Claude Fable 5 is now live on MetaChat 🚀
Anthropic's first Mythos-class model for the public — and it's a leap:
• Stripe compressed months of engineering into a single day
• #1 on finance reasoning, vision tasks & production-grade coding benchmarks
• Runs longer, more autonomously than any Claude before it
Available now ��� https://t.co/uikmEclnje
🔥The Rise of Multi Model AI Platforms: Why MetaChat Is the All-in-One Solution You Need in 2026
🙌MetaChat Description: Discover how MetaChat — the leading multi model AI platform — gives you instant access to 30+ AI models including ChatGPT, Claude, Gemini, Grok, Midjourney, and more from a single unified workspace.
https://t.co/cNdvB4LEn3
🚨 BREAKING: Google just turned Gemini into an AI operating system.
At I/O 2025, they launched a full suite of tools that go way beyond chat.
Here’s what just dropped and it’s wild: 🧵👇
Gemini is very suitable for learners, as it tends to explain everything in great detail, almost to the point of being overly talkative, fearing that you might not understand. Unlike ChatGPT, which can sometimes be a bit more concise with its words.
#gemini#ChatGPT
Gemini Advanced now connects with @github, making it a more powerful coding assistant.
Directly connect to public or private GitHub repos to generate/modify functions, explain complex code, ask questions about your codebase, debug and more.
Click the + button in the prompt bar, select "import code" and paste the GitHub URL to get started.
Also, many people have paid for various useless AI tools, only to later find out they are not very practical. That's why you can use our all-in-one service to access a wide range of AI tools in one place. With a single platform, one payment solves the need for multiple AI tool usages.
We just dropped Gemini 2.5 Pro (I/O edition). It’s our most intelligent model that’s even better at coding.
Now, you can build interactive web apps in Canvas with fewer prompts.
Head to https://t.co/OtJcjTFyhR and select “Canvas” in the prompt bar to try it out, and let us know what you’re building in the comments.
There are so many great AI models that we are pretty close to doing almost anything.
o3 - brilliant at planning and deciding what to do
Sonnet 3.5 - tool-use and coding
Gemini Flash - cheap and reliable
Qwen-235b - open-source leader
GPT-4.1 - everyday tasks
Grok-3 - real-time information
Kling 2.0 - video
ChatGPT image-gen - image gen
These models can work in concert to outpace most humans TODAY
Midjourney just killed ChatGPT.
Omnireference puts YOU inside cinematic worlds.
Now you can travel through time, across genres, and become the star of any film.
Step-by-step guide below 👇🏼🧵
11 Comparison Examples: Sora vs. Higgsfield
Which one’s better? Is Higgs just hype? Includes prompts + all the details 🧵👇
1. A flamingo on a skateboard
It's a fantastic work. We have communicated with some of the leading designers in the industry, and they generally believe that the image generation capabilities of 4o and Google's imagen3 have already surpassed MidJourney.
#gpt4o#imagen3
Day 25 of #30DaysOf4oVisuals:
Wanted: custom fantasy map.
Delivered: hand-drawn style world map with labeled regions, forests, oceans, and dragons 🗺️🐉
GPT-4o doesn’t just draw — it builds worlds.👇
#GPT4o#MapDesign#Worldbuilding
Let’s talk about the fundamental differences between Magi-1 and other AI video models.
On GitHub, you can see that Magi-1’s Physics-IQ score reaches 56%, whereas Google’s Videopoet scores only 30.2%, and Sora achieves a mere 10%.
How should we understand Physics-IQ?
Previous AI video models did not distinguish between “what happens first and what happens next.” They lacked a concept of time and couldn’t follow causality. This limitation is why many existing AI video tools might create scenes with good realism, but during video production, users often have to rely on trial and error (like drawing cards repeatedly) because they have no control over the story’s progression from one second to the next.
Magi-1, however, breaks that mold and achieves something groundbreaking—and achieves it exceptionally well.
Magi-1 adopts a chunk-by-chunk structure, where video is generated one segment at a time before transitioning to the next. The content of each subsequent segment is generated based on the previous one. This structure inherently preserves causality, meaning that time unfolds sequentially within the model, rather than being pieced together as an afterthought.
Magi-1 enables continuous video generation, allowing it to "keep generating" seamlessly, and it comes with three critical advantages:
1)No fixed length limit – the video can be as long as needed without constraints.
2)Customizable prompts for each chunk – you can precisely specify what happens at any given moment by applying different prompts to each segment.
3)Consistent computational cost for each chunk – even for long videos, it avoids memory overload or GPU crashes.
This innovation is a milestone in the field of AI video. Magi-1 not only introduces a sense of time and causality into AI video generation but also offers users unprecedented control and flexibility.
Let’s talk about the fundamental differences between Magi-1 and other AI video models.
On GitHub, you can see that Magi-1’s Physics-IQ score reaches 56%, whereas Google’s Videopoet scores only 30.2%, and Sora achieves a mere 10%.
How should we understand Physics-IQ?
Previous AI video models did not distinguish between “what happens first and what happens next.” They lacked a concept of time and couldn’t follow causality. This limitation is why many existing AI video tools might create scenes with good realism, but during video production, users often have to rely on trial and error (like drawing cards repeatedly) because they have no control over the story’s progression from one second to the next.
