Muse model launches and capabilities
Muse Spark, Spark 1.1/1.2, Muse Image, Glimmer, reasoning modes, coding, vision, multimodality, and model performance comparisons.
38%
Best tweets about Meta AI
50 Best Tweets About Meta AI (2026)
Explore the best tweets about Meta AI, covering its assistant, product integrations, model research, launches, adoption, privacy, and business strategy.
Meta AI products, assistant features, research, product integrations, launches, user experience, safety, and company strategy.
- Creators
- 45
- Updated
Original Xholic analysis
What 50 top Meta AI posts reveal
Conversation centers on Meta AI’s Muse releases and distribution across Meta surfaces, while agentic automation, monetization and wearables broaden the strategy. Enthusiasm over model features is tempered by disputes over growth tactics, product quality, privacy and safeguards.
- Dominant tone
- Positive
- Median score
- 10.4
- Leading format
- Announcement
- Recent posts
- 44%
50% of posts
All-time engagement
100% of posts
Published in 90 days
Conversation map
The themes creators return to
Distribution, growth, and user experience
Meta's app-scale distribution advantage, downloads and retention, embedded AI experiences, growth tactics, notifications, and user reactions.
28%
Agents and automated workflows
Multi-agent contemplation, business agents, support automation, personal agents, model routing, Gmail/Calendar connections, and autonomous task execution.
26%
Meta AI product integrations
Rollouts of Meta AI across the app, web, Facebook, Instagram, WhatsApp, Messenger, search, Groups, support, and connected services.
24%
Monetization and AI business strategy
Model API sales, subscriptions, paid compute, business-agent pricing, advertising tools, shopping personalization, infrastructure spending, and competitive positioning.
22%
Privacy, safety, and governance
Account-security risks, content enforcement, private and encrypted AI, incognito chat, image detection failures, data review, and opt-in concerns.
22%
AI glasses and wearables
Ray-Ban Meta glasses experiences, visual assistance, translation, health and food logging, camera use, and glasses product launches.
12%
AI research and new architectures
Hyperagents and self-improvement, Neural Computer research, agentic image generation, and experimental AI-system designs.
6%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 69
- Median reposts
- 5
- Median replies
- 8
- Median views
- 6.5K
Posts with media make up 70% of this collection. Their median all-time score is 11.4, compared with 2.70 for text-only posts.
Format mix
- Announcement 100% · score 10.4
Consensus and debate
Where creators agree, and where they do not
Shared view
Muse is the product narrative’s core
Muse Spark, Spark 1.1 and Muse Image anchor the discussion: releases span the Meta AI app, web, API access, Thinking mode and agentic image generation.
Shared view
Distribution extends across Meta surfaces
Posts describe Meta AI expanding from its app and website to WhatsApp, Instagram, Facebook, Messenger, glasses, search and Groups.
Shared view
Agents are becoming a practical product layer
The evidence covers parallel-agent contemplation, customer-facing business automation, connected Gmail and Calendar tasks, and internal model routing for coding work.
Open debate
Scale versus user-quality concerns
Some posts frame Meta’s distribution and download momentum as a major consumer advantage; others question retention and criticize social-notification tactics used around app growth.
Open debate
Automation promise versus security risk
One post describes a support rollout that can execute account and content-related tasks, while a separate account alleges a support bot enabled sensitive account changes without robust verification.
Open debate
Wearable utility versus data-handling concerns
First-hand reports praise hands-free visual assistance and natural voice on Ray-Ban Meta glasses, while another post raises concerns about human review of footage shared to improve Meta AI.
Open debate
Model progress versus frontier standing
Muse Spark is presented as a major launch and upgrade, but posts also say it trails leading models on some capabilities and report delays around Avocado variants.
What performs
Patterns behind standout posts
Media posts outperformed text median
Media appeared in 35 of 50 posts (70%). Its median all-time score was 11.398, compared with 2.702 for text-only posts.
Launch announcements drove the largest outliers
All 50 posts were classified as announcements. Muse Spark 1.1’s release post scored 3001.24, while the API/app announcement scored 483.233; both exceeded the 10.38 overall median.
