Posted by Travis Webb
This blog post describes common pitfalls and antipatterns to consider when migrating your mainframe workloads. It also helps you to understand and avoid them. Migrating or modernizing your mainframe workloads is complex and challenging, even under ideal conditions. If you avoid the antipatterns discussed in this document, you increase the odds of a successful transformation.
This blog post is useful whether you're planning to migrate your mainframe workloads to Google Cloud, to on-premises virtual machines, or to another cloud provider. It demonstrates how to remedy certain mainframe migration antipatterns using technology offerings from Google. In principle, however, you could apply these remedies to many kinds of transformations with different target platforms and architectures.
This blog post describes three common antipatterns:
These approaches can work in some narrow circumstances when migrating mainframe workloads. Avoid them, however, because they have a high probability of failure. For each antipattern discussed, you are given an overview of the antipattern, the typical rationale used to justify it, and the business and technical reasons that lead to failure.
In a big bang rewrite, you or your team manually rewrite and re-architect the legacy mainframe code into a modern language using modern design patterns. For example, you might form a development team to build a new Java application that replicates the business logic from a collection of legacy COBOL programs. Senior engineers who are familiar with the system often teach junior engineers the rationale behind the business logic to preserve institutional knowledge. The result is a new codebase using new programming languages and new documentation on a new platform.
Of the three antipatterns discussed in this document, the big bang rewrite requires the largest investment of capital and time to achieve success. It is capital-intensive and time-intensive because most organizations can’t resist the temptation to re-engineer and to improve business logic.
Re-engineering your systems using modern technologies allows for future innovation. Your senior engineers are moving on—to management, competitors, or retirement—and you need to transfer institutional knowledge to incoming staff. You expect those incoming staffers to re-engineer the system using the latest programming best practices. These less experienced engineers can rewrite module by module, and take advantage of current development methodologies and tools. Because you have all the code, you have an exact specification for what the new software needs to do, and can test against it. Access to the original code lets you compress the decades of investment into your original mainframe software into a modern application. At the same time, you are transferring institutional knowledge from your senior engineers to your junior engineers. At the end of the process, you'll have a new system consisting of well-engineered software built against modern design patterns and best practices.
This case is compelling and can help to convince your IT decision-makers. Though the approach appears rational, there are hidden pitfalls and risks that your team doesn’t recognize at the outset. Risks like budget overruns, unanticipated complexity, and staff turnover can derail a significant rewrite before realizing the benefits. As a result, big bang rewrites rarely equal the best-case scenarios presented to stakeholders. Often, they fail.
Big bang rewrites often suffer from the second system effect. Early in the project, they fall behind in schedule and budget. While you quickly develop prototypes, getting them to function in the same way as the original code is a long-tail effort that most teams underestimate. This unanticipated setback leads to the first major decision point in your project: How do I overcome these challenges but still achieve the outcomes that I need to make the project successful?
The first option: Continue to diligently plod the long path and adhere exactly to the original functionality. However, matching the new system precisely to the original functionality always takes longer than expected. This is true because the original code provides little or no improvement in productivity over a conventional specification. That means a significant engineering investment to understand the original code and reproduce it.
The second option: Implement the business logic differently. However, changes in business logic necessarily require changes to the business processes and downstream systems on which the original business logic depends. For example, you could have a web application that depends on the idiosyncratic behavior of your mainframe applications. Rather than incorporate these idiosyncrasies into the new, rewritten application, it is tempting to simplify and improve this behavior. However, that adds scope to the project. The chain reaction of further changes that are required in downstream systems introduce additional risk and prolong the rewrite effort.
If your production mainframe system requires ongoing maintenance or updates during the rewrite, you can compound these problems. For example, you might have a rules engine that powers a billing system on your mainframe. To support a new product launch, you need to add a feature to the rules engine to accommodate a new customer billing type. You also need to implement this new type in the current system and replicate it in the new system—possibly after the billing component was rewritten and tested. This maintenance and update scenario can occur many times during a big bang rewrite, setting the project back at each step, and increasing the odds of failure.
