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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... show more

AI at Meta

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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:

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

119,626 görüntüleme • 1 ay önce

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

50,615 görüntüleme • 10 gün önce

Here’s an example of Muse Code’s multimodal visual coding capabilities. In this demo, the user inputs a fly-through video of a home into the terminal as an mp4 file. Muse Code interprets the video and produces a visually rich website with booking capabilities.

Here’s an example of Muse Code’s multimodal visual coding capabilities. In this demo, the user inputs a fly-through video of a home into the terminal as an mp4 file. Muse Code interprets the video and produces a visually rich website with booking capabilities.

AI at Meta

21,570 görüntüleme • 25 gün önce

Introducing Meta Perception Encoder: a vision encoder setting new standards in image & video tasks. It excels in zero-shot classification & retrieval, surpassing existing models. Learn more about Meta Perception Encoder, read the research paper, and download the code and dataset

Introducing Meta Perception Encoder: a vision encoder setting new standards in image & video tasks. It excels in zero-shot classification & retrieval, surpassing existing models. Learn more about Meta Perception Encoder, read the research paper, and download the code and dataset

AI at Meta

74,588 görüntüleme • 1 yıl önce

META 🔥: Muse Image, the first image-gen model from MSL, is now available on Meta AI. > It uses advanced reasoning to understand complex prompts, seamlessly blending multiple photos into high-quality creations you can download and share anywhere. Users can also access Presets, mention images in the prompt, and edit images directly. Muse Image is also available on WhatsApp and Instagram and is planned to arrive on Messenger and Facebook later. Testing time 👀

META 🔥: Muse Image, the first image-gen model from MSL, is now available on Meta AI. > It uses advanced reasoning to understand complex prompts, seamlessly blending multiple photos into high-quality creations you can download and share anywhere. Users can also access Presets, mention images in the prompt, and edit images directly. Muse Image is also available on WhatsApp and Instagram and is planned to arrive on Messenger and Facebook later. Testing time 👀

🚨 AI News | TestingCatalog

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Code Arena can handle image inputs for agentic web dev tasks, reasoning through multi-step problems and using tools along the way. Watch how it works with Aryan Vichare in this clip. Find a link to the full walkthrough on how Code Arena creates sites and apps from images in thread.

Code Arena can handle image inputs for agentic web dev tasks, reasoning through multi-step problems and using tools along the way. Watch how it works with Aryan Vichare in this clip. Find a link to the full walkthrough on how Code Arena creates sites and apps from images in thread.

Arena.ai

20,064 görüntüleme • 4 ay önce

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 görüntüleme • 1 ay önce

Most AI demos stop at text prompts. I wanted to see what Qwen3.7-Plus could do beyond chat, so I tested it through the API using an Airbnb interface screenshot. The model analyzed the UI, identified key components, explained the visual hierarchy, highlighted usability strengths, suggested UX improvements, and even generated a React + Tailwind structure to recreate the interface. A few things that stood out: 👁️ Multimodal understanding from a single screenshot 🧠 Strong reasoning around UX and product design decisions 💻 Able to bridge visual inputs and code generation ⚡ OpenAI-compatible API that was easy to integrate 💰 Impressive capability-to-cost ratio Qwen3.7-Plus isn't just a chatbot. It's a multimodal agent model designed for workflows that combine vision, reasoning, coding, and automation. Try Qwen3.7-Plus with limited time 20% off:

Most AI demos stop at text prompts. I wanted to see what Qwen3.7-Plus could do beyond chat, so I tested it through the API using an Airbnb interface screenshot. The model analyzed the UI, identified key components, explained the visual hierarchy, highlighted usability strengths, suggested UX improvements, and even generated a React + Tailwind structure to recreate the interface. A few things that stood out: 👁️ Multimodal understanding from a single screenshot 🧠 Strong reasoning around UX and product design decisions 💻 Able to bridge visual inputs and code generation ⚡ OpenAI-compatible API that was easy to integrate 💰 Impressive capability-to-cost ratio Qwen3.7-Plus isn't just a chatbot. It's a multimodal agent model designed for workflows that combine vision, reasoning, coding, and automation. Try Qwen3.7-Plus with limited time 20% off:

Kuria | AI

17,044 görüntüleme • 2 ay önce

In this demo, you’ll see Gemini 3 Flash’s frontier-level reasoning and multimodal capabilities on display. The model is able to simultaneously conduct complex geometric calculations while processing complex inputs (video and image). You can play around with the slingshot in Google AI Studio here and share your favorite examples below:

In this demo, you’ll see Gemini 3 Flash’s frontier-level reasoning and multimodal capabilities on display. The model is able to simultaneously conduct complex geometric calculations while processing complex inputs (video and image). You can play around with the slingshot in Google AI Studio here and share your favorite examples below:

Google AI

55,622 görüntüleme • 8 ay önce

Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

Tencent Hy

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1/2 Meet Wan2.7-Video — The Comprehensive Model for Controllable Video Storytelling! From single clips to full-scale narrative direction, we’ve built more than just a generator. We’ve built a director’s suite: • Multimodal control over performance and style via text, image, audio, and video. • Character customization with up to 5 reference inputs and voice profiles. • Video editing with simple, intuitive instructions. • Full-stack creative toolkit: generation, editing, cloning, restyling, continuation, and more. • Sustained improvements in visual fidelity, motion stability, and prompt adherence.

