Tag Archives: Solve

Carbon Limit’s concrete technology is saving the environment using AI

Posted by Lillian Chen – Global Brand and Content Marketing Manager, Google Accelerator Programs

Located in Boca Raton, Carbon Limit aims to decarbonize the industry and take part in saving, protecting, and healing the environment. Cofounder Tim Sperry explains that for him and his cofounders Oro Padron, and Christina Stavridi, the mission is personal. “I’ve lost family members [to polluted air]. Oro has his own story, Christina has her own story, and our other core team member Angel just had kids. All of us have our own connection to our mission. And with that, we've developed a really strong company culture,” he says.

Today, Carbon Limit is evolving to create sustainable solutions for the built environment. Their flagship product, CaptureCrete, is an additive that gives concrete the ability to capture and store CO2 directly from the air.

Carbon Limit’s initial prototype — a portable shipping container fitted with solar panels, filtered media, and intake fans — was a direct air capture system. With a business model that was dependent on tax credits and carbon credits, the team decided to pivot. “We took our original technology, which was always meant to capture CO2 to store in concrete as a permanent storage solution to CO2 in the air, and turned that into concrete technology,” explains Tim. “We’re lowering the carbon footprint of concrete projects and problems, and providing the ability to generate valuable carbon credits. It actually pays to use our technology: you’re quantifiably lowering the carbon footprint and improving the environment, and you can make money from these carbon credits.”


How Carbon Limit uses AI

Combating climate change is a race against time, as cofounder and CMO Oro explains: “We are in an industry that moves at a pace that when technology catches up, sometimes it’s too late.”

“We have found that AI actually is not eliminating, it is creating—it is letting our own people discover things about themselves and possibilities that they didn’t know about,” says Oro. “We embrace AI because we are embracing the future, and we strive to be pioneers.”

Artificial intelligence also allows for transparency in a space that can become congested by unreliable data. “We’re developing tools, specifically the digital MRV, which stands for measurement, reporting, and verification of carbon credits,” says Tim. “There is bad press that there’s a lot of fake or unverified carbon credits being sold, generated, or created.” AI gives real-time, real-world data, exposure, and quantification of the carbon credits. Carbon Limit is generating carbon credits with hard tech, bringing trust into tech.


How Carbon Limit uses Google technology

Carbon Limit is a team of developers, programmers, and data scientists working across multiple operating systems, so they needed a centralized system for collaborating. “Google Workspace has allowed us to build our own CRMs with Google Sheets and Google Docs, which we’ve found to be the easiest way to onboard quickly. Google has been an amazing tool for us to communicate internally.” Christina adds, “We have a small but diverse team with ages that vary. Not every single team member is used to using the same tools, so the way Oro has onboarded the team and utilized these tools in a customizable way where they’re easily adoptable and used by every single team member to optimize our work has been super beneficial.”

Additionally, the Carbon Limit team uses Google data for training their CO2-related data, and Google Colab to train their models. “We have some models that were made in Python, but utilizing Google Cloud has helped us predict models faster,” says Oro.


Participating in Google for Startups Accelerator: Climate Change

Before Carbon Limit started the Google for Startups Accelerator: Climate Change program, the Carbon Limit team considered integrating artificial intelligence (AI) and machine learning (ML) into their process but wanted to ensure that they were making the right decision. With Google mentorship and support, they went full force with AI and ML algorithms. “Accelerator: Climate Change helped us realize exactly what we needed to do,” says Oro.

Participating in the program also gave Carbon Limit access to resources that helped enhance their SEO. “We learned how to increment our backlinks and how to improve performance, which has been extremely helpful to put us on the map. Our whole backbone has been built thanks to Google Workspace,” says Oro.

“The Google for Startups Accelerator program gave us valuable resources and guidance on what we can do, how we can do it, and what not to do” says Tim. “The mentorship and learning from people who developed the technology, use the technology, and work with it every day was invaluable for us.” Christina adds, “The mentors also helped us refine our pitch when communicating our solution on different platforms. That was very useful to understand how to speak to different customers and investors.”

The program also led to a new client for Carbon Limit: Google. “That was critical because with Google as an early adopter, that helped us build a significant amount of credibility and validation,” Tim tells us.


What’s next for Carbon Limit

Looking ahead, Carbon Limit will be launching a new technology that can be used in data centers to mitigate electricity as well as reduce and remove CO2 pollution.

“We went from a carbon capture solution to sustainable solutions because we wanted to go even bigger,” says Tim. “We want to inspire others to do what we’re doing and help create more awareness and a more environmentally friendly world.”

Tim shares, “I love what I do. I love to be able to invent something that didn’t exist. But more importantly, it helps protect my family, my loved ones, future generations, and the environment. And I get to do it with this amazing group of people at Carbon Limit.”

Learn about how to get involved in Google accelerator programs here.

HealthPulse AI Leverages MediaPipe to Increase Health Equity

A guest post by Rouella Mendonca, AI Product Lead and Matt Brown, Machine Learning Engineer at Audere

Please note that the information, uses, and applications expressed in the below post are solely those of our guest authors from Audere.


About HealthPulse AI and its application in the real world

Preventable and treatable diseases like HIV, COVID-19, and malaria infect ~12 million per year globally with a disproportionate number of cases impacting already underserved and under-resourced communities1. Communicable and non-communicable diseases are impeding human development by their negative impact on education, income, life expectancy, and other health indicators2. Lack of access to timely, accurate, and affordable diagnostics and care is a key contributor to high mortality rates.

Due to their low cost and relative ease of use, ~1 billion rapid diagnostic tests (RDTs) are used globally per year and growing. However, there are challenges with RDT use.

  • Where RDT data is reported, results are hard to trust due to inflated case counts, lack of reported expected seasonal fluctuations, and non-adherence to treatment regimens.
  • They are used in decentralized care settings by those with limited or no training, increasing the risk of misadministration and misinterpretation of test results.

HealthPulse AI, developed by a digital health non-profit Audere, leverages MediaPipe to address these issues by providing digital building blocks to increase trust in the world’s most widely used RDTs.

HealthPulse AI is a set of building blocks that can turn any digital solution into a Rapid Diagnostic Test (RDT) reader. These building blocks solve prominent global health problems by improving rapid diagnostic test accuracy, reducing misadministration of tests, and expanding the availability of testing for conditions including malaria, COVID, and HIV in decentralized care settings. With just a low-end smartphone, HealthPulse AI improves the accuracy of rapid diagnostic test results while automatically digitizing data for surveillance, program reporting, and test validation. It provides AI facilitated digital capture and result interpretation; quality, accessible digital use instructions for provider and self-tests; and standards based real-time reporting of test results.