Even for companies that have the tenacity to see through a multi-year transformation effort, the raw cost of a rewrite is often prohibitive. When compared to all other approaches, a big bang rewrite is the costliest way to modernize your mainframe software. Often it has the least convincing return on investment (ROI) when factoring in the risks, unanticipated costs, and delays.
A lift-and-shift migration is an established method of moving an application from one system to another with minimal changes and downtime. It's commonly used to migrate virtual machines running on commodity hardware to virtual machines in a public cloud. You can take a similar approach with your mainframe migration.
Mainframe platforms are based on proprietary hardware rather than x86-based commodity hardware. Therefore, you must emulate your mainframe environment on x86-based machines. Doing so is required to move your applications directly from the mainframe into the cloud, as you would with virtual machines. To run your applications in the emulated environment, you recompile them using a compiler provided by your emulation vendor.
Lift-and-shift migration is often seen as the quickest way to get from an on-premises environment to the cloud. You can apply this same thinking to mainframe workloads. Strategic IT decisions are often most palatable when facing a key transition, such as a hardware refresh. Mainframe hardware investments are capital-intensive. Financing the purchase often adds debt or lease liabilities to your company's balance sheet. By moving to the public cloud, mainframe workloads can scale both up and down to optimize resource use and operational cost. When compared to other migration or modernization options, you can make a strong business case that a lift-and-shift migration provides the quickest ROI and carries the lowest risk.
The business risks of a lift-and-shift migration appear small compared to other approaches, but the potential benefits are even smaller. The benefits of migrating off the mainframe platform to the cloud don’t materialize, because you remain locked into the same mainframe ecosystem, but now with an extra dependency on an emulation layer. That dependency can result in a new set of technical challenges. Challenges that are often unfamiliar to the teams maintaining the mainframe software. Unfamiliarity can lead to additional reliance on a new, single-vendor cloud ecosystem.
By not changing your mainframe software, you avoid solving many important problems: scarce and shrinking mainframe talent, a static ecosystem, a lack of agility, and an inability to innovate. You're now running your legacy workloads in the cloud, but remain locked out of cloud innovations due to your continued reliance on proprietary platforms.
In this antipattern, the cost benefits that you relied on to justify the investment don’t materialize. While you might spend less after combining your cloud infrastructure costs with your new, ongoing, emulation software license fees, your savings don’t justify the investment. The outcome is that you've taken all the risks inherent in any migration, but have realized few of the benefits, if any.
In an in-place modernization, you focus on improving the quality, maintainability, and testability of your software while keeping it on your mainframe computers. You might choose this antipattern because you see mainframes as part of your future and know that you must modernize your application software accordingly.
You can rewrite your application software to use modern languages that run on the mainframe, or you can re-architect it in place. For a partial cloud-like experience you can install orchestration technologies, like Kubernetes.
Mainframe software presents challenges related to maintainability, innovation, agility, and extensibility. By re-architecting and re-engineering this software to align with modern standards and design patterns, you can avoid many of the pitfalls that disrupt large replatforming efforts. Moving off the mainframe is the single largest risk. By avoiding that move, you can improve the odds that your project succeeds. Of all the mainframe modernization approaches you might consider, an in-place modernization appears to be the lowest risk. There's no migration component, so there's no risk of downtime.
There is an ecosystem of vendors offering tools to help with mainframe development using modern methodologies. Therefore, the risk of being left to support the software on your own is low. An in-place modernization often takes longer than a lift-and-shift migration or a code conversion. By modernizing slowly, however, you afford your teams the time they need to learn new development processes. When you re-engineer and re-architect the codebase, you can perform a more rational analysis to better understand whether the mainframe is the appropriate long-term platform.
An in-place modernization suffers from many of the same challenges as the big bang rewrite. Any approach involving manually updating your mainframe software can have budget and time constraints. These efforts also often suffer from the second-system effect. Performance and correctness issues inevitably arise because rewriting business logic in a new language requires extensive testing before it aligns with the previous functionality. When management learns more about the modest benefits gained by running updated software on the same mainframe platform, expect their willingness to see through such a drawn-out and costly transformation to wane.
