A multi-gig approach that puts customers first — next in internet starts in Iowa

When GFiber first brought gigabit speed to Kansas City in 2012, we didn’t just offer a faster internet connection; we established a new benchmark for what you should expect from your internet service provider. 


Today, we’re doing it again. We’re thrilled to announce that Des Moines is officially our first market where multi-gig internet is the standard, and we’re rolling it out to customers at the same prices we’ve always had (in fact, some customers will be paying less per month!). At the same time, we are also leveling up our highest speed  products to Wi-Fi 7, our most powerful Wi-Fi ever. This isn't just about more speed; it’s about continuing to create and add value, not just for the newest customers, but those that have always had GFiber. In fact, many of our existing customers woke up this morning to up to 3x higher speeds for the same price they’ve been paying, without having to do anything (and with just a click of the button on our side).


More speed. Same price.


At GFiber, we take immense pride in knowing our customers love … not like … LOVE their GFiber service. We’re in this relationship for the long haul and we know that to keep love like that going we have to keep investing in our relationship—surprising, delighting, and growing to flex and meet changing needs. So, starting with Des Moines, we’re leveling up our lifestyle products without changing the prices:


  • Core 1 Gig becomes Core 3 Gig ($70/month): 3x the symmetrical speed of our original internet and still the same price since 2012.

  • Home 3 Gig becomes Home 5 Gig ($100/month): Two more gigs of symmetrical speed plus the GFiber Multi-Gig Wi-Fi 7 Router with our best Wi-Fi ever.

  • Edge 8 Gig ($150/month): Multi-gig gets supercharged with the new GFiber Multi-Gig Wi-Fi 7 Router. 


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Additionally, the debut of these Home 5 Gig and Edge 8 Gig products come with the new GFiber Multi-Gig Wi-Fi 7 Router included so you can take advantage of the smartest traffic handling, best security, and multi-gig speeds even over wireless throughout your home. This isn’t an add-on; it’s GFiber’s new standard with a 10 Gig port plus three additional multi-gig ports, it’s built to handle more devices simultaneously —  imagine wired speed and quality without the wires.


Building a network for today’s challenges and tomorrow’s opportunities


This next giant step forward is powered by our deployment of advanced 25 Gig PON technology. We’re building our networks in all our cities to be truly future-proof, which allows us to increase speeds for years to come via simple software updates rather than additional construction which can take a long time and be disruptive to residents. This effort, starting here in Des Moines, is the true proving grounds for what a next gen network can do, demonstrating what is possible and empowering our customers to do whatever they need to do online and anything  else their imaginations bring to life.


And it is a challenge for our industry. We can’t rest on our laurels. The internet and its bandwidth demands are growing everyday. Adding value with a product that serves every corner of the home, redefining the customer experience while keeping prices steady, and doing the right thing for customers is the future of internet service. And our job at GFiber is to make sure we’re not just ready for that future, but helping chart that course.


Bringing the future to every neighborhood


Des Moines is a major first step in showcasing what our network is capable of, setting a new industry standard, and being future-ready to upgrade customers with no or minimal work on their part. It’s a big endeavor, and we’ll be learning from this process as we take multi-gig mainstream and upgrade our Des Moines customers. If you are in Des Moines, join us tonight at Flix Brewhouse to learn more (and see Disney’s Up on us!).  We’re kicking off the new age of internet in Des Moines, and we’re excited to keep building what’s next for everyone.


Posted by Melani Griffith, Chief Growth Officer

How Automated Prompt Optimization Unlocks Quality Gains for ML Kit’s GenAI Prompt API

Posted by Chetan Tekur, PM at AI Innovation and Research, Chao Zhao, SWE at AI Innovation and Research, Paul Zhou, Prompt Quality Lead at GCP Cloud AI and Industry Solutions, and Caren Chang, Developer Relations Engineer at Android


To further help bring your ML Kit Prompt API use cases to production, we are excited to announce Automated Prompt Optimization (APO) targeting On-Device models on Vertex AI. Automated Prompt Optimization is a tool that helps you automatically find the optimal prompt for your use cases.

The era of On-Device AI is no longer a promise—it is a production reality. With the release of Gemini Nano v3, we are placing unprecedented language understanding and multimodal capabilities directly into the palms of users. Through the Gemini Nano family of models, we have wide coverage of supported devices across the Android Ecosystem. But for developers building the next generation of intelligent apps, access to a powerful model is only step one. The real challenge lies in customization: How do you tailor a foundation model to expert-level performance for your specific use case without breaking the constraints of mobile hardware?

In the server-side world, the larger LLMs tend to be highly capable and require less domain adaptation. Even when needed, more advanced options such as LoRA (Low-Rank Adaptation) fine-tuning can be feasible options. However, the unique architecture of Android AICore prioritizes a shared, memory-efficient system model. This means that deploying custom LoRA adapters for every individual app comes with challenges on these shared system services.

But there is an alternate path that can be equally impactful. By leveraging Automated Prompt Optimization (APO) on Vertex AI, developers can achieve quality approaching fine-tuning, all while working seamlessly within the native Android execution environment. By focusing on superior system instruction, APO enables developers to tailor model behavior with greater robustness and scalability than traditional fine-tuning solutions.

Note: Gemini Nano V3 is a quality optimized version of the highly acclaimed Gemma 3N model. Any prompt optimizations that are made on the open source Gemma 3N model will apply to Gemini Nano V3 as well. On supported devices, ML Kit GenAI APIs leverage the nano-v3 model to maximize the quality for Android Developers.


Automated Prompt Optimization (APO)

To further help bring your ML Kit Prompt API use cases to production, we are excited to announce Automated Prompt Optimization (APO) targeting On-Device models on Vertex AI. Automated Prompt Optimization is a tool that helps you automatically find the optimal prompt for your use cases.

