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Thinking Machines@thinkymachinesAI score43AIThinking Machines' new model is natively multimodal and encoder-free, processing audio and images jointly with text. It nearly matches Inkling across multimodal evaluations and can run Python to crop, zoom, and inspect images while reasoning over documents and charts.
AIGoogle is rolling out Lyria 3.5, its newest music generation model, in Google Flow Music today. The update improves musicality, lyric quality and prompt adherence, and vocal expressiveness and pronunciation, and gives users more control over tempo and duration.
Why it matters: The post names the specific capability changes and where users can access them, which helps readers judge fit for music creation workflows.
AIAlibaba NLP has released UEmbed-9B, a decoder-only multimodal embedding model built on Qwen3.5 9B that outputs both dense and SPLADE-style sparse embeddings from one forward pass. It supports text, image, video, and mixed-modal inputs for retrieval and multimodal search, and the family also includes 2B and 4B variants. The model is available on Hugging Face, with transformers and vLLM inference support.
AIAlibaba NLP has released UEmbed-4B, a decoder-only multimodal embedding model built on Qwen3.5 4B that outputs both dense and sparse embeddings from one forward pass. It handles text, image, video, and mixed-modal inputs for retrieval and visual-document search, and sparse activations map to vocabulary terms usable with inverted indexes. The model is available on Hugging Face in a family that also includes 2B and 9B variants.
AIAlibaba-NLP's UEmbed-2B, a decoder-only multimodal embedding model built on Qwen3.5 2B, produces both dense and SPLADE-style sparse embeddings from a single forward pass. It supports text, image, video, and mixed-modal inputs for retrieval, and the 4B and 9B variants are also available. The team reports state-of-the-art results on the text and agent tracks of MMEB-v3.
AIMiniMax released H3, an open-weights omni-modal model that generates video with native stereo audio up to 2K and 15 seconds. The system combines H3-Context-IR preprocessing, the H3-Base generator at 768p, and H3-Regenerate-2K for 2K output, with the Context-IR and 2K modules available only through API.
Why it matters: The source details a three-module pipeline and open weights with deployment paths, showing how a video model is served and reproduced locally.
Intern Large Models@intern_lmAI score62AIIntern Large Models introduces Visual Pretraining, a pretraining paradigm for foundation models that learns directly from visual documents. The post says it outperforms text-only pretraining across backbones and benchmarks, and links the arXiv paper 2607.09657 along with Intern-S2-Preview (35B) and Intern-S2-Preview-397B on Hugging Face, the latter presented as a multimodal foundation model trained with this recipe.
Kimi.ai@Kimi_MoonshotAI score46AIMoonshot AI's Kimi has released PerceptionBench, a benchmark that evaluates visual perception as atomic capabilities derived from frontier-model failures across 42 benchmarks. It contains 3,000 verified questions, each isolating a single capability and answerable by looking alone, without reasoning or external knowledge.
Meta@MetaAI score26AIUniversity of Pittsburgh researchers used Meta's SAM3 and DINO models to help build one of the most advanced wheelchairs in the world. The post provides no further technical details on performance or availability.
Kimi.ai@Kimi_MoonshotAI score38AIMoonshot AI's Kimi K3 is now available on DigitalOcean's Serverless Inference, letting developers start building in minutes. DigitalOcean describes K3 as supporting a 1M-token context, native vision, and multi-hour agentic tasks, and it is accessible through the Inference Router.
Lisa Su@LisaSuAI score20AIAMD CEO Lisa Su thanked Korean minister @msitminister for attending AMD's Advancing AI event and said the two sides expanded their partnership. She said the goal is to bring together technology, talent, and deep technical collaboration to advance an open and innovative AI ecosystem in Korea.
Kimi.ai@Kimi_MoonshotPickAI score86AIMoonshot AI is releasing the model weights and technical report for Kimi K3, a 2.8T-parameter MoE model with native visual understanding and a 1M-token context window. The post says the new architecture delivers 2.5x the intelligence per unit of compute, and the company is also opening high-performance attention kernels, an MoE communication library, and infrastructure for running agent environments at scale.
Why it matters: The source names the model size, context window, and released weights, which helps readers compare its scale and openness with other frontier releases.
AIBlack Forest Labs released FLUX 3, a multimodal model trained on images, video, and audio, and mimic built FLUX-mimic on its video backbone to control robots. In a soft-body kitting task, mimic reports a 95% success rate without single-task fine-tuning, compared with 55% for an adapted π0.5 model. FLUX 3 Video is in early access, with action prediction offered to selected partners and an open-weight backbone planned.
Midjourney@midjourneyAI score60AIMidjourney is releasing V8.2 today and making it the default model on the platform. The release focuses on aesthetics, personalization, and image quality, with a bolder and more creative new style. The post's two image pairs compare earlier outputs with the new style.
