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BREAKING 🚨: META AI IS PREPARING TO GET NEW AVACADO MODELS, MANUS BROWSER AGENT AND INTEGRATION WITH OPENCLAW! What’s new? - Meta AI website got migrated to the new stack while retaining the same user experience. - New effort selector, email, and calendar connectors are already available to users....

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188,973 просмотров • 6 месяцев назад •via X (Twitter)

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ANTHROPIC AND OPENAi ARE IN TROUBLE⚠️ $META released their new Ai model and Zuckerberg broke down their gameplay. They dont need to be the BEST Ai, but what if they are the most affordable? This is where Facebook is finding the biggest opportunity Has $META made a Bottom?👇
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ANTHROPIC AND OPENAi ARE IN TROUBLE⚠️ $META released their new Ai model and Zuckerberg broke down their gameplay. They dont need to be the BEST Ai, but what if they are the most affordable? This is where Facebook is finding the biggest opportunity Has $META made a Bottom?👇

JEFE TRADES 🔪

31,453 просмотров • 1 месяц назад

Meta AI Chief Alex Wang comes on the Core Memory podcast this week . . . . He's been rather quiet for many months. But now Meta has a new model and more to come. We get into it all in this exclusive interview on Wed, including asking if Meta is as mercenary as it seems.
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Meta AI Chief Alex Wang comes on the Core Memory podcast this week . . . . He's been rather quiet for many months. But now Meta has a new model and more to come. We get into it all in this exclusive interview on Wed, including asking if Meta is as mercenary as it seems.

Ashlee Vance

31,063 просмотров • 3 месяцев назад

🇺🇸 ZUCK: META AI IS THE BIGGEST, BEST, IN THE WORLD!! Has anyone ever used it?... “Meta AI now has nearly 600 million monthly actives and as promised is on track to be the most used AI assistant in the world by the end of the year. Llama 3.3 is a new 70 billion parameter text model that performs about as well as our 405 billion parameter model but now it is easier and more efficient to run. So that is the last Lama 3.0 release. The next stop is Lama 4.0.” Source: Instagram
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🇺🇸 ZUCK: META AI IS THE BIGGEST, BEST, IN THE WORLD!! Has anyone ever used it?... “Meta AI now has nearly 600 million monthly actives and as promised is on track to be the most used AI assistant in the world by the end of the year. Llama 3.3 is a new 70 billion parameter text model that performs about as well as our 405 billion parameter model but now it is easier and more efficient to run. So that is the last Lama 3.0 release. The next stop is Lama 4.0.” Source: Instagram

Mario Nawfal

482,099 просмотров • 1 год назад

Big News! Meta just released Segment Anything, a new AI model that can "cut out" any object, in any image/video, with a single click. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.
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Big News! Meta just released Segment Anything, a new AI model that can "cut out" any object, in any image/video, with a single click. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.

Lior Alexander

290,330 просмотров • 3 лет назад

The new SpaceX-Cursor model is going to be insane. It is being trained from scratch, unlike previous Composer models, which were built off Kimi. This new model is Elon’s attempt to get back in the AI race, and he might rejoin as the frontrunner. Composer 2.5 is already a world-class model, only losing to models like GPT-5.5, Opus 4.8, and Fable 5. Grok Composer 3, with SpaceX’s massive compute and xAI’s team, could become the best AI model on the planet. Elon is back.
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The new SpaceX-Cursor model is going to be insane. It is being trained from scratch, unlike previous Composer models, which were built off Kimi. This new model is Elon’s attempt to get back in the AI race, and he might rejoin as the frontrunner. Composer 2.5 is already a world-class model, only losing to models like GPT-5.5, Opus 4.8, and Fable 5. Grok Composer 3, with SpaceX’s massive compute and xAI’s team, could become the best AI model on the planet. Elon is back.

