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#Multimodal

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Oct 6

Oct 6Tue
  1. Sundar PichaiAI score62

    Google releases EmbeddingGemma 2, an open multimodal embedding model for on-device use

    AIGoogle introduces EmbeddingGemma 2, its first open, natively multimodal embedding model, covering text, code, image, video, and audio tasks. It has a 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and the post claims it outperforms some specialist models more than twice its size. Weights are available now on Hugging Face.

    Video from @sundarpichai's post
  2. Google DeepMind · The KeywordAI score72

    Google releases EmbeddingGemma 2, an open multimodal embedding model for on-device use

    AIGoogle DeepMind has released EmbeddingGemma 2, a 740-million-parameter embedding model that maps text, images, audio, and video into a shared space and runs on local hardware under an Apache 2.0 license. Matryoshka Representation Learning lets developers truncate output vectors from 768 dimensions to 512, 256, or 128, and the model supports an 8K-token context window. The model weights are available on Hugging Face and Kaggle, with Gemini Enterprise Agent Platform availability coming soon.

    Why it matters: The release shows how a 740M-parameter multimodal embedder runs locally with a 768-to-128 dimension truncation option, useful for judging on-device retrieval designs.

  3. merveAI score72

    Mistral Large 4 will open its weights at the end of October

    AIMistral announced Mistral Large 4, which it describes as a natively multimodal model with 1T parameters and 49B active. Mistral says it is available via API now, with open weights to follow at the end of October, and a Hugging Face page is listed for the release.

    Why it matters: The quoted Mistral announcement gives specific size, activation, and API details, and the open-weights timing matters for teams weighing open model options.

    Image from @mervenoyann's post
  4. Simon WillisonAI score36

    Mistral's Pelican SVG Test Passes, Tied to Mistral Large 4 Context

    AISimon Willison reports that Mistral can now generate his pelican SVG test, shared via a Markdown SVG renderer. The post links to a rendered result but gives no benchmark or scoring details. Background from Mistral's own announcement describes Mistral Large 4 as a 1T-parameter, natively multimodal model with 49B active parameters, available via API today and with open weights planned for end of October.

    Image from @simonw's post
  5. Julien ChaumondAI score70

    Mistral Large 4 announced with open weights due end of October

    AIJulien Chaumond reposted Mistral's announcement of Mistral Large 4, a 1T-parameter natively multimodal model with 49B active parameters. Mistral says it is available via API today, with open weights scheduled for release at the end of October, and is working privately with cybersecurity partners.

    Why it matters: The post lays out Mistral Large 4's scale, multimodal design, and availability timeline, which helps readers gauge the open-weights landscape outside China.

  6. Guillaume Lample @ NeurIPS 2024AI score42

    Mistral's ML4 matches top open-weight models on coding and agentic benchmarks

    AIMistral's ML4 model matches the best open-weight models on DeepSWE, AutomationBench, and AA-Briefcase, and reaches state-of-the-art results on finance and legal workflows and complex multimodal grounding benchmarks. The post says it can navigate terminal workflows, work across spreadsheets, slides, and PDFs, and reason over scientific and multimodal tasks.

    Image from @GuillaumeLample's post
  7. Guillaume Lample @ NeurIPS 2024AI score78

    Mistral launches Large 4 preview with 1T parameters and open weights due October

    AIMistral has launched a preview of Mistral Large 4 (ML4), a 1T-parameter multimodal model with 49B active parameters. The company says it is the strongest open-weight model from the US or Europe on aggregated benchmarks and is available via API now, with open weights planned for the end of October.

    Why it matters: The post gives parameter counts, a preview timeline, and an open-weights release date, which help readers judge how Mistral's model compares with other open-weight options.

    Image from @GuillaumeLample's post
  8. Mistral AIAI score80

    Mistral Large 4 launches as a public preview with weights due end of month

    AIMistral AI launched a public preview API for Mistral Large 4, a 1 trillion-parameter natively multimodal model with 52 billion active parameters, and says it will release the weights by the end of the month. The company reports 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, 28.3% on Terminal-Bench 4, and 59.9% on AutomationBench. The model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's datacenters in Europe.

    Why it matters: The post gives benchmark figures and a weights timeline for an open-weight model, letting readers compare it with other open models and judge its access terms.

  9. GeekParkAI score46

    Huawei Mate 90 Pro Max starts at 9,499 yuan, with camera upgrades leading

    AIHuawei's Mate 90 Pro Max, launched October 1, starts at 9,499 yuan for the 12GB+512GB model, and the Collector's Edition starts at 10,999 yuan. The phone's main upgrade is its camera, including a new "Portrait Original" mode that preserves skin tone and makeup, a 200-megapixel telephoto lens with about 4x optical zoom, and generative-AI glare removal for night shots.