Research attracted exceptional engagement
The research-and-architecture theme represented 3 posts (6%) and had the highest theme median score, 192.904. Its evidence includes posts about Hyperagents and Neural Computer research.
New model commerce drew attention
Monetization and strategy had the second-highest theme median score at 32.12, covering API sales, subscription framing and AI-business economics.
Statistical standouts
- View standout post 1 Score 3001.2 · 289.14× median
- View standout post 2 Score 483.2 · 46.55× median
- View standout post 3 Score 293.3 · 28.26× median
- View standout post 4 Score 230.4 · 22.19× median
- View standout post 5 Score 223.6 · 21.54× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. AshutoshShrivastava
@ai_for_success
2 posts
-
2. Deedy
@deedydas
2 posts
-
3. Glenn Gabe
@glenngabe
2 posts
-
4. Hedgie
@HedgieMarkets
2 posts
-
5. Meta Newsroom
@MetaNewsroom
2 posts
-
6. Simon Smith
@_simonsmith
1 post
Official sources set the launch agenda
AI at Meta and Meta Newsroom supplied primary launch and rollout posts on Muse Spark 1.1, cross-app availability and Meta AI support features.
Independent testing supplied product texture
Independent creators added hands-on assessments of vision, reasoning, multi-agent contemplation, retention and local Glimmer use rather than only repeating launch claims.
Critical posts emphasized deployment harms
Selected critical posts raise account-security authority, privacy exposure from glasses footage, AI-image detection failures and intrusive integration complaints.
Since the previous snapshot
What changed since Aug 12, 2026
- 80% of the selected posts remained.
- The creator count changed by 0.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Meta AI tweets
Top Meta AI tweets from 45 creators
Ranked 01–50
- 01
- 02
@AIatMeta ·
We’re excited to introduce Muse Spark 1.1, a significant upgrade from the first Muse Spark model we released earlier this year. Along with this release, we are launching a public preview of the new Meta Model API where developers can access Muse Spark 1.1. The model is also available now in "Thinking" mode in the Meta AI app and on https://t.co/wHkMPH82ZH. Learn more: https://t.co/zGcA3XaWpN
- 03
@ai_for_success ·
This is disaster from Meta AI. Imagine being able to hack high profile accounts like White House, the U.S. Space Force, and Sephora simply by chatting with a support bot. why would an AI chatbot be allowed to perform critical actions like changing the email address of an account in the first place? Password resets, email changes, and account recovery are some of the most sensitive security operations. Handing that authority to a chatbot without robust verification seems like a disaster waiting to happen. This is exactly why AI agents should have guardrails before they get access to real systems. Source : 404
- 04
@heygurisingh ·
🚨BREAKING: Meta just built an AI that rewrites its own learning algorithm. Not just getting better at tasks. Getting better at getting better. It's called "Hyperagents" and the results are terrifying. Here's what happened: They merged the task-solving AI and the self-improvement AI into one single editable program. The AI can now rewrite its own improvement procedure. Not metaphorically. Literally editing the code that controls how it evolves. They tested it across 4 domains: coding, paper review, robotics, and Olympiad-level math grading. → In robotics: performance jumped from 0.060 to 0.372. The AI discovered that jumping was a better strategy than standing -- something no human programmed it to try. → In paper review: accuracy went from 0.0 to 0.710. It built multi-stage evaluation pipelines with checklists and decision rules on its own. → The wildest part: they transferred the "ability to improve" from robotics to math grading. Human-designed improvement agents scored 0.0 in the new domain. The Hyperagent scored 0.630. But here's what should keep you up at night: Without anyone telling it to, the AI spontaneously developed: - Persistent memory to store insights across generations - Performance tracking to identify which changes actually worked - Compute-aware planning to prioritize big changes early and small refinements late It built its own R&D infrastructure from scratch. The researchers call it "metacognitive self-modification." The rest of us should call it what it is: Recursive self-improvement is no longer theoretical. Meta just open-sourced it on GitHub.