The biggest issue with an in-place modernization is that the ideal outcome leaves you many of the same problems that you started with. The mainframe is more than a piece of hardware. Using mainframes encompasses a talent pool, a software platform, and a vendor ecosystem. The trend for each of these variables is moving in the wrong direction. Every year the talent pool shrinks, the software platform becomes more isolated, and the vendor ecosystem consolidates.
Google Cloud offers various options and resources for you to find the necessary help and support to best use Google Cloud services:
There are more resources to help you to migrate workloads to Google Cloud in the Google Cloud migration center.
For more information about these resources, see the finding help section of Migration to Google Cloud: Getting started.
Posted by Jason Scott, Head of Startup Developer Ecosystem, USA & Saurabh Sharma, Head of Assistant Investments
In December 2020, we announced our inaugural Google for Startups Accelerator: Voice AI program, a 10-week digital accelerator designed to help North American voice technology startups to take their businesses to the next level. Today, we are proud to announce our cohort of 12 companies - collectively leveraging voice user interfaces to solve complex challenges across accessibility, education, and care:
Babbly provides parents real-time insights on their child’s speech and language skills and recommends personalized activities that promote their child's development.
Bespoken is the leader in automated testing, training, and monitoring for voice applications and devices. If you can talk to it, Bespoken can test it!
conversationHEALTH enables conversational agents for patients and healthcare professionals in clinical trials, medical affairs, and commercial lines of business.
nēdl is democratizing access to the microphone by giving everyone their own live call-in radio station that transcribes, amplifies, and monetizes the audio creator's words as they speak.
OTO is building an acoustic engine capable of delivering non-semantic insights (intonation, emotions, laughter,etc.) from voice streams in real-time, on a small compute footprint.
Piffle is a voice gaming platform that aims to nurture professional wellness through conversational gameplay.
Powow is a SaaS platform which unleashes the power of AI in business meetings. Powow uses proprietary AI algorithms to transcribe and analyze meetings, transforming them into actionable insights.
SiMBi combines learners' narrations with the text of a story to create an engaging audiovisual book that learners worldwide can read along to.
Talkatoo is a dictation software explicitly designed for veterinary and medical professionals, enabling them to save time in their practice.
tinychef is a voice-first Culinary AI™ platform that helps consumers in their kitchen from their dinner dilemma, to grocery planning, grocery shopping, and cooking their meals with interactive experiences on smart speakers.
Voicify’s SaaS platform allows brands and large enterprises to easily design, build, and deploy voice apps, chatbots, and other conversational experiences across voice assistants, chatbots, and social media platforms.
Vowel brings the best of productivity and communication platforms into a single, integrated meeting tool.
The program kicks off on Monday, March 15th and will focus on product design, technical infrastructure, customer acquisition, and leadership development - granting our founders access to an expansive network of mentors, senior executives, and industry leaders,
We are incredibly excited to support this group of entrepreneurs over the next three months, connecting them with the best of our people, products, and programming to advance their companies and solutions.
We look forward to augmenting the work of these 12 innovators and to showcasing their accomplishments on Thursday, May 20th at 12:30pm EST at our Google for Startups Accelerator: Voice AI Demo Day.
Posted by Jermaine Robinson, Google Registry Team
It’s been two years since the Google Registry team launched its #MyDomain video series, which highlights creators in tech. While we’re proud of the initiatives we’ve featured so far, we want to do a better job of representing all voices. In honor of Black History Month, we’re featuring six Black creators who are making waves in the digital space.
Dairien Boyd, #MyDomain Video
Dairien Boyd is a founding member and principal designer at All Turtles, a mission-driven product studio. He’s responsible for building experiences that are both fun and useful within mmhmm.app — a project born out of the COVID-19 pandemic. The new reality of working remotely set Dairien and his team on a path to design a better way to deliver presentations — one that works in an all-video conferencing world. They created a powerful presentation tool that provides immersive backgrounds and visual effects to help add a bit of fun to virtual meetings.
Benjamin Williams, #MyDomain Video
Benjamin Williams also found new sources of inspiration during the pandemic. A software engineer at Google by day, Williams launched floward.app — a journaling and creative writing application that encourages “imperfection” — as a way to cope with the challenges and stresses that come from being stuck at home. By providing daily thought-provoking prompts, users can get their thoughts down on “paper” within a simple UI that intentionally prevents going back and making revisions; this way, they stay in the flow of writing instead of fixating on what they’ve already written.