The era of On-Device AI is no longer a promise—it is a production reality. With the release of Gemini Nano v3, we are placing unprecedented language understanding and multimodal capabilities directly into the palms of users. Through the Gemini Nano family of models, we have wide coverage of supported devices across the Android Ecosystem. But for developers building the next generation of intelligent apps, access to a powerful model is only step one. The real challenge lies in customization: How do you tailor a foundation model to expert-level performance for your specific use case without breaking the constraints of mobile hardware?

In the server-side world, the larger LLMs tend to be highly capable and require less domain adaptation. Even when needed, more advanced options such as LoRA (Low-Rank Adaptation) fine-tuning can be feasible options. However, the unique architecture of Android AICore prioritizes a shared, memory-efficient system model. This means that deploying custom LoRA adapters for every individual app comes with challenges on these shared system services.

But there is an alternate path that can be equally impactful. By leveraging Automated Prompt Optimization (APO) on Vertex AI, developers can achieve quality approaching fine-tuning, all while working seamlessly within the native Android execution environment. By focusing on superior system instruction, APO enables developers to tailor model behavior with greater robustness and scalability than traditional fine-tuning solutions.

Note: Gemini Nano V3 is a quality optimized version of the highly acclaimed Gemma 3N model. Any prompt optimizations that are made on the open source Gemma 3N model will apply to Gemini Nano V3 as well. On supported devices, ML Kit GenAI APIs leverage the nano-v3 model to maximize the quality for Android Developers


APO treats the prompt not as a static text, but as a programmable surface that can be optimized. It leverages server-side models (like Gemini Pro and Flash) to propose prompts, evaluate variations and find the optimal one for your specific task. This process employs three specific technical mechanisms to maximize performance:

  1. Automated Error Analysis: APO analyzes error patterns from training data to Automatically identify specific weaknesses in the initial prompt.

  2. Semantic Instruction Distillation: It analyzes massive training examples to distill the "true intent" of a task, creating instructions that more accurately reflect the real data distribution.

  3. Parallel Candidate Testing: Instead of testing one idea at a time, APO generates and tests numerous prompt candidates in parallel to identify the global maximum for quality.


Why APO Can Approach Fine Tuning Quality

It is a common misconception that fine-tuning always yields better quality than prompting. For modern foundation models like Gemini Nano v3, prompt engineering can be impactful by itself:

  • Preserving General capabilities: Fine-tuning ( PEFT/LoRA) forces a model's weights to over-index on a specific distribution of data. This often leads to "catastrophic forgetting," where the model gets better at your specific syntax but worse at general logic and safety. APO leaves the weights untouched, preserving the capabilities of the base model.

  • Instruction Following & Strategy Discovery: Gemini Nano v3 has been rigorously trained to follow complex system instructions. APO exploits this by finding the exact instruction structure that unlocks the model's latent capabilities, often discovering strategies that might be hard for human engineers to find. 

To validate this approach, we evaluated APO across diverse production workloads. Our validation has shown consistent 5-8% accuracy gains across various use cases.Across multiple deployed on-device features, APO provided significant quality lifts.



Use Case

Task Type

Task Description

Metric

APO Improvement

Topic classification

Text classification

Classify a news article into topics such as finance, sports, etc

Accuracy

+5%

Intent classification

Text classification

Classify a customer service query into intents

Accuracy

+8.0%

Webpage translation

Text translation

Translate a webpage from English to a local language

BLEU

+8.57%

A Seamless, End-to-End Developer Workflow

It is a common misconception that fine-tuning always yields better quality than prompting. For modern foundation models like Gemini Nano v3, prompt engineering can be impactful by itself:

  • Preserving General capabilities: Fine-tuning ( PEFT/LoRA) forces a model's weights to over-index on a specific distribution of data. This often leads to "catastrophic forgetting," where the model gets better at your specific syntax but worse at general logic and safety. APO leaves the weights untouched, preserving the capabilities of the base model.

  • Instruction Following & Strategy Discovery: Gemini Nano v3 has been rigorously trained to follow complex system instructions. APO exploits this by finding the exact instruction structure that unlocks the model's latent capabilities, often discovering strategies that might be hard for human engineers to find. 

To validate this approach, we evaluated APO across diverse production workloads. Our validation has shown consistent 5-8% accuracy gains across various use cases.Across multiple deployed on-device features, APO provided significant quality lifts.

Conclusion

The release of Automated Prompt Optimization (APO) marks a turning point for on-device generative AI. By bridging the gap between foundation models and expert-level performance, we are giving developers the tools to build more robust mobile applications. Whether you are just starting with Zero-Shot Optimization or scaling to production with Data-Driven refinement, the path to high-quality on-device intelligence is now clearer. Launch your on-device use cases to production today with ML Kit’s Prompt API and Vertex AI’s Automated Prompt Optimization. 

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Calendar event color labels now also accessible to users with “Make changes to events” permission

Google Calendar offers event color labels for events on your primary calendar, which help users to visually organize their meetings and categorize them with Time Insights. Currently, color labels are only visible to users who have “Make changes to events and manage sharing” permissions for a primary calendar. 

Starting February 27, 2026, we are expanding this to include users who have  “Make changes to events” permissions. Currently, these users are only able to see the colors, not the labels — which made color categorizing events harder.

Getting started

  • Admins: There is no additional admin control for this feature.
  • End users: There is no end user setting for this feature. Visit the Help Center to learn more.

Rollout pace

Availability

  • Business Standard, Plus
  • Enterprise Starter, Standard, and Plus
  • Education Fundamentals, Standard, Plus
  • Nonprofits