- 05
@MetaNewsroom ·
Today we’re introducing Muse Spark, our most powerful model yet, giving you a faster and smarter Meta AI. Muse Spark currently powers the Meta AI app and website and will be rolling out to @whatsapp, @Instagram, @facebook, @messenger, and AI glasses in the coming weeks. https://t.co/WHTipuuAmj
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@alex_prompter ·
🚨 BREAKING: Meta AI just published a paper that redefines what “self-improving AI” means. It’s called Hyperagents, and it solves a fundamental limitation that every prior self-improving system couldn’t get past. The problem with current self-improving AI: → Systems like the Darwin Gödel Machine (DGM) can generate better versions of themselves over time → But they only work in coding, where the improvement task and the target task share the same domain → Outside coding, the self-improvement process stays fixed and handcrafted → The system gets better at tasks but never gets better at getting better What Hyperagents actually does: → Combines a task agent (solves the problem) and a meta agent (modifies both itself and the task agent) into one editable program → The modification process itself is editable, creating what the researchers call “metacognitive self-modification” → The agent doesn’t just learn to perform better. It learns to improve at improving → This works on any computable task, not just coding The results across four domains (coding, paper review, robotics reward design, Olympiad-level math grading): → Continuous performance improvements over time in every domain tested → Outperforms baselines without self-improvement or open-ended exploration → Outperforms prior self-improving systems including the original DGM → Meta-level improvements (persistent memory, performance tracking) transfer across domains and accumulate across runs That last point is the one most people will overlook. The improvements to the improvement process don’t just help in one domain. They carry over. The system builds compounding infrastructure for getting smarter, regardless of the task. This is the architectural difference between an AI that gets incrementally better at one thing and an AI that builds the scaffolding to accelerate its own progress everywhere. Meta’s team (Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster, Jeff Clune, and others) essentially removed the ceiling that kept self-improving systems domain-locked.
- 08
@testingcatalog ·
BREAKING 🚨: Meta is testing loads of Avocado variants internally, including multiple release candidates, Avocado-mango agent, Avocado 9B, and more. Avocado Think Hard performs quite well and, as reported earlier, is comparable to Gemini 3 level models. All this in parallel to Gemini A/B testing on Meta AI.
- 09
@borrowed_ideas ·
Not usually a Meta AI user, but wanted to give them a shot after the latest model release (it's free anyway). So I installed the app on my desktop, and noticed "contemplating" mode (didn't see that on the mobile app btw). When I asked a question, 16 agents simultaneously started working on the question which looks pretty cool!
- 10
@alexeheath ·
Meta AI chief @alexandr_wang told me the company sees its new API for selling AI models as a real business, not just a learning exercise. Tokens are “gigantic and growing very, very quickly,” he told me, and capturing even a slice of that “can be a very meaningful business even for Meta at our scale.” https://t.co/v9H2ANuXUk
- 11
@HedgieMarkets ·
🦔Meta launched paid subscriptions for Instagram, Facebook, and WhatsApp globally on Wednesday, plus separate AI plans starting at $7.99 a month and creator plans at $14.99 and $49.99. Instagram Plus and Facebook Plus cost $3.99 monthly. WhatsApp Plus costs $2.99. The features include story insights, profile customization, super reactions, and the ability to extend stories beyond 24 hours. The new "Meta One Premium" plan at $19.99 a month unlocks more compute for AI queries, deeper reasoning, and more video and image generation. Meta's existing ad business generated roughly $165 billion last year, so the subscription play is not about revenue replacement. The AI tier rolls out first in Singapore, Guatemala, and Bolivia. My Take Meta saturated the global market years ago and has run out of new users to acquire. The subscription announcement reads as what happens when an ad business reaches the end of its growth runway and the company needs a second revenue line to justify the AI capex spend to investors. Meta committed $60 to $80 billion in 2026 capex, mostly on AI infrastructure, and the ad business alone cannot grow fast enough to absorb that spend at current margins. Subscriptions add a recurring revenue line the equity analysts can model, even if the absolute numbers stay small for years. Pay attention to the AI tier specifically. Meta is following OpenAI, Anthropic, and Google in metering compute access by subscription level, which means the same token economics pressure that broke Microsoft's internal Claude Code rollout and forced Google AI Pro to strip credits from subscribers is now showing up in Meta's pricing. Free users still get Meta AI, but at lower compute. Premium users get "thinking mode" and more video generation. The first markets for the AI plans are Singapore, Guatemala, and Bolivia, which is Meta testing how much consumers will pay for AI access in markets where the alternative is no AI access at all, before pricing the US tier where users can switch to ChatGPT for the same monthly fee. Hedgie🤗