Rhianna Jones, #MyDomain Video
A writer and model by day, Rhianna Jones started a campaign for “Afrovisibility” as a true passion project. Her campaign, which pushes for more widespread adoption of natural hair emojis within universal keyboards (including Android and iOS), went viral. It wasn’t long before her domain — afrohairmatters.page — helped Jones connect with industry leaders. “The opportunity to collaborate only helps the culture move forward in a direction that better represents the rainbow of tech users,” Jones says. While it might seem small to some, the addition of natural hair emojis is a major step towards promoting Afrovisibility in everyone’s daily digital language and lives — especially for a younger generation that is all about ✊🏿 🤗 👩🏾 💕.
Michael Broughton, #MyDomain Video
Michael Broughton, CEO of Perch, launched his credit-building app after getting denied a loan to cover the remainder of his college tuition while attending the University of Southern California. “I was told to get a credit card in order to build credit, but when I applied for a credit card, they said I needed to build my credit score first,” he says. “This made me realize how difficult it can be for individuals to develop their personal finances without already having a foot in the door.” Instead of feeling defeated, he channeled his frustrations into launching getperch.app, a service that helps others build credit history and boost their credit scores.
Edward Cunningham, #MyDomain Video
Edward Cunningham is cofounder and CTO of NXSTEP.app, a platform that allows high-school seniors to connect with current college students to get deeper insights into life within the walls of various academic institutions. By connecting with currently-enrolled college students, seniors can better determine the right college for them. It’s like matchmaking for higher education: helping students decide on their future alma mater based on personality, interests, and goals.
Adesina Tyler, #MyDomain Video
Adesina Tyler is our youngest creator in this month’s #MyDomain series. Tyler is a junior in high school, juggling the complexities that come with distance learning, schoolwork and extracurricular activities. As busy as he’s been, he somehow found the time to launch wondershop.page as part of his participation in Google’s technology program, Code Next. He built his website (an online retail store) as a way to better understand the basic building blocks of e-commerce.
Videos of everyone featured above are available at goo.gle/mydomain. Ensuring proper representation of all groups is crucial for everyone in tech. We all benefit and learn from hearing the full spectrum of voices — especially the voices of those who’ve been underrepresented for far too long.
We want to actively do our part in moving the industry in the right direction by celebrating all entrepreneurs, founders and creators. If you have a unique story to share about an .app. ,dev, or .page domain and would like to be considered for our next series, please fill out this short application form and help us produce and share content that better represents all of us in an industry that still has a long way to go.
Posted by Miguel Guevara, Product Manager, Privacy and Data Protection Office
At Google, we believe that innovation and privacy must go hand in hand. Earlier this month, we shared our work to keep people safe online, including our investments in leading privacy technologies such as differential privacy. Today, on Data Privacy Day, we want to share some updates on new ways we’re applying differential privacy technologies in our own products and making it more accessible to developers and businesses globally—providing them with greater access to data and insights while keeping people’s personal information private and secure.
Strengthening our core products with differential privacy
We first deployed our world-class differential privacy anonymization technology in Chrome nearly seven years ago and are continually expanding its use across our products including Google Maps and the Assistant. And as the world combats COVID-19, last year we published our COVID-19 Community Mobility Reports, which uses differential privacy to help public health officials, economists and policymakers globally as they make critical decisions for their communities while ensuring no personally identifiable information is made available at any point.
This year in the Google Play console, we’ll provide new app metrics and benchmarks to developers in a differentially private manner. When launched, developers will be able to easily access metrics related to how successfully their apps are engaging their users, such as Daily Active Users and Revenue per Active user, in a manner that helps ensure individual users cannot be identified or re-identified. By adding differential privacy to these new app metrics, we’ll provide meaningful insights to help developers improve their apps without compromising people’s privacy, or developer confidentiality. Moving forward, we plan to expand the number of metrics we provide to developers using differential privacy.
