#Google

AIin which the model decides what to watch, at what speed, and through which modality. It fetches only the moments and signals it needs instead of ingesting media at a fixed frame rate. The post says this cuts costs by up to 66% and token consumption by up to 88% while boosting accuracy, and it is available now via the Gemini API and in AI Studio.

AIGoogle has released GNM v3.0, a parametric 3D statistical model of the human head, with weights published on Hugging Face and Kaggle under the Apache 2.0 license. The model gives controllable identity, expression, head pose, and internal anatomy including eyeballs, teeth, and tongue, and supports NumPy, JAX, PyTorch, and TensorFlow backends.

AIGoogle released agentic video understanding for Gemini 3.7 Flash, Gemini 3.6 Flash, and Gemini 3.5 Flash-Lite across the Interactions and GenerateContent APIs. The model dynamically navigates video timelines, requesting transcripts, frames, or audio tracks on demand. The source says this approach uses up to 88% fewer tokens for long-form content than static processing.

Why it matters: The changelog names the affected models and API surfaces, and states a token-use figure that helps developers judge the cost of long video workloads.

AIOpenClaw 2.0 (v2026.8.1) can be installed via npm and linked to Google's Gemini 3.8 Flash using a Gemini API key, with Google Search grounding enabled by default. The guide covers five CLI steps, from installation and authentication to starting the local gateway and Control UI. Gemini 3.8 Flash is described as up to 300 tokens per second and suited to coding and agent tasks.

AIJosh Woodward, who leads Google's Gemini app, praises the author letter included in the Expert Intelligence feature of Gemini Notebook. He calls it a nice touch and names it as one of his favorite parts of the feature.

AIGoogle's Gemini Notebook, launched yesterday, is pitched as a way to make buying books for AI context frictionless. The post by Josh Woodward responds to @shreyas, who said users increasingly buy books within Claude or ChatGPT to use as context and wants that process simplified in the UI.

AIReplicate has made Google's Gemini Omni 1.1 available to try through a hosted model page. The post provides only a link to the model on replicate.com, with no specifications, benchmarks, or pricing mentioned.

AIReplicate announced that Gemini Omni 1.1 Flash from Google DeepMind is now live on its platform. The update adds scene extension, control of a shot's starting and ending frames, video input references, upscaling to 4K, and 360p fast prototyping.

AIGoogle is rolling out Gemini Omni 1.1 Flash updates in Google Flow, adding start and end frame controls to keep characters and narrative consistent between transitions. Creators can draft at 360p on lower credit cost, then export final clips in 720p, 1080p, or 4K for digital, social, or broadcast workflows.

AIGoogle DeepMind has welcomed back Barret Zoph, who will work on reinforcement learning and post-training. Zoph, who began his AI career in the Google Brain Residency class, said he is excited to rejoin the company and contribute to its AI mission. Further details are to be announced later.

AIThe Google Developers Blog post describes how deep learning can analyze the large, image-like sensor data from astroparticle observatories such as the Pierre Auger Observatory and IceCube. The author argues these methods could improve instrument sensitivity and reveal patterns in cosmic-ray and neutrino signals that traditional analysis techniques miss.

AIThe post links to a Google blog page about Gemini 3.5 Transcribe, presented as a model developers can start building with. The post itself gives no benchmark scores, pricing, or capability details beyond the link.

AIGoogle has released Gemini 3.5 Transcribe, a speech-to-text model that filters out spoken hesitations and accurately grounds technical terms, file names, and code variables against the active context. The model uses visual biasing to incorporate screen-aware context into developer workflows, as demonstrated in Antigravity.

AIGoogle has released Gemini 3.5 Transcribe, a speech-to-text model that auto-detects over 85 languages and handles multiple speakers. It also supports custom vocabulary adaptation for specialized jargon. The API is available now in Google AI Studio and Gemini Enterprise.

AIGoogle DeepMind VP of research Zoubin Ghahramani, a Cambridge professor, discusses how machines can represent uncertainty, a line of work he has pursued for about 30 years. The video covers correctness versus confidence, Bayesian thinking in AI, and whether improving machine uncertainty is a missing piece for future AI progress.

AIGoogle Cloud has added native TPU support to vLLM and engineered optimizations to serve the Qwen3-Embedding-8B model on Cloud TPU, targeting 4K+ token text and 15K+ token multimodal inputs. The work addresses tensor alignment, lazy-loading, compilation pre-warming, and long-context pooling, with a cosine similarity pass threshold of at least 0.999 for text and 0.995 for multimodal inputs against XPU reference vectors.

AIGoogle Labs has introduced Play with Putty, an experimental collaborative vibe coding tool that lets users build tools and websites together in real time. It is available through a waitlist at labs.google/playwithputty and is limited to users in the US aged 18 and over.

AIGoogle Research has published the official PyTorch weights and configurations for TimesFM 3.0, a pretrained time-series foundation model for forecasting. The model uses a Stacked Mixing Transformer with 20 layers, a model dimension of 1280, and 16 heads, and it is released under the TimesFM Non-Commercial License v1.0.

