The ChromeOS Beta channel is being updated to OS version 16581.24.0 (Browser version 146.0.7680.60) for most ChromeOS devices.
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Luis Menezes
Google ChromeOS
The Beta channel has been updated to 146.0.7680.65 for Windows, Mac and Linux.
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.
Chrome Release Team
Google Chrome
Hi everyone! We've just released Chrome Beta 146 (146.0.7680.65) for Android. It's now available on Google Play.
You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.
If you find a new issue, please let us know by filing a bug.
Chrome Release Team
Google Chrome
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GFiber delivers an essential upgrade over traditional internet providers, offering service built on a 100% fiber optic network designed to support symmetrical upload and download speeds and to meet the ever-growing bandwidth demands of a modern digital life.
100% fiber optic network with 25G PON technology
Internet demand is growing by up to 40% every year. To meet this demand, GFiber uses a 100% fiber optic network and is proactively upgrading our network in GFiber cities to 25G PON technology, which is capable of 20 gigabits-per-second (Gbps) symmetrical speeds. Many traditional providers use a network that relies on a mix of coaxial and fiber lines, but the GFiber network is built on 100% fiber optic technology.
Symmetrical speeds for gaming and video calls
While the average U.S. household internet speed is approximately 214 Mbps, GFiber’s entry-level product is much faster, providing more capacity for both download and upload-heavy tasks, like video conferencing or content creation.
1 Gig Plan: Up to 1,000 Mbps symmetrical upload and download speeds.*
Performance: This provides 4.6x higher upload speeds than the national average.**
In addition, every GFiber product includes high-quality equipment at no extra cost, like a Wi-Fi router and mesh extender. Professional installation is also included.
"There's arguably no better multi-gig provider."
Source: CNET, "Google Fiber Review 2024," Oct. 2024
Award-winning customer service and support
You can get live support by phone, chat, or through the GFiber App any time. In fact, on average, customers reach a live agent in 10 seconds or less.*** This commitment to service has resulted in consistent national recognition.
Named 2x winner or back to back winner Best Overall Internet Provider in the US by HighSpeedInternet.com in 2024 & 2025.****
PCMag's Most Awarded Internet Service Provider
CNET Best Fiber Internet of 2025 (National)*****
J.D. Power Best in Customer Satisfaction for Home Wired Internet Service in the South Region for 3 years in a row.******
Named a Best Internet Provider by Forbes Home 3 years in a row.*******
Transparent pricing with no hidden fees
GFiber has flat-rate pricing with no annual contracts or equipment rental fees. The price you see on the Broadband Facts label is the price you pay. Unlike many competitors that offer introductory rates that increase after 12 months, the price for our Core 1 Gig product has remained unchanged since 2012.
Note: Products and pricing vary by market. Visit google.com/fiber for availability.
GFiber is more than just a service built on a 100% fiber optic network, with symmetrical speeds, and transparent pricing. It’s a new standard for home internet and a service that puts you first. Our goal is for you to never have to think about your internet. But on the rare occasion that you do, we’ve made sure those moments are as painless as possible. Finally, an internet provider you may actually love.
*Up to 1,000 Mbps symmetrical download and upload speeds. Internet speeds are not guaranteed and may vary by various factors including equipment, market conditions, and network congestion.
**Based on the 2023 FCC Measuring Broadband America Fixed Broadband Report. ***Based on average data from January 1, 2024 - January 1, 2025 data.
****GFiber received the highest score in the HighSpeedInternet.com 2025 Annual Satisfaction Survey (2025). HighSpeedInternet.com is a third-party organization, and the ranking may change annually.
*****A trademark of Ziff Davis, LLC. Used under license; Reprinted with permission. © 2025 Ziff Davis, LLC. All Rights Reserved.
******GFiber received the highest score in the South region of the J.D. Power 2023-2025 U.S. Residential Internet Service Provider Satisfaction Studies, which measures customers’ satisfaction of service with their current internet provider. Visit jdpower.com/awards for more details.
*******Awarded Best Fiber Internet: Best Internet Providers Of 2026, Forbes Home.
Posted by Mayuri Khinvasara Khabya, Developer Relations Engineer (LinkedIn and X)
In the dynamic world of social media, user attention is won or lost quickly. Meta apps (Facebook and Instagram) are among the world's largest social platforms and serve billions of users globally. For Meta, delivering videos seamlessly isn't just a feature, it's the core of their user experience. Short-form videos, particularly Facebook Newsfeed and Instagram Reels, have become a primary driver of engagement. They enable creative expression and rapid content consumption; connecting and entertaining people around the world.