Noah Zweben@noahzwebenAI score20AIAnthropic's Noah Zweben asked "Is this AGI?" in a post about a Rocket League clone reportedly built by Opus 5. The quoted post credits Opus 5 with building a game and 3D model that it calls the best it has seen, using 27% of a 5x Max subscription.
AIEthan Mollick's guide says ChatGPT and Claude are the main choices for real work, since their agent modes can act on a computer. He separates agent modes that run on the company's computers from those that access the user's own computer. He recommends keeping approval settings on for sending, spending, or deleting, because of prompt injection risk. He also notes that Gemini currently lags for agentic work, though its Notebook and video tools are useful.
Andrej Karpathy@karpathyAI score30AIAndrej Karpathy describes using /voice to ramble for about 10 minutes, sometimes as a short interview, to give an LLM context that would be tedious to type. He says LLMs reconstruct these messy streams of thought remarkably well, often returning a cleaner version than the speaker started with, which improves shared understanding and reduces later corrections.
Fei-Fei Li@drfeifeiAI score33AIFei-Fei Li announced that SceniX is joining World Labs. She framed spatial intelligence as more than perceiving and generating worlds, emphasizing interaction with them.
koray kavukcuoglu@koraykvPickAI score72AIGoogle introduces Gemini 3.6 Flash as its workhorse model, with better coding, knowledge work, and multimodal performance while reducing token usage. It also launches Gemini 3.5 Flash-Lite, described as the fastest and most cost-effective 3.5-class model for high-throughput applications, and 3.5 Flash Cyber, a version of 3.5 Flash fine-tuned to find and fix cybersecurity vulnerabilities.
Why it matters: The post lists three distinct models, each aimed at a different job, so readers can map which one fits coding, high-volume, or security workloads.
AIAt RAAIS, DeepMind VP of Research Raia Hadsell argued that the field focuses too much on language and should apply large-model training to worlds, robots, biology, and weather. The article cites DeepMind's DiffusionGemma, a 26-billion-parameter open text model that generates blocks by denoising rather than one token at a time, and the Genie-3 world model, which runs in real time for several minutes. It also describes world models as a source of synthetic training data for robots.
Soumith Chintala@soumithchintalaAI score60AIMoonshot AI announced Kimi K3, a native multimodal model with 2.8 trillion parameters and a 1 million token context window. The announcement cites up to 6.3x faster decoding in million-token contexts and about 25% higher training efficiency, and says open weights arrive by July 27, 2026. The author, Soumith Chintala, reposted it with a brief note of congratulations.
AIMistral AI released Shieldstral-1.0-3B, a 3B-parameter multimodal safety classifier that judges content against natural-language policies and outputs a continuous safety score. It moderates text, image, and text-plus-image content in a single forward pass and can be retargeted to new policies at inference time without retraining. The Apache 2.0 open-weight model is built on Ministral-3-3B-Base-2512 and trained on sequences up to 32k tokens.
Junyang Lin@JustinLin610AI score42AIJunyang Lin praised Thinking Machines' new architecture in Inkling, which reasons across text, image, and audio, and asked whether the small model is open-sourced. The quoted announcement says the full weights are available and that Inkling is available for fine-tuning on Tinker.
AISable has created "Aidan," an AI employee that leads customer calls using vision, voice, video and real-time browser interaction. Frontier companies including Notion and Decagon are already using Sable to explain their products, and more than 150 companies are on its waitlist. Sequoia Capital led Sable's seed round and co-led its Series A.
Leandro von Werra@lvwerraAI score42AIThinking Machines has introduced Inkling, a model that reasons across text, image, and audio, with full weights made available. It is available for fine-tuning on Tinker and can be tried in the Inkling Playground. Hugging Face's Leandro von Werra praised the release for its grounded writing, interesting details, and strong ecosystem integration.
Lilian Weng@lilianwengAI score62AIThinking Machines has introduced Inkling, an open-weights model that reasons across text, image, and audio, with full weights made available. The model is available today for fine-tuning on Tinker, and the company also offers an Inkling Playground for trying it out. The author describes Inkling as a foundation model intended for broad capabilities in practical use and customization.
John Schulman@johnschulman2PickAI score75AIThinking Machines introduced Inkling, a model that reasons across text, image, and audio, and is making its full weights available. It is available today for fine-tuning on Tinker and can be tried in the Inkling Playground. John Schulman says pretraining began last winter and a small team added coding, reasoning, and agentic training starting in mid-January.
Why it matters: The post links an open-weights release to a stated training timeline, showing how a small team moved from pretraining to coding, reasoning, and agentic training.