As we have in the last year, we’ll continue to make our existing differential privacy library even easier for developers to use. For example, this month we’re open sourcing a new differentially private SQL database query language extension that is used in thousands of queries done every day at Google. These queries help our analysts obtain business insights, and observe product trends. This is a step forward in democratizing privacy safe data analysis, empowering data scientists around the world to uncover powerful insights while protecting and respecting the privacy of individuals.
Partnering with OpenMined to make differential privacy more widely accessible
As we continue to make advancements with privacy-preserving technologies in our own products, it’s also important to us that developers have access to this technology. That’s why in 2019, we open-sourced our differential privacy library and made it freely accessible, easy to deploy and useful to developers globally. Since then, hundreds of developers, researchers and institutions have incorporated Google’s differential privacy algorithms into their work, enabling them to tackle new problems while using data in a responsible and privacy protective way. One of these companies is French healthcare startup Arkhn. For Arkhn, differential privacy is making it possible to pursue its mission to revolutionize the healthcare industry with artificial intelligence, enabling them to gather, query and analyze cross-department hospital data in a secure, and safe way.
To help bring our world class differential privacy library to more developer teams, like the one at Arkhn, today we’re excited to announce a new partnership with OpenMined, a group of open-source developers that is focused on taking privacy preserving technologies and expanding their usage around the world. Together with OpenMined, we will develop a version of our differential privacy library specifically for python developers. By replicating Google’s differentially private infrastructure, Python developers will have access to a new and unique way to treat their data with world-class privacy.
A collaborative approach to improving the state of privacy in Machine Learning
Two years ago, we introduced TensorFlow Privacy (GitHub), an open source library that makes it easier not only for developers to train machine-learning models with privacy, but also for researchers to advance the state of the art in machine learning with strong privacy guarantees. In the past year, we've expanded the library to include support for TensorFlow 2, as well as both the Keras Model interface and TensorFlow's premade estimators. Thanks to a collaboration with researchers from University of Waterloo, we’ve improved performance, with our new release making it four times faster or more to train on common workloads.
We also recognize that training with privacy might be expensive, or not feasible. So we set out to understand how private machine learning models are. Last year we open-sourced our attack library to help address this and help anyone using the library get a broader privacy picture of their machine models. Since then, we partnered with researchers at Princeton University, and the National University of Singapore who have added new features that expand the library’s scope to test generative models and non-neural network models. Recently, researchers at Stanford Medical School tried it on some of their models, to test for memorization. This testing helped them understand the privacy behavior of their models, something that wasn’t possible beforehand.
We’ve also published new research studying the trade-offs between differential privacy and robustness, another property at the core of AI ethics, privacy and safety.
Our work continues as we invest in world-class-privacy that provides algorithmic protections to the people who use our products while nurturing and expanding a healthy open-source ecosystem. We strongly believe that everyone globally deserves world-class privacy, and we’ll continue partnering with organizations to fulfill that mission.
Posted by Nikita Gandhi (Community Manager, GDG India), Nilay Yener (Program Manager, Flutter DevRel)
Happy New Year folks. It’s the perfect time of year to learn something new! Do you have an app idea you’ve been dreaming of over the holidays? If so, we have just the opportunity for you! Starting February 1st, leading up to our big event on March 3rd, join us for #30DaysOfFlutter to kickstart your learning journey and meet Flutter experts in the community. Whether you are building your first Flutter app or looking to improve your Flutter skills, we have curated content, code labs, and demos!
Flutter is Google’s open source UI toolkit for building beautiful, natively compiled applications for mobile, web, and desktop from a single codebase. It’s one of the fastest growing, most in-demand cross platform frameworks to learn and is used by freelance developers and large organizations around the world. Flutter uses the Dart language, so it will feel natural to many of you familiar with object-oriented languages.
Along with the curated content, we will also have four live AskMeAnything sessions (#AMAs), where you can meet members of Google’s Flutter team and community. You can also join us on the FlutterDev Discord channel, where you can meet the other members of the community, ask and answer questions, and maybe make some new Flutter friends too!
Does this sound exciting? Visit the 30 Days of Flutter website to get more information and to register to join.
Your learning journey with Flutter for the month will look like this::
Week 1
Receive curated content to your inbox. Meet other Flutter Devs on Discord. Attend Kick Off Webinar on February 1st.
