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Muse Spark 1.1 is here! Available today via API and on Of all the agentic use cases, I’m most excited about this one: going from a single video shot straight to task completion. 🚀 We’ve also polished and launched Computer Use and Video-Audio Perception for Muse Spark 1.1. Read more:

Jiahui Yu

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119,626 次观看 • 1 个月前 •via X (Twitter)

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Muse Spark 1.1 also excels in perception and multimodal reasoning, inspecting visual and audio inputs, preserving details across long workflows, and acting on them in real execution environments. It shows particular strengths in visual-to-code generation, rich image/video captioning, and agentic computer use. In this demo, using video shot from a smartphone, Muse Spark 1.1 extracts useful photos and reasons about the product to operate a user's browser and make a Facebook Marketplace listing on the user's behalf.

Muse Spark 1.1 also excels in perception and multimodal reasoning, inspecting visual and audio inputs, preserving details across long workflows, and acting on them in real execution environments. It shows particular strengths in visual-to-code generation, rich image/video captioning, and agentic computer use. In this demo, using video shot from a smartphone, Muse Spark 1.1 extracts useful photos and reasons about the product to operate a user's browser and make a Facebook Marketplace listing on the user's behalf.

AI at Meta

73,367 次观看 • 1 个月前

Meta's Muse Spark 1.1 beat Gemini 3.6 at making 3D arcade games for 3x cheaper! We gave two models the same task: build three 3D tabletop games that play themselves Prompts: - An 8-ball pool break scattering the rack - An air hockey rally that ends in a goal - A foosball table where the rods play the ball Outputs: Muse Spark 1.1: 31.3K tokens, $0.026 Gemini 3.6: 28.8K tokens, $0.073 Muse Spark played all three tables clean with far more detail than Gemini, which barely rendered its backgrounds. Its air hockey glitched and its foosball lagged into a buggy still picture. Muse Spark 1.1 ran the full set perfectly for 3x less

Meta's Muse Spark 1.1 beat Gemini 3.6 at making 3D arcade games for 3x cheaper! We gave two models the same task: build three 3D tabletop games that play themselves Prompts: - An 8-ball pool break scattering the rack - An air hockey rally that ends in a goal - A foosball table where the rods play the ball Outputs: Muse Spark 1.1: 31.3K tokens, $0.026 Gemini 3.6: 28.8K tokens, $0.073 Muse Spark played all three tables clean with far more detail than Gemini, which barely rendered its backgrounds. Its air hockey glitched and its foosball lagged into a buggy still picture. Muse Spark 1.1 ran the full set perfectly for 3x less

atomic.chat

125,857 次观看 • 1 个月前

Muse Spark 1.2 supports a broad range of multimodal tasks, from turning visuals into working code to translating perception into physical action. It also brings robust audio-visual understanding to enable video-heavy workflows common in real-world enterprise use. Today, we’re sharing new evals and demos that illustrate the breadth of the model’s visual understanding and reasoning capabilities. Let’s start with a demo that shows how Muse Spark parses multimodal observations and calls tools to guide a robot to navigate in an unstructured environment to find a rubber duck. 🧵👇

Muse Spark 1.2 supports a broad range of multimodal tasks, from turning visuals into working code to translating perception into physical action. It also brings robust audio-visual understanding to enable video-heavy workflows common in real-world enterprise use. Today, we’re sharing new evals and demos that illustrate the breadth of the model’s visual understanding and reasoning capabilities. Let’s start with a demo that shows how Muse Spark parses multimodal observations and calls tools to guide a robot to navigate in an unstructured environment to find a rubber duck. 🧵👇

AI at Meta

50,527 次观看 • 10 天前

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

🚨 AI News | TestingCatalog

37,973 次观看 • 1 个月前

We're excited to announce Stream's new Video API, which is now available for general use! 🎥🚀 The video API aims to make it easier for developers to build live in-app video calling, audio rooms, and live streaming. 🎧 During our eight-month beta program, our team worked closely with several companies to help integrate, test, and develop our API and SDKs. Learn more here:

We're excited to announce Stream's new Video API, which is now available for general use! 🎥🚀 The video API aims to make it easier for developers to build live in-app video calling, audio rooms, and live streaming. 🎧 During our eight-month beta program, our team worked closely with several companies to help integrate, test, and develop our API and SDKs. Learn more here:

Stream

134,124 次观看 • 2 年前

1/ I’m excited for Speak to be a featured use case for OpenAI’s Realtime API launch today! We’ve been working closely with them on this API for a while. And speech-to-speech is going to be a game changer not just for Speak but for all software.

