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๐—”๐—ด๐—ป๐—ฒ๐˜€ ๐Ÿฎ.๐Ÿฌ ๐—ถ๐˜€ ๐—ณ๐—ฟ๐—ฒ๐—ฒ. ๐—œ๐—ป๐—ฑ๐—ฒ๐—ณ๐—ถ๐—ป๐—ถ๐˜๐—ฒ๐—น๐˜†. ๐—ก๐—ผ ๐˜„๐—ฎ๐—ถ๐˜๐—น๐—ถ๐˜€๐˜. Text. Image. Video. One model series. We built Agnes-2.0 for the developers who got priced out โ€” high token costs, API limits, geography. That ends today. ๐—ช๐—ต๐—ฎ๐˜'๐˜€ ๐—น๐—ถ๐˜ƒ๐—ฒ: โ†’ ๐—”๐—ด๐—ป๐—ฒ๐˜€-๐Ÿฎ.๐Ÿฌ-๐—™๐—น๐—ฎ๐˜€๐—ต โ€” text and agentic, top 10 on Claw-Eval, ahead of Gemini and...

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Agnes AI

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๐—”๐—ด๐—ป๐—ฒ๐˜€ ๐Ÿฎ.๐Ÿฌ: AI models ranked among the world's best. One suite for text, image, and video generation. Top 10 across leading AI benchmarks. Trained in Singapore and available now with no waitlist.
2:19

๐—”๐—ด๐—ป๐—ฒ๐˜€ ๐Ÿฎ.๐Ÿฌ: AI models ranked among the world's best. One suite for text, image, and video generation. Top 10 across leading AI benchmarks. Trained in Singapore and available now with no waitlist.

Tech in Asia

4,765,968 Aufrufe โ€ข vor 1 Monat

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: โ€œThe internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, thereโ€™s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not theyโ€™re using it to train, I donโ€™t know. Iโ€™ve heard from insiders theyโ€™re not allowed to yet and would have to redo the terms of service.โ€ Source: AIFinInsights david friedberg
2:12

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: โ€œThe internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, thereโ€™s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not theyโ€™re using it to train, I donโ€™t know. Iโ€™ve heard from insiders theyโ€™re not allowed to yet and would have to redo the terms of service.โ€ Source: AIFinInsights david friedberg

Mario Nawfal

15,855 Aufrufe โ€ข vor 7 Monaten

[CLIP] by Hand โœ๏ธ The CLIP (Contrastive Languageโ€“Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: ๐ŸŸจ Learn a shared embedding space for text and image [1] Given โ†ณ A mini batch of 3 text-image pairs โ†ณ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] ๐ŸŸช Text โ†’ 2 Vectors (3D) โ†ณ Look up word embedding vectors using word2vec. [3] ๐ŸŸฉ Image โ†’ 2 Vectors (4D) โ†ณ Divide the image into two patches. โ†ณ Flatten each patch [4] Process other pairs โ†ณ Repeat [2]-[3] [5] ๐ŸŸช Text Encoder & ๐ŸŸฉ Image Encoder โ†ณ Encode input vectors into feature vectors โ†ณ Here, both encoders are simple one layer perceptron (linear + ReLU) โ†ณ In practice, the encoders are usually transformer models. [6] ๐ŸŸช ๐ŸŸฉ Mean Pooling: 2 โ†’ 1 vector โ†ณ Average 2 feature vectors into a single vector by averaging across the columns โ†ณ The goal is to have one vector to represent each image or text [7] ๐ŸŸช ๐ŸŸฉ -> ๐ŸŸจ Projection โ†ณ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). โ†ณ Use a linear layer to project image and text vectors to a 2D shared embedding space. ๐Ÿ‹๏ธ Contrastive Pre-training ๐Ÿ‹๏ธ [8] Prepare for MatMul โ†ณ Copy text vectors (T1,T2,T3) โ†ณ Copy the transpose of image vectors (I1,I2,I3) โ†ณ They are all in the 2D shared embedding space. [9] ๐ŸŸฆ MatMul โ†ณ Multiply T and I matrices. โ†ณ This is equivalent to taking dot product between every pair of image and text vectors. โ†ณ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] ๐ŸŸฆ Softmax: e^x โ†ณ Raise e to the power of the number in each cell โ†ณ To simplify hand calculation, we approximate e^โ–ก with 3^โ–ก. [11] ๐ŸŸฆ Softmax: โˆ‘ โ†ณ Sum each row for ๐ŸŸฉ imageโ†’๐ŸŸช text โ†ณ Sum each column for ๐ŸŸช textโ†’ ๐ŸŸฉ image [12] ๐ŸŸฆ Softmax: 1 / sum โ†ณ Divide each element by the column sum to obtain a similarity matrix for ๐ŸŸช textโ†’๐ŸŸฉ image โ†ณ Divide each element by the row sum to obtain a similarity matrix for ๐ŸŸฉ imageโ†’๐ŸŸช text [13] ๐ŸŸฅ Loss Gradients โ†ณ The "Targets" for the similarity matrices are Identity Matrices. โ†ณ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. โ†ณ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. โ†ณ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. โ†ณ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).
1:20

