Lyria 3.5, our best-sounding music generation model, is now available in the Gemini app and the Gemini API. Lyria 3.5 brings more expressive vocals and richer musical arrangements, allowing you to craft tracks with higher fidelity.

In the Gemini app, you can now:

• Easily select or describe your genre and choose between vocal or instrumental styles
• Use our new templates to jumpstart your creativity for anything from background music to custom birthday tracks
• Plus, you’ll now have the flexibility to choose short or longer tracks

Whether you need a custom backing track for a video, a unique brand jingle, or just a personalized ringtone, it’s easier than ever to bring your idea to life.

Lyria 3.5 is available to all users globally on the web and in the mobile app. It’s also available for artists and AI creatives in Google Flow Music, and for developers and technologists through Google AI Studio, and Google Vids.

Feature

Gemini Enterprise: Latency and error rate views for agents

To monitor operational telemetry for your agents, use the two new views on the Observability tab:

• Latency: Shows response times for your agents. This view displays p50 (median) and p95 (95th percentile) metrics for Time to First Token (TTFT), Time to First Answer (TTFA), and Time to Last Token (TTLT). TTFT counts the first token of any kind, including the model's thinking, while TTFA counts only the first token of the answer itself. You can also compare latencies by specific agent features, such as web search, media generation, or parametric interactions.
• Error rate: Shows how your agent's requests resolve by tracking request volume and error rates. This view groups results by response class (OK, client errors, server errors, and canceled) and displays the associated client and server error codes.

This feature is generally available (GA). For more information, see Access metrics.

Feature

Gemini Enterprise: General availability of Workflow Builder (formerly Agent Designer)

Workflow Builder (formerly known as Agent Designer) is generally available (GA) in Gemini Enterprise.

Workflow Builder enables users across your organization to build multi-step automated workflows to streamline tasks and connect to enterprise data.

Key capabilities in this release include:

• Workflows and on-demand execution: Build multi-step workflows that can run on an automated schedule, trigger manually on demand, or execute via @-mention directly within Gemini Enterprise chat conversations.
• Chat agents: Call chat agents from chat conversations using @-mention.
• In conversation: Call workflows from a chat conversation using @-mention.
• Agent import: Import existing A2A and ADK agents into Gemini Enterprise for centralized management, sharing, and enterprise governance.
• Enterprise connectors: Connect workflows to enterprise data and applications—including Google Workspace (Gmail, Google Calendar, Google Chat, Google Drive) and third-party tools (Slack, Jira, ServiceNow, Confluence, Microsoft OneDrive, SharePoint, and Outlook)—to search data and execute actions.
• Enhanced Agent Gallery: Discover and organize organization-wide and Google-created agents using keyword search, filter chips, and pinned items.
• Administrative controls: Administrators can manage feature availability org-wide in the Google Cloud console, including dedicated toggles for workflows and chat agents.

For more information, see Workflow Builder.

Our flagship AI weather forecasting model now includes real-time satellite data, hourly refreshes, higher resolution, precise precipitation forecasting, and clean energy variables. It’s now integrated across Search, Gemini, Maps, Google Maps Platform, and Cloud.

The WeatherNext team

Rapid weather prediction at unprecedented resolution

Every day, the weather influences billions of decisions. Some are as simple as grabbing an umbrella before heading out the door, but others are far more consequential. Wind, rain, and extreme weather events, like heatwaves and droughts, have cascading impacts across agriculture, global supply chains, clean energy production, and national economies.

In recent years, AI has revolutionized weather forecasting, using historical records to make faster and more accurate predictions than traditional methods. Yet predicting highly local and rapidly changing weather has remained a challenge. Previous models often lacked sufficient spatial resolution, and struggled to incorporate real-time weather data from sources like satellites.

Today, Google DeepMind and Google Research are introducing WeatherNext 3, the most advanced and accurate global weather model to date, according to independent live evaluations by Brightband. Our model learns directly from real-time observations, enabling it to provide timely and more localized predictions for the weather events that impact people the most. By using raw satellite data to produce a forecast every hour in high resolution, our model makes reliable forecasts accessible across Google products worldwide.

A forecast's utility often comes down to detail and how finely it resolves both time and space. WeatherNext 3 generates hourly forecasts at multiple spatial resolutions, maintaining physical consistency from broad global wind patterns all the way down to local topography.