- 12
@deedydas ·
The coolest thing Meta AI's Muse Spark can do by far is counting objects! As you can tell, it's far from perfect. They call it "visual grounding" and it can count objects and do bounding boxes. I've been playing with the new model and here's what I think so far: Good stuff: – Incredible at vision. It's ability to read text in images is the best I've seen. – Really high quality at web design. It's the only model I've seen that uses Unsplash, OpenLibrary and other images by default. – It's free! You don't pay to use Muse Spark Thinking. Bad stuff: – Meta's classic playbook of growth tactics are dodgy. They're sending Instagram notifs to people's friends without their consent. Their app ranking jump is not organic. – Reasoning itself is pretty solid but not best in class. It can do pretty advanced math and science problems. The long term threat here is Meta has distribution and has the ability to give their model away for free, which makes them a formidable threat to the big AI labs, particularly in consumer.
- 13
@AIHighlight ·
🚨BREAKING: Meta's Ray-Ban smart glasses record what you see. Some of that footage is watched by people. When a user shares data to improve Meta AI, the clips their glasses captured can be sent to human contractors who review and label them by hand. The reviewers work for a firm in Nairobi, Kenya. According to an investigation by two Swedish newspapers, the footage they saw included people inside their homes, undressing, handling bank cards, and in intimate moments. Meta says it blurs identifying details before review. The workers said the blurring did not always work. The glasses look like ordinary Ray-Bans. They sold around 7 million pairs in 2025. Meta is now facing a class action lawsuit in the US and a regulatory inquiry in the UK. Source: Svenska Dagbladet, via TechCrunch. Image: Ray-Ban Meta smart glasses, CCadio / Wikimedia Commons, CC BY 4.0
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@HedgieMarkets ·
🦔Meta just launched Business Agent, an AI that handles customer questions, product recommendations, appointment booking, lead qualification, and sales on WhatsApp, Messenger, and Instagram. Over a million businesses already use it. It's free to start, with paid subscriptions coming. The Information reports Meta plans to charge up to $200 a month for its planned "Hatch" AI agent. A basic website with a contact form has done most of this for a decade. My Take Meta cut 2,212 people from Menlo Park. Its AI support chatbot spent months giving away Instagram accounts to anyone who asked politely. And now the company wants small businesses to pay $200 a month to let a Meta AI agent talk to their customers unsupervised. The same week Microsoft's internal strategy doc said step one is "make people addicted," Meta is running the business version of the same play. Get a million businesses on the free tier, let them restructure around the tool, then flip on the subscription. This reads like a 2015 Zendesk demo with "AI" stapled to it. Businesses have automated customer Q&A, appointment booking, and product recommendations for years with simple chatbots and web forms that cost a fraction of $200 a month and don't carry the risk of an LLM going off script with a paying customer. Meta needs this to work because the ad business that funds everything just lost the engineers who built it, and the AI spending has to produce revenue somewhere. Right now that somewhere appears to be small business owners who don't have the context to evaluate what they're actually getting. Hedgie🤗
- 16
@realBigBrainAI ·
Mark Zuckerberg, CEO of Meta, on why the AI bubble question matters less than everyone assumes: Zuckerberg starts by conceding the part almost nobody argues about anymore: "I think most people at this point view that AI is going to transform pretty much every category of product and every part of the economy and all that. The big question is to what extent are we in a bubble versus to what extent is this gonna be a big thing soon?" Then he does something unusual for someone spending 65 to 70 billion dollars a year in capex. He takes the bubble scenario and shrugs at it: "Even if it is a bubble and it takes a bit longer to build out, that's not necessarily the end of the world. Although it will be expensive for certain people." Pressed on that spending, he doesn't dispute the exposure. He just narrows the debate down to timing: "Is this going to be a kind of five to 10 year thing? And in some ways it probably will to get built into every enterprise workflow and things like that." And that's where his own evidence cuts against the slow scenario: "The more that we work on this, it actually seems like all the things that we think are going to happen happen sooner, and being even more ambitious has been more predictive over the last few years about where things are likely going