[CLIP] by Hand โœ๏ธ The CLIP (Contrastive Languageโ€“Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: ๐ŸŸจ Learn a shared embedding space for text and image [1] Given โ†ณ A mini batch of 3 text-image pairs โ†ณ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] ๐ŸŸช Text โ†’ 2 Vectors (3D) โ†ณ Look up word embedding vectors using word2vec. [3] ๐ŸŸฉ Image โ†’ 2 Vectors (4D) โ†ณ Divide the image into two patches. โ†ณ Flatten each patch [4] Process other pairs โ†ณ Repeat [2]-[3] [5] ๐ŸŸช Text Encoder & ๐ŸŸฉ Image Encoder โ†ณ Encode input vectors into feature vectors โ†ณ Here, both encoders are simple one layer perceptron (linear + ReLU) โ†ณ In practice, the encoders are usually transformer models. [6] ๐ŸŸช ๐ŸŸฉ Mean Pooling: 2 โ†’ 1 vector โ†ณ Average 2 feature vectors into a single vector by averaging across the columns โ†ณ The goal is to have one vector to represent each image or text [7] ๐ŸŸช ๐ŸŸฉ -> ๐ŸŸจ Projection โ†ณ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). โ†ณ Use a linear layer to project image and text vectors to a 2D shared embedding space. ๐Ÿ‹๏ธ Contrastive Pre-training ๐Ÿ‹๏ธ [8] Prepare for MatMul โ†ณ Copy text vectors (T1,T2,T3) โ†ณ Copy the transpose of image vectors (I1,I2,I3) โ†ณ They are all in the 2D shared embedding space. [9] ๐ŸŸฆ MatMul โ†ณ Multiply T and I matrices. โ†ณ This is equivalent to taking dot product between every pair of image and text vectors. โ†ณ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] ๐ŸŸฆ Softmax: e^x โ†ณ Raise e to the power of the number in each cell โ†ณ To simplify hand calculation, we approximate e^โ–ก with 3^โ–ก. [11] ๐ŸŸฆ Softmax: โˆ‘ โ†ณ Sum each row for ๐ŸŸฉ imageโ†’๐ŸŸช text โ†ณ Sum each column for ๐ŸŸช textโ†’ ๐ŸŸฉ image [12] ๐ŸŸฆ Softmax: 1 / sum โ†ณ Divide each element by the column sum to obtain a similarity matrix for ๐ŸŸช textโ†’๐ŸŸฉ image โ†ณ Divide each element by the row sum to obtain a similarity matrix for ๐ŸŸฉ imageโ†’๐ŸŸช text [13] ๐ŸŸฅ Loss Gradients โ†ณ The "Targets" for the similarity matrices are Identity Matrices. โ†ณ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. โ†ณ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. โ†ณ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. โ†ณ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).