With WeatherNext 3, we can visualize key surface variables — like temperature and moisture — at a 5-kilometer resolution, other surface variables at 10 kilometers, and atmospheric variables, like wind speed, at 25 kilometers. Overall, this provides a global weather picture roughly five times sharper than our previous model, WeatherNext 2, which produced forecasts on a 25-kilometer grid in 6-hour increments.

Real-world data at continuous global scale

WeatherNext 3's biggest leap forward is what it learns from. Most AI weather models, including WeatherNext 2, are trained on data from numerical weather prediction (NWP) models. Although useful, NWP models are complex, supercomputer-driven physics simulations that carry a six-hour data lag. This lag can lead to biases for fast-changing variables like rain or surface temperature.

By ingesting a mosaic of live, global geostationary satellite data, our new model gains a rich, continuously updating view of the atmosphere. This allows the model to generate a new forecast every hour, each one grounded in the most recent satellite observations available, at up to 5-kilometer resolution.

This is important because critical weather develops fast. When storms, fronts, or precipitation systems materialize suddenly, our rapid update cycle and higher resolution provides earlier, more detailed insights needed to help drive an effective response.

Some variables, like temperature and humidity, can fluctuate dramatically over just a few kilometers, which is particularly relevant for communities near coastlines, valleys, or mountain ranges. Traditional models struggle here because they train on representations of the atmosphere that lack detail and miss extreme local variations.

To address this, WeatherNext 3 instead trains directly on sparse weather station observation data. This allows us to make global forecasts on a 5-kilometer grid that account for regional details like topography.

This breakthrough is particularly vital for regions across Latin America, Africa, and Asia-Pacific that have historically been underserved by high-resolution forecasting due to the immense supercomputing costs of traditional regional models. It brings localized, high-fidelity forecasting to billions of people and local businesses in these areas.

Beyond improved resolution and forecast frequency, our model introduces predictions specifically engineered for renewable energy production. The model forecasts 100-meter wind speeds (roughly at turbine-height) for precise wind-energy output, alongside high-resolution cloud cover and sun radiation levels to help solar farms estimate how much light they will receive on the ground.

This data is crucial for global clean energy planning, allowing grid operators and renewables developers to accurately predict how much power their clean energy assets will generate and match it with consumer demand.

Precipitation forecasting at breakthrough accuracy

Global weather models notoriously struggle to accurately predict precipitation. Rain and snow systems are driven by fast-moving cloud processes on tiny scales that are hard to model accurately using traditional physics-based simulations. Consequently, AI forecasts often produce blurry estimates or miss the boundaries of severe storms entirely.

To solve this, we train our model on two exceptionally high-quality sources of precipitation data: NASA’s satellite-based Integrated Multi-satellite Retrievals for GPM (IMERG) and our own global precipitation reanalysis based on satellite radar.

The result is a significant leap in precipitation forecasting accuracy. In medium-range global forecasts, evaluations against baselines show a Continuous Ranked Probability Score (CRPS) improvement of up to 60% against IMERG, 30% for MRMS, and 10% against rain gauge measurements for early lead times.

Research applied across the ecosystem

Our primary goal is to advance weather intelligence to make it universally useful — whether for an emergency responder tracking sudden wind shifts, an air traffic controller planning flight paths, or a farmer managing crops.

To bring these breakthroughs out of the lab and into the real world, we’re integrating WeatherNext 3 across Google’s core ecosystem and beyond:

• High-resolution forecast data: We’re making global weather predictions, updated hourly and ready to integrate into your workflows with no model setup required. This enables researchers, developers and businesses to query the data in BigQuery and Earth Engine, or bulk-download from Google Cloud Storage.
• Available globally: WeatherNext 3 will begin powering weather experiences within Google Search, Gemini app, Google Maps, Google Maps Platform Weather API, and Google Earth Engine starting today. The update dramatically improves longer term forecasts. When planning a day or more ahead, people will see up to 50% more accurate precipitation forecasts — with the greatest improvements in regions where forecasts have historically been less reliable. So if you’re packing for a weekend trip or deciding the best day for an outdoor activity, you’ll now get more accurate predictions to help you plan.

