Tag Archives: cloud

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.

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 IVirus Protection

 built ivirus 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 IVirus Protection

 built ivirus 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.

Finding Stability in Open Source Work

At Google, open source is at the core of our infrastructure, processes, and culture. For the last 19 years, Google’s Open Source Programs Office (OSPO) has enabled our organization to support open source ecosystems through funding, training, mentorship and direct contribution. Every year for the last 5 years, roughly 10% of our workforce has contributed to open source projects as part of their work as well as in their personal time. We’re focused on investing in and protecting open source communities and infrastructure, as well as expanding access to open source opportunities around the world. Every day we seek to promote open and connected ecosystems as the foundation of technological advancement.

For the last four years, researchers in Google's Open Source Programs Office (OSPO) have analyzed our open source contribution activity annually to identify trends and changes in behavior. The goal of this effort has been to increase transparency and accountability across all of the communities we engage with, as well as provide feedback indicators for Alphabet’s internal tools, processes, and policies. In this iteration, our 2022 open source contribution metrics were remarkably consistent with what we found in 2021, which gives us confidence that what we're measuring is a good representation of open source behavior, especially after the extreme outlier year of 2020.


Security remains a priority

At Alphabet, open source software remains a critical component of our infrastructure, products, and services and we continue to rely on the health and availability of open source projects. Through internal efforts and collaboration with industry-led efforts such as OpenSSF, Alphabet is committed to bolstering the security posture of projects, users, and developers of open source software.

In 2021, Google began funding two Linux Foundation contractors to focus exclusively on security, and in 2022 we've continued to sponsor their work to eliminate fragile C language features and APIs in the kernel. We also continue to support the Rust-in-Linux project, with the goal of improving memory safety, strengthening APIs, and reducing the number of bugs overall in the project. In late 2022, Rust infrastructure support landed in the upstream kernel.

The deps.dev project released a public BigQuery dataset, allowing anyone to explore and analyze the dependencies, advisories, ownership, license, and other metadata of open source packages across supported ecosystems, and explore how this metadata has changed over time.

In 2022 we announced:

  • The OSV-Scanner, a free tool enabling open source developers and users to identify and remediate known vulnerabilities in their project's OSS dependencies. The OSV-Scanner provides a supported frontend to the OSV database which connects a project’s list of dependencies with the vulnerabilities that affect them.
  • The GOSST Upstream Team, a dedicated staff of Google open source security engineers who spend 100% of their time working closely with upstream maintainers to improve the security of critical open source projects.
  • Graph for Understanding Artifact Composition (GUAC) which aggregates software security metadata into a high fidelity graph database–normalizing entity identities and mapping standard relationships between them.

Our contributions continue to scale with our growing workforce

In 2022, roughly 10% of Alphabet's full-time workforce contributed to open source projects hosted on GitHub or Git-on-Borg - our internal production Git service (more details below). This percentage has remained roughly consistent over the last five years, indicating that our open source contribution has continued to scale with the growth of Alphabet. Similar to last year, FTEs represented over 95% of our open source workers, while the remainder includes vendors, independent contractors, temporary staff, and interns who contributed to open source projects during their tenure at Alphabet.

As open source work is core to our ongoing operations, we continue to track engagement over time, helping to compare continuous and sporadic participation. On average, over 45% of our active* contributing population for the year logged an activity on GitHub or Git-on-Borg in an average month. (see Figure 1)
This chart shows Alphabet's monthly active users on GitHub and Git-on-Borg. Over the last five years, the trajectory of monthly active users has continued to increase on both GitHub and Git-on-Borg by more than 15% year over year per month

Our portfolio of projects remains active

We estimate that more than 2000 projects that originated from Alphabet teams and employees were still active* (not archived). To make this estimate, we chose a broad and variable definition of an open source project, including developer tools, utilities, languages, frameworks, libraries, demos, sample code, models, raw data, designs, and more.

Project counts should not be confused with repositories as projects can include many repositories. Within Alphabet, we maintain over 7500 public repositories on GitHub and 1600 public repositories on Git-on-Borg. Our total repositories under management have reduced over time with the enforcement of a new archiving policy that flags repositories for archiving based on activity levels and owner feedback. Most of these repositories are open to outside contribution: more than 500,000 unique GitHub accounts not affiliated with Alphabet workers contributed to Alphabet projects in 2022.

The majority of our open source work happens outside of Alphabet organizations

The majority of repositories we work on are outside of Alphabet organizations: Over the last five years, more than 70% of non-personal GitHub repositories Alphabet contributors interacted with were outside of Google-managed organizations. We updated the methodology behind this metric since our last edition to filter out forks created in the pull request workflow. The top projects (by unique contributors at Alphabet) include Google-initiated projects such as Kuberenetes, Apache Beam, and gRPC as well as community-led projects such as LLVM, Envoy, and Rust.