Week 2
Receive more content. Start building your first Flutter app. Join the webinar and ask your questions.
Week 3
Work on your app and attend the 3rd webinar to ask your questions.
Week 4
Complete your project and learn how to share it with the Flutter community.
Are you ready to learn one of the most in demand developer skills in the world?
Sign up to be a part of the journey and be sure to follow @FlutterDev on Twitter, to get updates about #30DaysOfFlutter.
Posted by Christina Yeh, Google Registry Team
Google Registry is always on the lookout for interesting websites that have launched using our top-level domains. 2020 was a rough year, so to help you make 2021 (at least a little bit) better, we’ve rounded up 21 ways you can start something .new, get .appy, turn a new .page, and make .dev(elopment) a breeze.
Start something .new:
Get .app(y):
Turn to the next .page:
Make .dev(elopment) a breeze:
Happy New Year from all of us at Google Registry! We hope these websites and apps help you get the most out of 2021.
Posted by Kübra Zengin, GDG North America Regional Lead
Image of participants in a recent Elevate workshop.
The North America Developer Ecosystem team recently hosted Elevate for Google Developer Groups organizers and Women Techmakers Ambassadors in US & Canada. The three-month professional development program met every Wednesday via Google Meet to help tech professionals upskill themselves with workshops on leadership, communication, thinking, and teamwork.
The first cohort of the seminar-style program recently came to a close, with 40+ Google Developer Groups organizers and Women Techmakers Ambassadors participating. Additionally, 18 guest speakers - 89% of whom were underrepresented genders - hosted specialized learning sessions over three months of events.
Elevate is just one example of the specialized applied skills training available to the Google Developer Groups community. As we look ahead to offering Elevate again in 2021, we wanted to share with you some of the key takeaways from the first installment of the program.
What the graduates had to say
From landing new roles at companies like Twitter and Accenture, to negotiating salary raises, the 40 graduates of Elevate have seen many successes. Here’s what a few of them had to say:
Whether it’s finding new jobs or moving to new countries, Elevate’s graduates have used their new skills to guide their careers towards their passions. Check out a few of the program’s key lessons below:
Bringing your best self to the table
One major focus of the program was to help community leaders develop their own professional identity and confidence by learning communication techniques that would help them stand out and define themselves in the workplace.
Entire learning sessions were dedicated to specific value-adding topics, including:
Along with other sessions on growth mindsets, problem solving, and more, attendees gained a deeper understanding of the best ways to present themselves, their ideas, and their worth in a professional setting - an essential ability that many feel has already helped them navigate job markets with more precision.
A team that feels valued brings value
The advice above, offered by a guest speaker during a teambuilding session, was one of the quotes that resonated with participants the most during the program. The emphasis on how coworkers think of each other and the best ways to build a culture of ownership over a team’s wins and losses embodies the key learnings central to Elevate’s mission.
The program further emphasized this message with learning sessions on:
With these trainings, paired with others on growth mindsets and decision making, Elevate’s participants were able to start analyzing the effectiveness of different work environments on productivity. Through breakout sessions, they quickly realized that the more secure and supported an employee feels, the more willing they are to go the extra mile for their team. Equipped with this new knowledge base, many participants have already started bringing these key takeaways to their own workplaces in an effort to build more inclusive and productive cultures.
Whether it’s finding a new role or improving your applied skills, we can’t wait to see how Google Developer programs can help members achieve their professional goals.
For similar opportunities, find out how to join a Google Developer Group near you, here. And register for upcoming applied skills trainings on the Elevate website, here.
Posted by Erica Hanson, Global Program Manager, Google Developer Student Clubs
Created by the United Nations in 2015 to be achieved by 2030, the 17 Sustainable Development Goals (SDGs) agreed upon by all 193 United Nations Member States aim to end poverty, ensure prosperity, and protect the planet.
Last year brought many challenges, but it also brought a greater spirit around helping each other and giving back to our communities. With that in mind, we invite students around the world to join the Google Developer Student Clubs 2021 Solution Challenge!