These capabilities are available to local implementers, global NGOs, governments, and private sector pharmacies via a web service for use with chatbots, apps or server implementations; a mobile SDK for offline use in any mobile application; or directly through native Android and iOS apps.

It enables innovative use cases such as quality-assured virtual care models which enables stigma-free, convenient HIV home testing with linkage to education, prevention, and treatment options.

HealthPulse AI Use Cases

HealthPulse AI can substantially democratize access to timely, quality care in the private sector (e.g. pharmacies), in the public sector (e.g. clinics), in community programs (e.g. community health workers), and self-testing use cases. Using only an RDT image captured on a low-end smartphone, HealthPulse AI can power virtual care models by providing valuable decision support and quality control to clinicians, especially in cases where lines may be faint and hard to detect with the human eye. In the private sector, it can automate and scale incentive programs so auditors only need to review automated alerts based on test anomalies; procedures which presently require human reviews of each incoming image and transaction. In community care programs, HealthPulse AI can be used as a training tool for health workers learning how to correctly administer and interpret tests. In the public sector, it can strengthen surveillance systems with real-time disease tracking and verification of results across all channels where care is delivered - enabling faster response and pandemic preparedness3.


HealthPulse AI algorithms

HealthPulse AI provides a library of AI algorithms for the top RDTs for malaria, HIV, and COVID. Each algorithm is a collection of Computer Vision (CV) models that are trained using machine learning (ML) algorithms. From an image of an RDT, our algorithms can:

  • Flag image quality issues common on low-end phones (blurriness, over/underexposure)
  • Detect the RDT type
  • Interpret the test result

Image Quality Assurance

When capturing an image of an RDT, it is important to ensure that the image captured is human and AI interpretable to power the use cases described above. Image quality issues are common, particularly when images are captured with low-end phones in settings that may have poor lighting or simply captured by users with shaky hands. As such, HealthPulse AI provides image quality assurance (IQA) to identify adversarial image conditions. IQA returns concerns detected and can be used to request users to retake the photo in real time. Without IQA, clients would have to retest due to uninterpretable images and expired RDT read windows in telehealth use cases, for example. With just-in-time quality concern flagging, additional cost and treatment delays can be avoided. Examples of some adversarial images that IQA would flag are shown in Figure 1 below.

Images of malaria, HIV and COVID tests that are dark, blurry, too bright, and too small.
Figure 1: Images of malaria, HIV and COVID tests that are dark, blurry, too bright, and too small.

Classification

With just an image captured on a 5MP camera from low-end smartphones commonly used in Africa, SE Asia, and Latin America where a disproportionate disease burden exists, HealthPulse AI can identify a specific test (brand, disease), individual test lines, and provide an interpretation of the test. Our current library of AI algorithms supports many of the most commonly used RDTs for malaria, HIV, and COVID-19 that are W.H.O. pre-qualified. Our AI is condition agnostic and can be easily extended to support any RDT for a range of communicable and non-communicable diseases (Diabetes, Influenza, Tuberculosis, Pregnancy, STIs and more).

HealthPulse AI is able to detect the type of RDT in the image (for supported RDTs that the model was trained for), detect the presence of lines, and return a classification for the particular test (e.g. positive, negative, invalid, uninterpretable). See Figure 2.

Figure 2: Interpretation of a supported lateral flow rapid test.
Figure 2: Interpretation of a supported lateral flow rapid test.

How and why we use MediaPipe

Deploying HealthPulse AI in decentralized care settings with unstable infrastructure comes with a number of challenges. The first challenge is a lack of reliable internet connectivity, often requiring our CV and ML algorithms to run locally. Secondly, phones available in these settings are often very old, lacking the latest hardware (< 1 GB of ram and comparable CPU specs), and on different platforms and versions ( iOS, Android, Huawei; very old versions - possibly no longer receiving OS updates) mobile platforms. This necessitates having a platform agnostic, highly efficient inference engine. MediaPipe’s out-of-the-box multi-platform support for image-focused machine learning processes makes it efficient to meet these needs.

As a non-profit operating in cost-recovery mode, it was important that solutions:

  • have broad reach globally,
  • are low-lift to maintain, and
  • meet the needs of our target population for offline, low resource, performant use.

Without needing to write a lot of glue code, HealthPulse AI can support Android, iOS, and cloud devices using the same library built on MediaPipe.

Our pipeline

MediaPipe’s graph definitions allow us to build and iterate our inference pipeline on the fly. After a user submits a picture, the pipeline determines the RDT type, and attempts to classify the test result by passing the detected result-window crop of the RDT image to our classifier.

For good human and AI interpretability, it is important to have good quality images. However, input images to the pipeline have a high level of variability we have little to no control over. Variability factors include (but are not limited to) varying image quality due to a range of smartphone camera features/megapixels/physical defects, decentralized testing settings which include differing and non-ideal lighting conditions, random orientations of the RDT cassettes, blurry and unfocused images, partial RDT images, and many other adversarial conditions that add challenges for the AI. As such, an important part of our solution is image quality assurance. Each image passes through a number of calculators geared towards highlighting quality concerns that may prevent the detector or classifier from doing its job accurately. The pipeline elevates these concerns to the host application, so an end-user can be requested in real-time to retake a photo when necessary. Since RDT results have a limited validity time (e.g. a time window specified by the RDT manufacturer for how long after processing a result can be accurately read), IQA is essential to ensure timely care and save costs. A high level flowchart of the pipeline is shown below in Figure 3.

Figure 3: HealthPulse AI pipeline
Figure 3: HealthPulse AI pipeline

Summary

HealthPulse AI is designed to improve the quality and richness of testing programs and data in underserved communities that are disproportionately impacted by preventable communicable and non-communicable diseases.

Towards this mission, MediaPipe plays a critical role by providing a platform that allows Audere to quickly iterate and support new rapid diagnostic tests. This is imperative as new rapid tests come to market regularly, and test availability for community and home use can change frequently. Additionally, the flexibility allows for lower overhead in maintaining the pipeline, which is crucial for cost-effective operations. This, in turn, reduces the cost of use for governments and organizations globally that provide services to people who need them most.