1/ I’m excited for Speak to be a featured use case for OpenAI’s Realtime API launch today! We’ve been working closely with them on this API for a while. And speech-to-speech is going to be a game changer not just for Speak but for all software.

Connor Zwick

60,705 次观看 • 1 年前

Excited to share Muse Spark! It's a strong natively multimodal model with many surprising properties that emerged. Here, the model is able to use Python tools to make a playable Sudoku game on the web from an image input of the board. ✨

Excited to share Muse Spark! It's a strong natively multimodal model with many surprising properties that emerged. Here, the model is able to use Python tools to make a playable Sudoku game on the web from an image input of the board. ✨

Matt Deitke

29,581 次观看 • 4 个月前

Here is the latest on the A14 mitigation.For the bright spark who said on here last year that was a video from two years ago,here it is this afternoon.Every single one of these trees have count that up and tell me that was a good use of money.

Here is the latest on the A14 mitigation.For the bright spark who said on here last year that was a video from two years ago,here it is this afternoon.Every single one of these trees have count that up and tell me that was a good use of money.

john lindsell

55,083 次观看 • 6 个月前

muse spark 1.1 in AdaL Engineer beats Opus4.8 in Claude Code with 20% of the cost loop engineering, when done right, is beyond just running longer, its delivering better results when contexts are managed well and when workers are better prompted to stay honest. how GANs had changed image generation is how loop engineering is changing agentic engineering. muse sparking is truly musing the world, bringing us to the next more autonomous era.

muse spark 1.1 in AdaL Engineer beats Opus4.8 in Claude Code with 20% of the cost loop engineering, when done right, is beyond just running longer, its delivering better results when contexts are managed well and when workers are better prompted to stay honest. how GANs had changed image generation is how loop engineering is changing agentic engineering. muse sparking is truly musing the world, bringing us to the next more autonomous era.

Li Yin

63,641 次观看 • 1 个月前

Here is something completely new you can do in one shot with Runway. This entire video was generated from a single long audio file. Runway analyzed the file, read the audio transcription, and generated one coherent video to bring it all together visually.

Here is something completely new you can do in one shot with Runway. This entire video was generated from a single long audio file. Runway analyzed the file, read the audio transcription, and generated one coherent video to bring it all together visually.

Cristóbal Valenzuela

21,853 次观看 • 1 个月前

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

135,556 次观看 • 1 个月前

SPK is now available on Revolut across the EU, and UK. This expands access to Spark through one of the largest consumer financial platforms in Europe. Spark is infrastructure for on-chain credit markets, allocating capital across lending markets and liquidity venues through a rules-based framework designed for predictable liquidity and transparent risk. Today, Spark coordinates over $11B in capital powering lending and savings activity across DeFi. More on Spark:

SPK is now available on Revolut across the EU, and UK. This expands access to Spark through one of the largest consumer financial platforms in Europe. Spark is infrastructure for on-chain credit markets, allocating capital across lending markets and liquidity venues through a rules-based framework designed for predictable liquidity and transparent risk. Today, Spark coordinates over $11B in capital powering lending and savings activity across DeFi. More on Spark:

Spark

10,299 次观看 • 4 个月前

A new set of crisp and diverse default voices is now available on the platform! We’ve expanded our voice offering with multiple accents and a plethora of use cases, ranging from cartoons, video games, audiobooks to even ASMR!

A new set of crisp and diverse default voices is now available on the platform! We’ve expanded our voice offering with multiple accents and a plethora of use cases, ranging from cartoons, video games, audiobooks to even ASMR!