The atmosphere will always retain a degree of unpredictability. However, by training on real-world observations and bypassing traditional modeling constraints, WeatherNext 3 brings us closer to a future where forecasts truly match what is happening on the ground.

To learn more about geospatial platforms and AI work at Google, check out Google Earth Engine, AlphaEarth Foundations, and Earth AI.

Disclaimer: For official weather forecasts, severe weather warnings, and public safety advisories, please refer to your local meteorological agency or national weather service.

Learn more about WeatherNext 3

• Read our paper
• Build with WeatherNext 3
• Explore Weather Lab to see WeatherNext 3 visualized in real-time
• See where WeatherNext 3 ranks on independent live leaderboards from Brightband.

Earlier this year, we introduced the ability for Workspace users to set persistent custom instructions for Gemini in Google Docs. We're now expanding support for these custom instructions to additional Gemini in Workspace surfaces, specifically:

• Ask Gemini in Drive
• Ask Gemini in Chat
• Gemini side panel in Slides, Sheets, and Gmail

These instructions help personalize your interactions with Gemini and ensure that Gemini adapts to your style, tone, and formatting preferences without needing to repeat them in every conversation, ultimately saving you time and ensuring consistency.

With this update, users can build a set of custom instructions that Gemini respects across these Gemini surfaces. The update ensures that users have a consistent personalization experience across the platform based on their individual needs or preferences.

You can declare preferences in any Gemini in Workspace Surface

All saved instruction can be viewed and managed in the Personalization Setting tab

These instructions are then used to personalize Gemini’s responses across Workspace

Getting started

• Admins: There is no admin control for this feature.
• End users: Get started by opening the side panel, Ask Gemini in Drive, or Ask Gemini in Chat. You can then prompt Gemini to store a specific instruction. You can also access the ‘Your Instructions for Gemini In Workspace” Menu by selecting the hamburger menu > Settings > Personalization. Visit the Help Center to learn more about customizing Gemini in Workspace's responses with your instructions.

Rollout pace

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on September 2, 2026

Availability

• Available to all Google Workspace customers with access to Ask Gemini in Drive, Ask Gemini in Chat, and/or the Gemini side panel in Gmail, Sheets, and Slides. See more details on feature availability here.

Resources

• Google Help: Customize Gemini in Workspace's responses with your instructions

Howdy gophers,

We have tagged version v0.56.0 of golang.org/x/crypto in order to address the following security issues:

ssh: prevent DoS on deadlocked established channel

Previously, after a channel has been established, a malicious peer could send crafted messages that would deadlock the entire connection.

Now, we handle all RFC 4254 channel messages; global requests are handled explicitly. Then, treat all other messages as a protocol error and tear the connection down instead of buffering and blocking.

Thanks to Will Mortensen for reporting this issue.
This is CVE-2026-56855 and Go issue https://go.dev/issue/81317.

ssh: prevent DoS on deadlocked undecided channel

Previously, a channel registered in the mux's chanList is not usable until it is established. A malicious peer was able flood the channel's incomingRequests, deadlocking the entire connection.

Now, we add an atomic established state, set when a channel becomes usable. Until such a time, handlePacket drops every packet other than the open confirmation/failure, without blocking and without tearing down the connection.

Thanks to Will Mortensen for reporting this issue.
This is CVE-2026-78662 and Go issue https://go.dev/issue/81316.

Cheers,
Go Security Team

Example: concurrent workers

Consider a function that processes work items concurrently:

type result struct {
    res workResult
    err error
}

func processWorkItems(ws []workItem) ([]workResult, error) {
    // Process work items in parallel, aggregating results in ch.
    ch := make(chan result)
    for _, w := range ws {
        go func() {
            res, err := processWorkItem(w)
            ch <- result{res, err}
        }()
    }

    // Collect the results from ch, or return an error if one is found.
    var results []workResult
    for range len(ws) {
        r := <-ch
        if r.err != nil {
            // This early return may cause goroutine leaks.
            return nil, r.err
        }
        results = append(results, r.res)
    }
    return results, nil
}

Because ch is an unbuffered channel, each worker goroutine blocks when sending its result until the main goroutine receives from the channel. If processWorkItems returns early due to an error, the receiving loop terminates, and all remaining sender goroutines block forever.