HealthPulse AI offerings allow organizations and governments to benefit from new innovations in the diagnostics space with minimal overhead. This is an essential component of the primary health journey - to ensure that populations in under-resourced communities have access to timely, cost-effective, and efficacious care.


About Audere

Audere is a global digital health nonprofit developing AI based solutions to address important problems in health delivery by providing innovative, scalable, interconnected tools to advance health equity in underserved communities worldwide. We operate at the unique intersection of global health and high tech, creating advanced, accessible software that revolutionizes the detection, prevention, and treatment of diseases — such as malaria, COVID-19, and HIV. Our diverse team of passionate, innovative minds combines human-centered design, smartphone technology, artificial intelligence (AI), open standards, and the best of cloud-based services to empower innovators globally to deliver healthcare in new ways in low-and-middle income settings. Audere operates primarily in Africa with projects in Nigeria, Kenya, Côte d’Ivoire, Benin, Uganda, Zambia, South Africa, and Ethiopia.


1 WHO malaria fact sheets

YouTube Ads Creative Analysis

Posted by Brian Craft, Satish Shreenivasa, Huikun Zhang, Manisha Arora and Paul Cubre – gTech Data Science Team


Introduction


Why analyze YouTube ads?

YouTube has billions of monthly logged-in users and every day people watch billions of hours of video and generate billions of views. Businesses can connect with YouTube users using YouTube ads, which are promotional videos that appear on YouTube's website and app, with a variety of video ad formats and goals.

Image of a sample YouTube in-stream skippable video ad
A sample YouTube in-stream skippable video ad

The Challenge

An effective video ad focuses on the ABCDs.

  • Attention: Capturing the viewer's attention till the end.
  • Branding: Helping them hear or visualize the brand.
  • Connection: Making them feel something about the brand.
  • Direction: Encouraging them to take action.

But each YouTube ad has a varying number of components, for instance, objects, background music or a logo. Each of these components affect the view through rate (which is referred to as VTR for the remainder of the post) of the video ad. Therefore, analyzing video ads through the lens of the components in the ad helps businesses understand what about the ad improves VTR. The insights from these analyses can be used to inform the creation of new creatives and to optimize existing creatives to improve VTR.


The Proposal

We propose a machine learning based approach for analyzing a company’s YouTube ads to assess which components affect VTR, for the purpose of optimizing a video ad’s performance. We illustrate how to:

  • Use Google Cloud Video Intelligence API to extract the components of each video ad, using the underlying video files.
  • Transform that extracted data to engineered features that map to actionable business questions.
  • Use a machine learning model to isolate the effect on VTR of each engineered feature.
  • Interpret and action on those insights to improve video ad performance, for instance altering existing creatives or create new creatives to be used in an AB test.

Approach


The Process

The proposed analysis has 5 steps, discussed below.

1. Define Business Questions
Align on a list of business questions that are actionable, for instance “does having a logo in the opening shot affect VTR?” We suggest taking feasibility into account ahead of time, for instance if a product disclaimer is necessary to have for legal reasons, there is no reason to assess the impact a disclaimer has on VTR.

2. Raw Component Extraction
Use Google Cloud technologies, such as the Google Cloud Video Intelligence API, and underlying video files to extract raw components from each video ad. For instance, but not limited to, objects appearing in the video at a particular timestamp, presence of text and its location on the screen, or the presence of specific sounds.

3. Feature Engineering
Using the raw components extracted in step 2, engineer features that align to the business questions defined in step 1. For example, if the business question is “does having a logo in the opening shot affect VTR”, create a feature that labels each video as either 1, having a logo in the opening shot or 0, not having a logo in the opening shot. Repeat this for each feature.

4. Modeling
Create an ML model using the engineered features from step 3, using VTR as the target in the model.

5. Interpretation
Extract statistically significant features from the ML model and interpret their effect on VTR. For example, “there is an xx% observed uplift in VTR when there is a logo in the opening shot.”


Feature Engineering


Data Extraction

Consider 2 different YouTube Video Ads for a web browser, each highlighting a different product feature. Ad A has text that says “Built In Virus Protection'', while Ad B has text that says “Automatic Password Saving”.

The raw text can be extracted from each video ad and allow for the creation of tabular datasets, such as the below. For brevity and simplicity, the example carried forward will deal with text features only and forgo the timestamp dimension.

 Ad

 Detected Raw Text

 Ad A

 Built In Virus Protection

 Ad B

 Automatic Password Saving

Preprocessing

After extracting the raw components in each ad, preprocessing may need to be applied, such as removing case sensitivity and punctuation.

 Ad

 Detected Raw Text

 Processed Text

 Ad A

 Built In Virus Protection

 built in virus protection

 Ad B

 Automatic Password Saving

 automatic password saving

Manual Feature Engineering

Consider a scenario where the goal is to answer the business question, “does having a textual reference to a product feature affect VTR?”

This feature could be built manually by exploring all the text in all the videos in the sample and creating a list of tokens or phrases that indicate a textual reference to a product feature. However, this approach can be time consuming and limits scaling.

Image of pseudo code for manual feature engineering
Pseudo code for manual feature engineering

AI Based Feature Engineering

Instead of manual feature engineering as described above, the text detected in each video ad creative can be passed to an LLM along with a prompt that performs the feature engineering automatically.

For example, if the goal is to explore the value of highlighting a product feature in a video ad, ask an LLM if the text “‘built in virus protection’ is a feature callout”, followed by asking the LLM if the text “‘automatic password saving’ is a feature callout”.

The answers can be extracted and transformed to a 0 or 1, to later be passed to a machine learning model.

 Ad

 Raw Text

 Processed Text

 Has Textual Reference to Feature

 Ad A

 Built In Virus Protection

 built in virus protection

 Yes

 Ad B

 Automatic Password Saving

 automatic password saving

 Yes



Modeling


Training Data

The result of the feature engineering step is a dataframe with columns that align to the initial business questions, which can be joined to a dataframe that has the VTR for each video ad in the sample.

 Ad

 Has Textual Reference to Feature

 VTR*

 Ad A

 Yes

 10%

 Ad B

 Yes

 50%

*Values are random and not to be interpreted in any way.

Modeling is done using fixed effects, bootstrapping and ElasticNet. More information can be found here in the post Introducing Discovery Ad Performance Analysis, written by Manisha Arora and Nithya Mahadevan.

Interpretation

The model output can be used to extract significant features, coefficient values, and standard deviation.

Coefficient Value (+/- X%)
Represents the absolute percentage uplift in VTR. Positive value indicates positive impact on VTR and a negative value indicates a negative impact on VTR.

