Everything to know about the new, rich Apple Watch complications on Series 4

Apple’s new watch brings a pair of faces specifically designed for the new complication families. Learn about the engaging, full-color complication on Series 4 and how they take full advantage of the new, rounder, fifteen percent larger display.

Apple Watch complications on Series 4: the hero image

Complications—small elements rendered on the Apple Watch face that provide quick access to frequently-used data—have received a boost on Series 4: all-new templates now let brand new elements, such as full-color images, text and gauges, follow curvature of the display.

Complications are a great way to connect users to their favorite apps with every wrist raise and keep them informed throughout their day. Tapping a complication launches its underlying app.

Complications are available for Apple’s own and third-party apps.

Apple Watch complications on Series 4: another example image

Complications put glanceable information snippets on the watch face

In an app called Dexcom, the complication allows for continuous glucose monitoring. Another example: the Streaks complication shows daily progress on tasks on the watch face.

With Series 4, Apple’s added improvements to its underlying APIs to let app makers create full-color complications for the new Infograph and the Infograph Modular faces on Series 4.

These dense new Series 4 complications are informative and way more precise. With support for the larger Series 4 screen, new templates let third-party makers offer a more detailed view of their apps from the watch face.

ClockKit gets a major boost

Apple Watch complications are realized through ClockKit.

ClockKit is a software framework in watchOS 2.0 and higher designed to support displaying apps data on your watch face. In watchmaking, these things are called complications.

ClockKit now supports enriched Series 4 complications so app makers can write compilations that take full advantage of the rounded display.

Apple Watch complications on Series 4 are much richer and denser

The new Series 4 complications work on the Infograph and Infograph Modular faces

ClockKit includes templates for laying out complication styles on the watch face that support custom arrangements of text, images, completion rings and other elements. Coupled with data providers, these templates let complications visualize formatted content on a small segment of the watch face.

In other words, data providers bring the data to the complication to display on the watch face while templates format the data appropriately for the different watch face designs.

Modular faces

As mentioned, the new and enhanced complication families available on Series 4 are only available through either the Infograph or the Infograph Modular face. Other watch faces can display old school complication styles, but not new ones like curly gauges.

Apple Watch complications on Series 4 include curly template allowing them to fit the rounded corners of the OLED display

Rounded complications are exclusive to Series 4 watches

In addition to the seven existing complication types—Modular Small, Modular Large, Utilitarian Small, Utilitarian Small Flat, Utilitarian Large, Circular Small and Extra Large—Series 4 adds four new families that determine how information is displayed onscreen:

  • Graphic Corner
  • Graphic Circular
  • Graphic Bezel
  • Graphic Rectangular

thanks to the updated system-provided templates on Series 4, these new complications now support features like extending into the corners of the display, presenting information in full color, using images in the middle of the watch face and so forth. Conveniently, ClockKit has gained a few new data providers that support the new complication families with modern new elements like gauges, images and so forth.

Colorful gauges

Gauges are progress bars used for illustrating progress completed or a value within a range. A time-interval gauge, such as that in the Timer complication, would automatically animate the values as they change.

Gauges can be single-color or use a custom gradient.

Apple Watch complications on Series 4 include many colorful gauges

The system uses your values and ranges to render the gradients

Either way, color can be set to fill the gauge as the data progresses. If the data is a range, it uses a ring to indicate the value. This is useful for illustrating progress on complications focused on weather, fitness, health and more.

Full-color images & text

Series 4 complications can also display full-color images on the watch face, which ups the visuals a few notches. On older models, complications were limited to monochrome images.

And with text providers, complications can display several text-based elements, including the current date and time, as well as time ranges and automatic count-downs/ups between two dates/times. Multiple text providers can be chained together easily, and each can have its own tint color.

Apple Watch complications on Series 4 can use differently-tinted text for visual or even branding purposes

Text data can be formatted with color

This is useful for creating differently-colored strings that a complication might want to use for branding purposes (i.e. to display the precise numerical values of your Activity rings).