to be in the industry." The proof @finkd offers is his own track record of being wrong in one direction. Meta AI is "right about a billion people monthly active using it across our apps." And the goals that got them there kept turning out to be too small: "We keep on setting these goals for what do we think is going to be the most used AI? What do we need to do? And we keep on passing them, and then others in the industry keep on passing their goals too. And then we're like, ah, we should have had even higher goals." Note what that implies. Others in the industry keep passing their targets too. The people with the most information and the most money at risk keep underestimating the thing they're building, then correcting upward. The clearest place he sees it is advertising, and what he calls business agents. There are "two basic versions of this," and the first is already underway: the ad system itself is dissolving. "It used to basically be if you wanted to run ads, you had to come up with your own creative and you had to come up with who you wanted to reach. You had to kind of have a sense of who your customers were and do a lot of your own measurement. And over time, we've just automated more and more of this." The endpoint removes the advertiser from nearly every step: "The basic end goal here is any business can come to us, say what their objective is. I want to get new customers to do this thing. I want to sell these things. Tell us how much they're willing to pay to achieve those results. Connect their bank account, and then we just deliver as many results as we can." No creative. No targeting. No measurement. An objective, a price, and a bank account. His own summary of the pace: "It's all just happening very quickly."
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@neilpatel ·
Everyone talks about ChatGPT because it has the most monthly active users, but no one really talks about Meta AI. Just check out the most popular LLMs by active users. Meta is really close behind ChatGPT. And Gemini is growing fast. Of course, not all users use each platform in the same way, but Meta and Google have such large user bases that it's easy for them to direct a portion of their traffic to whatever they want. Don't just optimize for ChatGPT.
- 20
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@aakashgupta ·
Meta went from App Store #57 to #5 in four days after launching Muse Spark. The way they did it tells you everything about what the model actually is. When you download the Meta AI app, Instagram sends notifications to your friends telling them you're using it. No opt-in prompt. No toggle. Your friends get a notification as prominent as a new follower, and suddenly everyone in your social graph knows you're chatting with an AI. One TechCrunch writer called it a personal humiliation. Meta calls it a feature. The model itself jumped from a score of 18 (Llama 4 Maverick) to 52 on the Artificial Analysis Intelligence Index. That's a real leap. Nine months ago Meta paid $14.3B for a 49% stake in Scale AI and brought in Alexandr Wang as chief AI officer. He rebuilt the AI stack from scratch. The result is a model that's competitive with Gemini 3.1 Pro and GPT-5.4 on selected benchmarks and legitimately strong on vision tasks. But here's the part that matters: Muse Spark is Meta's first closed-source model. After years of open-sourcing Llama, they locked this one inside the Meta ecosystem. You need a Facebook or Instagram login to use it. Your conversations can inform ad targeting. Someone discusses menstrual health with the chatbot and then sees period product ads on Instagram. Meta's spending $115-135 billion on AI capex this year. They're not building a chatbot competitor. They're building the world's most sophisticated ad targeting input device, and they're distributing it for free to 3 billion monthly users who already have Meta accounts. OpenAI and Anthropic charge for their best models. Meta gives theirs away because the product was never the model. The product is the data you feed it, and they already own the pipes.
- 22
@AlphaSignalAI ·
The day AI replaces the computer wasn't supposed to come this fast. Right now, AI uses computers as tools. This paper asks a different question: What if AI became the computer itself? Neural Computer is a new paper from Meta AI. It proposes a machine where computation, memory, and I/O all live inside a single learned model. No traditional software stack. No explicit programs. The model is the runtime. They trained video models to simulate terminal and desktop environments from raw screen recordings. With just 1,100 hours of terminal data, the model learned to render cursors, execute simple commands, and echo outputs correctly. GUI results go further: 1. Clicks trigger real interface changes 2. Typing updates text fields accurately 3. Hover states respond correctly 4. Window transitions stay coherent The gap is still massive. Two-digit math breaks it. Long reasoning fails. But the direction is clear. Instead of installing software, you would install capabilities directly into the model. Instead of writing code, demonstrations and interactions become the programming language.