Ali Romman

119,837 просмотров • 2 месяцев назад

New AI Package with DeepSeek AI Model TesseractAI continues to expand and improve our Custom AI Agent. You will now be able to add AI packages to your agent in addition to custom information. 🌟 This new feature is being developed using #DeepSeek AI model. In particular, DeepSeek R1 is designed for greater efficiency and more goal-oriented tasks, requiring fewer computational resources while still delivering powerful results. Additionally, we’re experimenting with DeepSeek’s technology for our existing Agent LLM to further enhance performance. Our goal is to make the Custom AI Agent even more efficient and effective for all your needs.
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New AI Package with DeepSeek AI Model TesseractAI continues to expand and improve our Custom AI Agent. You will now be able to add AI packages to your agent in addition to custom information. 🌟 This new feature is being developed using #DeepSeek AI model. In particular, DeepSeek R1 is designed for greater efficiency and more goal-oriented tasks, requiring fewer computational resources while still delivering powerful results. Additionally, we’re experimenting with DeepSeek’s technology for our existing Agent LLM to further enhance performance. Our goal is to make the Custom AI Agent even more efficient and effective for all your needs.

Tesseract AI

13,236 просмотров • 1 год назад

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview
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A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 просмотров • 4 месяцев назад

3. Agent factory: Foundry is the complete app platform for building apps and agents. We are adding support for more models from Grok, Hugging Face, Meta, Mistral, and more. Plus: Agentic retrieval in Azure AI Search, Foundry Agent Service, integration with Copilot Studio, and more. And we are ensuring the tools you already use for identity, management, and security will now all extend to agents too. The NFL has already used Foundry, along with our data stack, to run its recent Combine.
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3. Agent factory: Foundry is the complete app platform for building apps and agents. We are adding support for more models from Grok, Hugging Face, Meta, Mistral, and more. Plus: Agentic retrieval in Azure AI Search, Foundry Agent Service, integration with Copilot Studio, and more. And we are ensuring the tools you already use for identity, management, and security will now all extend to agents too. The NFL has already used Foundry, along with our data stack, to run its recent Combine.

