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Google says its Project Suncatcher satellite successfully booted onboard TPUs and used the Gemma 4 model to generate a haiku from orbit, marking the system's first transmitted AI response. The company plans further tests of TPU performance under thermal stress and radiation and says this work paves the way toward twin interconnected satellites planned for 2027.
Project Suncatcher launched a prototype satellite on Oct 1 and, after commissioning, uplinked a prompt to an onboard Gemma 4 open model running on data-center-class TPU hardware; the satellite downlinked a haiku as the first successful AI response while passing Svalbard. The team will continue tests of TPU performance under thermal stress and radiation ahead of planned twin interconnected satellites in 2027.
Google has released EmbeddingGemma 2, an open-weight multimodal embedding model based on Gemma 4 and licensed under Apache 2.0. The model produces unified 768-dimensional embeddings for text, code, images, video and audio, scales from ~270M to ~740M parameters depending on loaded encoders, supports Matryoshka Representation Learning (MRL) for truncated vectors, and is positioned for low-latency on-device search and retrieval; it is available on Hugging Face.
Google released a Mac app called Google AI Edge Foresight that captures meeting notes across apps and in‑person meetings and can run completely offline on Apple Silicon. The app uses an on‑device EmbeddingGemma 2 (740M parameters) for embeddings, offers a split view like Granola, lets users view transcripts and chat with a Gemma 4–powered assistant, and can ingest PDFs, Google Docs, Office files, text, Markdown and bookmarks to build a local knowledge base for real‑time Q&A.
EmbeddingGemma 2 is a 740M-parameter, Apache 2.0–licensed multimodal embedding model from Google that natively maps text, images, audio, video and code into a single embedding space and is optimized for on-device use. It is built on the Gemma 4 architecture, supports modular encoder configurations, an 8K-token context window, Matryoshka Representation Learning for configurable vector sizes, and is available on Hugging Face and Kaggle.
Cohere published a post promoting small language models (SLMs) for enterprise use, arguing they cut compute, data and energy needs while enabling local deployment and lower costs. The post highlights Cohere models — Command R7B, the Tiny Aya family (3.35B) and North Mini Code (30B MoE with 3B active) — and cites benchmark claims that North Mini Code and Tiny Aya outperform several larger competitors on coding and multilingual translation benchmarks respectively.
Google DeepMind announced Gemma 4 12B, which it describes as a unified, encoder-free multimodal model. The announcement presents Gemma 4 12B as part of the Gemma 4 family.
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