Google DeepMind’s Gemma 4 12B model brings agentic, multimodal AI capabilities to everyday laptops with 16GB of RAM, enabling local data processing and visual insight generation. Users can leverage this model on macOS through the Google AI Edge Gallery for dynamic Python code execution and visualization, as well as via Google AI Edge Eloquent for completely offline voice dictation and text editing. Additionally, developer workflows are enhanced by the LiteRT-LM CLI's new serve command, which creates an industry-compatible local endpoint to power fully-local AI tools and agents.
The newly released Gemma 4 12B is a dense, multimodal model designed for high-performance local AI execution on consumer devices. By introducing a novel, encoder-free architecture, it bypasses traditional visual and audio encoders to feed multimodal data directly into the LLM backbone.
Data loss prevention (DLP) for Google Calendar is now generally available to protect sensitive information shared within event details. Previously available in beta, this feature allows you to create and apply data protection rules that scan calendar event titles, descriptions, and locations for sensitive content, such as credit card numbers or national identification numbers.
Key functionalities include:
Choice of actions: Admins can choose to audit when an event is saved with sensitive content, warn users about sensitive content in their event, or block event creation or updates if a DLP policy is violated.
Event details: DLP rules scan free-text fields in the event, including the event’s title, description, and location fields.
Owner-based policies: Rules are applied based on the organizational unit (OU) of the owner (event organizer on primary calendars or calendar owner on secondary calendars), consistent with other Workspace DLP configurations.
User notifications: With DLP policies for Calendar, users receive immediate feedback when sensitive data is detected. On the web, users see a pop-up notification explaining the issue. Admins can also customize this message with more specific details. If a meeting update is blocked on Android, iOS, or via the Calendar API, the user will receive an automated email notification explaining the policy violation and why changes to the meeting invite were not successful.
Getting started
Admins: The feature will be OFF by default and can be enabled at the organizational unit (OU) or group level. Visit the Help Center to learn more about DLP for Calendar.
End users: There is no end user setting for this feature.
DLP settings in the admin console to configure policies for sensitive data, including actions and alerts when creating Calendar events
An end user is prompted with a message asking them to remove sensitive information
Data loss prevention (DLP) rules for non-Workspace file attachments and associated proximity conditions are now generally available. These new capabilities enable organizations to target files with specific parameters, such as blocking the sharing of sensitive file formats or identifying files that contain specific strings in their titles.
Using these new content conditions, admins can set up various DLP rules for added protection, such as:
File names: Block files containing text string “funkyword”
File extensions: Block .java files
File types: Block custom mime type such as application/custom_app
Proximity matching: Detect “routing number” in proximity of 100 characters of “account number”
Additional details
In addition to file-based conditions, administrators can utilize associated proximity conditions to identify sensitive information in the file. This feature allows for the detection of sensitive data that appears within a specified distance of other predefined data types, regular expressions, or word lists.
For example, a rule can be configured to trigger when a bank account number is found within 100 characters of a routing number. By identifying data in context, proximity matching helps administrators reduce false positives and more accurately secure financial information or proprietary content.
Key functionality in DLP rules for file attachments and associated proximity conditions include:
Ability to match against common or custom MIME types and system file categories
Support for scanning attachments in Gmail, Drive, and Chat to set rules across communication channels
Granular distance settings for proximity matching, allowing admins to define a range of up to 1,000 characters between matched conditions
Getting started
Admins: When configuring DLP rules in the admin console, admins can locate the new content conditions of file extension, file name, and file type under content conditions. Admins can also select the option of proximity matching to set a maximum distance between two pieces of matched texts. Visit the Help Center to learn more.
End users: There is no end user setting for this feature.
Content conditions for DLP in the Admin console to configure policies for sensitive file attachments
by Jyrki Alakuijala, Zoltán Szabadka & Luca Versari, Paradigms of Intelligence, Google Technology & Society
Building the Next Generation Image Standard
The internet runs on images. Since the early days of the web, there has been a relentless tension between visual fidelity and bandwidth. For decades, the industry relied on the venerable JPEG standard for images loading fast. It served us remarkably well, but as displays moved to High Dynamic Range (HDR) and Wide Color Gamut (WCG), the format began to show its limits.