We continue to invest in the sustainability of open source ecosystems

The mission of the Google Open Source Programs Office remains the same: we sponsor, create, and invest in projects and programs that enable everyone to join and contribute to the global open source ecosystem. In 2022, OSPO provided $5.7M in membership fees and sponsorship funding to 60 key open source projects and organizations. This funding was in addition to our established annual programs:

  • In its 18th year, Google Summer of Code enabled more than 1000 individuals to contribute to more than 150 organizations. Over the lifetime of this program, more than 19,000 individuals from 112 countries have contributed to more than 800 open source organizations across the globe.
  • In its fourth year, Google Season of Docs provided direct grants to 30 open source projects to hire more than 50 technical writers to improve open source project documentation, and published its second case study report highlighting useful open source documentation metrics. More than half of the documentation created in the 2022 program were how-tos, tutorials, and reference documentation; projects primarily wanted to add documentation for missing use cases and fix disorganized documentation.
  • Since 2011, the Google Open Source Peer Bonus Program has awarded bonuses for open source contributions to members of our extended community. In 2022 more than 300 contributors received awards, working in over 40 countries on more than 200 open source projects.

Our open source work will continue to grow and evolve to support the changing needs of our communities. Thank you to our colleagues and community members who continue to dedicate their personal and professional time supporting the open source ecosystem. Follow our work at opensource.google.

By Sophia Vargas – Researcher, Google Open Source Programs Office


About this data:

This report features metrics provided by many teams and programs across Alphabet. In regards to the code and code-adjacent activities data, we wanted to share more details about the derivation of those metrics.

2022 updates: This year, we decided to remove event counts as it is increasingly difficult to differentiate automated activities from human-centered work. Even after filtering out non-human accounts, we couldn’t correlate these events to employee time spent on open source projects, and so we reduced our reporting to focus on our population and scope of effort.

  • Data sources: These data represent activities on repositories hosted on GitHub and our internal production Git service Git-on-Borg. These sources represent a subset of open source activity currently tracked by Google OSPO.
    • GitHub: We continue to use GitHub Archive as the primary source for GitHub data, which is available as a public dataset on BigQuery. Alphabet activity within GitHub is identified by self-registered accounts, which we estimate underreports actual activity.
    • Git-on-Borg: This is our primary platform for internal projects and some of our larger, long running public projects such as Android and Chromium. While we continue to develop on this platform, most of our open source activity has moved to GitHub to increase exposure and encourage community growth.
    • Distinct event types: Note that Git-on-Borg and GitHub APIs produce distinct sets of events—so we report activity metrics per platform. Where GitHub Event logs capture a wide range of activity from code creation and review to issue creation and comments, the Gerrit Event stream (used by Git-on-Borg) only captures code changes and reviews.
  • Driven by humans: We have created many automated bots and systems that can propose changes on various hosting platforms. We have intentionally filtered these data to focus on human-initiated activities.
  • Business and personal: Activity on GitHub reflects a mixture of Alphabet projects, third party projects, experimental efforts, and personal projects. Our metrics report on all of the above unless otherwise specified.
  • Alphabet contributors: Please note that unless additional detail is specified, activity counts attributed to Alphabet open source contributors will include our full-time employees as well as our extended Alphabet community (temps, vendors, contractors, and interns).
  • GitHub Accounts: For counts of GitHub accounts not affiliated with Alphabet, we cannot assume that one account is equivalent to one person, as multiple accounts could be tied to one individual or bot account.
  • *Active counts: Where possible, we will show ‘active users’ defined by logged activity (excluding ‘WatchEvent’) within a specified timeframe (a month, year, etc.) and ‘active repositories’ and ‘active projects’ as those that have enough activity to meet our internal criteria and have not been archived.

Celebrating 25 years of Google Search: developer trends and history

Posted by Google for Developers

This month, Google Search turns 25. A lot has changed over the last quarter of a century when it comes to the development space, but one thing has remained a constant - whether you’re stuck on a problem, reading documentation, learning about new technology, or figuring out the best tech stack for your project, Search has been a helpful tool in getting your questions answered.

What you searched for is a strong signal when it comes to developer trends across web, mobile, cloud, and AI over the years. Let’s take a look at some of the interesting things you’ve looked up* – and some funny queries too – because everyone loves a good retrospective.

*Note: Google Trends data goes as far back as 2004.