Tom Yeh

67,834 Aufrufe โ€ข vor 2 Jahren

Weโ€™re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the otherโ€”all at a fraction of the cost ๐ŸŒโšก๏ธ 1๏ธโƒฃ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2๏ธโƒฃ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:
0:27

Weโ€™re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the otherโ€”all at a fraction of the cost ๐ŸŒโšก๏ธ 1๏ธโƒฃ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2๏ธโƒฃ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:

Google AI

119,113 Aufrufe โ€ข vor 19 Tagen

Today we're launching FilmArena [dot] ai. Biggest release we've ever shipped. The problem: a new SOTA AI image & video model drops every week. OpenAI shipped GPT Image 2 literally yesterday. 1000 endpoints, no honest answer to "which one is best right now?" Arena AI and Artificial Analysis cracked LLM benchmarking. But for creative AI, they're solving the wrong problem. There is no "best image-to-video model." Best at what? World building? Action Sequences? Expressions? Every category has a different winner, and it flips weekly. So we benchmarked 40+ SOTA models with 2M+ votes across 190 countries. By subcategory. Re-ranked every week as new models ship. With thousands of real humans. But that's just half of it. Then there's the SANDBOX. Higgsfield and other aggregators put models in one place. But creators still juggle twelve subscriptions to do one job. FilmArena collapses it into one tab. Every frontier model. One interface. Same price. Call the best, or call 3 at once and compare. The AI film stack has been fragmented since day one. Today it isn't. Go try it free: filmarena [dot] ai Already wild results from this week: 1. OpenAI GPT Image 2 (released yesterday) dominates image editing, but Google DeepMind Nano Banana 2 still wins animated 2. PixVerse C-1 beats Dreamina AI Seedance 2 on animated 3. Veo 3.1 Fast outperforms Veo 3.1 on realistic video And a lot more. Go check it out.
1:13

Today we're launching FilmArena [dot] ai. Biggest release we've ever shipped. The problem: a new SOTA AI image & video model drops every week. OpenAI shipped GPT Image 2 literally yesterday. 1000 endpoints, no honest answer to "which one is best right now?" Arena AI and Artificial Analysis cracked LLM benchmarking. But for creative AI, they're solving the wrong problem. There is no "best image-to-video model." Best at what? World building? Action Sequences? Expressions? Every category has a different winner, and it flips weekly. So we benchmarked 40+ SOTA models with 2M+ votes across 190 countries. By subcategory. Re-ranked every week as new models ship. With thousands of real humans. But that's just half of it. Then there's the SANDBOX. Higgsfield and other aggregators put models in one place. But creators still juggle twelve subscriptions to do one job. FilmArena collapses it into one tab. Every frontier model. One interface. Same price. Call the best, or call 3 at once and compare. The AI film stack has been fragmented since day one. Today it isn't. Go try it free: filmarena [dot] ai Already wild results from this week: 1. OpenAI GPT Image 2 (released yesterday) dominates image editing, but Google DeepMind Nano Banana 2 still wins animated 2. PixVerse C-1 beats Dreamina AI Seedance 2 on animated 3. Veo 3.1 Fast outperforms Veo 3.1 on realistic video And a lot more. Go check it out.

Mehul Agarwal

43,247 Aufrufe โ€ข vor 2 Monaten

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.
2:43

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.