Satya Nadella

184,995 просмотров • 1 год назад

Meta literally spent $72 BILLION building AI infrastructure that generates ZERO revenue. Then a Chinese company launches an AI agent in March, hits $125 million in revenue by December, and Zuck writes a $2 billion check in 10 days. But this isn't a strategic acquisition... It's panic. Here's what happened: Meta has been burning cash on AI for years. Building data centers. Hiring researchers. Training models. Claiming they're building "superintelligence." The problem: None of it makes money. Meta AI is free. Their models are open source. Their chatbots generate zero revenue. Meanwhile, investors are getting twitchy about the $72 billion infrastructure spending spree with no clear path to profitability. Enter Manus. A startup that launched 8 months ago. Founded in Beijing. Chinese founders. Moved to Singapore in June. March 2025: Manus launches with a viral demo video showing an AI agent that screens job candidates, plans vacations, analyzes stock portfolios. April 2025: Benchmark leads a $75M funding round at $500M valuation. US Senator John Cornyn immediately drags them for investing in a Chinese AI company, asking "who thought it was a good idea for American investors to subsidize our biggest adversary in AI?" December 2025: Manus announces $100M in annual recurring revenue. The fastest startup in HISTORY to hit that milestone. Revenue run rate: $125M. That's when Meta started negotiating. The deal closed in 10 days for ~$2 billion. Meta paid 4x the valuation from 8 months ago for a company that's ACTUALLY making money from AI. Here's why this matters: Manus hit $125M revenue in 8 months. Meta spent $72B on AI and has generated exactly $0 in AI-specific revenue. Zuck couldn't build profitability, so he bought it. But there's a problem. Manus has Chinese founders. Started in Beijing. Backed by Tencent and HongShan Capital (formerly Sequoia China). In the current geopolitical climate, that's radioactive. So Meta immediately issued a statement: "There will be no continuing Chinese ownership interests in Manus following the transaction, and Manus will discontinue its services and operations in China." Translation: We're buying your revenue and your team, cutting all Chinese ties, and pretending this was always an American company. This is geopolitical cleanup. The numbers tell the real story: Manus processed 147 trillion tokens in 8 months. Created 80 million virtual computers. Hit $100M ARR faster than any startup in history. Meta spent years and $72 billion trying to build this and failed. So they panic-bought the Chinese company that figured it out in 8 months. Meanwhile, this is Meta's third major AI acquisition THIS YEAR: June: Bought 49% of Scale AI for $14 billion to get CEO Alexandr Wang. Earlier this month: Acquired AI-wearables startup Limitless. Now: Manus for $2B. Meta's AI strategy is literally pay-to-win. Because after burning $72 billion, they still can't answer the one question investors keep asking: "When does AI make money?" Manus answered that question in 8 months. Meta couldn't answer it in 3 years. The craziest part: Manus charges $39-$199/month for subscriptions. That's it. No fancy enterprise deals. No complex pricing. Just a simple SaaS model that actually works. And it took a Chinese startup to figure out what Silicon Valley couldn't: people will pay for AI that actually does work instead of just answering questions. So Zuck wrote a $2 billion check, promised to cut all Chinese ownership, and is now claiming credit for "accelerating AI innovation." But everyone watching knows the truth... Meta spent $72B building infrastructure for a business they couldn't figure out how to monetize. Then they bought the company that cracked the code in 8 months. The AI race isn't about who builds the best models. It's about who builds a business that actually makes money. And right now, Meta just admitted they can't do it alone.
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Meta literally spent $72 BILLION building AI infrastructure that generates ZERO revenue. Then a Chinese company launches an AI agent in March, hits $125 million in revenue by December, and Zuck writes a $2 billion check in 10 days. But this isn't a strategic acquisition... It's panic. Here's what happened: Meta has been burning cash on AI for years. Building data centers. Hiring researchers. Training models. Claiming they're building "superintelligence." The problem: None of it makes money. Meta AI is free. Their models are open source. Their chatbots generate zero revenue. Meanwhile, investors are getting twitchy about the $72 billion infrastructure spending spree with no clear path to profitability. Enter Manus. A startup that launched 8 months ago. Founded in Beijing. Chinese founders. Moved to Singapore in June. March 2025: Manus launches with a viral demo video showing an AI agent that screens job candidates, plans vacations, analyzes stock portfolios. April 2025: Benchmark leads a $75M funding round at $500M valuation. US Senator John Cornyn immediately drags them for investing in a Chinese AI company, asking "who thought it was a good idea for American investors to subsidize our biggest adversary in AI?" December 2025: Manus announces $100M in annual recurring revenue. The fastest startup in HISTORY to hit that milestone. Revenue run rate: $125M. That's when Meta started negotiating. The deal closed in 10 days for ~$2 billion. Meta paid 4x the valuation from 8 months ago for a company that's ACTUALLY making money from AI. Here's why this matters: Manus hit $125M revenue in 8 months. Meta spent $72B on AI and has generated exactly $0 in AI-specific revenue. Zuck couldn't build profitability, so he bought it. But there's a problem. Manus has Chinese founders. Started in Beijing. Backed by Tencent and HongShan Capital (formerly Sequoia China). In the current geopolitical climate, that's radioactive. So Meta immediately issued a statement: "There will be no continuing Chinese ownership interests in Manus following the transaction, and Manus will discontinue its services and operations in China." Translation: We're buying your revenue and your team, cutting all Chinese ties, and pretending this was always an American company. This is geopolitical cleanup. The numbers tell the real story: Manus processed 147 trillion tokens in 8 months. Created 80 million virtual computers. Hit $100M ARR faster than any startup in history. Meta spent years and $72 billion trying to build this and failed. So they panic-bought the Chinese company that figured it out in 8 months. Meanwhile, this is Meta's third major AI acquisition THIS YEAR: June: Bought 49% of Scale AI for $14 billion to get CEO Alexandr Wang. Earlier this month: Acquired AI-wearables startup Limitless. Now: Manus for $2B. Meta's AI strategy is literally pay-to-win. Because after burning $72 billion, they still can't answer the one question investors keep asking: "When does AI make money?" Manus answered that question in 8 months. Meta couldn't answer it in 3 years. The craziest part: Manus charges $39-$199/month for subscriptions. That's it. No fancy enterprise deals. No complex pricing. Just a simple SaaS model that actually works. And it took a Chinese startup to figure out what Silicon Valley couldn't: people will pay for AI that actually does work instead of just answering questions. So Zuck wrote a $2 billion check, promised to cut all Chinese ownership, and is now claiming credit for "accelerating AI innovation." But everyone watching knows the truth... Meta spent $72B building infrastructure for a business they couldn't figure out how to monetize. Then they bought the company that cracked the code in 8 months. The AI race isn't about who builds the best models. It's about who builds a business that actually makes money. And right now, Meta just admitted they can't do it alone.