Building a better web

After the internet dot-com bubble popped in 2000–2001, the web continued to advance and the internet exploded. Web development responded by enabling designers to incorporate multimedia into web pages. Cascading Style Sheets (CSS) (released in 1997) and Flash video (1996-2017) changed the way web pages looked and moved, and streaming changed the way people consumed video. However, the basic interface and structure of the web page remained the same. With the variety of browsers that came to market, JavaScript frameworks and libraries rose along since it can be run everywhere with both CSS and HTML. All these shifts led to some fun searches.

How to center a div

You can’t think of web development without CSS. And it turns out, “how to center a div” has been searched for from the beginning - it’s also provided the internet with a wealth of memes over the years.

JavaScript libraries

JavaScript is a front-end programming language that is used to add interactivity and dynamic behavior to web pages. It is one of the most popular programming languages in the world, and it is essential for building modern web applications. But at some point, most developers have to ask themselves what kind of JavaScript they should use. Vanilla? A framework? A library?

Starting in 2007 there was an uptick of searches for jQuery, which peaked in 2013 and started to fall after that. Meanwhile, developers started to show more interest in React and Angular right around the same time as jQuery’s peak. By April of 2018 they all had a similar volume of searches, and soon after React took over, followed by Angular. Nigeria searched for React the most, while Japan preferred jQuery, and Ecuador preferred Angular. Nowadays, the choice of JavaScript framework is the subject of a lot of controversy - what's your favorite? Share your thoughts with us.

Graph showing search term volume for “React”,” jQuery”, and “Angular” from 2004-present day
Search term volume for “React”,” jQuery”, and “Angular” from 2004-present day


The rise of mobile

As the web improved, so did mobile. Phones went from cellular to smart. The app economy blossomed. Due to low infrastructure and financial restraints, many emerging markets in Asia, Africa, and Latin America skipped the desktop era in favor of mobile to get their information and entertainment. Mobile development –Android in particular– kicked into high gear as a response.

Android development

Starting in 2007, Android was released as a developer platform before devices were on the market, along with the first Android Developer Challenge which launched to support and recognize developers who build great applications. In 2008, the Android OS was released and open sourced, along with T-Mobile’s G1 as the first smartphone to run Android. That same year, the Android Market was released, allowing developers an easy way to distribute apps to the Android community. In 2012, the marketplace got rebranded to Google Play. All of this momentum helped add to the frenzy, but searches really took off starting in 2012.

Graph of search term volume for “Android development” from 2007-2012
Search term volume for “Android development” from 2007-2012

Mobilegeddon

Even web developers couldn’t escape the importance of mobile in its heyday. By 2010, “mobile-first” and “responsive design” became best practices for the web in order to support mobile traffic. As a response to the clear indication that mobile wasn’t going anywhere, by 2015, Google’s search ranking algorithm changed to favor content that is mobile-friendly. Dubbed ‘Mobilegeddon’ by Chuck Price in a post written on Search Engine Watch, developers quickly searched for the term and adjusted their best practices such as responsive and mobile-first design. By 2017, mobile traffic accounted for approximately half of web traffic worldwide before permanently surpassing it in 2020.


Moving to the cloud

Over the last 25 years, cloud development has evolved from a niche technology to a mainstream solution for organizations of all sizes. Being free from managing infrastructure and operations provides a number of advantages like cost savings, speed, and scalability. In the early days, it was mainly used for hosting static websites and applications. But as technology matured, it became increasingly popular for a wider range of applications, including IoT, big data, real-time data, and ML in addition to more modern development practices like containers, microservices, and security.

Cloud computing

As development continued to modernize, developers, IT, and operations figured out fairly quickly that managing infrastructure and servers was painful and expensive. In response, many cloud environment providers launched between 2002-2010, including Google Cloud Platform.

Graph of search term volume for “cloud computing” from 2004-2012
Search term volume for “cloud computing” from 2004-2012

Cloud databases

Cloud services extend to storage, databases, and so much more – a necessity as technology becomes more robust, supporting large amounts of data in real time from IoT devices or use cases like ML and large language models. While there were searches for the term “cloud database” as far back as 2004, it spiked in 2017, coinciding with Google Cloud’s Cloud Spanner. And with the latest renaissance of AI technology, it’s pretty likely that this search term will keep going up in the coming months and years.


Present day innovations

Disruptive developer technology like artificial intelligence and machine learning are infused in development today. From AI-assisted coding to solving problems leveraging big data, AI is permeating our lives. So it’s no wonder developers are searching for some key terms.

Artificial intelligence, machine learning, and more

While some applications of AI, ML, deep learning, large language models (LLMs) are new, most of the terms aren’t. Even in 2004, AI and ML were search terms of interest. In 2015, most of these terms started to pick back up and continue to trend upwards, with a sheer spike in interest in 2022. That same year, ‘generative AI’ was formally introduced to the world. Python is the most searched coding language closely associated with AI, becoming the most popularly searched language in 2019, finally surpassing Java.

