M-148, ChromeOS version 16640.57.0 (Browser version 148.0.7778.250) has rolled out to ChromeOS devices on the Stable channel. 

If you find new issues, please let us know one of the following ways:

1. File a bug

2. Visit our ChromeOS communities

1. General: Chromebook Help Community

2. Beta Specific: ChromeOS Beta Help Community

3. Report an issue or send feedback on Chrome

4. Interested in switching channels? Find out how.

Andy Wu

Google ChromeOS

Gmail sources in Ask Gemini in Drive is now generally available and has started rolling out to eligible Google Workspace and Google AI plans.

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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.

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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.

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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.

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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.

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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.

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.

A new LTS-144  version 144.0.7559.254(Platform Version: 16503.86.0), is being rolled out for most ChromeOS devices. 

This version includes selected security fixes including:

501576946 High CVE-2026-9934: Use after free in Aura.

491685406 High CVE-2026-9895: Out of bounds read in GPU.

511776372 High CVE-2026-9980: Insufficient validation of untrusted input in Printing.

513730012 High CVE-2026-10004: Insufficient validation of untrusted input in Passwords.

514715455 High CVE-2026-10013: Use after free in WebCodecs.

507707838 High CVE-2026-9894: Use after free in GPU.

513177826 High CVE-2026-9992: Use after free in Network.

498205735 High CVE-2026-9902: Use after free in Accessibility.

478560268 High CVE-2026-2314: Heap buffer overflow in Codecs.

499119490 High CVE-2026-7351: Race in MHTML.

511249104 Critical CVE-2026-9887: Use after free in Proxy.

507365348 Critical CVE-2026-9873: Use after free in Network.

491080830 Medium CVE-2026-4462: Out of bounds read in Blink.

513505927 High CVE-2026-10001: Use after free in PerformanceManager.

513508128 Critical CVE-2026-9891: Use after free in Extensions.

Release notes for LTS-144 can be found here 

Want to know more about Long-term Support? Click here

Andy Wu

Google Chrome OS

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.

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