AK

429,307 Aufrufe โ€ข vor 3 Jahren

๐ŸŽฅ Today weโ€™re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. Weโ€™re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do โžก๏ธ ๐Ÿ› ๏ธ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound โ€” delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements โ€” or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. Weโ€™re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.
0:30

๐ŸŽฅ Today weโ€™re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. Weโ€™re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do โžก๏ธ ๐Ÿ› ๏ธ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound โ€” delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements โ€” or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. Weโ€™re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

AI at Meta

2,264,846 Aufrufe โ€ข vor 1 Jahr

WHY IS NO ONE TALKING ABOUT THIS?? Gemma 3n model was one of the best surprises for me. The fact that you can run it on edge devices even with just 2GB of RAM is impressive. A few weeks back, I was on holiday and used the Gemini Live feature a lot. But I kept running into issues whenever I was in a place where the network wasnโ€™t reliable. Gemma 3n : > Multimodal: Supports text and image inputs; video/audio in development. > Context Window: Up to 128K tokens (32K for 1B model). > Multilingual: Trained on 140+ languages. > Privacy: Offline, on-device processing for data security. > Model Sizes: E2B (5B parameters, 2GB RAM) and E4B (10โ€“12B parameters, 3GB RAM). Video credit: Google YT
1:34

WHY IS NO ONE TALKING ABOUT THIS?? Gemma 3n model was one of the best surprises for me. The fact that you can run it on edge devices even with just 2GB of RAM is impressive. A few weeks back, I was on holiday and used the Gemini Live feature a lot. But I kept running into issues whenever I was in a place where the network wasnโ€™t reliable. Gemma 3n : > Multimodal: Supports text and image inputs; video/audio in development. > Context Window: Up to 128K tokens (32K for 1B model). > Multilingual: Trained on 140+ languages. > Privacy: Offline, on-device processing for data security. > Model Sizes: E2B (5B parameters, 2GB RAM) and E4B (10โ€“12B parameters, 3GB RAM). Video credit: Google YT

AshutoshShrivastava

210,490 Aufrufe โ€ข vor 1 Jahr

Are open source models catching up to proprietary models? Weโ€™ve looked back at 3 years of Arenaโ€™s data to show how the race has evolved. For comparison, weโ€™ve taken the top 20% of the models and uncovered the following: - Before mid 2024: The gap was between 100-150 points - In the second half of 2024, the gap rapidly narrowed to ~50 points - This was driven by the fact that proprietary models have not significantly improved between early 2023 and November 2024 - hovering around the 1350 score for the top 20% of proprietary models - Then since Jan 2025, we saw a parallel improvement in both proprietary and open source models - Meaning that for the last ~14 months the gap stayed steady at around 50-60 points For context, 50 points is the gap between the 1st and 20th place in the Overall leaderboard Today, proprietary models take up the first 20 spots on the Text Arena, with Anthropic, Google DeepMind , xAI and OpenAI leading in the rankings. The top open source models are ranked #20 (GLM-5 by Z.ai ), #23 (Kimi-K2.5-Thinking by Kimi.ai ) and #27 (Qwen3.5-397b-a17b by Qwen ).
0:21

Are open source models catching up to proprietary models? Weโ€™ve looked back at 3 years of Arenaโ€™s data to show how the race has evolved. For comparison, weโ€™ve taken the top 20% of the models and uncovered the following: - Before mid 2024: The gap was between 100-150 points - In the second half of 2024, the gap rapidly narrowed to ~50 points - This was driven by the fact that proprietary models have not significantly improved between early 2023 and November 2024 - hovering around the 1350 score for the top 20% of proprietary models - Then since Jan 2025, we saw a parallel improvement in both proprietary and open source models - Meaning that for the last ~14 months the gap stayed steady at around 50-60 points For context, 50 points is the gap between the 1st and 20th place in the Overall leaderboard Today, proprietary models take up the first 20 spots on the Text Arena, with Anthropic, Google DeepMind , xAI and OpenAI leading in the rankings. The top open source models are ranked #20 (GLM-5 by Z.ai ), #23 (Kimi-K2.5-Thinking by Kimi.ai ) and #27 (Qwen3.5-397b-a17b by Qwen ).