Graph of search term volume for “artificial intelligence”, “machine learning”, “deep learning”, and “generative AI” from 2004-present day
Search term volume for “artificial intelligence”, “machine learning”, “deep learning”, and “generative AI” from 2004-present day

Looking ahead

While some aspects of development have gotten progressively cleaner, more modern, and more lightweight - there’s now more choice and complexity when it comes to your tech stack. So it’s no wonder “why is my code not working” spiked in both the early days and today. At Google, we’ll do our best to help streamline and simplify technology to help you build smarter and ship faster with new technology like Project IDX, Android Studio Bot, and coding for Bard.

Graph of search term volume for “why is my code not working?” from 2004-present day
Search term volume for “why is my code not working?” from 2004-present day

It’s inspiring to see what you have done with the answers to your questions, whether you’re trying to solve specific problems, learning new skills or best practices, figuring out what technology you want to use, or dreaming up your next big idea. We look forward to seeing what the next 25 years bring.

Follow more developer trends and insights on Google for Developers across YouTube, LinkedIn, and Instagram.

Announcing the Inaugural Google for Startups Accelerator: AI First cohort

Posted by Yariv Adan, Director of Cloud Conversational AI and Pati Jurek, Google for startups Accelerator Regional Lead

This article is also shared on Google Cloud Blog

Today’s startups are addressing the world's most pressing issues, and artificial intelligence (AI) is one of their most powerful tools. To empower startups to scale their business towards success in the rapidly evolving AI landscape, Google for Startups Accelerator: AI First offers a 10-week, equity-free program for AI-first startups in partnership with Google Cloud. Designed for seed to series A startups based in Europe and Israel, the program helps them grow and build responsibly with AI and machine learning (ML) from the ground up, with access to experts from Google Cloud and Google DeepMind, a mix of in-person and virtual activities, 1:1 mentoring, and group learning sessions.

In addition, the program features deep dives and workshops focused on product design, business growth, and leadership development. Startups that are selected for the cohort also benefit from dedicated Google AI technical expertise and receive credits via the Google for Startups Cloud Program.

Out of hundreds of impressive applications, today we welcome the inaugural cohort of the Google for Startups Accelerator: AI First. The program includes 13 groundbreaking startups from eight different countries, all focused on different verticals and with a diverse array of founder and executive backgrounds. All participants are leveraging AI and ML technologies to solve significant problems and have the potential to transform their respective industries.


Congratulations to the cohort!

We are thrilled to present the inaugural Google for Startups Accelerator: AI First cohort:

  • Annea.Ai (Germany) utilizes AI and Digital Twin technology to forecast and prevent possible breakdowns in renewable energy assets, such as wind turbines.
  • Checktur.io (Germany) empowers businesses to manage their commercial vehicle fleets efficiently via an end-to-end fleet asset management ecosystem while using AI models and data-driven insights.
  • Exactly.ai (UK) lets artists create images in their own unique style with a simple written description.
  • Neurons (Denmark) has developed a precise AI model that can measure human subconscious signals to predict marketing responses.
  • PACTA (Germany) provides AI-driven contract lifecycle management with an intelligent no-code workflow on one central legal platform.
  • Quantic Brains (Spain) empowers users to generate movies and video games using AI.
  • Sarus (France) builds a privacy layer for Analytics & AI and allows data practitioners to query sensitive data without having direct access to it.
  • Releva (Bulgaria) provides an all-in-one AI automation solution for eCommerce marketing.
  • Semantic Hub (Switzerland) uses AI leveraging multilingual Natural Language Understanding to help global biopharmaceutical companies understand the patient experience through first-hand testimonies on social media.
  • Vazy Data (France) allows anyone to analyze data without technical knowledge by using AI.
  • Visionary.AI (Israel) leverages cutting-edge AI to improve real-time video quality in challenging visual conditions like extreme low-light.
  • ZENPULSAR (UK) provides social media analytics from over 10 social media platforms to financial institutions and corporations to facilitate investment and business decisions.
  • Zaya AI (Romania) uses machine learning to better understand and diagnose diseases, assisting healthcare professionals to make timely and informed medical decisions.
Grid image of logos and executives of all startups listed in the inaugural Google for Startups Accelerator

To learn more about the AI-first program, and to signal your interest in nominating your startup for future cohorts, visit the program page here.

Programmatically access working locations with the Calendar API

Posted by Chanel Greco, Developer Advocate

Giving Google Workspace users the ability to set their working location and working hours in Google Calendar was an important step in helping our customers’ employees adapt to a hybrid world. Sending a Chat message asking “Will you be in the office tomorrow?” soon became obsolete as anyone could share where and when they would be working within Calendar.