This example is emblematic of a common mistake discovered in real Go programs, including Uber production services. Let’s see how we can find these leaks by using the new goroutine leak profiler.

Debugging with the goroutine leak profiler

The profile is available through the runtime/pprof package, as the goroutineleak profile type, or by installing the profile handlers defined by the net/http/pprof package. If you already have net/http/pprof set up in your service, then you don’t need to do anything else! The profile will be automatically made available for collection at the /debug/pprof/goroutineleak endpoint on whatever host and port the handlers are installed.

[Example program and usage instructions omitted for brevity]

Collecting the profile

It won’t take long for the program to start accumulating leaks, which you can then view by using the web UI at http://localhost:6060/debug/pprof.

Alternatively, you can collect the goroutine leak profile using curl, and then examine it with go tool pprof.

The profile reveals the goroutines leaked at ch <- result{res, err} (line 33), pinpointing the culprit operation. Notably, the longer the program is running, the larger the number of leaked goroutines.

Addressing the leak

This leak can be simply fixed by giving ch a buffer:

ch := make(chan result, len(ws))

This allows all the work item goroutines to send a message without blocking in the event of a premature return of processWorkItems.

Implementation

The article explains the core concept of leak detection based on liveness of goroutines and how the Go runtime garbage collector is adapted to detect leaked goroutines by marking only unblocked goroutines as live and iteratively marking others reachable from them.

Limitations

The garbage collector based leak detection has limitations such as memory overreach (if concurrency primitives are reachable from global variables or runnable goroutines, leaks may not be detected), non-standard blocking (only Go first-class concurrency primitives are considered), and non-determinism (leaks can only be detected after occurrence).

Performance impact

Leak detection adds some overhead, especially in pathological cases, but is designed to minimize impact and can be tuned for profiling frequency.

Acknowledgements

Goroutine leak detection is the result of a research collaboration between Aarhus University, Washington University in St. Louis, and Uber, presented in “Dynamic Partial Deadlock Detection and Recovery via Garbage Collection” (Saioc et al., ASPLOS 2025). Transition to a Go feature was guided by members of the Go team at Google.

Additional examples

The article provides multiple real-world examples of goroutine leaks and how the goroutine leak profiler can detect them, including common patterns like double sends on channels, early returns causing blocked sends, timeouts with context cancellation, range over channels without closing, method contract violations, missing unlocks, unexpected channel operation orderings, mutual blocking between channels and mutexes, and misuse of sync.WaitGroup.

Each example includes code snippets illustrating the leak and how the profiler highlights the issue, along with suggested fixes.

Today, we’re launching our Fairwind Program, a limited access program for governments and trusted partners to use our most advanced cyber defense capabilities.

Defenders wanting to use advanced AI have faced a difficult dilemma: adopt enormous frontier models that could be expensive to deploy and difficult to control across enterprise codebases, or turn to smaller open-weight models that might struggle with complex vulnerability remediation and require teams to build their own tooling and infrastructure from scratch. Until now.

Today, we’re launching our Fairwind Program to bring the best of Google’s AI and cyber defense capabilities to a trusted group of Google Cloud customers, government agencies, and cybersecurity partners, to help them proactively solve cyber risks at scale. As a first step, the Fairwind Program will give defenders access to powerful and advanced Gemini models to help them autonomously find and fix vulnerabilities, protecting critical infrastructure, public services, and national security.

Finding and autonomously fixing vulnerabilities

The Fairwind Program offerings bring together our most advanced cyber model, Gemini 3.8 Flash Cyber, with our CodeMender harness, to help defenders find, verify, and fix vulnerabilities at agentic scale. Spotting weaknesses creates awareness and fear; autonomously finding and fixing vulnerabilities delivers security.

CodeMender with Gemini 3.8 Flash Cyber delivers the specialized reasoning to write and validate code fixes, at a fraction of the operating cost of traditional frontier models. Instead of taking weeks to manually fix vulnerabilities, defenders can now generate verified, deployment-ready patches in minutes — within an organization’s secure cloud environment.