Ricardo

98,070 просмотров • 7 месяцев назад

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️
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Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️

AI at Meta

380,822 просмотров • 2 лет назад

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”
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Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,611 просмотров • 4 месяцев назад

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)
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Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

441,746 просмотров • 27 дней назад

Exciting update for AI developers! The Hugging Face Hub is now more natively integrated into Google Cloud Vertex AI Model Garden. Search through thousands of open Generative AI models from Hugging Face models & deploy them with one click to Vertex AI or GKE. 🤯 What's new: 🔎 Browse and search thousands of Hugging Face models directly within the Vertex AI Model Garden and filter based on what is currently trending in the community. 🚀 Accelerate your AI projects by leveraging readily available one-click deploy your model to Vertex AI or Google Kubernetes Engine (GKE). ⭐️ Featuring popular open models from BlackForestLabsAI - Unofficial FLUX.1, AI at Meta Llama 3.1, Mistral AI, Google DeepMind Gemma, and countless others. Get Started:
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Exciting update for AI developers! The Hugging Face Hub is now more natively integrated into Google Cloud Vertex AI Model Garden. Search through thousands of open Generative AI models from Hugging Face models & deploy them with one click to Vertex AI or GKE. 🤯 What's new: 🔎 Browse and search thousands of Hugging Face models directly within the Vertex AI Model Garden and filter based on what is currently trending in the community. 🚀 Accelerate your AI projects by leveraging readily available one-click deploy your model to Vertex AI or Google Kubernetes Engine (GKE). ⭐️ Featuring popular open models from BlackForestLabsAI - Unofficial FLUX.1, AI at Meta Llama 3.1, Mistral AI, Google DeepMind Gemma, and countless others. Get Started:

Philipp Schmid

34,757 просмотров • 1 год назад

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.
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As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.

Sui_Builds

12,853 просмотров • 7 месяцев назад

🤖Neurai AI Chat: New version We have updated our AI chat to improve its performance with mobiles and tablets. It has also been updated with a free LLM selector. With a mobile phone, a chrome based browser and by selecting llama 3.2 1B, it is possible to have an AI in your pocket. ✨Why is an AI chat necessary for Neurai? The value of running an AI locally is very important to avoid exposing our information to large corporations and allows autonomy to anyone. With a cell phone it is possible to improve in programming, translate between languages, find information without advertising and have a small AI agent in your pocket. ✨Integration at Neurai In the future, we will add a feature that will allow to anchor a Neurai wallet to the chat and extend the information between different devices such as IoT equipment through the blockchain's own encryption systems. #Neurai #XNA #AI #IoT #DePin #RWA If you want to try our chat on your cell phone, the link is in the next post 👇👇👇
0:48