To improve the hybrid working experience, many organizations rely on third-party or company-internal tools to enable tasks like hot desk booking or scheduling days in the office. Until recently, there was no way to programmatically synchronize the working location set in Calendar with such tools.


Image showing working locations visible via Google Calendar in the Robin app
Robin displays the working location from Google Calendar in their application and updates the user's Google Calendar when they book a desk in Robin

Programmatically read and write working locations

We are pleased to announce that the Calendar API has been updated to make working locations available and this added functionality is generally available (feature is only available for eligible Workspace editions). This enables developers to programmatically read and write the working location of Google Workspace users. This can be especially useful in three use cases that have surfaced in discussions with customers which we are going to explore together.

1.     Synchronize with third-party tools

Enhancing the Calendar API enables developers to synchronize user’s working location with third-party tools like Robin and Comeen. For example, some companies provide their employees with desk booking tools so they can book their workplace in advance for the days they will be on-site. HR management tools are also common for employees to request and set “Work from home” days. In both situations the user had to set their working location in two separate tools: their desk booking tool and/or HR management system and Google Calendar.

Thanks to the working location being accessible through the Calendar API this duplicate work is no longer necessary since a user’s working location can be programmatically set. And if a user's calendar is the single source of truth? In that case, the API can be used to read the working location from the user’s calendar and write it to any permissioned third-party tool.


Image showing Google Workspace Add-on synchronizing users' working locations in the Comeen app.
Comeen’s Google Workspace Add-on synchronizes the user’s’ working locations whenever the user updates their working location, either in Google Calendar or in Comeen's add-on

2.     Display working location on other surfaces

The API enables the surfacing of the user's working location in other tools, creating interesting opportunities. For instance, some of our customers have asked for ways to better coordinate in-office days. Imagine you are planning to be at the office tomorrow. Who else from your team will be there? Who from a neighboring team might be on-site for a coffee chat?

With the Calendar API, a user's working location can be displayed in tools like directories, or a hybrid-work scheduling tool. The goal is to make a user’s working location available in the systems that are relevant to our customers.

3.     Analyze patterns

The third use case that surfaced from discussions with our customers is analyzing working location patterns. With many of our customers having a hybrid work approach it’s vital to have a good understanding of the working patterns. For example, which days do locations reach maximal legal capacity? Or, when does the on-campus restaurant have to prepare more meals for employees working on-site?

The API answers these and other questions so that facility management can adapt their resources to the needs of their employees.


How to get started

Now that you have an idea of the possibilities the updated Calendar API creates, we want to guide you on how you can get started using it.

  • Check out the developer documentation for reading and writing a user's working locations.
  • Watch the announcement video on the Google Workspace Developers YouTube channel.
  • Check the original post about the launch of the working location feature for a list of all Google Workspace plans that have access to the feature.

A vision for more efficient media management

Petit Press’ new open source, cloud-based DAM platform helps publishers get rich media content in front of their audience at pace and scale.

Picture the scene: You’re an investigative journalist that has just wrapped up a new piece of video content that offers incisive, timely commentary on a pressing issue of the day. Your editor wants to get the content in front of your audience as quickly as possible and you soon find yourself bogged down in a laborious, manual process of archiving and uploading files. A process that is subject to human error, and involves repeating the same tasks as you prepare the content for YouTube and embedding within an article.

With the development of a new open source digital asset management (DAM) system, Slovak publishing house, Petit Press, is hoping to help the wider publishing ecosystem overcome these types of challenges.

Striving towards a universal, open source solution

Like many publishers in today’s fast-paced, fast-changing news landscape, Petit Press was feeling the pressure to be more efficient and do more with less, while at the same time maximizing the amount of high-quality, rich media content its journalists could deliver. “We wanted to find a solution to two main asset delivery issues in particular,” says Ondrej Podstupka, deputy editor in chief of SME.sk. “Firstly, to reduce the volume of work involved in transferring files from our journalists to our admin teams to the various platforms and CMS we use. Secondly, to avoid the risk of misplacing archived files or losing them entirely in an archive built on legacy technologies.”

As a publisher of over 35 print and digital titles, including one of Slovakia’s most-visited news portal, SME.sk, Petit Press also had a first-hand understanding of how useful the solution might be if it could flex to the different publishing scales, schedules, and platforms found across the news industry. With encouragement and support from GNI, Petit Press challenged themselves to build an entirely open source, API-based DAM system that flexes beyond their own use cases and can be easily integrated with any CMS, which means that other publishers can adapt and add functionality with minimal development costs.