Scaling frontline defense

Providing early access to these powerful cyber capabilities gives trusted defenders a vital adaptation window to harden their systems before bad actors have a chance to exploit new capabilities. We’re staging initial access to government and enterprise partners most critical to society’s resilience:

• Governments and national cyber authorities: Hardening public-sector networks and citizen services against targeted intrusions.
• Critical infrastructure operators: Protecting essential services across healthcare, telecommunications, energy, and financial networks from operational disruption.
• Core technology platforms: Securing widespread software foundations to uplift digital security for millions of downstream users at once.

To ensure these powerful AI capabilities are used responsibly, participating organizations agree to strict operational standards, including limiting access to employees within their internal cybersecurity, incident response, or penetration testing teams and deploying protections like multi-factor authentication.

We have more than 650 participating partners globally, including:

What our Fairwind Program partners are saying

Trusted defenders and industry leaders are already putting Gemini 3.8 Flash Cyber into practice:

The Fairwind Program will evolve alongside our partners and users' needs. We will adapt our product offerings and expand partner access, collaborating closely with industry, governments, and open-weight community leaders to strike the right balance between open access and robust security.

While we are prioritizing Gemini 3.8 Flash Cyber access for customers in the Fairwind Program, any Google Cloud customer can proactively secure their code by using CodeMender with publicly available models hosted on Gemini Enterprise Agent Platform, in combination with industry-leading solutions offered through AI Threat Defense.

Making an ecosystem-scale impact on cyber defense

Years of Google’s pioneering zero-trust architecture, advanced AI defenses, and built-in security allow us to protect billions of accounts daily – keeping more people and organizations safe online than anyone else. The Fairwind Program builds on this experience and is part of our broader commitment to global cyber resilience across the entire digital ecosystem, including helping to fortify grassroots cyber defense.

Through Google.org, our latest commitment brings our total cybersecurity funding to more than $100 million globally. We’re pleased to release our 2026 Google.org US Cybersecurity Impact Report, which details $36 million in funding for 35 cyber clinics to date, providing free, hands-on security support to over 1,250 hospitals, public school districts, and municipal utilities in the U.S.

Providing a security advantage

The defender’s edge comes from shrinking the time between detecting a flaw and patching it. Through Google’s Fairwind Program, government and enterprise partners gain autonomous tools to repair systems faster and at scale, keeping them one step ahead of agentic-speed threats.

Our newest Gemini models deliver next-generation intelligence for agentic workflows and cybersecurity.

Building on the momentum of 3.7 Flash from three weeks ago and marking our third Flash release in only six weeks, today we’re introducing Gemini 3.8, our best reasoning & coding model yet, at the same speed and low cost of 3.7. Gemini 3.8 introduces 2 variants:

• Gemini 3.8 Flash: our most intelligent workhorse model, delivering significant improvements from 3.7 Flash across software engineering, agentic tasks, and critical, multi-step reasoning in specialized domains. It is available at the same introductory price1 as 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.
• Gemini 3.8 Flash Cyber: our most capable cybersecurity model with frontier-level performance in vulnerability detection and automated patching, available to trusted defenders through our new Fairwind Program.

While tailored for different deployment environments, both of today's releases are powered by the same foundational intelligence, and further accelerated by long-running agentic loops designed to recursively evaluate and refine the underlying models. The significant coding and reasoning gains across this shared core were driven by a number of innovations, including rigorous training in the highly demanding domain of cybersecurity.

Gemini 3.8 Flash: built for long-horizon coding and autonomous agents

Gemini 3.8 Flash delivers substantial gains from 3.7 Flash, often approaching the performance of higher-cost frontier models.

On DeepSWE v1.1 (Long-Horizon Software Engineering) 3.8 Flash outperforms most larger frontier models in autonomously solving complex engineering problems end to end, only at a fraction of the cost.

Additionally, 3.8 Flash exhibits the dependability required for critical enterprise autonomy, across specialized knowledge domains.

In quantitative and professional fields that require advanced analysis and reporting, 3.8 Flash outperforms 3.7 Flash and other frontier models in benchmarks like Vals Finance Agent V2 and Harvey's Legal Agent Benchmark. 3.8 Flash also achieves a 54.9% on HLE-Verified, demonstrating its ability to handle multi-step reasoning across STEM, humanities, and professional fields.