AISundar Pichai says Gemini 3.7 Flash set new Gemini growth records in its first week, making it the fastest-growing Gemini model so far. The model is now running in Search and the Gemini app. A quoted ARC-AGI post reports 84.6% on ARC-AGI-2 at $0.25 per task and 95.5% on ARC-AGI-1 at $0.12 per task.

AIAntigravity is now available to all Gemini enterprise subscribers, broadening its access across the board. It can also be installed as an extension in all the major IDEs.

AIGoogle has released TIPS g/14 low-res (v1) on Hugging Face, a Text-Image Pre-training with Spatial awareness vision-language model with 1.1B vision parameters and 389M text parameters. The model produces spatially rich image features aligned with text embeddings at 224 resolution, under the Apache 2.0 license. It supports image encoding, text encoding, and zero-shot classification via the transformers library.

AIGoogle has released the original TIPS g/14 (v1) vision-language model on Hugging Face under Apache 2.0, with 1.1B vision parameters and 389M text parameters at 448 resolution. The TIPS family, presented at ICLR 2025, produces spatially rich image features aligned with text embeddings, and the release includes a low-res 224 variant.

AIGoogle released google/tipsv1-so400m14, the original v1 So400m/14 checkpoint of TIPS, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The model has 413M vision parameters and 448M text parameters at 448 resolution, and is licensed under Apache 2.0.

AIGoogle has published google/tipsv1-l14, the original v1 L/14 release of TIPS, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The L/14 variant has 304M vision parameters and 184M text parameters at 448 resolution, with an embedding dimension of 1024, and is licensed under Apache 2.0.

AIGoogle has published TIPS B/14 (v1) on Hugging Face, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The model has 86M vision parameters and 110M text parameters at native 448 resolution, and is licensed under Apache 2.0. The release includes usage code for image and text encoding, zero-shot classification, and spatial feature visualization.

AIVarun Mohan says users building with Gemini 3.7 Flash on Antigravity are seeing tasks that once took an hour now finish in minutes. He adds that more is coming.

AIGoogle AI Developers shared a GitHub repository, nroadley/Creating-Oneshot-Hero-Landing-Pages, for building your own one-shot hero landing pages. The post contains no further details about the project's features or methods.

AIGoogle shows Gemini 3.7 Flash, paired with Nano Banana and Omni, generating a fully animated parallax landing page from a single prompt. The model calls tools to write the copy, create image references, and render parallax-ready Omni videos.

AIGoogle Labs has opened a waitlist for CC, its experimental AI productivity agent in Gmail, in Australia and New Zealand, and is expanding availability in the US and Canada. CC now helps manage calendars by connecting to Gmail and automatically creating events in a dedicated Google Calendar that update as plans change. Invitations to waitlisted users in the US and Canada begin rolling out today.

AIGoogle AI Developers showcased a Chrome extension built in Antigravity with Gemini 3.7 Flash that generates rich visual definitions when users highlight text while browsing. The post says the tool uses Nano Banana and Gemini Omni to turn the web into an interactive visual encyclopedia, with a demo video linked.

AIGoogle fed Gemini 3.7 Flash hundreds of PDFs from a Victorian botanical illustration book and had it extract and classify the historical plants under the modern APG IV system. The model's results are shown in an interactive visualization built in Antigravity.

AIThe author built a Python agent that uses Gemini 3.7 Flash to control an Android emulator from raw screenshots, returning normalized 0–999 coordinates that are scaled to 1080x1920 pixels over ADB. In a test, the agent opened Chrome, closed popups, and solved one round of Wordle in two guesses without accessibility IDs or DOM access. The article presents the loop as usable for UI testing and task automation across native apps, webviews, and canvas interfaces, with code in an open-source quickstart repository.

AIGoogle Labs is promoting two experimental AI tools: Pomelli, for building marketing campaigns tailored to a brand at scale, and Flow, for crafting visual stories. The post invites users who have not yet tried them to explore both.

AIGoogle AI Developers is promoting marketing and campaign tools for apps built during the Build with Gemini @XPrize Hackathon, which runs through August 17. The post highlights Flow by Google and Pomelli by Google as options for promoting a product and deploying creative campaigns.

AIGoogle AI Developers directs readers to the Gemini X Prize website for more details and submission information before August 17. The post itself contains no further specifics about the competition's rules, prizes, or eligibility.

AIDemis Hassabis says Gemini 3.7 Flash is also lightning fast. Logan Kilpatrick's post identifies the model as 3.7 Flash, with no speed figures given.

AIGoogle has introduced Gemini 3.7 Flash, linking to an official blog post for more details. The post itself provides no further specifications, benchmarks, pricing, or availability information.

AIGoogle's Gemini 3.7 Flash in Antigravity turns one architecture spec into production-ready code for five mobile frameworks: Flutter, SwiftUI, Jetpack Compose, React Native, and NativeScript. The post presents this as generating native apps without boilerplate from a single specification.

AIGoogle has made Gemini 3.7 Flash available in Google Antigravity, described as its most intelligent workhorse model yet for coding and agents. Users can download or upgrade Antigravity to try the model. The author, Varun Mohan, says the model brings a big capability improvement at half the API cost.

AIGoogle has introduced Gemini 3.7 Flash, linking to an official blog post for more details. The post itself provides no further specifications, benchmarks, pricing, or availability information.