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Gemma

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Headlines · 7

  1. Google DeepMind and Singapore build on-device scam detection model

    Google DeepMind said it worked with GovTech Singapore to fine-tune its open Gemma model into an on-device scam-detection model, presented at Singapore International Cyber Week (SICW 2026). The system runs in two stages: a lightweight, low-power classifier screens inbound messages, then the fine-tuned Gemma analyzes a larger snapshot of the conversation to confirm a threat or discard a false alarm. DeepMind said fine-tuning happened inside GovTech Singapore's managed GCP environment to keep training data isolated, that training excluded private or personally identifiable information and used anonymized data derived from scam reports and intelligence feeds, and that the model was stress-tested on synthetic dialogues and local corpora; TechRepublic reported that public availability is still unconfirmed.

    Google DeepMind Blog · 🔥 0
  2. Satlyt raises $8M seed to run AI models on satellites

    Satlyt, co-founded by former Google and SpaceX product manager Rama Afullo, has raised an $8 million seed round led by Houston-based Non Sibi Ventures. Headquartered in Sunnyvale, California and Nairobi, the company builds software for running AI models on satellites rather than its own spacecraft, aiming to be a horizontally integrated layer across many operators' hardware. It previously deployed Google DeepMind's Gemma model on a Momentus-operated spacecraft, which the company says cut the size of a transmission about onboard software errors by more than 60%. This week its software launches on a SpaceX rocket aboard a satellite built by Indian startup TakeMe2Space, serving NASA and space-surveillance startup Stellerian, and Satlyt plans next year to attempt a shared compute system spanning two satellites.

    TechCrunch AI · 🔥 0
  3. Flo Health fine-tunes open models with Databricks to boost medical safety and usefulness

    A Databricks blog post describes how Flo Health, a women's health app with 81 million monthly active users, built safer and more useful custom open models on Databricks AI Runtime. Flo first created an evaluation system of 26 AI judges (12 medical-safety, 6 micro-behavior, 8 usefulness) to turn medical standards into a training signal, generated more than 15,000 privacy-first synthetic questions, then ran supervised fine-tuning and DPO on 32 NVIDIA H100 GPUs using LLM Foundry and Compose-RL with MLflow tracking. Flo reports medical safety rising from 34% to about 61%, usefulness from 42% to 76%, and the share of responses meeting both from 16% to 47%, and estimates tailored models can cut inference costs by an order of magnitude versus frontier APIs; it plans to apply the pipeline to newer open-weight models such as Gemma, Qwen and Kimi.

    Databricks Blog · 🔥 0
  4. a16z leads $11M round for Ghost, whose screenless Core AI computer costs $3,499

    Ghost, founded by 19-year-old CEO Zain Javaid, came out of stealth with an $11 million seed round led by Andreessen Horowitz (a16z), with Abstract, Audacious Ventures, SV Angel and Nova participating, and launched its first product: Core, a screenless, keyboard-less personal AI computer priced at $3,499 that its website says has already sold out, with shipping expected in the last week of October. Core packs an NVIDIA RTX PRO 4000 Blackwell SFF Edition GPU with 24GB GDDR7, 64GB DDR5 memory, a 1TB NVMe SSD and an AMD Ryzen 5 7600, and ships preinstalled with models including Qwen 3.8-Next, Qwen 3.8-27B and Gemma 4-31B, with users free to install others. Ghost says models, memories and personal data run and are stored encrypted mainly on-device with no usage-based AI subscription, and that it is building an "agent firewall" that monitors network requests and blocks unauthorized actions, plus OTA-style model updates.

    36氪 人工智能 · 🔥 0
  5. Unsloth ships desktop app and Studio web UI

    The open-source Unsloth project is trending on GitHub's Python list, and its README now describes three ways to use it: the native Unsloth Desktop app for Windows, macOS and Linux, the Unsloth Studio web UI, and code-based Unsloth Core. The project says it can run and train LLMs plus diffusion, embedding and audio models, naming Qwen3.8, DeepSeek-V4, Gemma 4, GLM-5.3-Flash, Kimi K3 and MiniMax-H3, and its Unsloth Start command wires local models into agents such as Claude Code and Codex. The README also claims 2x faster training with up to 70% less VRAM and no accuracy loss — figures that come from the project's own documentation.

    GitHub Trending · Python · 🔥 0
  6. Google open-sources ML Drift, an on-device GPU inference engine

    Google's AI Edge team announced on October 8 the open-source release of ML Drift under the Apache 2.0 license, a cross-platform on-device GPU compute engine for AI/ML inference that abstracts low-level graphics APIs including OpenGL ES, OpenCL, Metal and WebGPU. It serves as the core GPU acceleration engine inside LiteRT and is also offered as a standalone library for custom inference runtimes; Google says it cuts memory overhead by up to 12% versus other frameworks in Gemma benchmarks and, via Dawn, brings its WebGPU backend to Windows and Linux desktops. Google adds that ML Drift already runs in production on millions of devices daily across Chrome, YouTube Shorts, Photos and Meet, with YouTube Shorts reporting up to 40% lower average frame latency after migrating its segmentation effects — figures that remain vendor-reported without independent verification.

    Google Developers Blog · 🔥 0
  7. Ghost raises $11M to build Core, a $3,499 screenless computer for local AI agents

    Ghost, a startup led by 19-year-old CEO Zain Javaid, emerged from stealth with $11 million in seed funding led by Andreessen Horowitz, with Abstract, Audacious Ventures, SV Angel and Nova participating. Its first product, Core, is a $3,499 screenless machine meant to run personal AI agents locally, packing an Nvidia RTX PRO 4000 Blackwell SFF GPU, AMD Ryzen 5 7600, 64GB of RAM and 1TB of storage, and shipping with open models including Qwen-3.8, Qwen-3.8-2.7B and Gemma-4-31B; users control it through a phone and web app. Ghost says all source code, logic, model weights and user data stay on the device, with the user holding the encryption keys and a built-in firewall watching agents' outbound requests.

    SiliconANGLE AI · 🔥 0

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