This blog post takes you through the journey of how Meta transformed video playback for billions by delivering true instant playback.
Short-form videos lead to highly fast paced interactions as users quickly scroll through their feeds. Delivering a seamless transition between videos in an ever-changing feed introduces unique hurdles for instantaneous playback. Hence we need solutions that go beyond traditional disk caching and standard reactive playback strategies.
To address the shifts in consumption habits from rise in short form content and the limitations of traditional long form playback architecture, Jetpack Media3 introduced PreloadManager. This component allows developers to move beyond disk caching, offering granular control and customization to keep media ready in memory before the user hits play. Read this blog series to understand technical details about media playback with PreloadManager.
Previously, Meta used a combination of warmup (to get players ready) and prefetch (to cache content on disk) for video delivery. While these methods helped improve network efficiency, they introduced significant challenges. Warmup required instantiating multiple player instances sequentially, which consumed significant memory and limited preloading to only a few videos. This high resource demand meant that a more scalable robust solution could be applied to deliver the instant playback expected on modern, fast-scrolling social feeds.
The team then performed extensive testing and iterations to optimize performance across Meta's diverse global device ecosystem. Initial aggressive preloading sometimes caused issues, including increased memory usage and scroll performance slowdowns. To solve this, they fine-tuned the implementation by using careful memory measurements, considering device fragmentation, and tailoring the system to specific UI patterns.
Meta applied different preloading strategies and tailored the behavior to match the specific UI patterns of each app:
Facebook Newsfeed: The UI prioritizes the video currently coming into view. The manager preloads only the current video to ensure it starts the moment the user pauses their scroll. This "current-only" focus minimizes data and memory footprints in an environment where users may see many static posts between videos. While the system is presently designed to preload just the video in view, it can be adjusted to also preload upcoming (future) videos.
Instagram Reels: This is a pure video environment where users swipe vertically. For this UI, the team implemented an "adjacent preload" strategy. The PreloadManager keeps the videos immediately after the current Reel ready in memory. This bi-directional approach ensures that whether a user swipes up or down, the transition remains instant and smooth. The result was a dramatic improvement in the Quality of Experience (QoE) including improvements in Playback Start and Time to First Frame for the user.
Scaling a high-performance video stack across billions of devices requires more than just aggressive preloading; it requires intelligence. Meta faced initial challenges with memory pressure and scroll lag, particularly on mid-to-low-end hardware. To solve this, they built a Device Stress Detection system around the Media3 implementation. The apps now monitor I/O and CPU signals in real-time. If a device is under heavy load, preloading is paused to prioritize UI responsiveness.
This device-aware optimization ensures that the benefit of instant playback doesn't come at the cost of system stability, allowing even users on older hardware to experience a smoother, uninterrupted feed.
Beyond the user-facing metrics, the migration to Media3 PreloadManageroffered long-term architectural benefits. While the integration and tuning process needed multiple iterations to balance performance, the resulting codebase is more maintainable. The team found that the PreloadManager API integrated cleanly with the existing Media3 ecosystem, allowing for better resource sharing. For Meta, the adoption of Media3 PreloadManager was a strategic investment in the future of video consumption.
By adopting preloading and adding device-intelligent gates, they successfully increased total watch time on their apps and improved the overall engagement of their global community.
The proactive architecture delivered immediate and measurable improvements across both platforms.
Facebook experienced faster playback starts, decreased playback stall rates and a reduction in bad sessions (like rebuffering, delayed start time, lower quality,etc) which overall resulted in higher watch time.
Instagram saw faster playback starts and an increase in total watch time. Eliminating join latency (the interval from the user's action to the first frame display) directly increased engagement metrics. The fewer interruptions due to reduced buffering meant users watched more content, which showed through engagement metrics.
As media consumption habits evolve, the demand for instant experiences will continue to grow. Implementing proactive memory management and optimizing for scale and device diversity ensures your application can meet these expectations efficiently.
Prioritize intelligent preloading
Focus on delivering a reliable experience by minimizing stutters and loading times through preloading. Rather than simple disk caching, leveraging memory-level preloading ensures that content is ready the moment a user interacts with it.