Significant Value (True/False)
Represents whether the feature has a statistically significant impact on VTR.

 Feature

 Coefficient*

 Standard Deviation*

 Significant?*

 Has Textual Reference to Feature

0.0222

0.000033

True

*Values are random and not to be interpreted in any way.

In the above hypothetical example, the feature “Has Feature Callout” has a statistically significant, positive impact of VTR. This can be interpreted as “there is an observed 2.22% absolute uplift in VTR when an ad has a textual reference to a product feature.”

Challenges

Challenges of the above approach are:

  • Interactions among the individual features input into the model are not considered. For example, if “has logo” and “has logo in the lower left” are individual features in the model, their interaction will not be assessed. However, a third feature can be engineered combining the above as “has large logo + has logo in the lower left”.
  • Inferences are based on historical data and not necessarily representative of future ad creative performance. There is no guarantee that insights will improve VTR.
  • Dimensionality can be a concern as given the number of components in a video ad.

Activation Strategies


Ads Creative Studio

Ads Creative Studio is an effective tool for businesses to create multiple versions of a video by quickly combining text, images, video clips or audio. Use this tool to create new videos quickly by adding/removing features in accordance with model output.

Image of sample video creation features in Ads creative studio
Sample video creation features in Ads creative studio

Video Experiments

Design a new creative, varying a component based on the insights from the analysis, and run an AB test. For example, change the size of the logo and set up an experiment using Video Experiments.


Summary


Identifying which components of a YouTube Ad affect VTR is difficult, due to the number of components contained in the ad, but there is an incentive for advertisers to optimize their creatives to improve VTR. Google Cloud technologies, GenAI models and ML can be used to answer creative centric business questions in a scalable and actionable way. The resulting insights can be used to optimize YouTube ads and achieve business outcomes.


Acknowledgements

We would like to thank our collaborators at Google, specifically Luyang Yu, Vijai Kasthuri Rangan, Ahmad Emad, Chuyi Wang, Kun Chang, Mike Anderson, Yan Sun, Nithya Mahadevan, Tommy Mulc, David Letts, Tony Coconate, Akash Roy Choudhury, Alex Pronin, Toby Yang, Felix Abreu and Anthony Lui.

Congratulations to the winners of Google’s Immersive Geospatial Challenge

Posted by Bradford Lee – Product Marketing Manager, Augmented Reality, and Ahsan Ashraf – Product Marketing Manager, Google Maps Platform

In September, we launched Google's Immersive Geospatial Challenge on Devpost where we invited developers and creators from all over the world to create an AR experience with Geospatial Creator or a virtual 3D immersive experience with Photorealistic 3D Tiles.

"We were impressed by the innovation and creativity of the projects submitted. Over 2,700 participants across 100+ countries joined to build something they were truly passionate about and to push the boundaries of what is possible. Congratulations to all the winners!" 

– Shahram Izadi, VP of AR at Google

We judged all submissions on five key criteria:

  • Functionality - How are the APIs used in the application?
  • Purpose - What problem is the application solving?
  • Content - How creative is the application?
  • User Experience - How easy is the application to use?
  • Technical Execution - How well are you showcasing Geospatial Creator and/or Photorealistic 3D Tiles?

Many of the entries are working prototypes, with which our judges thoroughly enjoyed experiencing and interacting. Thank you to everyone who participated in this hackathon.



From our outstanding list of submissions, here are the winners of Google’s Immersive Geospatial Challenge:


Category: Best of Entertainment and Events

Winner, AR Experience: World Ensemble

Description: World Ensemble is an audio-visual app that positions sound objects in 3D, creating an immersive audio-visual experience.


Winner, Virtual 3D Experience: Realistic Event Showcaser

Description: Realistic Event Showcaser is a fully configurable and immersive platform to customize your event experience and showcase its unique location stories and charm.


Winner, Virtual 3D Experience: navigAtoR

Description: navigAtoR is an augmented reality app that is changing the way you navigate through cities by providing a 3 dimensional map of your surroundings.



Category: Best of Commerce

Winner, AR Experience: love ya

Description: love ya showcases three user scenarios for a special time of year that connect local businesses with users.



Category: Best of Travel and Local Discovery

Winner, AR Experience: Sutro Baths AR Tour

Description: This guided tour through the Sutro Baths historical landmark using an illuminated walking path, information panels with text and images, and a 3D rendering of how the Sutro Baths swimming pool complex would appear to those attending.


Winner, Virtual 3D Experience: Hyper Immersive Panorama

Description: Hyper Immersive Panorama uses real time facial detection to allow the user to look left, right, up or down, in the virtual 3D environment.


Winner, Virtual 3D Experience: The World is Flooding!

Description: The World is Flooding! allows you to visualize a 3D, realistic flooding view of your neighborhood.


Category: Best of Productivity and Business

Winner, AR Experience: GeoViz

Description: GeoViz revolutionizes architectural design, allowing users to create, modify, and visualize architectural designs in their intended context. The platform facilitates real-time collaboration, letting multiple users contribute to designs and view them in AR on location.



Category: Best of Sustainability

Winner, AR Experience: Geospatial Solar

Description: Geospatial Solar combines the Google Geospatial API with the Google Solar API for instant analysis of a building's solar potential by simply tapping it.


Winner, Virtual 3D Experience: EarthLink - Geospatial Social Media

Description: EarthLink is the first geospatial social media platform that uses 3D photorealistic tiles to enable users to create and share immersive experiences with their friends.


Honorable Mentions

In addition, we have five projects that earned honorable mentions:

  1. Simmy
  2. FrameView
  3. City Hopper
  4. GEOMAZE - The Urban Quest
  5. Geospatial Route Check

Congratulations to the winners and thank you to all the participants! Check out all the amazing projects submitted. We can't wait to see you at the next hackathon.

NordVPN boosted the speed of its login user flow by 60% using Baseline Profiles

Posted by Ben Weiss, Senior Developer Relations Engineer

NordVPN is a virtual private network (VPN) app that protects users while they’re browsing the web by providing them a more secure and private connection. As a network utility, NordVPN’s users deserve a responsive UI, allowing them to set up their protections at a moment’s notice. That's why NordVPN developers recently integrated Baseline Profiles, a profile-guided optimization that helps Android developers improve an app's startup and runtime performance using ahead-of-time compilation.