Putting it all to work

It’s time we illustrate some of the creative possibilities afforded by the new system templates.

Graphic Corner

Available on the Infograph face, this one curves along the corners of the display, allowing for way more content while making stunning use of the rounded Series 4 display.

Apple Watch complications on Series 4 include rounded gauges accompanied by text or images

New Series 4 complications account for the round display corners

Graphic Circular

Available on both the Infograph and Infograph Modular watch faces, this template gives a complication the ability to combine gauges and text. It typically shows gauges at the bottom of the display, with handy values at the end of the range included in the gauge.

Apple Watch complications on Series 4 support circular gauges, open or closed, at the bottom of the watch face

Open gauges can have text, values or images at their bottom

As the image right below attest, a closed gauge is also possible. These gauges can use full-color images or text in the middle, or even an image across the entire complication area.

Apple Watch complications on Series 4 even support displaying a full-color image across the entire complication area

Closed gauges can be styled with text, images and so forth

Graphic Bezel

Available only on the Infograph watch face, this template does something clever: it wraps a custom UPPERCASE text within the time bezel. Seeing text information like weather forecast curved along the time bezel looks really neat, as evidenced by the screenshot.

Apple Watch complications on Series 4 permit the text to flow along the time bezel

Complication text can fill 180 degrees of the time bezel before it’s truncated

Graphic Rectangular

Lastly, the Graphic Rectangular template allows you to use highly visual, full-color images in the center of the watch face. These can be used for anything from displaying bio images of the user’s favorite contacts to showing a more detailed heart rate graph and beyond.

Apple Watch complications on Series 4 support displaying images in the center of the watch face

The main image can be coupled with gauges and text for added effect.

Apple Watch complications on Series 4 can also display rich text information in place of an image at the center of the watch face

This template is available exclusively on the Infograph Modular watch face.

Wrapping it all up

These new Series 4 complications available on the Infograph and Infograph Modular watch faces are updated with the same frequency as your old complications on previous watches, meaning 50 guaranteed push updates every 24 hours.

If the app containing a complication uses the Background App Refresh feature (managed through My Watch → General → Background App Refresh in the companion Watch app), or is in your recently-used list in the Dock, it can update the complication once every hour.

The only potential downside of the new complications on Series 4: the system templates that allow for image-heavy or curly layouts, like Graphic Circular or Graphic Rectangular, may tax the CPU and affect battery life.

Apple Watch complications on Series 4 : an example image showing circular contact images at 3, 6, 9 o'clock and midnight

As a result, and this is pure speculation on our part, stuffing your watch face with a bunch of curly complications in the corners might affect battery life.

And last but certainly not least—in appeasing visually-impaired users, Apple has permitted developers to boost their complications with VoiceOver. This is especially important if a complication only uses an image and no accompanying text that VoiceOver could read.

To learn more about how the new Infograph and Infograph Modular watch faces on Series 4 allow for all new ways to create engaging, full-color complications, be sure to watch Apple’s Tech Talks developer video, titled “Developing Complications for Apple Watch Series 4”.

Complicated?

We are very eager to hear your opinion.

These new complications with their assorted templates really do take the best advantage of that gorgeous OLED screen on Series 4, coming alive with color, curves and images.

To reiterate, only the new Infograph and Infograph Modular watch faces support the new complication families brought by Apple Watch Series 4. You cannot get them on older watches as they lack bigger screens to support Series 4’s much denser complications.

Tick-tock, hit us in comments!


"Everything to know about the new, rich Apple Watch complications on Series 4" is an article by iDownloadBlog.com.
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New iPhones can read NFC tags in the background, no app needed whatsoever

The new iPhones feature updated hardware for NFC which supports background tag reading. This permits the new phones to scan for and reads NFC tags without requiring users to launch an app for that beforehand.