If you’re new to the Solution Challenge, it is an annual competition that invites university students to develop solutions for real world problems using one or more Google products or platforms.
This year, see how you can use Android, TensorFlow, Google Cloud, Flutter, or any of your favorite Google technologies to promote employment for all, economic growth, and climate action, by building a solution for one or more of the UN Sustainable Development Goals.
Participants will receive specialized prizes at different stages:
There are four main steps to joining the Solution Challenge and getting started on your project:
Google will provide Solution Challenge participants with various resources to help students build strong projects for their contest submission.
Once all the projects are submitted after the March 31st deadline, judges will evaluate and score each submission from around the world using the criteria listed on the website. From there, winning solutions will be announced in three rounds.
Round 1 (May): The Top 50 teams will be announced.
Round 2 (July): After the top 50 teams submit their new and improved solutions, 10 finalists will be announced.
Round 3 (August): In the finale, the top 3 grand prize winners will be announced live on YouTube during the 2021 Solution Challenge Demo Day.
With a passion for building a better world, savvy coding skills, and a little help from Google technology, we can’t wait to see the solutions students create.
Learn more and sign up for the 2021 Solution Challenge, here.
Posted by Toni Klopfenstein, Developer Advocate
When a user connects a smart device to the Google Assistant via the Home app, the user must select the appropriate related Action from the list of all available Actions. The user then clicks through multiple screens to complete their device setup. Today, we're releasing two new features to improve this device discovery process and drive customer adoption of your Smart Home Action through the Google Home app. App Discovery and Deep Linking are two convenience features that help users find your Google-Assistant compatible smart devices quickly and onboard faster.
App Discovery enables users to quickly find your smart home Action thanks to suggestion chips within the Google Home app. You can implement this new feature through the Actions Console by creating a verified brand link between your Action, your website, and your mobile app. App Discovery doesn't require any coding work to implement, making this a development-light feature that provides great improvements to the user experience of device linking.
In addition to helping users discover your Action directly through suggestion chips, Deep Linking enables you to guide users to your account linking flow within the Google Home app in one step. These deep links are easily added to your mobile app or web content, guiding users to your smart home integration with a single tap.
Deep Linking and App Discovery can help you create a more streamlined onboarding experience for your users, driving increased engagement and user satisfaction, and can be implemented with minimal engineering work.
To implement App Discovery and Deep Linking for your Smart Home Action, check out the developer documents, or watch the video covering these new features.
You can also check out the smart home codelabs if you are just starting to build out your Action.
We want to hear from you, so continue sharing your feedback with us through the issue tracker, and engage with other smart home developers in the /r/GoogleAssistantDev community. Follow @ActionsOnGoogle on Twitter for more of our team's updates, and tweet using #AoGDevs to share what you’re working on. We can’t wait to see what you build!
Posted by Jason Scott, Head of Startup Developer Ecosystem, U.S., Google
At Google, we have long understood that voice user interfaces can help millions of people accomplish their goals more effectively. Our journey in voice began in 2008 with Voice Search -- with notable milestones since, such as building our first deep neural network in 2012, our first sequence-to-sequence network in 2015, launching Google Assistant in 2016, and processing speech fully on device in 2019. These building blocks have enabled the unique voice experiences across Google products that our users rely on everyday.
Voice AI startups play a key role in helping build and deliver innovative voice-enabled experiences to users. And, Google is committed to helping tech startups deliver high impact solutions in the voice space. This month, we are excited to announce the Google for Startups Accelerator: Voice AI program, which will bring together the best of Google’s programs, products, people and technology with a joint mission to advance and support the most promising voice-enabled AI startups across North America.
As part of this Google for Startups Accelerator, selected startups will be paired with experts to help tackle the top technical challenges facing their startup. With an emphasis on product development and machine learning, founders will connect with voice technology and AI/ML experts from across Google to take their innovative solutions to the next level.
We are proud to launch our first ever Google for Startups Accelerator: Voice AI -- building upon Google’s longstanding efforts to advance the future of voice-based computing. The accelerator will kick off in March 2021, bringing together a cohort of 10 to 12 innovative voice technology startups. If this sounds like your startup, we'd love to hear from you. Applications are open until January 28, 2021.