  10. Luma AI NewsAI score22

    Cyberpunk AI Prompts Guide Covers Video and Image Generation Workflows

    AIThe guide offers a prompt structure for cyberpunk visuals built from subject, environment, lighting, camera, style, and quality modifiers, with magenta and cyan neon, rain-slicked reflections, and fog named as key mood elements. It presents 15 ready-to-use prompts and argues that free tools suit testing directions, while full access is needed for commercial campaigns.

Oct 5

Oct 5Mon
  1. Google Developers BlogAI score62

    EmbeddingGemma 2 releases multimodal embeddings with modular encoder loading

    AIGoogle released EmbeddingGemma 2, an open embedding model under the Apache 2.0 license that maps text, code, images, video, and audio into a shared 768-dimensional space. Developers can load a 270M-parameter text and code setup, or add vision and audio encoders up to a 740M-parameter full multimodal model. Matryoshka truncation to 256 or 128 dimensions reduces vector storage, with the guide noting quality losses on image, video, and speech retrieval at lower dimensions.

    Why it matters: The guide gives concrete encoder sizes and dimension-storage tradeoffs, showing how to choose a configuration for text, code, image, video, and audio retrieval.

  2. Google Developers BlogAI score67

    Google releases EmbeddingGemma 2, a multimodal embedding model for on-device search

    AIGoogle DeepMind launched EmbeddingGemma 2, an open-weight 740M parameter model that maps text, images, video frames, and audio into one vector space. The model can run on-device, with about 567MB active RAM for the full multimodal model on a Google Pixel 11 Pro, and is available through Google AI Edge Gallery, Google AI Edge Foresight on Mac, and MediaPipe Tasks, with ML Kit support coming in the weeks ahead.

    Why it matters: The post names concrete on-device apps, memory footprints, and latency figures, showing how a multimodal embedding model can power local search without cloud calls.

  3. Liquid AIAI score37

    Liquid AI's d1 decision model adds vision, rivaling GPT-6.1 Sol at lower cost

    AILiquid AI released d1 with vision support, accepting images, text, or both as inputs. In tests on six real applications, d1 matched or beat GPT-6.1 Sol on four while costing 19x to 200x less than both GPT-6.1 Sol and Claude Opus 5.5. It returns probabilities for yes/no, choice, or score questions in one forward pass, with text decisions in 200 to 300 ms.

    Image from @liquidai's post
  4. Liquid AIAI score36

    Liquid AI's d1 model inspects parts from camera images with 85-97% accuracy

    AILiquid AI's vision-enabled decision model d1 inspects parts directly from camera images and is described as the best such model currently on the market. It reaches 85% to 97% accuracy across four VisA inspection tasks covering circuit boards, candles, cashews, and chewing gum. It understands each task from a short description without task-specific training.

    Video from @liquidai's post
  5. Liquid AI · new models on Hugging FaceAI score44

    LiquidAI releases d1-omni-600M, a 600M decision model for text, image and audio

    AILiquidAI has released d1-omni-600M on Hugging Face, a 587M-parameter model that answers named yes/no, choice and score questions over text, images or up to 30 seconds of speech in a single forward pass. It returns typed answers with zero output tokens by reading the model's distribution over options, and is built on LFM2.5-Encoder-350M with a 16,384-token context length. The model is not a chat model and does not generate text.

  6. Liquid AI · new models on Hugging FaceAI score67

    Liquid AI releases d1-3B, a 3B multimodal decision model for edge deployment

    AILiquid AI has released d1-3B, a 3B parameter multimodal model post-trained to return calibrated, typed answers to yes/no, choice, and score questions in one forward pass. The source reports a Decision Index 0.2.1 score of 48.57, the highest among models under 10B in its table, and 8 ms per decision on an NVIDIA RTX 4090.

    Why it matters: The source gives benchmark scores against named peer models and edge latency figures across several hardware targets, helping readers judge fit for on-device decision pipelines.

Oct 4

Oct 4Sun
  1. Liquid AI BlogAI score70

    Liquid AI releases d1 decision model with image input support

    AILiquid AI introduces d1, its first decision model, now accepting both text and images. The company says d1 matches or beats GPT-6.1 Sol on four of six tested applications, at 19x to 200x lower cost and with faster answers on every task. d1 is available on the Liquid AI API and through Vercel and OpenRouter, with text-only support on those two platforms for now.

    Why it matters: The post gives benchmark comparisons against named models along with per-token pricing and latency figures, which makes the cost and speed tradeoff checkable.

  2. Kling AIAI score36

    Kling 4.0 powers "The Beat," a viral short film with 5M+ impressions

    AIKling AI shares behind-the-scenes details of its short film "The Beat," which has passed 5 million impressions across social platforms. The post says the film used Kling 4.0 features including a 30-second continuous shot, Omni Reference supporting up to 15 multi-modal references, Multi-Keyframe control for up to 10 keyframes, and 10-bit HDR output.

Oct 3

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Oct 2

Oct 2Fri