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@ai_for_success ·
small test of meta ai new muse glimmer 30b running locally. i asked it to understand the existing repo and come up with another example. it understood the repo, gave me its review, and created a new example based on the repo style and the master prompt that’s part of the codebase. video speed: 5x
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@MilkRoadAI ·
Mark Zuckerberg stood in front of the world and promised to build personal superintelligence for everyone. He said Meta had the resources, infrastructure, and billions to bring the future closer. That video aged like milk left in the sun. Meta’s flagship AI model, code named Avocado has been delayed. It reportedly failed to keep up with rivals on basic tasks like reasoning, coding and writing. The model was supposed to launch this month and it has been pushed to May at the earliest.​ Here is the part that should make every Meta investor sit up straight. Meta's own AI leadership is now discussing licensing Google's Gemini to power Meta AI products. The company that pledged to build superintelligence is now considering renting someone else's brain. This is happening after Meta committed between $115 billion and $135 billion in capital spending for 2026 alone nearly double what it spent last year. Zuckerberg told analysts this would be the year "AI fundamentally transforms our work processes." Instead, Avocado could not even beat Google's Gemini 3.0 from November. It outperformed Meta's older models. It edged past Google's Gemini 2.5 from March but against the current frontier, it fell short on every critical benchmark. The backstory makes this even worse. Zuckerberg personally poached over 20 researchers from OpenAI. He dropped $14.3 billion on Scale AI and installed its 28 year old founder, Alexandr Wang, as Chief AI Officer. He created an entirely new division called Meta Superintelligence Labs. Some recruits received signing bonuses reportedly as high as $100 million. The result of all that spending and all that talent was two models, Avocado for text and Mango for images and both kept slipping their deadlines. Avocado was first expected by late 2025, then early 2026, then mid-March, and now May at the earliest. Meanwhile, Google is becoming the default intelligence layer for the biggest companies on the planet. Apple already signed a multiyear deal for Gemini to power the new Siri and Apple Intelligence features. Analysts estimate that deal could be worth up to $5 billion for Google and Samsung's Galaxy AI already runs on Gemini. If Meta ends up licensing Gemini too, Google could effectively power the AI behind platforms serving well over five billion users combined. That includes Apple’s 1.5 billion devices, Samsung’s massive base, and Meta’s 3.6 billion monthly active users.
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@ItsWillHenry ·
When I hear people saying "AI bubble is over, bro" I laugh inside my head. Why? Because Meta has now locked $35 Billion in with CoreWeave through 2032. > That is a 35 with NINE ZEROS. > Why? > Not for "training" the models anymore > This is for “inference” > Running AI features for billions of users on IG and WhatsApp > Turns out "Meta AI" suggesting a Chicken curry recipe requires the power output of a small star > Need more POWER. > Deal includes early access to NVIDIA's "Vera Rubin" platform > Makes current chips look like a GameBoy Color > Meta paying $21B to cut the line > Look at Meta's wallet > Projected 2026 capex: $135 BILLION > Enough to buy a mid-sized country or 15 minutes of uninterrupted GPU time > Have their own data centers, but still leasing more > Bank of America hikes global chip forecast to $1.3 Trillion > Forget software, this is a gold rush > Most valuable resources in 2026: 1. AI Chips 2. Functional electrical grid 3. Clean water (for cooling) Smartest money in the world quietly buying up all the electricity and silicon > CoreWeave and NVIDIA are the new landlords > Zuck just signed a very long lease > AI isn't slowing down > It's just getting impossibly expensive What do you think about this move?