Getting out of the comfort zone to overcome complexity

For the publisher, creating an open source project requires collaboration, skill development, and a strong sense of purpose. GNI inspired our team members to work together in a positive, creative, and supportive environment. Crucial resources from GNI also enabled the team to broaden the scope of the project beyond Petit Press’ direct requirements to cover the edge use cases and automations that a truly open source piece of software requires.

“GNI has enabled our organization to make our code open source, helping to create a more collaborative and innovative environment in the media industry.” 
– Ondrej Podstupka, deputy editor in chief of SME.sk

Building and developing the tool was difficult at times with a team of software engineers, product managers, newsroom managers, UX designers, testers, and cloud engineers all coming together to see the project to completion. For a team not used to working on GitHub, the open source aspect of the project proved the primary challenge. The team, however, also worked to overcome everything from understanding the complexities of integrating a podcast feature, to creating an interface all users felt comfortable with, to ensuring compliance with YouTube’s security requirements.

Unburdening the newsroom and minimizing costs

The hard work paid off though, when the system initially launched in early 2023. Serving as a unified distribution platform, asset delivery service and long term archive, the single solution is already unburdening the newsroom. It also benefits the tech/admin teams, by addressing concerns about the long-term costs of various media storage services.

On Petit Press’ own platforms, the DAM system has already been successfully integrated into SME.sk’s user-generated content (UGC) blog. This integration allows for seamless content management and curation, enhancing the overall user experience. The system also makes regulatory compliance easier, thanks to its GDPR-compliant user deletion process.

In addition to the UGC Blog system, the DAM system has now launched for internal Petit Press users—specifically for managing video and podcast content, which has led to increased efficiency and organization within the team. By streamlining the video and podcast creation and distribution processes, Petit Press has already seen a 5-10% productivity boost. The new DAM system saves an estimated 15-20 minutes of admin time off every piece of video/podcast content Petit Press produces.

Working towards bigger-picture benefits

Zooming out, the DAM system is also playing a central part in Petit Press’ year-long, org-wide migration to the cloud. This transformation was set in motion to enhance infrastructure, streamline processes, and improve overall efficiency within the department.

Podstupka also illustrates how the system might benefit other publishers. “It could be used as an effective standalone, automated archive for videos and podcasts,” he says. For larger publishing houses, “if you use [the DAM system] to distribute videos to YouTube and archive podcasts, there is minimal traffic cost and very low storage cost. But you still have full control over the content in case you decide to switch to a new distribution platform or video hosting service.”

As the team at Petit Press continues to get to grips with the new system, there is a clear goal in mind: To have virtually zero administrative overhead related to audio or video.

Beyond the automation-powered efficiency savings, the team at Petit Press are also exploring the new monetisation opportunities that the DAM system presents. They are currently working on a way to automatically redistribute audio and image assets to their video hosting platform, to automatically create video from every podcast they produce. This video is then pushed to their CMS and optimized for monetisation on the site with very little additional development required.

Ultimately, though, the open source nature of the system makes the whole team excited to see where other publishers and developers might take the product. “It’s a futureproof way to leverage media content with new services, platforms and ideas that emerge in technology or media landscapes,” says Igor, Head Of Development & Infrastructure. A succinct, but undeniably compelling way of summing up the system’s wide-ranging potential.

A guest post by the Petit Press team

Champion Innovator David Cardozo, based in Victoriaville, Quebec

Posted by Max Saltonstall, Developer Relations Engineer

Google Cloud Champion Innovators are a global network of more than 500 non-Google professionals, who are technical experts in Google Cloud products and services. Each Champion specializes in one of nine different technical categories: cloud AI/ML, data analytics, hybrid multi-cloud, modern architecture, security and networking, serverless app development, storage, Workspace and databases.

In our ongoing interview series we sit down with Champion Innovators across the world to learn more about their journeys, their technology focus, and what excites them.

Today we're talking to David Cardozo, a Machine Learning Scientist, Kubeflow Community member and ML GDE.

Headshot of David Cardozo, smiling

What tech area has you most fascinated right now, and why?

I love all the creative ways people are using Machine Learning (ML) to solve problems. There are a ton of cool applications that I see through my consulting work – counting cranberries from drone footage, tallying fish in fish farms, classifying plastics for recycling – and there's great stuff going on in both the public and private sector.

I'm also digging into the Kubeflow community right now, learning from that group. It's a melting pot of languages: Go, Python, etc. By participating in the working group and meetings I'm understanding so much more about current issues, blockers to progress, and get a deeper understanding of the technology itself. I love gaining that insight.

How do you like to learn new services, tools, and applications?

I read a lot: engineering blogs, books, documentation. Right now I'm learning system design from a variety of Google blogs, which helps me learn how to scale up the things I design. I'm also learning how to make ML models, and how to improve the ones I've deployed.