Improving performance with Baseline profiles

As part of its product roadmap for 2023, the NordVPN team wanted to boost the application’s performance. Before implementing Baseline Profiles, NordVPN’s startup times on Android devices didn’t meet the team’s standards, prompting them to examine new ways to make the app run better.

After exploring ways to improve its runtime performance and streamline the login process for users, NordVPN developers identified an opportunity to make the app faster using Baseline Profiles. Baseline Profiles lets the Android Runtime (ART) know which code paths to optimize through Ahead-of-Time (AOT) compilation before an app launches, boosting speed, stability, and overall responsiveness during startup, when navigating through the app, and while viewing content.

“App speed and stability are essential for a better user experience, so we’re always looking for new ways to improve NordVPN’s performance,” said Himanshu Singh, senior Android engineer at NordVPN. “We wanted to speed up the app’s load time and make launch and navigation faster than ever.”

By applying Baseline Profiles, NordVPN improved its launch speed by an average of 24%. Using tools like Android Vitals, the NordVPN team measured that it had reduced the application’s cold start time from 4.3 seconds to 3.2 seconds, the warm start time from 2.7 seconds to 1.8 seconds, and the hot start time from 1 second to 0.7 seconds.

After implementation, NordVPN developers' also noticed that Baseline Profiles made it faster for users to login to the app, improving the user login flow. The login flow is measured from when a user starts an app to when a user is logged into it. Using the Macrobenchmark library to monitor the improvements, the team observed that the NordVPN app runs its login flow 60% faster than before.

A black quote card featuring a headshot if Ernestas on the right with text in white that reads “The introduction of Baseline Profiles helped us achieve outstanding results, elevating our application’s speed with minimal effort.” — Ernestas Balčiūnas, engineering lead at NordVPN

Integrating and testing Baseline Profiles is easy

The ease of implementing Baseline Profiles impressed NordVPN developers. The available resources, in-depth documentation, and codelabs from Android allowed them to enhance the app’s UX without having to write an extensive amount of code themselves.

Using the Macrobenchmark library, NordVPN developers quickly generated Baseline Profiles for the application. To do this, they used a Gradle managed device, which enabled them to create new profiles without a physical device. Using a Gradle Managed Device also allowed NordVPN developers to create fresh profiles for each app release build on their Continuous Integration platform. Looking forward, NordVPN developers plan to migrate Baseline Profile generation to the official Gradle plugin, which will further automate profile generation.

NordVPN developers combined development workflows to create an integration pipeline, allowing them to test the app under various conditions. Then, the Macrobenchmark library ran Baseline generation tests, pushing the latest Baseline Profiles into the code base.

A black Quote card featuring droid on the right side and a green half oval overlay on the left with white text that reads, 'Applying Baseline Profiles in the NordVPN application led to a 29% improvement to overall in-app speed.'

A quick boost to app quality

After integrating Baseline Profiles into NordVPN’s code, its developers saw immediate speed improvements. The engineering team assessed the app’s overall speed after finishing the project and found that, beyond improving the app’s launch times, applying Baseline Profiles led to a 29% improvement to overall in-app speed.

"We’re constantly working to improve app quality, and Baseline Profiles integration has proven to be one of the most successful steps we’ve taken,” said Šarūnas Rimša, product owner at NordVPN. “We’re helping users access the services they’re entitled to faster. What's not to like?"

Get started

Learn how you can improve your app’s performance using Baseline Profiles.

Snapchat integrated new camera features 50% faster with the Camera2 Extensions API

Posted by Fred Chung, Android Developer Relations

Snapchat is a visual messaging app that enhances Snapchatters’ relationships with friends, family, and the world. It opens to the camera and offers millions of augmented reality and AI-powered Lenses for self expression, learning, and play. Ensuring Snapchatters can easily capture and share their lives with close friends and family is a priority for Snapchat, and they're always exploring new ways to improve the overall app experience.

As part of this, the Snapchat team added new camera features into the app using Android’s Camera2 Extensions API, which allows developers to access various capabilities that OEMs have implemented on various devices, like Night Mode and Bokeh. Thanks to Android’s intuitive API, the Snapchat team implemented new camera features 50% faster than before.

Camera2 Extensions API gives access to advanced features

The Snapchat team wanted to optimize the application for the expanding selection of Android devices, knowing many OEMs differentiate their devices with their respective camera technologies. As Snapchat is a primarily visual app that works with a device’s camera, the team optimizes the app to take full advantage of each device’s unique hardware.

“We wanted to leverage each OEM’s software to enhance the Snapchat experience on Android,” said Ye Tian, a software engineer at Snapchat. “This would help the app achieve higher-quality Snaps that are comparable to what a device's native camera offers.”

https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiU-F-r5DnCnu0YdvwT1OYmjJinR-gH1LNVq_wmImMPU4rwXDfroLQlvht1k8640kMaTabS8maaYYeRgfDQwBYrjv8Gi5QygnmWMb1nw-X8OfxSxEoSjp3V56uhg3lbdoaXRruZHzHuvscejVS-9dsqeQHzJ9QDytZQuQmmZRQcfLYb42v578M4Ln8OX9g/s1600/image3.gif
Snapchat developers enhanced the app’s zoom and night mode camera capabilities using the Camera2 Extensions API

What started as a goal to improve the app’s low-light capabilities led to much more. The Snapchat team worked on finding new ways to improve the app’s camera capabilities by implementing features like night mode, portrait mode, face retouch, tap-to-focus, zoom, and more.

“Our collaboration with Google Pixel paved the way for collaboration with other OEMs to implement night mode and super-night mode in their devices with very minimal code changes,” said Ye. “The Camera2 Extensions API is flexible and extensive. Snapchat can now use it to build full-fledged applications on demand without negatively impacting performance and stability.”

The implementation via the Camera2 Extension API made it easy for Snapchat developers to add more camera features into the app. And using the extensions made available with Android’s camera API, Snapchat integrated new camera features 50% faster when compared to the typical industry-standard approaches it used in the past.

The Camera2 Extensions API is flexible and extensive. Snapchat can now use it to build full-fledged applications on demand without negatively impacting performance and stability.” — Ye Tian, Software Engineer at Snapchat

More opportunities on more devices

The Snapchat team was happy to give its users a more cohesive experience using the Camera2 Extensions API. Thanks to the extensions provided in the API, developers easily improved the app’s camera on a range of manufacturer devices using the Android platform, and much faster than before.