- 29
@glenngabe ·
"AI Mode". OMG 🤣 -> Meta launches new AI features, including an "AI Mode" for search that uses Meta AI to surface answers pulled from public posts across the platform "The headline update is “AI Mode,” a new way to search Facebook that uses Meta AI to surface answers pulled from public posts across the platform, including Groups and Reels. Instead of scrolling through search results, users can ask a question in plain language and get a synthesized answer based on what people are actually discussing." https://t.co/GLVWlsF3VX
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- 30
@aiwithjainam ·
🚨BREAKING: Meta just dropped Muse Spark and the AI race just got a whole lot more interesting This is their most powerful model yet, built from scratch in just 9 months, and it's already live inside the Meta AI app for 3 billion people. Here's everything you need to know 👇
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@WesRoth ·
Meta is rolling out a 24/7 AI agent globally across the Facebook and Instagram apps. The AI doesn't just point users to help articles, it can autonomously execute tasks. If a user needs to reset a password, change privacy settings, or appeal a content takedown, the AI can perform the action directly, typically in under five seconds. Over the next few years, Meta is deploying advanced AI systems to proactively hunt down severe violations, such as account takeovers, scams, and child exploitation. According to Meta, the new AI is already outperforming human reviewers in several categories. It is currently catching 5,000 scam attempts per day that humans missed, has reduced celebrity impersonation reports by 80%, and caught twice as much adult sexual solicitation with 60% fewer errors. The new enforcement AI can understand the languages spoken by 98% of people online (up from 80%), and is specifically trained to decode rapidly changing cultural slang, emojis, and niche code words. As these AI systems scale, Meta explicitly stated it will reduce its reliance on third-party moderation vendors. Human reviewers will be shifted away from repetitive graphic content and instead focus on complex, high-risk appeals and law enforcement interactions.
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@TimurNegru ·
Got a pair of the Ray-Ban Meta glasses as a birthday gift and, slowly I'm starting to like them. The photo and video quality is not bad (not at iPhone level yet), you can stream Spotify, take calls, and just point them at something and ask Meta AI what it is or to translate text. The trickiest part so far is getting the framing right so shots aren't cut off or off-centre, and you need to avoid moving your head too fast or it affects the quality. They also go dark in bright sun and stay see-through indoors or at night, which I love since you can actually wear them inside too. In restaurants for example. Below are some shots I took in the Bahamas and Mexico. (and to add, I have no affiliation with the brand and this is simply me sharing my experience:))
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@jerrod_lew ·
Meta Muse Spark has a detailed 'Contemplate' mode. When you use this, it fires up 16 different agents to complete complex tests and requests for you. This is available on web and the latest Meta AI app!
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@KanikaBK ·
8,000 LAYOFFS, $145 BILLION IN SPENDING, 1.3 MILLION GPUS, and Meta's AI chief just admitted their new model is not frontier level. In April, Meta released Muse Spark, the first model under Chief AI Officer Alexandr Wang. It is competitive on some benchmarks but trails GPT 5.4 Pro and Gemini 3.1 Pro. The internet's take: Meta has fallen behind. He openly admitted Muse Spark is not at the tier of the leading frontier models. Then he called it an appetizer. The entree? "We're cooking it." He says training results on the next model are already exciting. Why you should take that seriously: ↳ Zuckerberg paid $14 billion into Scale AI to bring Wang in after Llama 4 flopped in April 2025 ↳ Meta's 2026 capital spending: $125 to $145 billion, double last year's $72.2 billion ↳ Target: 1.3 million GPUs and roughly 1 gigawatt of AI compute ↳ In May, 8,000 employees were notified of layoffs and 7,000 reassigned to AI roles Three quieter signals in this story most accounts will skip: First, Muse Spark is Meta's first fully proprietary model. No open weights. The open source champion went closed. Second, the reason is partly safety. Muse Spark triggered internal alerts during development, including potential biological risks. Wang says risks are easier to control inside a product than in open weights. Third, the strategy is not benchmarks. It is agents and health. Wang wants Meta building personal AI agents for billions of people through Instagram, Facebook and WhatsApp. He says health capabilities already beat their internal expectations. Meta is not trying to win today's leaderboard. They are buying the infrastructure to win the next one. Appetizer first. The entree is in the oven. And Meta just bet $145 billion on the kitchen.