The road to JPEG XL (JXL) wasn't a straight line. It was a decade-long exploration, creating a series of milestone projects testing radical ideas in psychovisual modeling, entropy coding, and optimization. Today, as JPEG XL sees rapid adoption across operating systems and professional standards, we’re looking back at the experiments that made it possible.
The Early Foundation: 2011–2017
Our study began with a focus on understanding the limits of existing technology. We didn't start by trying to write a new standard; we started by trying to make the current ones better, and learning their limitations. This allowed us to make the new formalism more flexible and efficient in the right places.
WebP Lossless and Brotli: Lossy WebP drew its lineage from video technology, the WebP Lossless (2011) represented an architectural and scoping departure. We debuted the entropy image concept, an innovative method utilizing a secondary image to orchestrate the selection of static entropy codes for the primary visual data. We reapplied this approach later with data-driven context modeling in the Brotli compression format, enabling rich context modeling without slowing decoding.
Butteraugli: Around 2014, we realized that raw mathematical compression (PSNR) wasn't enough, and simple psychovisual approximations (SSIM and similar) failed in color-rich environments. We built Butteraugli and the XYB color space to mimic the human visual system's edge detection and opponent-color processes in varying scale, allowing us to compress images more effectively.
We pushed the legacy JPEG 1 standard (ISO/IEC 10918, introduced in 1992) to its absolute limits through two key projects: Guetzli and Brunsli. These initiatives provided invaluable insights into the strengths and limitations of traditional JPEG compression methods. Guetzli (2016) is a slow high-density perceptual encoder that used Butteraugli to find the optimal quantization tables, pushing legacy JPEGs to be 20-30% smaller. Brunsli (2015) meanwhile, focuses on lossless recompression, allowing users to repack existing JPEGs into a smaller footprint without losing a single bit of original data. After finishing with JPEG XL standardization, we returned to Guetzli's scope in 2024 and made the encoding much faster and HDR-compatible in Jpegli.
The feedback from these launches, ranging from the technical details of WebP Lossless to the psychovisual audits of Guetzli, proved indispensable. While we already targeted the highest visual fidelity, feedback from detail-critical e-commerce helped us to refine the requirements.
The Convergence: 2017–2019 PIK Era and the 2019 FUIF Integration
By 2017 we had powerful separate tools and it was time to fuse them. In open sourcing PIK we combined the efficiency of Brunsli with the psychovisual optimizations of Guetzli. Further, PIK introduced a real adaptive quantization field and other optimizations. PIK formed our proposal to the ISO standardization body. The committee's final call for proposals pushed toward extreme density, requiring bit rates as low as 0.06 BPP, equivalent to 35 times the compression of internet-quality images and 80 times that of camera output. This expansion of scope necessitated a significant complexification of the format and the encoder, leading to the Variable-block-size Discrete Cosine Transform (VarDCT) architecture that remains central to JPEG XL today.
We proposed to merge our PIK proposal with the FUIF (Free Universal Image Format) proposal from Cloudinary. PIK used Brotli-style distribution selection at encoding time, while FUIF refined codes incrementally during decoding. The final JPEG XL standard became a best-of-both-worlds compromise: we used PIK's faster-to-decode distribution selection with FUIF's sophisticated context trees. The merger represented a departure from conventional one platform driven standardization, and prioritized technical synergy and collaboration.
JPEG XL Today: An Ecosystem Takes Root
JPEG XL's efficiency, psychovisually-optimized quality, file size, and coding speed, are being noticed. We are seeing bottom-up adoption in various industries, the most demanding fields are leading the way. Because of its ability to handle high bit-depth, high quality and even lossless data efficiently and robustly, JPEG XL has become foundational in several fields:
Photography: Used in Digital Negative (DNG 1.7), Apple's ProRAW, and others.
Medical: Adopted by DICOM, the international standard for medical images.
Publishing: Integration into future versions of the PDF and EPUB standards.