Align your implementation with UI patterns
Customize preloading behavior as per your apps’s UI. For example, use a "current-only" focus for mixed feeds like Facebook to save memory, and an "adjacent preload" strategy for vertical environments like Instagram Reels.
Leverage Media3 for long-term code health
Integrating with Media3 APIs rather than a custom caching solution allows for better resource sharing between the player and the PreloadManager, enabling you to manage multiple videos with a single player instance. This results in a future-proof codebase that is easier for engineering teams to not only maintain and optimize over time but also benefit from the latest feature updates.
Implement device aware optimizations
Broaden your market reach by testing on various devices, including mid-to-low-end models. Use real-time signals like CPU, memory, and I/O to adapt features and resource usage dynamically.
To get started and learn more, visit
Explore the Media3 PreloadManager documentation.
Read the blog series for advanced technical and implementation details.
Check out the sample app to see preloading in action.
Now you know the secrets for instant playback. Go try them out!
Learn more about AI Mode in Search’s query fan-out method for visual search.
This new unboxing video from Google shows off the Pixel 10a’s sleek, durable design and brand new Berry color.
Here are Google’s latest AI updates from February 2026
Superpollutants have been responsible for close to half of planetary warming, and without action they’ll continue to warm the planet rapidly in the decades to come.That’…
Posted by Matthew McCullough, VP of Product Management, Android Developer
We want to make it faster and easier for you to build high-quality Android apps, and one way we’re helping you be more productive is by putting AI at your fingertips. We know you want AI that truly understands the nuances of the Android platform, which is why we’ve been measuring how LLMs perform Android development tasks. Today we released the first version of Android Bench, our official leaderboard of LLMs for Android development.
Our goal is to provide model creators with a benchmark to evaluate LLM capabilities for Android development. By establishing a clear, reliable baseline for what high quality Android development looks like, we’re helping model creators identify gaps and accelerate improvements—which empowers developers to work more efficiently with a wider range of helpful models to choose for AI assistance—which ultimately will lead to higher quality apps across the Android ecosystem.
Designed with real-world Android development tasks
We created the benchmark by curating a task set against a range of common Android development areas. It is composed of real challenges of varying difficulty, sourced from public GitHub Android repositories. Scenarios include resolving breaking changes across Android releases, domain-specific tasks like networking on wearables, and migrating to the latest version of Jetpack Compose, to name a few.
Each evaluation attempts to have an LLM fix the issue reported in the task, which we then verify using unit or instrumentation tests. This model-agnostic approach allows us to measure a model’s ability to navigate complex codebases, understand dependencies, and solve the kind of problems you encounter every day.
We validated this methodology with several LLM makers, including JetBrains.
“Measuring AI’s impact on Android is a massive challenge, so it’s great to see a framework that’s this sound and realistic. While we’re active in benchmarking ourselves, Android Bench is a unique and welcome addition. This methodology is exactly the kind of rigorous evaluation Android developers need right now.”
- Kirill Smelov, Head of AI Integrations at JetBrains.
The first Android Bench results
For this initial release, we wanted to purely measure model performance and not focus on agentic or tool use. The models were able to successfully complete 16-72% of the tasks. This is a wide range that demonstrates some LLMs already have a strong baseline for Android knowledge, while others have more room for improvement. Regardless of where the models are at now, we’re anticipating continued improvement as we encourage LLM makers to enhance their models for Android development.
The LLM with the highest average score for this first release is Gemini 3.1 Pro, followed closely by Claude Opus 4.6. You can try all of the models we evaluated for AI assistance for your Android projects by using API keys in the latest stable version of Android Studio.
Providing developers and LLM makers with transparency
We value an open and transparent approach, so we made our methodology, dataset, and test harness publicly available on GitHub.
One challenge for any public benchmark is the risk of data contamination, where models may have seen evaluation tasks during their training process. We have taken measures to ensure our results reflect genuine reasoning rather than memorization or guessing, including a thorough manual review of agent trajectories, or the integration of a canary string to discourage training.
Looking ahead, we will continue to evolve our methodology to preserve the integrity of the dataset, while also making improvements for future releases of the benchmark—for example, growing the quantity and complexity of tasks.
We’re looking forward to how Android Bench can improve AI assistance long-term. Our vision is to close the gap between concept and quality code. We're building the foundation for a future where no matter what you imagine, you can build it on Android.