Arena.ai

22,447 Aufrufe โ€ข vor 3 Monaten

Happy GPT-5 Day to all who celebrate. Here's what I expect: It will be agentic-first (this is all but confirmed by OpenAI) It will be heavily multimodal (audio, video, music, tool use, and more) It will be a reasoning-first model as well (math, planning, etc) Directionally, I suspect we're looking at moving towards a convergence of "everything to everything" end-to-end models. Sort of like how Tesla improved FSD by doing neural networks for end-to-end perception-planning-control. Likewise, I suspect that, for the foreseeable future on general purpose models, we're going to converge towards "all in one" models. They will just tend to get smarter and smarter the more modalities we stuff into them (world models, math, image, text, video, audio, etc).
9:02

Happy GPT-5 Day to all who celebrate. Here's what I expect: It will be agentic-first (this is all but confirmed by OpenAI) It will be heavily multimodal (audio, video, music, tool use, and more) It will be a reasoning-first model as well (math, planning, etc) Directionally, I suspect we're looking at moving towards a convergence of "everything to everything" end-to-end models. Sort of like how Tesla improved FSD by doing neural networks for end-to-end perception-planning-control. Likewise, I suspect that, for the foreseeable future on general purpose models, we're going to converge towards "all in one" models. They will just tend to get smarter and smarter the more modalities we stuff into them (world models, math, image, text, video, audio, etc).

David Shapiro (L/0)

19,305 Aufrufe โ€ข vor 11 Monaten

๐Ÿš€ Introducing the Kling 3.0 Model: Everyone a Director. Itโ€™s Time. An all-in-one creative engine that enables truly native multimodal creation. - Superb Consistency: Your characters and elements, always locked in. - Flexible Video Production: Create 15s clips with precise control, excellent video realism and customizable multi-shots. - Upgraded Native Audio: Now supports multi-character reference, more languages and accents. - Enhanced Image Generation: 4K Image output, new image series mode and more cinematic visuals. Ultra subscribers get exclusive early access โ€” now live on Web -> Kling AI. Get ready to unlock your creative potential with our most advanced model yet. [For the next 24 hours ONLY] Follow, Comment & Retweet to win exclusive early access - we will select 10 winners in this post from the top ten 'Most liked replies'. So which feature are you most excited about? Let us know in the comment! We will contact you via your DM.
1:54

๐Ÿš€ Introducing the Kling 3.0 Model: Everyone a Director. Itโ€™s Time. An all-in-one creative engine that enables truly native multimodal creation. - Superb Consistency: Your characters and elements, always locked in. - Flexible Video Production: Create 15s clips with precise control, excellent video realism and customizable multi-shots. - Upgraded Native Audio: Now supports multi-character reference, more languages and accents. - Enhanced Image Generation: 4K Image output, new image series mode and more cinematic visuals. Ultra subscribers get exclusive early access โ€” now live on Web -> Kling AI. Get ready to unlock your creative potential with our most advanced model yet. [For the next 24 hours ONLY] Follow, Comment & Retweet to win exclusive early access - we will select 10 winners in this post from the top ten 'Most liked replies'. So which feature are you most excited about? Let us know in the comment! We will contact you via your DM.

Kling AI

1,195,161 Aufrufe โ€ข vor 5 Monaten

This is probably the most complex workflow Iโ€™ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but hereโ€™s the first preview. Hereโ€™s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as Iโ€™ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I donโ€™t know yet how Iโ€™ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and letโ€™s always keep pushing further! :)
0:55

This is probably the most complex workflow Iโ€™ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but hereโ€™s the first preview. Hereโ€™s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as Iโ€™ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I donโ€™t know yet how Iโ€™ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and letโ€™s always keep pushing further! :)