“I enjoy the diversity of the Android platform and utilizing the unique advantages of each mobile phone manufacturers’ devices,” said Ye. “It helps us bring their cutting-edge innovations into the Snapchat app, allowing Snapchatters to better capture their life moments.”

Snapchat’s team looks forward to working with more OEMs to further improve the app’s processing capabilities across devices using the Camera2 Extensions API. They’re also looking forward to improving the app’s backward compatibility using the new API, which will allow even more users to benefit from the extensions.

“I would recommend using Camera2 Extension API. It provides extensive functionalities and stable performance to improve the velocity that developers can deliver features,” said Ye.

Get started

Learn how to increase your app’s camera capabilities with the Camera2 Extensions API.

All treats, no tricks: 6 solutions to common developers challenges

Posted by Google for Developers

For many, Halloween is the perfect excuse to dress up and celebrate the things that haunt us. Google for Developers is embracing the spirit of the season by diving into the spine-chilling challenges that spook software developers and engineers. Read on to uncover these lurking terrors and discover the tricks – and treats – to conquer them.


The code cemetery

Resilient code requires regular updates, and when it comes to solving bugs, it’s much easier to find them when there are fewer lines of code. When faced with legacy or lengthy code, consider simplifying and refreshing it to make it more manageable – because no one likes an ancient or overly complex codebase. Here are some best practices.

Start small: Don't try to update your entire codebase at once. Instead, start by updating small, isolated parts of the codebase to minimize the risk of introducing new bugs.

Use a version control system: Track your changes and easily revert to a previous version if necessary.

Consider a refactoring tool: This can help you to make changes to your code without breaking it.

Test thoroughly: Make sure to test your changes thoroughly before deploying them to production. This includes testing the changes in isolation, as well as testing them in conjunction with the rest of the codebase. See more tips about testing motivation below.

Document your changes: Include new tooling, updated APIs, and any changes so other developers understand what you have done and why.


Testing terrors

When you want to build and ship quickly, it’s tempting to avoid writing tests for your code because they might slow you down in the short term. But beware, untested code will come back to haunt you later. Testing is a best practice that can save you time, money, and angst in the long run. Even if you know you should run tests, it doesn’t mean you want to. Use these tips to help make writing tests easier.

Test gamification: Turn test writing into a game. Challenge yourself to write tests faster than your coworker can say "code coverage."

Pair programming: Write tests together with a colleague. It's like having a workout buddy – more fun and motivating.

Set up test automation: Automate tests wherever possible– it's better AND more efficient.


A monster problem: not being able to choose your tech stack

Many developers have strong preferences when it comes to products, but sometimes legacy technology or organizational needs can limit choices. This can be deflating, especially if it prevents you from using the latest tools. If you’re faced with a similar situation, it’s worth expressing your recommendations to your team. Here’s how:

Lobby for change: If the current tech stack really isn't working out, advocate for a change. This may require documentation over a series of events, but you can use that to build your case.

Pitch the benefits: If you’re ready to share your preferences, explain how your tech stack of choice benefits the project, similarly to how optimized code improves performance.

Showcase expertise: Demonstrate your knowledge in your preferred stack, whether it’s through a Proof of Concept or a presentation.

Upskill: If you have to dive into a top-down tech stack that you are not familiar with, consider it a learning opportunity. It’s like exploring a new coding language.

Compromise is key: First, recognize that all of the points above are still well-worth aiming for, but sometimes, you do have to compromise. Think of it as working with legacy code - not ideal, but doable. So if you aren’t able to influence in your favor, don’t be dismayed.


Not a trick: ship your code smarter

The only thing worse than spending the end of the week fixing buggy code isexcept for spending the weekend fixing buggy code when you had other plans. Between less time to react to problems, taking up personal time, and fewer people available to help troubleshoot – shipping code when you don’t have the proper resources in place to help is risky at best. Here are a handful of best practices to help you build a better schedule and avoid the Saturday and Sunday Scaries.

Consider business hours and user impact: Schedule deployments during off-peak times when fewer users will be impacted. For B2B companies, Friday afternoons can minimize disruption for customers, but for smaller companies, Friday deployments might mean spending your weekend fixing critical issues. Pick a schedule that works for you.

Automate testing: Implement automated testing in your development process to catch issues early.

Make sure your staging environment is right: Thoroughly test changes in a staging environment that mirrors production.

Be rollback-ready: Have a rollback plan ready to revert quickly if problems arise.

Monitoring and alerts: Set up monitoring and alerts to catch issues 24/7.

Communication: Ensure clear communication among team members regarding deployment schedules and procedures.

Scheduled deployments: If you’re a team who doesn’t regularly ship at the end of the week, consider READ-ONLY Fridays. Or if necessary, schedule Friday deployments for the morning or early afternoon.

Weekend on-call: Consider a weekend on-call rotation to address critical issues.

Post-deployment review: Analyze and learn from each deployment's challenges to improve processes.

Plan thoroughly: Ensure deployment processes are well-documented and communication is clear across teams and stakeholders.

Evaluate risks: Assess potential business and user impact to determine deployment frequency and timing.


A nightmare come true: getting hacked

Realizing you've been hacked is a heart-stopping event, but even the most tech-savvy developers are vulnerable to attacks. Before it happens to you, remember to implement these best practices.

Keep your systems and software up-to-date: Think of it as patching vulnerabilities in your code.

Use strong passwords: Just like strong encryption, use robust passwords.

Use two-factor authentication: Always add a second layer of security.

Beware of phishermen: Don't take the bait. Be as cautious with suspicious emails as you are with untested code.

Perform security audits: Regularly audit your systems for vulnerabilities, like running code reviews but for your cybersecurity.

Backup plan: Just like version control, maintain backups. They're your safety net in case things go full horror-movie.


The horror: third party data breaches

Data breaches are arguably the most terrifying yet plausible threat to developer happiness. No company wants to be associated with them, let alone the dev who chose the service or API to work with. Here are some tips for minimizing issues with third party vendors to help you avoid this scenario.

Perform due diligence on third-party vendors: Before working with a third-party vendor, carefully review their security practices and policies. Ask about security certifications, vulnerability management practices, and their incident response plan.

Require vendors to comply with security requirements: Create or add your input in a written contract with each third-party vendor that outlines the security requirements that the vendor must meet. This contract should include requirements for data encryption, access control, and incident reporting.

Monitor vendor activity: Ensure vendors comply with the security requirements in the contract by reviewing audit logs and conducting security assessments. Only grant access to data that a vendor needs to perform their job duties to help to minimize the impact of a data breach if the vendor is compromised.