🤖Neurai AI Chat: New version We have updated our AI chat to improve its performance with mobiles and tablets. It has also been updated with a free LLM selector. With a mobile phone, a chrome based browser and by selecting llama 3.2 1B, it is possible to have an AI in your pocket. ✨Why is an AI chat necessary for Neurai? The value of running an AI locally is very important to avoid exposing our information to large corporations and allows autonomy to anyone. With a cell phone it is possible to improve in programming, translate between languages, find information without advertising and have a small AI agent in your pocket. ✨Integration at Neurai In the future, we will add a feature that will allow to anchor a Neurai wallet to the chat and extend the information between different devices such as IoT equipment through the blockchain's own encryption systems. #Neurai #XNA #AI #IoT #DePin #RWA If you want to try our chat on your cell phone, the link is in the next post 👇👇👇

NeurAI Project / XNA

13,561 просмотров • 1 год назад

Meta just launched its 1st image model after Mark Zuckerberg’s AI shake-up. Muse Image is Meta Superintelligence Labs' first image generator after Muse Spark. They said the model will power new editing features in Meta’s Instagram photo app and be added to its marketer tools for creating platform ads. Meta had relied on Midjourney and Black Forest Labs for generation inside Meta AI. Muse Image brings that layer in-house, so Meta controls quality, cost, and product timing. Consumers get free access through Meta AI, WhatsApp chats, and Instagram Stories. Power users need Meta One or another monthly plan when free limits run out. Users can start from prompts, add photos, annotate edits, or sketch changes directly. Meta says it can erase photobombers, create QR codes, and keep visual text readable. Advertisers get variants through Advantage+ creative, with edits, style swaps, and brand-matched versions. Meta says internal tests trail GPT Image 2 but beat Nano Banana 2 on editing. This is another move in Meta's effort in trying to convert AI infrastructure spending into revenue beyond social ads.
0:10

Meta just launched its 1st image model after Mark Zuckerberg’s AI shake-up. Muse Image is Meta Superintelligence Labs' first image generator after Muse Spark. They said the model will power new editing features in Meta’s Instagram photo app and be added to its marketer tools for creating platform ads. Meta had relied on Midjourney and Black Forest Labs for generation inside Meta AI. Muse Image brings that layer in-house, so Meta controls quality, cost, and product timing. Consumers get free access through Meta AI, WhatsApp chats, and Instagram Stories. Power users need Meta One or another monthly plan when free limits run out. Users can start from prompts, add photos, annotate edits, or sketch changes directly. Meta says it can erase photobombers, create QR codes, and keep visual text readable. Advertisers get variants through Advantage+ creative, with edits, style swaps, and brand-matched versions. Meta says internal tests trail GPT Image 2 but beat Nano Banana 2 on editing. This is another move in Meta's effort in trying to convert AI infrastructure spending into revenue beyond social ads.

Rohan Paul

20,377 просмотров • 1 месяц назад

AI is coming for taste and judgment Claude Code's Boris Cherny: "We have this tendency to think....here's a thing the agent can't do yet, this must be a thing it can never do. But the rate of progress is so quick that something like taste, the agent will be able to do quite soon" co-lead cat says you can see the jump in the models directly. The front-end apps Claude builds have gotten visibly nicer between Sonnet 3.5 and Opus 4.7, and you no longer need to handhold the agent.
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AI is coming for taste and judgment Claude Code's Boris Cherny: "We have this tendency to think....here's a thing the agent can't do yet, this must be a thing it can never do. But the rate of progress is so quick that something like taste, the agent will be able to do quite soon" co-lead cat says you can see the jump in the models directly. The front-end apps Claude builds have gotten visibly nicer between Sonnet 3.5 and Opus 4.7, and you no longer need to handhold the agent.

Deirdre Bosa

21,115 просмотров • 3 месяцев назад

Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️
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Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️

AI at Meta

150,406 просмотров • 1 год назад