Lovis Odin

58,769 Aufrufe โ€ข vor 10 Monaten

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!
1:41

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 Aufrufe โ€ข vor 2 Monaten

Dylan on why access to the most intelligent AI models will determine who wins in every industry: "What really matters is access to the most intelligent tokens and leveraging them at the highest-value tasks. A lot of folks will want tokens, but the shitty SaaS startup in SF who is using Claude is not necessarily creating a ton of value, and therefore they're going to get priced out of tokens. Anthropic only released Mythos to certain companies for cybersecurity. You also don't want people to distill your model so you release them to a fewer and fewer customers. Those customers are now wrestling over the tokens. Token usage and therefore the additional value generated on top of those tokens aggregates among fewer and fewer companies. I don't have Mythos but you know who has Mythos, top banks. There are people like Ken Griffin of Citadel who can just sign a deal with OpenAI or Antrhopic for access to your models. I'll buy the first $10 billion worth of tokens each year. So whenever you release the model, I'll spend the first 10 billion tokens and then everyone else can get the model after that. Now, he's going to crush everyone in the markets. I can envision because I have an enterprise Anthropic contract and Anthropic people like me, they're willing to give us slightly earlier access or higher rate limits, and then my competitor doesn't have that, and I'm able to f*cking crush them."
2:22

Dylan on why access to the most intelligent AI models will determine who wins in every industry: "What really matters is access to the most intelligent tokens and leveraging them at the highest-value tasks. A lot of folks will want tokens, but the shitty SaaS startup in SF who is using Claude is not necessarily creating a ton of value, and therefore they're going to get priced out of tokens. Anthropic only released Mythos to certain companies for cybersecurity. You also don't want people to distill your model so you release them to a fewer and fewer customers. Those customers are now wrestling over the tokens. Token usage and therefore the additional value generated on top of those tokens aggregates among fewer and fewer companies. I don't have Mythos but you know who has Mythos, top banks. There are people like Ken Griffin of Citadel who can just sign a deal with OpenAI or Antrhopic for access to your models. I'll buy the first $10 billion worth of tokens each year. So whenever you release the model, I'll spend the first 10 billion tokens and then everyone else can get the model after that. Now, he's going to crush everyone in the markets. I can envision because I have an enterprise Anthropic contract and Anthropic people like me, they're willing to give us slightly earlier access or higher rate limits, and then my competitor doesn't have that, and I'm able to f*cking crush them."

Patrick OShaughnessy

36,100 Aufrufe โ€ข vor 2 Monaten

Really happy to see the interest around our โ€œHands-on with Geminiโ€ video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities โ€” image and text in this case โ€” and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 ๐Ÿš€. The knitting demo used Ultraโšก All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When youโ€™re building an app, you can get similar results (thereโ€™s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting โ€œyou are an expert in science โ€ฆโ€ before a user can engage in the same kind of back and forth dialogue. Hereโ€™s a clip of what this looks like in AI Studio with Gemini Pro. Weโ€™ve come a long way since Flamingo ๐Ÿฆฉ & PALI, looking forward to seeing what people build with it!
2:24

Really happy to see the interest around our โ€œHands-on with Geminiโ€ video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities โ€” image and text in this case โ€” and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 ๐Ÿš€. The knitting demo used Ultraโšก All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When youโ€™re building an app, you can get similar results (thereโ€™s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting โ€œyou are an expert in science โ€ฆโ€ before a user can engage in the same kind of back and forth dialogue. Hereโ€™s a clip of what this looks like in AI Studio with Gemini Pro. Weโ€™ve come a long way since Flamingo ๐Ÿฆฉ & PALI, looking forward to seeing what people build with it!