The ecosystem has been maturing rapidly. Adobe's photography software, Apple's iOS, macOS, and visionOS have native support, as do Linux distributions like Ubuntu and Microsoft's JPEG XL Image Extension for Windows. Our libjxl-tiny inspired Shikino High-Tech, Inc. and CAST to release the first commercial JPEG XL encoder IP core for ASIC and FPGA designs, aimed at real-time, low-power image capture. Safari (2023) led among major browsers, while Firefox and Chrome currently maintain experimental support.
JPEG XL design was not only countless hours of optimization, experimentation and eye-balling the results, but also creative discussions at a whiteboard. In this Gemini-reconstructed scene, Luca Versari and Jyrki Alakuijala (left-to-right) debate VarDCT block selection heuristics.
Looking Forward
The story of JPEG XL stands as a testament to the efficacy of long-horizon planning validated by intermediate functional milestones—with minimum-viable prototypes like Guetzli and practical tools like Brunsli and Brotli—that invite feedback from the open-source community. A small research team can innovate by crystallizing solutions through quick iterations, with thousands, if not tens of thousands, of experiments in psychovisual modeling, entropy, coding speed and complexity, and the entire industry can eventually navigate toward a more efficient, beautiful future.
We started by trying to squeeze a few more bytes out of a 1992 JPEG 1 standard; with JPEG XL we hope to have established a foundation for digital imaging that can last for the next three decades.
Tired of scanning one page at a time? With the new Document Scanner in Google Drive on Android, you can now scan multiple pages at the same time. Flip through the pages of a book or lay your receipts out on a table and our Document Scanner will identify, separate, and capture each page within the camera view. It even detects duplicates to prevent accidental re-scans.
Note this is only available for Android devices with 8GB+ RAM.
New multi-page scanning with Android Document Scanner
Getting started
Admins: There is no admin control for this feature.
Scholar Labs is a search mode that helps you answer detailed research questions. Today, we’re releasing an update that:
Finds results ~10x faster
Scans up to 3x more papers, to find the most helpful results
Allows users to do 10x more searches every day
Whether you’re starting your research journey with a literature survey, mid-way through a project and looking up the details of a specific method, or making sure you haven’t missed an important citation before sending off your manuscript, Scholar Labs is designed to help you find the papers you need.
For every question, Labs identifies its key topics, aspects, and relationships. It then searches Scholar and scans the results to find papers that best answer your overall research question. For each paper, it provides a brief description of how the paper might be helpful. And it includes all the familiar Scholar features that you depend upon.
You can also ask follow up questions to dig deeper and explore specific nuances.
Scholar Labs is available for all logged in users and supports questions in all languages. The updated version of Labs is available in English starting today, and we hope to roll this out to all other languages shortly.
Happy searching!
Posted by: Sam Yuan, Namit Shetty,Alex Verstak, Anurag Acharya
Previously, data transfers from corporate Google Workspace accounts to third-party apps were restricted. We’re now recalibrating these restrictions to allow for a "trusted ecosystem" between managed apps.
With this release, users are now able to maintain efficient workflows and securely move data between corporate Workspace accounts and other authorized, managed third-party applications without being blocked. For added data protection, this capability strictly prevents the transfer of that data to personal accounts within the same managed Google application or to unmanaged personal apps.
For example, a user is able to copy a client's email address from their corporate Gmail account and successfully paste it into a managed third-party CRM application. When they try to paste that same email address into their personal Gmail account, they are blocked and receive the following message: "This information can only be shared within your organization's Google Workspace apps.”
Getting started
Admins: This feature will be OFF by default and can be enabled at the OU level. Admins can leverage iOS AppConfig through their MDM provider to enforce these restrictions for each Workspace app. Visit the Help Center to learn more about preventing accidental data leaks on iOS devices.
End users: There is no end-user setting for this feature.
The Chrome team is excited to announce the promotion of Chrome 150 to the Beta channel for Windows, Mac and Linux. Chrome 150.0.7871.4 contains our usual under-the-hood performance and stability tweaks, but there are also some cool new features to explore - please head to the Chromium blog to learn more!
A partial list of changes is available in the Git log. Interested in switching release channels? Find out how. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.