1/2 Meet Wan2.7-Video — The Comprehensive Model for Controllable Video Storytelling! From single clips to full-scale narrative direction, we’ve built more than just a generator. We’ve built a director’s suite: • Multimodal control over performance and style via text, image, audio, and video. • Character customization with up to 5 reference inputs and voice profiles. • Video editing with simple, intuitive instructions. • Full-stack creative toolkit: generation, editing, cloning, restyling, continuation, and more. • Sustained improvements in visual fidelity, motion stability, and prompt adherence.

Wan

25,572,774 görüntüleme • 4 ay önce

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

Wenhu Chen

82,829 görüntüleme • 1 yıl önce

Transformer Explainer Really cool interactive tool to learn about the inner workings of a Transformer model. Apparently, it runs a GPT-2 instance locally in the user's browser and allows you to experiment with your own inputs. This is a nice tool to learn more about the different components inside the Transformer and the transformations that occur. Tool:

Transformer Explainer Really cool interactive tool to learn about the inner workings of a Transformer model. Apparently, it runs a GPT-2 instance locally in the user's browser and allows you to experiment with your own inputs. This is a nice tool to learn more about the different components inside the Transformer and the transformations that occur. Tool:

elvis

121,978 görüntüleme • 2 yıl önce

new Ox Alpha is kind of absurd One prompt, 64,745 output tokens, and no external assets: Ox Alpha generated the code for an entire Three.js dreamcore 3D scene/world in a single shot. AI/ML API ran this demo and they are also giving Ox Alpha free on aimlapi[.]com - 1M context window - Max output: ~131k tokens - Multimodal: text + image + video input → text output - Explicitly positioned as a reasoning model for coding, long-horizon/agentic software engineering, sustained production workloads, and tasks that mix text with visual context

new Ox Alpha is kind of absurd One prompt, 64,745 output tokens, and no external assets: Ox Alpha generated the code for an entire Three.js dreamcore 3D scene/world in a single shot. AI/ML API ran this demo and they are also giving Ox Alpha free on aimlapi[.]com - 1M context window - Max output: ~131k tokens - Multimodal: text + image + video input → text output - Explicitly positioned as a reasoning model for coding, long-horizon/agentic software engineering, sustained production workloads, and tasks that mix text with visual context

Rohan Paul

13,576 görüntüleme • 9 gün önce

Got the vibe but not the words? Starting today, you can simply show or tell AI Mode about what you're looking for and get rich, visual results to explore. ✨ This experience works by bringing together Search’s world-class visual understanding from Lens and Image search with Gemini 2.5’s advanced multimodal capabilities. When you show AI Mode what you’re searching for it uses our new “visual search fan-out” technique to get a deeper understanding of what’s in an image (including subtle details and secondary objects), and runs multiple queries in the background. This helps it understand the visual context and nuance of your request to deliver relevant visual responses. You can also tell AI Mode what vibe you’re going for (no image needed). For example, ask AI Mode for bedroom design inspo and go back and forth to refine your vision. We’re rolling out this experience in English in the U.S. starting this week. Try it out and let us know what you think!

Got the vibe but not the words? Starting today, you can simply show or tell AI Mode about what you're looking for and get rich, visual results to explore. ✨ This experience works by bringing together Search’s world-class visual understanding from Lens and Image search with Gemini 2.5’s advanced multimodal capabilities. When you show AI Mode what you’re searching for it uses our new “visual search fan-out” technique to get a deeper understanding of what’s in an image (including subtle details and secondary objects), and runs multiple queries in the background. This helps it understand the visual context and nuance of your request to deliver relevant visual responses. You can also tell AI Mode what vibe you’re going for (no image needed). For example, ask AI Mode for bedroom design inspo and go back and forth to refine your vision. We’re rolling out this experience in English in the U.S. starting this week. Try it out and let us know what you think!

Google AI

68,285 görüntüleme • 11 ay önce

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 görüntüleme • 1 ay önce

AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!

AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!

Jason Cui

36,205 görüntüleme • 4 ay önce

Anthropic just announced Computer Use It allows Claude to control your computer screen based on a prompt and take actions on your behalf The use cases in agentic coding with automated debugging, customer support, and education are going to be INSANE

Anthropic just announced Computer Use It allows Claude to control your computer screen based on a prompt and take actions on your behalf The use cases in agentic coding with automated debugging, customer support, and education are going to be INSANE

Rowan Cheung

1,024,718 görüntüleme • 1 yıl önce

Kling O1 is GOATED! No Cap! Kling AI -Replace characters with a single reference image or element -Change styles with a video reference and prompt -Add visual FX with ease -Change environments -Quality coherency and outputs from both text and image to video. -So much more! Try it now on OpenArt (Link in the comments! 👇)

Kling O1 is GOATED! No Cap! Kling AI -Replace characters with a single reference image or element -Change styles with a video reference and prompt -Add visual FX with ease -Change environments -Quality coherency and outputs from both text and image to video. -So much more! Try it now on OpenArt (Link in the comments! 👇)

OpenArt

32,119 görüntüleme • 8 ay önce

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 görüntüleme • 1 ay önce