Oriol Vinyals

180,952 Aufrufe โ€ข vor 2 Jahren

ANTARCTICA by invideo Creative Residency partner 44creator. Built entirely on invideo Agents & Models. What is Agents & Models inside invideo? A workspace designed for creators who want granular control over consistency and realism. โ†’ Nano Banana Pro: For establishing the perfect visual style. โ†’ Kling 2.6: For high-fidelity, natural motion. Itโ€™s not just about prompting; itโ€™s about directing the scene. How does the workflow work? โ†’ Generate the Master Frame: Use Nano Banana Pro to create a photorealistic base image. โ†’ Apply the Motion: Pass that image through Kling 2.6 inside the editor. โ†’ Direct with Text: Describe the movement you wantโ€”drifting ice, blowing snow, or slow reveal. โ†’ Render: High-definition video that respects physics and texture. โŒ No complex keyframing โŒ No jumping between apps Just pure cinematic creation in one place. Nano Banana Pro is UNLIMITED for 365 days on all paid plans. Try it today.
0:42

ANTARCTICA by invideo Creative Residency partner 44creator. Built entirely on invideo Agents & Models. What is Agents & Models inside invideo? A workspace designed for creators who want granular control over consistency and realism. โ†’ Nano Banana Pro: For establishing the perfect visual style. โ†’ Kling 2.6: For high-fidelity, natural motion. Itโ€™s not just about prompting; itโ€™s about directing the scene. How does the workflow work? โ†’ Generate the Master Frame: Use Nano Banana Pro to create a photorealistic base image. โ†’ Apply the Motion: Pass that image through Kling 2.6 inside the editor. โ†’ Direct with Text: Describe the movement you wantโ€”drifting ice, blowing snow, or slow reveal. โ†’ Render: High-definition video that respects physics and texture. โŒ No complex keyframing โŒ No jumping between apps Just pure cinematic creation in one place. Nano Banana Pro is UNLIMITED for 365 days on all paid plans. Try it today.

Invideo

421,778 Aufrufe โ€ข vor 6 Monaten

I usually skip benchmark comparisons. They're almost always picked to flatter. But EgoSchema is different. It measures first-person, egocentric video understanding. The AI equivalent of โ€œlook through the robot's eyes and tell me what happened.โ€ I spent some time testing Perceptron Mk1 (โ€œMark Oneโ€) on the robotics workflows Perceptron walked me through, and the EgoSchema result suddenly made a lot more sense. Perceptron Mk1 scored 80.60% on it. Gemini 3.1 Flash Lite scored 62.4%. That's a completely different level of understanding. Most frontier models were trained on the internet through text, images, and static content. The physical world works differently. Context lives across time. The critical moment rarely exists in a single frame. And that's exactly where production deployments start mattering. Manufacturing QC. Robotics data pipelines. Security footage. Everything is temporal. Native video reasoning. Temporal grounding. Structured output. Perceptron calls it โ€œMk1โ€ because it marks the beginning of their closed-model series for physical-world intelligence, and honestly, the name fits. It feels engineered for deployment, not just demos. Feels like one of the first video AI systems being built around how the physical world actually behaves instead of adapting static-image intelligence into video later.
0:21

I usually skip benchmark comparisons. They're almost always picked to flatter. But EgoSchema is different. It measures first-person, egocentric video understanding. The AI equivalent of โ€œlook through the robot's eyes and tell me what happened.โ€ I spent some time testing Perceptron Mk1 (โ€œMark Oneโ€) on the robotics workflows Perceptron walked me through, and the EgoSchema result suddenly made a lot more sense. Perceptron Mk1 scored 80.60% on it. Gemini 3.1 Flash Lite scored 62.4%. That's a completely different level of understanding. Most frontier models were trained on the internet through text, images, and static content. The physical world works differently. Context lives across time. The critical moment rarely exists in a single frame. And that's exactly where production deployments start mattering. Manufacturing QC. Robotics data pipelines. Security footage. Everything is temporal. Native video reasoning. Temporal grounding. Structured output. Perceptron calls it โ€œMk1โ€ because it marks the beginning of their closed-model series for physical-world intelligence, and honestly, the name fits. It feels engineered for deployment, not just demos. Feels like one of the first video AI systems being built around how the physical world actually behaves instead of adapting static-image intelligence into video later.

