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Aug 26

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  1. Google Developers BlogAI score35

    Google Brings Qwen3-Embedding-8B to Cloud TPU via vLLM with Long-Context Support

    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.

Aug 24

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  1. Google · new models on Hugging FaceAI score40

    Google releases TimesFM 3.0 time-series forecasting model weights on Hugging Face

    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.

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  1. Google · new models on Hugging FaceAI score22

    Google releases TIPS g/14 low-res v1 vision-language model on Hugging Face

    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.

  2. Google · new models on Hugging FaceAI score26

    Google releases TIPS g/14 v1 vision-language model on Hugging Face

    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.

  3. Google · new models on Hugging FaceAI score22

    Google releases TIPS L/14 v1 vision-language model on Hugging Face

    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.

  4. Google · new models on Hugging FaceAI score22

    Google releases TIPS B/14 v1 vision-language model on Hugging Face

    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.

Aug 18

Aug 18Tue
  1. Google LabsAI score43

    Google's CC Gmail agent expands waitlist to Australia and New Zealand

    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.

Aug 17

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  1. Google AI DevelopersAI score22

    Google demos Chrome extension generating visual definitions from highlighted text

    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.

    Video from @googleaidevs's post

Aug 16

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  1. Philipp SchmidAI score58

    Controlling Android with Gemini 3.7 Flash and 150 lines of Python

    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.

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  1. Google AI DevelopersAI score75

    Google releases Gemini 3.7 Flash for coding and agentic tasks

    AIGoogle AI Developers announced Gemini 3.7 Flash as its most intelligent workhorse model yet for coding and agents, citing higher instruction adherence, first-pass code accuracy, and high-quality agentic execution. The post shows the model building a complex 3D web game in Antigravity, covering Three.js engine logic, asset orchestration with PBR textures and Nano Banana sprite sheets, and procedural sound effects.

    Why it matters: The post shows a concrete build workflow across engine logic, assets, and audio, which helps readers judge how the model handles multi-step agentic coding.

    Video from @googleaidevs's post
  2. Demis HassabisAI score67

    Google releases Gemini 3.7 Flash with coding and web development upgrades

    AIGoogle DeepMind has released Gemini 3.7 Flash, which the post says is stronger for coding, knowledge work, and web development. Its introductory price is half the original cost of Gemini 3.6 Flash.

    Why it matters: The post names concrete upgrade areas and a price change against the prior version, which helps readers compare it with earlier Flash releases.

  3. koray kavukcuogluAI score72

    Google launches Gemini 3.7 Flash for coding and agentic workflows

    AIGoogle launches Gemini 3.7 Flash, its latest Flash model for coding and agentic workflows, with an introductory price at half the original cost of 3.6 Flash. The post reports gains from 3.5 to 3.7 Flash, including DeepSWE v1.1 rising from 37.0% to 65.3%, Code Arena Elo from 1506 to 1588, and AutomationBench from 13.4% to 30.4%.

    Why it matters: The post pairs a launch with specific before-and-after benchmark gains and an introductory price, letting readers weigh capability against cost for coding and agent work.

    Image from @koraykv's post

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