications, and permissions. This was a capped, English-language Latest search on September 30, 2026—not a representative X sample or a measure of how prevalent any view is. The search term “muse.ai” also pulled in Meta-agent promotions and unrelated posts, so it provides little reliable evidence about the video-hosting product.

The best next place is public app-store reviews, if we’re tracking Meta’s Muse agent. They may surface more first-hand reports about what people tried, what worked, and what failed than another broad keyword search. Reviews still skew toward people motivated to post; they cannot establish how typical an experience is.

After that, I’d look at:

1. Replies to specific X posts and Reddit threads. Follow accounts describing actual tasks—such as this booking attempt that needed correction—rather than collecting more invite-code posts. Replies can add corroboration or disagreement, but one thread is not a platform-wide trend.
2. Public developer and product forums. Look for connector problems, permission questions, and reproducible failures. These are useful for technical detail, though developers are not representative of ordinary users.
3. Threads, Bluesky, and comments on short-form demonstrations. These could reveal different communities’ reactions, but creator promotions and reposted news need to stay separate from first-hand use.

There’s also an entity fork: if you mean muse.ai video hosting, I’d search its product-specific reviews and hosting comparisons instead. Our existing video-hosting review concerns a different product from Meta’s agent, so combining their reactions would be misleading.

I’d prioritize app reviews, then targeted replies, and label every result by product and source type. For a broader assessment of product claims or security incidents—not just what people are saying—Meaningful’s separate Deep Research feature is the better route.

I’m treating this as Meta’s Muse agent, not the separate muse.ai video-hosting product. I can’t present an app-store review analysis yet: no Apple App Store or Google Play reviews have been collected into this workspace.

The closest existing evidence is a Reddit commenter describing what they saw in app-store reviews. They say critical reviews often concern login or waitlist access. That is a secondhand account, not review text I can verify or use to characterize reviewers overall. I wouldn’t treat the rating or review counts quoted there as confirmed either.

Understood—you want firsthand App Store and Google Play reviews of Meta’s Muse agent. I haven’t retrieved those reviews, so I can’t yet say what reviewers are reporting. The Reddit comment about app-store ratings and login problems is secondhand and isn’t a substitute for the reviews themselves.

Yes. I fetched 50 US Apple App Store reviews and 50 Google Play reviews for Muse from Meta, not the separate muse.ai video-hosting service. Both samples consist of reviews dated September 29, 2026. These are reviewers’ accounts of their experiences, not independently verified tests.

The most useful firsthand examples are:

Takeaway: Specific praise centers on delegated errands, reminders, and connected-account work. Specific complaints concern task accuracy, payments, controls, and trust in account access. I kept vague praise and a review about the advertisement’s music out of that interpretation.

Link and coverage limitation: The review scrapers returned IDs and text, but not working individual-review links: Apple supplied the same generic review URL for every item, and Google Play supplied no review URL. The links above therefore lead to the relevant app’s reviews/listing, not to the identified review itself. These two 50-review, one-day slices cannot establish overall ratings, how common a complaint is, or whether a reported event occurred as described.

The biggest gap in the app-review evidence was time: the earlier Apple and Google Play samples contained only reviews dated September 29. I collected a separate, capped sample of 40 one-star Google Play reviews of Meta’s Muse, dated September 8–18, 2026. Their review IDs do not overlap with the earlier 50-review Google Play sample.

The older reviews add more specific accounts of where people got stuck:

What this changes: the earlier newest-review slice showed both useful tasks and failures; this older, deliberately critical slice supplies more detail on connection failures, task accuracy, and access barriers. It does not show how common those problems are, or whether Muse improved between September 18 and 29. These are selected one-star reviews, not a representative sample.

The links above lead to Muse’s Google Play listing, not individual reviews: the collection returned review IDs and text but no public review-specific URLs. I kept these reviewer accounts separate from brand demonstrations and from the unrelated muse.ai video-hosting product.

Meta’s Muse: what public users report

Bottom line: Firsthand reports describe useful assistance with connected tasks, but also concrete failures in accuracy, follow-through, and account connections. These reports show what happened to particular reviewers; they do not establish how common those experiences are.

• Where it helped: A Google Play reviewer says Muse spotted an overlooked appointment email and guided phone-storage cleanup (review 3b59160a-6c9a-47f2-9ec5-fe010be63534, product listing). An Apple reviewer says it helped organize a large collection of Instagram saves and corrected mistakes when prompted (review 14607921497, Apple review page).
• Where tasks went wrong: An Apple reviewer says a movie-ticket purchase went through Fandango and incurred an avoidable fee (review 14607796724, Apple review page). Google Play reviewers report an image returned unchanged after a requested 3D edit (6a14ee7b-d297-4269-8ebc-33d7aa0b63ff) and a Gmail connection stuck in a browser–app loop (bf0a3d8c-2b8a-422d-952f-d1c14836bfa7; product listing).
• Trust and access matter even apart from performance: One favorable Google Play reviewer says they are not yet comfortable connecting other accounts (6492f13d-5851-4d62-899e-3a855623046a); another reports reaching a waitlist after downloading the app (e318ee4e-a732-415c-91cf-3b89075976f4; product listing). The latter is an access complaint, not a task test.

A firsthand X comparison similarly praises Muse’s free usage while preferring a competing agent for browser-heavy tasks. Repeated invite-code posts are promotional, not independent usage evidence. The older muse.ai video-hosting review is about a different product.

Scope: This review covers bounded samples of 50 Apple and 50 Google Play newest reviews dated September 29, 2026, plus 40 deliberately selected one-star Google Play reviews dated September 8–18. The critical sample cannot be used to estimate complaint rates or a trend. The review scrapers returned IDs but no individual public review links: the linked pages identify the product, not the specific review. Privacy and account-restriction allegations in user posts have not been independently verified.