These performance gains stem from a core design choice: 3.8 Flash works harder. On complex tasks, it exhibits greater diligence — executing extra reasoning steps, and calling tools iteratively. At times, the model might use more tokens to maximize performance, especially at higher effort levels.

For applications where compute efficiency is the primary constraint, developers can utilize lower effort levels to minimize token overhead or continue to rely on Gemini 3.7 Flash, which remains fully supported for efficiency-first workloads.

Gemini 3.8 Flash Cyber: expert cyber performance

Gemini 3.8 Flash Cyber, available to a set of trusted defenders via the Fairwind Program, provides a decisive advantage in today’s complex cybersecurity landscape, with the Flash speed and cost that enables quick iteration.

Autonomous vulnerability discovery

On the standard industry benchmark for finding vulnerabilities, CyberGym, Gemini 3.8 Flash Cyber demonstrates frontier-level performance in autonomous vulnerability discovery. It surpasses both 3.5 Flash Cyber as well as significantly larger frontier models.

To better capture real-world defensive needs which are not limited to just C/C++ codebases like in CyberGym, we also evaluated Gemini 3.8 Flash Cyber against a comprehensive internal benchmark in which the model has to discover a wide range of vulnerabilities across complex codebases spanning 20 programming languages. Here, the model showcases an impressive leap over our previous models and reaches a success rate exceeding 70%.

Automated patching

With Gemini 3.8 Flash Cyber, we focused specifically on equipping defenders with expert capabilities that give them an advantage over attackers. This is why we have invested in vulnerability fixing from the start, and prioritized it over offensive capabilities like exploitation.

CWE-Bench, run by Collinear, is a challenging external benchmark for patching capabilities. On this benchmark, Gemini 3.8 Flash Cyber is on the Pareto frontier: with a pass@1 of 47.2% compared to a leading frontier model at 47.8%, yet offered at a significantly lower cost.

Real-world impact: securing Google’s code

We’re already using Gemini 3.8 Flash Cyber to secure code across Google. For example:

• The Chrome Security team found that 3.8 Flash Cyber produced 2.6 times more correct patches to vulnerabilities in Chrome than the best commercial models that are much larger.
• Wiz found that Gemini 3.8 Flash Cyber achieves +7.5-9.7% higher recall on their internal penetration testing benchmark for a 2.3-5.2x lower cost compared to other leading frontier models.
• Google’s Cloud Vulnerability Research team leveraged the 3.8 Flash Cyber model to find a critical foundational vulnerability in less than 2 hours, a vulnerability for which research and discovery usually takes months.

What our Fairwind Program partners are saying

[Quotes from partners shown as images]

Built with safety in mind

3.8 Flash ships with safeguards against misuse in the domains of Chemical, Biological, Radiological, and Nuclear (CBRN) and cyber offense, while enabling beneficial use cases, as per our Frontier Safety Framework. 3.8 Flash Cyber ships with a more permissive set of mitigations for cybersecurity, and as such, is only available to trusted defenders who require a more comprehensive set of cyber capabilities.

Gemini 3.8 models have also made a significant leap in prompt injection robustness as measured by Gray Swan, protecting Gemini model users from prompt-injection related malicious attacks.

Gemini 3.8 Flash and Cyber: get started today

• Developers: Build with 3.8 Flash and explore agent-first workflows in Google Antigravity or start building today in the Gemini API via Google AI Studio and Android Studio, or generate UIs in Stitch. Get started with our developer docs.
• Enterprises: Access 3.8 Flash in Gemini Enterprise.
• Consumers: 3.8 Flash is available to Google AI Pro and Ultra subscribers across the Gemini app, AI Mode in Google Search and Gemini in Google Sheets.
• Cyber: Through our new Fairwind Program, we’re providing trusted government authorities, as well as critical infrastructure operators and software maintainers with prioritized access to Gemini 3.8 Flash Cyber. Apply for access.

Feature

Gemini Enterprise: Overage controls available for all invoiced Cloud Billing accounts

Configuring overage controls in Gemini Enterprise is available to all projects linked to an invoiced Cloud Billing account. Previously, customers that received an email with the subject line [Billing Update] New Gemini Enterprise overage billing controls launching Aug 17, 2026 couldn't enable overages despite having an invoiced Cloud Billing account. This restriction no longer applies.

For more information, see Overview of overages and spend controls.