Jaymin Shah

116,425 Aufrufe โ€ข vor 2 Monaten

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. โ€” 1๏ธโƒฃ NotebookLM โ€” your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise โ€” 2๏ธโƒฃ Google AI Studio โ€” the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits โ€” 3๏ธโƒฃ Gemini CLI โ€” google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) โ€” 4๏ธโƒฃ Jules โ€” async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5๏ธโƒฃ Stitch โ€” text โ†’ UI โ†’ code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work โ€” 6๏ธโƒฃ Gemma 4 โ€” open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference โ€” 7๏ธโƒฃ Illuminate โ€” papers โ†’ podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading โ€” 8๏ธโƒฃ Learn About (LearnLM) โ€” adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics โ€” 9๏ธโƒฃ Google Labs FX (ImageFX + Flow + MusicFX) โ€” free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music โ€” those stay free. replaces: midjourney + suno (free tier only โ€” runway-level video gen is paid) โ€” ๐Ÿ”Ÿ Google Colab โ€” free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals โ€” a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done โ€” especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this ๐Ÿ””
0:34

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. โ€” 1๏ธโƒฃ NotebookLM โ€” your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise โ€” 2๏ธโƒฃ Google AI Studio โ€” the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits โ€” 3๏ธโƒฃ Gemini CLI โ€” google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) โ€” 4๏ธโƒฃ Jules โ€” async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5๏ธโƒฃ Stitch โ€” text โ†’ UI โ†’ code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work โ€” 6๏ธโƒฃ Gemma 4 โ€” open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference โ€” 7๏ธโƒฃ Illuminate โ€” papers โ†’ podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading โ€” 8๏ธโƒฃ Learn About (LearnLM) โ€” adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics โ€” 9๏ธโƒฃ Google Labs FX (ImageFX + Flow + MusicFX) โ€” free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music โ€” those stay free. replaces: midjourney + suno (free tier only โ€” runway-level video gen is paid) โ€” ๐Ÿ”Ÿ Google Colab โ€” free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals โ€” a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done โ€” especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this ๐Ÿ””

m0h

11,673 Aufrufe โ€ข vor 1 Monat

Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw๐Ÿฆž and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw
1:03

Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw๐Ÿฆž and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw

BytePlus

95,947 Aufrufe โ€ข vor 3 Monaten

Decades of hardware development led to strong, fast, and precise robot arms. The moment we can put in general intelligence in these things, we can leverage the full spectra of capabilities that they were always meant to capture. Weโ€™re betting on a future where robot hardware will continue to improve, and we intend to build the best models on top of the best hardware to push the frontier of capabilities and reliability. Hereโ€™s an old video from TossingBot that I think helps make this point extra clear. Industrial-grade repeatability started out as a crutch for dumb software -- but if you pair it with the right AI models, then it becomes an advantage that is superhuman. There are factories where the same UR robot arms are still being used to precisely and repeatably build the same car parts, operating for 10 yrs straight without a single failure or shutdown. Humanoids will get there too (among other form factors). Not yet today, but eventually. And our models will be ready to meet them when they do.
0:29

Decades of hardware development led to strong, fast, and precise robot arms. The moment we can put in general intelligence in these things, we can leverage the full spectra of capabilities that they were always meant to capture. Weโ€™re betting on a future where robot hardware will continue to improve, and we intend to build the best models on top of the best hardware to push the frontier of capabilities and reliability. Hereโ€™s an old video from TossingBot that I think helps make this point extra clear. Industrial-grade repeatability started out as a crutch for dumb software -- but if you pair it with the right AI models, then it becomes an advantage that is superhuman. There are factories where the same UR robot arms are still being used to precisely and repeatably build the same car parts, operating for 10 yrs straight without a single failure or shutdown. Humanoids will get there too (among other form factors). Not yet today, but eventually. And our models will be ready to meet them when they do.

Andy Zeng

19,174 Aufrufe โ€ข vor 2 Monaten