Magi-1, however, breaks that mold and achieves something groundbreaking—and achieves it exceptionally well.
Magi-1 adopts a chunk-by-chunk structure, where video is generated one segment at a time before transitioning to the next. The content of each subsequent segment is generated based on the previous one. This structure inherently preserves causality, meaning that time unfolds sequentially within the model, rather than being pieced together as an afterthought.
Magi-1 enables continuous video generation, allowing it to "keep generating" seamlessly, and it comes with three critical advantages:
1)No fixed length limit – the video can be as long as needed without constraints.
2)Customizable prompts for each chunk – you can precisely specify what happens at any given moment by applying different prompts to each segment.
3)Consistent computational cost for each chunk – even for long videos, it avoids memory overload or GPU crashes.
This innovation is a milestone in the field of AI video. Magi-1 not only introduces a sense of time and causality into AI video generation but also offers users unprecedented control and flexibility.
Let’s talk about the fundamental differences between Magi-1 and other AI video models.
On GitHub, you can see that Magi-1’s Physics-IQ score reaches 56%, whereas Google’s Videopoet scores only 30.2%, and Sora achieves a mere 10%.
How should we understand Physics-IQ?
Previous AI video models did not distinguish between “what happens first and what happens next.” They lacked a concept of time and couldn’t follow causality. This limitation is why many existing AI video tools might create scenes with good realism, but during video production, users often have to rely on trial and error (like drawing cards repeatedly) because they have no control over the story’s progression from one second to the next.
Magi-1, however, breaks that mold and achieves something groundbreaking—and achieves it exceptionally well.
Magi-1 adopts a chunk-by-chunk structure, where video is generated one segment at a time before transitioning to the next. The content of each subsequent segment is generated based on the previous one. This structure inherently preserves causality, meaning that time unfolds sequentially within the model, rather than being pieced together as an afterthought.
Magi-1 enables continuous video generation, allowing it to "keep generating" seamlessly, and it comes with three critical advantages:
1)No fixed length limit – the video can be as long as needed without constraints.
2)Customizable prompts for each chunk – you can precisely specify what happens at any given moment by applying different prompts to each segment.
3)Consistent computational cost for each chunk – even for long videos, it avoids memory overload or GPU crashes.
This innovation is a milestone in the field of AI video. Magi-1 not only introduces a sense of time and causality into AI video generation but also offers users unprecedented control and flexibility.
Let’s talk about the fundamental differences between Magi-1 and other AI video models.
On GitHub, you can see that Magi-1’s Physics-IQ score reaches 56%, whereas Google’s Videopoet scores only 30.2%, and Sora achieves a mere 10%.
How should we understand Physics-IQ?
Previous AI video models did not distinguish between “what happens first and what happens next.” They lacked a concept of time and couldn’t follow causality. This limitation is why many existing AI video tools might create scenes with good realism, but during video production, users often have to rely on trial and error (like drawing cards repeatedly) because they have no control over the story’s progression from one second to the next.
Magi-1, however, breaks that mold and achieves something groundbreaking—and achieves it exceptionally well.
Magi-1 adopts a chunk-by-chunk structure, where video is generated one segment at a time before transitioning to the next. The content of each subsequent segment is generated based on the previous one. This structure inherently preserves causality, meaning that time unfolds sequentially within the model, rather than being pieced together as an afterthought.
Magi-1 enables continuous video generation, allowing it to "keep generating" seamlessly, and it comes with three critical advantages:
1)No fixed length limit – the video can be as long as needed without constraints.
2)Customizable prompts for each chunk – you can precisely specify what happens at any given moment by applying different prompts to each segment.
3)Consistent computational cost for each chunk – even for long videos, it avoids memory overload or GPU crashes.
This innovation is a milestone in the field of AI video. Magi-1 not only introduces a sense of time and causality into AI video generation but also offers users unprecedented control and flexibility.
Let’s talk about the fundamental differences between Magi-1 and other AI video models.
On GitHub, you can see that Magi-1’s Physics-IQ score reaches 56%, whereas Google’s Videopoet scores only 30.2%, and Sora achieves a mere 10%.
How should we understand Physics-IQ?
Previous AI video models did not distinguish between “what happens first and what happens next.” They lacked a concept of time and couldn’t follow causality. This limitation is why many existing AI video tools might create scenes with good realism, but during video production, users often have to rely on trial and error (like drawing cards repeatedly) because they have no control over the story’s progression from one second to the next.
Magi-1, however, breaks that mold and achieves something groundbreaking—and achieves it exceptionally well.
Magi-1 adopts a chunk-by-chunk structure, where video is generated one segment at a time before transitioning to the next. The content of each subsequent segment is generated based on the previous one. This structure inherently preserves causality, meaning that time unfolds sequentially within the model, rather than being pieced together as an afterthought.
Magi-1 enables continuous video generation, allowing it to "keep generating" seamlessly, and it comes with three critical advantages:
1)No fixed length limit – the video can be as long as needed without constraints.
2)Customizable prompts for each chunk – you can precisely specify what happens at any given moment by applying different prompts to each segment.
3)Consistent computational cost for each chunk – even for long videos, it avoids memory overload or GPU crashes.
This innovation is a milestone in the field of AI video. Magi-1 not only introduces a sense of time and causality into AI video generation but also offers users unprecedented control and flexibility.