Soumith Chintala@soumithchintalaAI score71AIThinking Machines introduced Inkling, an open-weight model with 975B parameters that reasons across text, image, and audio. The full weights are available, with fine-tuning on Tinker and access through the Inkling Playground and Hugging Face and partners.
Mira Murati@miramuratiAI score62AIMira Murati announced Inkling, the first model from Thinking Machines, trained from scratch with its full weights made available. The model reasons across text, image, and audio, and is available today for fine-tuning on Tinker and testing in the Inkling Playground.
Thinking Machines@thinkymachinesAI score48AIThinking Machines has released Inkling, a model that natively understands and reasons across text, audio, and images. It is particularly strong on audio, ranking among the top open-weights models on VoiceBench, MMAU, and AudioMC.
Jason Wei@_jasonweiAI score36AIOn Radiology's Last Exam, Meta's Muse Spark 1.1 outperforms OpenAI's GPT-5.6 Sol and Gemini 3.1, but still trails Fable and human experts. The result comes from a post by Jason Wei, with the benchmark context coming from a separate post about RadLE 2.0, an uncertainty-aware radiology diagnosis benchmark.
AIByteDance's UniVR-34B-Planning, built on Emu3.5 at 34B parameters, learns visual reasoning, physical dynamics, and long-term planning from visual demonstrations using a next-token objective and two-stage training on the VR-X dataset with VR-GRPO reinforcement learning. On the VR-X benchmark it scores 58.2 overall, up 18.4 points from the Emu3.5 34B baseline of 39.8. The Planning checkpoint is available on Hugging Face under CC BY 4.0, alongside a General checkpoint.
BAAI@BAAIBeijingAI score47AIBAAI introduced RoboBrain Orca, which learns world latent representations from multimodal data and models world-state transitions through Next-State-Prediction instead of only next-token, next-frame, or next-action prediction.
AIMeta Superintelligence Labs has introduced Muse Spark 1.1, a multimodal reasoning model aimed at agentic tasks, with gains in tool use, computer use, coding, and multimodal understanding. It supports a 1 million token context window and is available in Thinking mode in the Meta AI app and on meta.ai, with developers able to access it through a public preview of the Meta Model API.
Why it matters: The post specifies Muse Spark 1.1's agent, coding, and multimodal gains and its Meta Model API preview access, which helps developers judge its fit for their workflows.
Max Woolf@minimaxirAI score22AIMax Woolf says Meta's new generative image model failed immediately on his prompt injection test, which asked it to render all previous text verbatim using refrigerator magnets. He links to Meta's announcement of Muse Image. The post gives no further details about the model's capabilities or the test results.
AIMistral AI released Leanstral 1.5 on Hugging Face as an open-source code agent model for Lean 4 proof assistant tasks. The model uses 119B total parameters with 6.5B activated per token, a 256k context length, and accepts text and image input. The source gives setup paths through Mistral Vibe and a local vLLM server, with recommended settings of temperature 1.0 and reasoning effort set to high for complex prompts. The model is licensed under Apache 2.0.
PaddlePaddle@PaddlePaddleAI score38AIPP-OCRv6's recognition module uses a CTC plus NRTR dual-head design so text is decoded from visual features rather than language priors, reducing hallucination. In hallucination tests, PP-OCRv6_medium reaches 93.2%, versus 85.0% for the best VLM, and recognition accuracy across 15 scenarios is 83.2%, above PP-OCRv5_server's 78.1%. NRTR is used only during training, adding language regularization at no inference cost, and it contributes +1.16% accuracy.
Fei-Fei Li@drfeifeiAI score24AIFei-Fei Li replied to smallfly's reflection on AI, automation, and human creativity, saying World Labs is founded on empowering human ingenuity and productivity. The post thanks smallfly for working with World Labs and links to a Fast Company article about World Labs, Marble, and spatial intelligence.
Xiaomi MiMo@XiaomiMiMoAI score38AIXiaomi has launched MiMo Claw, an agent built on its flagship MiMo model and integrated with Kingsoft Office for Word, Excel, PowerPoint, and PDF workflows. The company says it consumes 40–60% fewer tokens than comparable solutions, and daily usage has been expanded from 1 hour to 4 hours, with free access and no deployment required. A limited-time subscription is priced at ¥14.9 per month.
AIByteDance has released Sa2VA-LLaVA-1.5-7B on Hugging Face, a model built on LLaVA-1.5-7B with a SAM2 grounding encoder that performs dense image and video referring segmentation alongside open-ended chat. The checkpoint is self-contained and loads with trust_remote_code=True without extra packages, and it is positioned as a LISA-comparable baseline within the Sa2VA family. Reported results include 80.3 cIoU on RefCOCO val and 54.8 J&F on MeViS (val_u).