I'm passionate about contributing to the open source community and actively participate in various projects. Right now with friends in the community we developed Elegy – a high level API for Deep Learning in JAX.

Writing about a topic also helps me learn. Right now, I am working on blogs focused on Kubeflow pipelines in version 2.0 and Vertex AI in Google Cloud.

When I'm diving into a brand new technology I try to join the working groups that are furthering its development, so I get an inside look at how things are moving. Those working groups, their discussions and notes, teach me a ton. I also use the Google Cloud Forum and StackOverflow communities to deepen my knowledge.

What are some exciting projects you have in flight right now?

Getting to play with Generative AI within Vertex (on Google Cloud) has been very fun. I like hearing about what the other Innovators are making; it's a very smart, creative group with cool projects. Learning more about the cutting edge of ML is very exciting.

I'm doing a bit more with Open Source in my free time, trying to understand more around Kubernetes and Kubeflow.

What engages you outside of the technology world?

I stay active: swimming, lots of soccer. I also have been learning about option trading, testing out the waters of active investing. The complexity of those economic systems stimulates my curiosity. I really want to understand how it works, and how to make it useful.

My background is in the social sciences, I'm a bit of a frustrated historian. My interest in school was history, but my family said that I shouldn't focus on social science, so I majored in Math and Physics, but never finished my degree. Right now, after a few life and career pivots, I'm working on completing my Bachelor's through Coursera via the University of London, and earning a history degree requires a lot of reading. This has inspired me to make an AI project that summarizes the knowledge from very long documents, making history research more accessible by giving people a format that's easier to consume.

What brought you into the Innovators program?

I started as one of the Google Developer Experts, but I always wanted more opportunities to talk with Google engineers and get more feedback on the cloud architectures I was building, for myself or my clients. I also wanted to be more involved in the Cloud community.

When I see members of the community encountering challenges, struggling as I did, I feel the pull to help them. As a native Spanish speaker I wanted to make more content in Spanish for folks like myself. I didn't have a mentor as I was learning, and I'd like to fill that gap for others.

So I began organizing meetups in Latin America, and in Spanish speaking communities. I sought out more data scientists. And I went through Qwiklabs and Cloud Skills Boost to learn to improve my own skills.

After I joined the Innovators program, I've had the chance to play with new AI technologies, work more closely with Google experts and received credits for more Cloud experimentation.

What's one thing our readers should do next?

I recommend using some of the open, public teaching resources in Computer Science (CS), especially if you're like me and didn't focus on CS in school. For me, computers came very late to Colombia and I didn't have a chance to major in CS as a student, so I got into it via Math, then information security.

I also suggest taking a look at Elegy, and being involved in solving first issues, providing feedback and also some pull requests :)

I've liked Stanford's course on Neural Networks (CS 231n), as well as MIT's open courseware classes and ML videos on YouTube by Joel Grus.


Each Champion Innovator is not affiliated with Google nor do they offer services on behalf of Google.

What’s new for developers building solutions on Google Workspace – mid-year recap

Posted by Chanel Greco, Developer Advocate Google Workspace

Google Workspace offers tools for productivity and collaboration for the ways we work. It also offers a rich set of APIs, SDKs, and no-code/low-code tools to create apps and integrate workflows that integrate directly into the surfaces across Google Workspace.

Leading software makers like Atlassian, Asana, LumApps and Miro are building integrations with Google Workspace apps—like Google Docs, Meet, and Chat—to make it easier than ever to access data and act right in the tools relied on by more than 3 billion users and 9 million paying customers.

At I/O’23 we had some exciting announcements for new features that give developers more options when integrating apps with Google Workspace.


Third-party smart chips in Google Docs

We announced the opening up of smart chips functionality to our partners. Smart chips allow you to tag and see critical information to linked resources, such as projects, customer records, and more. This preview information provides users with context and critical information right in the flow of their work. These capabilities are now generally available to developers to build their own smart chips.

Some of our partners have built and launched integrations using this new smart chips functionality. For example, Figma is integrated into Docs with smart chips, allowing users to tag Figma projects which allows readers to hover over a Figma link in a doc to see a preview of the design project. Atlassian is leveraging smart chips so users can seamlessly access Jira issues and Confluence pages within Google Docs.

Tableau uses smart chips to show the user the Tableau Viz's name, last updated date, and a preview image. With the Miro smart chip solution users have an easy way to get context, request access and open a Miro board from any document. The Whimsical smart chip integration allows users to see up-to-date previews of their Whimsical boards.

Moving image showing functionality of Figma smart chips in Google docs, allowing users to tag and preview projects in docs.