ElevenLabs

103,283 次观看 • 3 年前

Watch our new video on community sentencing to learn more about the use of Community Payback Orders and what they involve. The full video is available here:

Watch our new video on community sentencing to learn more about the use of Community Payback Orders and what they involve. The full video is available here:

Scottish Sentencing Council

12,379 次观看 • 2 年前

🚨 Announcing Smart Model Router - Create Your Own Custom Router To Route To Your Favorite LLM - Route to GPT Sol, Muse Spark, Grok 4.5 and Fable - Optimize for cost or performance - Use Fable only for hard coding Once you create a router, you can use it on our Chat, Agent or in an API! Coming on Monday - Support for all CLI clients including Code, Claude Code and others

🚨 Announcing Smart Model Router - Create Your Own Custom Router To Route To Your Favorite LLM - Route to GPT Sol, Muse Spark, Grok 4.5 and Fable - Optimize for cost or performance - Use Fable only for hard coding Once you create a router, you can use it on our Chat, Agent or in an API! Coming on Monday - Support for all CLI clients including Code, Claude Code and others

Bindu Reddy

2,572,380 次观看 • 1 个月前

introducing Gemini Omni 1.1 Flash this model brings a new suite of creative controls and generative video capabilities to developers - extend scenes for longer storytelling - specify first and last frames - draft videos more efficiently in 360p - upscale up to 4K resolution - add video references in your multimodal input now available via the Gemini API and in AI Studio

introducing Gemini Omni 1.1 Flash this model brings a new suite of creative controls and generative video capabilities to developers - extend scenes for longer storytelling - specify first and last frames - draft videos more efficiently in 360p - upscale up to 4K resolution - add video references in your multimodal input now available via the Gemini API and in AI Studio

Google AI Studio

178,203 次观看 • 3 天前

We’re excited to announce we’ve opened up slots to participate in a limited preview of Llama API - our developer platform for Llama app development. This is just step 1 for our API, and we look forward to hearing the community's feedback on it as we continue to iterate. Llama API has easy one-click API key creation, interactive playgrounds to explore Llama models, and dedicated compatibility endpoints for easy integration - it’s free to use during this preview period, so get started today! Join the waitlist: Learn more about Llama API: Read more on our blog:

We’re excited to announce we’ve opened up slots to participate in a limited preview of Llama API - our developer platform for Llama app development. This is just step 1 for our API, and we look forward to hearing the community's feedback on it as we continue to iterate. Llama API has easy one-click API key creation, interactive playgrounds to explore Llama models, and dedicated compatibility endpoints for easy integration - it’s free to use during this preview period, so get started today! Join the waitlist: Learn more about Llama API: Read more on our blog:

Meta for Developers

40,655 次观看 • 1 年前

Claude Sonnet 4.6 just released, and it's incredible It's just as good as Opus for most agentic tasks, but faster and a fifth of the price In this video I show you all the key differences, how to hook this up to OpenClaw, and give you 2 mind blowing use cases you can use NOW:

Claude Sonnet 4.6 just released, and it's incredible It's just as good as Opus for most agentic tasks, but faster and a fifth of the price In this video I show you all the key differences, how to hook this up to OpenClaw, and give you 2 mind blowing use cases you can use NOW:

Alex Finn

101,167 次观看 • 6 个月前

Today we're excited to launch Action: a Claude computer use launcher for macOS. Action is a macOS launcher that can take actions (click, type, and more) on your Mac using Claude’s computer use API. The interface is a floating window triggered by a keyboard shortcut, similar to Spotlight. This lets you see what the model outputs as it performs actions.

Today we're excited to launch Action: a Claude computer use launcher for macOS. Action is a macOS launcher that can take actions (click, type, and more) on your Mac using Claude’s computer use API. The interface is a floating window triggered by a keyboard shortcut, similar to Spotlight. This lets you see what the model outputs as it performs actions.

Lawrence Chen

21,622 次观看 • 1 年前

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,425 次观看 • 1 年前