Implement strong security controls: Within your own systems, protect data from unauthorized access through firewalls, intrusion detection systems, and data encryption.

Be wary of third-party APIs: Vet all security risks. Carefully review the API documentation to understand the permissions that are required and to ensure the API uses strong security practices.

Use secure coding practices: Use input validation, escaping output, and strong cryptography.

Keep software up to date: Always update with the latest security patch to help to protect against known vulnerabilities.


Creepin' it real

It’s easy to get spooked knowing what can go wrong, but by implementing these best practices, the chance of your work going awry goes down significantly.

What other spine-chilling developer challenges have you experienced? Share them with the community.

Simple and secure sign-in on Android with Credential Manager and passkeys

Posted by Diego Zavala, Product Manager

We are excited to announce that the public release of Credential Manager will be available starting on November 1st. Credential Manager brings the future of authentication to Android, simplifying how users sign in to their apps and websites, and at the same time, making it more secure.

Signing in can be challenging - passwords are widely used, and often forgotten. They are reused, phished, and washed, making them less secure. Furthermore, there is a proliferation of ways to log in to apps; passwords, email links, OTP, ‘Sign in with…’, and users carry the burden of remembering what to use where. And for developers, this adds complexity - they need to support multiple sign-in methods, increasing integration and maintenance costs.

To address this, Android is rolling out Credential Manager, which brings support for passkeys, a new passwordless authentication, together with traditional sign-in methods, such as passwords and federated identity, in a unified interface.

Let’s take a look at how it can help make users’ and developers’ lives easier.


1.    Passkeys enable passwordless authentication

Passkeys are the future of online authentication - they are more secure and convenient than passwords. With a passkey, signing in is as simple as selecting the right account and confirming with a device face scan, fingerprint or PIN - that’s it. No need to manually type username or passwords, copy-paste a one-time code from SMS, or tap a link in an email inbox. This has resulted in apps reducing the sign-in time by 50% when they implemented passkeys. Logging in with passkeys is also more secure, as they provide phishing-resistant protection.

Image showing step-by-step passwordless authentication experience to sign in to Shrine app from an Android device

Several apps are already integrated with Credential Manager and support passkeys, including Uber and Whatsapp.

“Passkeys add an additional layer of security for WhatsApp users. Simplifying the way users can securely get into their account will help our users, which is why the Credential Manager API is so important.” 
– Nitin Gupta, Head of Engineering, WhatsApp

 

“At Uber, we are relentless in our push to create magical experiences without compromising user safety. Passkeys simplify the user experience and promote accessibility, while enhancing the security that comes from reducing the dependency on traditional passwords. Ultimately this is a win-win for Uber and Uber’s customers.

The Credential Manager offers a developer-friendly suite of APIs that enable seamless integration with our apps, eliminating concerns about device fragmentation. We’ve seen great results from launching passkeys across our apps and encourage all users to adopt passkeys.” 

– Ramsin Betyousef, Sr. Director of Engineering at Uber


2.    All accounts available in a single tap, in a simplified interface

Users often end up with different sign-in methods for the same account - they may use a password on their phone, and a “Sign in with…” on a browser, and then be offered a passkey on their desktop. To simplify users’ lives, Credential Manager lets them choose the account they want, and use smart defaults to pick the best technology to do it (e.g. a passkey, password, or federated identity). That way, users don’t need to think whether they want to sign-in with a password or a passkey; they just choose the account, and they are in.

Let’s take a look at how it works. Imagine that Elisa has 2 accounts on the Shrine app

  • a personal account for which she had a password and just created a new passkey
  • a shared family account with just a password.

To facilitate her experience, Credential Manager shows her 2 accounts and that’s it. Credential Manager uses a password for her family account and a passkey for her personal account (because it’s simpler and safer). Elisa doesn’t need to think about it.

Image showing Credential Manager on an Android device allowing user to choose a saved sign in from list of two accounts

3.    Open to the ecosystem

One of the reasons why users prefer Android is because they are able to customize their experience. In the case of authentication, some users prefer to use the password manager that’s shipped with their device, and others prefer to use a different one. Credential Manager gives users the ability to do so, by being open to any credential provider and allowing multiple enabled at the same time.

Image showing Credential Manager in app allowing user to choose a saved sign in from list of two accounts

Several leading credential providers already integrated with Credential Manager.


"We're at an inflection point in the history of authentication as passkeys represent the perfect balance between ease and security. Since 1Password launched support for passkeys earlier this year, we’ve had over 230,000 passkeys created and see thousands added each day. The data indicates strong user demand but we must continue to prioritize support for apps and services, making it simpler for developers to integrate passkey authentication." 
– Anna Pobletts, Head of Passwordless at 1Password

 

“At Enpass, we quickly recognized the potential of passkeys. Thanks to the Android Credential Manager framework, Enpass is fully prepared to serve as a passkey provider for Android 14. This integration empowers our customers to embrace a secure alternative to traditional passwords wherever it's available.” 
– Vinod Kumar, Chief Technology Officer at Enpass.


How to integrate with Credential Manager?

To get started, take a look at the resources below:

Simple and secure sign-in on Android with Credential Manager and passkeys

Posted by Diego Zavala, Product Manager

We are excited to announce that the public release of Credential Manager will be available starting on November 1st. Credential Manager brings the future of authentication to Android, simplifying how users sign in to their apps and websites, and at the same time, making it more secure.

Signing in can be challenging - passwords are widely used, and often forgotten. They are reused, phished, and washed, making them less secure. Furthermore, there is a proliferation of ways to log in to apps; passwords, email links, OTP, ‘Sign in with…’, and users carry the burden of remembering what to use where. And for developers, this adds complexity - they need to support multiple sign-in methods, increasing integration and maintenance costs.

To address this, Android is rolling out Credential Manager, which brings support for passkeys, a new passwordless authentication, together with traditional sign-in methods, such as passwords and federated identity, in a unified interface.

Let’s take a look at how it can help make users’ and developers’ lives easier.

1.    Passkeys enable passwordless authentication

Passkeys are the future of online authentication - they are more secure and convenient than passwords. With a passkey, signing in is as simple as selecting the right account and confirming with a device face scan, fingerprint or PIN - that’s it. No need to manually type username or passwords, copy-paste a one-time code from SMS, or tap a link in an email inbox. This has resulted in apps reducing the sign-in time by 50% when they implemented passkeys. Logging in with passkeys is also more secure, as they provide phishing-resistant protection.