Google Chat REST API and Chat apps

Developers and solution builders can use the Google Chat REST API to create Chat apps and automate workflows to send alerts, create spaces, and share critical data right in the flow of the conversation. For instance, LumApps is integrating with the Chat APIs to allow users to start conversations in Chat right from within the employee experience platform.

The Chat REST API is now generally available.

Using the Chat API and the Google Workspace UI-kit, developers can build Chat apps that bring information and workflows right into the conversation. Developers can also build low code Chat apps using AppSheet.

Moving image showing interactive Google Meet add-ons by partner Jira

There are already Chat apps available from partners like Atlassian’s Jira, Asana, PagerDuty and Zendesk. Jira for Google Chat to collaborate on projects, create issues, and update tickets – all without having to switch context.

Google Workspace UI-kit

We are continuing to evolve the Workspace UI-kit to provide a more seamless experience across Google Workspace surfaces with easy to use widgets and visual optimizations.

For example, there is a new date and time picker widget for Google Chat apps and there is the new two-column layout to optimize space and organize information.

Google Meet SDKs and APIs

There are exciting new capabilities which will soon be launched in preview for Google Meet.

For example, the Google Meet Live Sharing SDK allows for the building of new shared experiences for users on Android, iOS, and web. Developers will be able to synchronize media content across participant’s devices in real-time and offer shared content controls for everyone in the meeting.

The Google Meet Add-ons SDK enables developers to embed their app into Meet via an iframe, and choose between the main stage or the side panel. This integration can be published on the Google Workspace Marketplace for discoverability.

Partners such as Atlassian, Figma, Lucid Software, Miro and Polly.ai, are already building Meet add-ons, and we’re excited to see what apps and workflows developers will build into Meet’s highly-interactive surfaces.

Image of interactive Google Meet add-on by partner Miro

With the Google Meet APIs developers can add the power of Google Meet to their applications by pre-configuring and launching video calls right from their apps. Developers will also be able to pull data and artifacts such as attendance reporting, recordings, and transcripts to make them available for their users post-meeting.

Google Calendar API

The ability to programmatically read and write the working location from Calendar is now available in preview. In the second half of this year, we plan to make these two capabilities, along with the writing of sub-day working locations, generally available.

These new capabilities can be used for integrating with desk booking systems and coordinating in-offices days, to mention just a few use cases. This information will help organizations adapt their setup to meet the needs of hybrid work.

Google Workspace API Dashboard and APIs Explorer

Two new tools were released to assist developers: the Google Workspace API Dashboard and the APIs Explorer.

The API Dashboard is a unified way to access Google Workspace APIs through the Google Cloud Console—APIs for Gmail, Google Drive, Docs, Sheets, Chat, Slides, Calendar, and many more. From there, you now have a central location to manage all your Google Workspace APIs and view all of the aggregated metrics, quotas, credentials, and more for the APIs in use.

The APIs Explorer allows you to explore and test Google Workspace APIs without having to write any code. It's a great way to get familiar with the capabilities of the many Google Workspace APIs.

Apps Script

The eagerly awaited project history capability for Google Apps Script will soon be generally available. This feature allows users to view the list of versions created for the script, their content, and different changes between the selected version and the current version.

It was also announced that admins will be able to add an allowlist for URLs per domain to help safer access controls and control where their data can be sent externally.

The V8 runtime for Apps Script was launched back in 2020 and it enables developers to use modern JavaScript syntax and features. If you still have legacy scripts on the old Rhino runtime, now is the time to migrate them to V8.

AppSheet

We have been further improving AppSheet, our no-code solution builder, and announced multiple new features at I/O.

Later this year we will be launching Duet AI in AppSheet to make it easier than ever to create no-code apps for Google Workspace. Using a natural-language and conversational interface, users can build an app in AppSheet by simply describing their needs as a step-by-step conversation in chat.

Moving image of no-code app creation in AppSheet

The no-code Chat apps feature for AppSheet is generally available which can be used to quickly create Google Chat apps and publish them with 1-click.

AppSheet databases are also generally available. With this native database feature, you can organize data with structured columns and references directly in AppSheet.

Check out the Build a no-code app using the native AppSheet database and Add Chat to your AppSheet apps codelabs to get you started with these two new capabilities.

Google Workspace Marketplace

The Google Workspace Marketplace is where developers can distribute their Workspace integrations for users to find, install, and use. We launched the Intelligent Apps category which spotlights the AI-enabled apps developers build and helps users discover tools to work smarter and be more productive (eligibility criteria here).

Image of Intelligent Apps in Google Workspace

Start building today

If you want early access to the features in preview, sign up for the Developer Preview Program. Subscribe to the Google Workspace Developers YouTube channel for the latest news and video tutorials to kickstart your Workspace development journey.

We can’t wait to see what you will build on the Google Workspace platform.