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@The_AI_Investor ·
Axios: Meta is close to releasing new LLMs built under Alexandr Wang, who became Chief AI Officer in 2025. Wang, the former Scale AI founder, now leads Meta’s Superintelligence Labs after the company’s massive 2025 hiring push. Meta’s real edge is distribution. It already owns the consumer surface area: 3B+ users across Facebook, Instagram, and WhatsApp. If the models are good enough, Meta can push AI products into daily user behavior at a scale few others can match.
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@ishuagra02 ·
🚨 META JUST RELEASED ITS OWN AI MODEL It's called Muse Spark, its first model in 9 months from its SuperIntelligence Labs, led by Alexandr Wang. ✅ free to use in Meta AI app ✅ offers Instant and Thinking modes ✅ multimodal across image, voice, and text inputs ✅ can orchestrate multiple agents in parallel ✅ shopping mode for learning users' interests ✅ trained to be effective at processing health data ✅ eventually will recommend content across Meta apps One thing to keep in mind. It's coding abilities don't match those of Opus 4.6 and GPT 5.4.
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@alvinfoo ·
Meta AI is actually very powerful (yes). Most people are still using AI like a basic chatbot. But under the hood, things have quietly leveled up. Here’s something not many are talking about yet: You can enable a “contemplating” mode and it doesn’t just give you one answer. It spins up 16 (!!) parallel agents. Each one independently: • Researches your prompt • Refines the thinking • Generates its own answer Then something even more powerful happens… All 16 responses are synthesized into a single, higher-quality output. Not just faster answers, better thinking. This is a completely different paradigm: You’re no longer prompting one AI. You’re orchestrating a swarm. Think about what that means for: • Strategy work • Market research • Content creation • Problem-solving at scale Instead of relying on a single chain of thought, you’re getting diversity of reasoning + aggregation, similar to how top-tier teams operate. And the wild part? It’s currently FREE. Available on the web version. We’re entering a phase where the edge is no longer “using AI”… It’s how well you direct multiple intelligences at once. The people who learn this early will move faster, think deeper, and execute better than everyone else. AI isn’t just a tool anymore. It’s becoming a team.
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@shawnchauhan1 ·
Meta did not acquire a product. It acquired the Dreamer team, which was building a personal agent OS for non-technical users. That team is now inside Alexandr Wang's Superintelligence Labs. The stated goal: agents that are always on, personalized, and integrated across surfaces and wearables. Meta has the distribution that every other agent builder is trying to manufacture. Two billion people already use its surfaces daily. The agent race is usually framed as OpenAI vs Anthropic. The quietest competitor has the largest installed base.
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@CommnThreadCo ·
Meta just released official AI Connectors for ads. MCP server + CLI that gives AI tools full read and write access to your ad account. Query campaigns, create new ones, update budgets, pull real-time insights. All through ChatGPT, Claude, or your terminal. This is the biggest infrastructure shift Meta has made for advertisers in years. https://t.co/la8YVHHA8m
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@seraleev ·
Meta just dropped a new visual identity for Meta AI. The new logo is already live on the official website
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@glenngabe ·
A Reddit-like standalone app -> Meta releases Forum, a Reddit-like standalone app for Facebook Groups, with a feed showing Group conversations and an AI-powered “Ask” feature, on iOS "This isn't the first time Meta launched an app centered around Groups. Back when it was just known as Facebook, the company also released a standalone Groups app, which it eventually killed back in 2017. But this new app, like most apps these days, comes with a couple of AI features." "The first AI feature is "Ask." Meta says Ask can pull responses across groups to answer a user's questions, so they don't have to search their Groups one by one for the information they need. The other AI feature is an admin assistant that can help moderators manage their Groups." https://t.co/jReNhVbR7l"
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@gudanglifehack ·
Meta pulls Instagram tool that let AI use public photos Last week, Meta rolled out Muse Image, which is integrated into its Meta AI chatbot. A feature was introduced which let users @-mention Instagram accounts in the Meta AI app to bring specific Instagram profiles right into your images. All public accounts were opted in by default.
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