Image showing step-by-step passwordless authentication experience to sign in to Shrine app from an Android device

Several apps are already integrated with Credential Manager and support passkeys, including Uber and Whatsapp.

“Passkeys add an additional layer of security for WhatsApp users. Simplifying the way users can securely get into their account will help our users, which is why the Credential Manager API is so important.” – Nitin Gupta, Head of Engineering, WhatsApp

“At Uber, we are relentless in our push to create magical experiences without compromising user safety. Passkeys simplify the user experience and promote accessibility, while enhancing the security that comes from reducing the dependency on traditional passwords. Ultimately this is a win-win for Uber and Uber’s customers.

The Credential Manager offers a developer-friendly suite of APIs that enable seamless integration with our apps, eliminating concerns about device fragmentation. We’ve seen great results from launching passkeys across our apps and encourage all users to adopt passkeys.”– Ramsin Betyousef, Sr. Director of Engineering at Uber

2.    All accounts available in a single tap, in a simplified interface

Users often end up with different sign-in methods for the same account - they may use a password on their phone, and a “Sign in with…” on a browser, and then be offered a passkey on their desktop. To simplify users’ lives, Credential Manager lets them choose the account they want, and use smart defaults to pick the best technology to do it (e.g. a passkey, password, or federated identity). That way, users don’t need to think whether they want to sign-in with a password or a passkey; they just choose the account, and they are in.

Let’s take a look at how it works. Imagine that Elisa has 2 accounts on the Shrine app

  • a personal account for which she had a password and just created a new passkey
  • a shared family account with just a password.

To facilitate her experience, Credential Manager shows her 2 accounts and that’s it. Credential Manager uses a password for her family account and a passkey for her personal account (because it’s simpler and safer). Elisa doesn’t need to think about it.

Image showing Credential Manager on an Android device allowing user to choose a saved sign in from list of two accounts

3.    Open to the ecosystem

One of the reasons why users prefer Android is because they are able to customize their experience. In the case of authentication, some users prefer to use the password manager that’s shipped with their device, and others prefer to use a different one. Credential Manager gives users the ability to do so, by being open to any credential provider and allowing multiple enabled at the same time.

Image showing Credential Manager in app allowing user to choose a saved sign in from list of two accounts

Several leading credential providers already integrated with Credential Manager.

"We're at an inflection point in the history of authentication as passkeys represent the perfect balance between ease and security. Since 1Password launched support for passkeys earlier this year, we’ve had over 230,000 passkeys created and see thousands added each day. The data indicates strong user demand but we must continue to prioritize support for apps and services, making it simpler for developers to integrate passkey authentication." – Anna Pobletts, Head of Passwordless at 1Password

“At Enpass, we quickly recognized the potential of passkeys. Thanks to the Android Credential Manager framework, Enpass is fully prepared to serve as a passkey provider for Android 14. This integration empowers our customers to embrace a secure alternative to traditional passwords wherever it's available.” – Vinod Kumar, Chief Technology Officer at Enpass.

How to integrate with Credential Manager?

To get started, take a look at the resources below:

Google’s Contacts app created a new widget 25% faster using Jetpack Glance

Posted by Andre Labonte, the Glance API.

With over a billion downloads on Google Play, Google’s Contacts app is many Android users’ primary tool for viewing and organizing their personal contact information. Contacts developers aim to make the app an easy way for users to connect with the people who matter most to them. To make connecting even simpler, the Contacts engineering team built an entirely new widget from scratch using Jetpack Glance, Android’s latest framework for designing and developing widgets.

Contacts users enjoy connecting through widgets

Widgets make it easy for users to quickly access their favorite app features. Instead of having to navigate to and through an application, widgets can be added directly to a device’s home screen to create a shortcut to the information users need. With this in mind, Contacts engineers developed a new widget for users’ favorite contacts that allows them to reach friends and family faster.

Contacts widget is moved and placed in position on the home screen

“With the increasing popularity of home screen widgets,” said Phil Groman, product manager at Contacts, “we wanted to make our widget as useful as possible so that users can more easily connect with their most important contacts.”

A Google Opinion Rewards survey of Contacts widget users showed that about 80% of users with the Favorites widget on their home screen found it helpful for finding and connecting with their contacts. The survey used a 5-point scale, with 1 signifying “Not at all helpful” and 5 signifying “Extremely helpful.” Roughly 80% of the polled users selected options 4 or 5.

Faster development using Jetpack Glance

Previously, the Contacts team built the app’s widgets using RemoteViews, but the team switched to Jetpack Glance for its familiar APIs, concise Compose syntax, and out-of-the-box support of Material You dynamic theming. Jetpack Glance is also built on top of the Jetpack Compose Runtime, which makes development simple thanks to its modern declarative approach to UI and Kotlin APIs.

Contacts engineers also liked Jetpack Glance because the toolkit is backward compatible with RemoteViews code. Using Jetpack Glance, they could create the new Favorites widget with support as far back as API 23 of RemoteViews. Currently, the Favorites widget supports back to API 31. Jetpack Glance also offers a WorkManager-backed update mechanism, making it easier for developers to load a widget’s display data.

Android has additional documentation available on the Jetpack Glance landing page to help guide the development and implementation of widgets, which Contacts engineers used to build the Favorites widget. These resources helped Contacts engineers learn how to build responsive dynamic layouts for resizing, incorporate material themes, add UIs for empty and error states, and verify accessibility features like content descriptions.

After the Contacts team built the new Favorites widget, engineers estimated end-to-end development using Jetpack Glance to be 25% faster compared to another Contacts widget built using RemoteViews.

Google’s Contacts engineers estimated a 25% increase in end-to-end development speed of building a widget using Jetpack Glance compared to RemoteViews.

Doing more with Jetpack Glance

Thanks to Jetpack Glance, the Contacts team greatly reduced the time it took to produce a widget. Jetpack Glance’s Compose-based framework and intuitive Kotlin APIs made development easier, allowing engineers to quickly get a new widget off the ground and out to users.

“Jetpack Glance helped a lot during development,” said Zhen Fan, a software engineer at Contacts. “It increased development speed and made developers happier overall.”

Right now, the Contacts team is beginning to migrate another one of its widgets from RemoteViews to Jetpack Glance. The team hopes to incorporate more complex UI versions by making the switch, allowing engineers to develop richer features for users to enjoy.

Get started

Learn more about developing engaging widgets with Jetpack Glance.