The new iPhone Xs and Xs Max, as well as a colorful family of the new iPhone Xr handsets, include advancements in near-field communication (NFC) technology that allow the handsets to automatically read nearby NDEF (NFC Data Exchange Format) tags and send any collected assets to appropriate apps without needing to open any specific app beforehand.... Read the rest of this post here


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Microsoft acquires Lobe, a drag-and-drop AI tool

Microsoft today announced that is has acquired Lobe, a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into beta earlier this year, to build upon its own efforts to make building AI models easier, though, for the time being, […]

Microsoft today announced that is has acquired Lobe, a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into beta earlier this year, to build upon its own efforts to make building AI models easier, though, for the time being, Lobe will operate as before.

“As part of Microsoft, Lobe will be able to leverage world-class AI research, global infrastructure, and decades of experience building developer tools,” the team writes. “We plan to continue developing Lobe as a standalone service, supporting open source standards and multiple platforms.”

Lobe was co-founded by Mike Matas, who previously worked on the iPhone and iPad, as well as Facebook’s Paper and Instant Articles products. The other co-founders are Adam Menges and Markus Beissinger.

In addition to Lobe, Microsoft also recently bought Bonsai.ai, a deep reinforcement learning platform, and Semantic Machines, a conversational AI platform. Last year, it acquired Disrupt Battlefield participant Maluuba. It’s no secret that machine learning talent is hard to come by, so it’s no surprise that all of the major tech firms are acquiring as much talent and technology as they can.

“In many ways though, we’re only just beginning to tap into the full potential AI can provide,” Microsoft’s EVP and CTO Kevin Scott writes in today’s announcement. “This in large part is because AI development and building deep learning models are slow and complex processes even for experienced data scientists and developers. To date, many people have been at a disadvantage when it comes to accessing AI, and we’re committed to changing that.”

It’s worth noting that Lobe’s approach complements Microsoft’s existing Azure ML Studio platform, which also offers a drag-and-drop interface for building machine learning models, though with a more utilitarian design than the slick interface that the Lobe team built. Both Lobe and Azure ML Studio aim to make machine learning easy to use for anybody, without having to know the ins and outs of TensorFlow, Keras or PyTorch. Those approaches always come with some limitations, but just like low-code tools, they do serve a purpose and work well enough for many use cases.

Microsoft acquires Lobe, a drag-and-drop AI tool

Microsoft today announced that is has acquired Lobe, a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into beta earlier this year, to build upon its own efforts to make building AI models easier, though, for the time being, […]

Microsoft today announced that is has acquired Lobe, a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into beta earlier this year, to build upon its own efforts to make building AI models easier, though, for the time being, Lobe will operate as before.

“As part of Microsoft, Lobe will be able to leverage world-class AI research, global infrastructure, and decades of experience building developer tools,” the team writes. “We plan to continue developing Lobe as a standalone service, supporting open source standards and multiple platforms.”

Lobe was co-founded by Mike Matas, who previously worked on the iPhone and iPad, as well as Facebook’s Paper and Instant Articles products. The other co-founders are Adam Menges and Markus Beissinger.

In addition to Lobe, Microsoft also recently bought Bonsai.ai, a deep reinforcement learning platform, and Semantic Machines, a conversational AI platform. Last year, it acquired Disrupt Battlefield participant Maluuba. It’s no secret that machine learning talent is hard to come by, so it’s no surprise that all of the major tech firms are acquiring as much talent and technology as they can.

“In many ways though, we’re only just beginning to tap into the full potential AI can provide,” Microsoft’s EVP and CTO Kevin Scott writes in today’s announcement. “This in large part is because AI development and building deep learning models are slow and complex processes even for experienced data scientists and developers. To date, many people have been at a disadvantage when it comes to accessing AI, and we’re committed to changing that.”

It’s worth noting that Lobe’s approach complements Microsoft’s existing Azure ML Studio platform, which also offers a drag-and-drop interface for building machine learning models, though with a more utilitarian design than the slick interface that the Lobe team built. Both Lobe and Azure ML Studio aim to make machine learning easy to use for anybody, without having to know the ins and outs of TensorFlow, Keras or PyTorch. Those approaches always come with some limitations, but just like low-code tools, they do serve a purpose and work well enough for many use cases.

Facebook’s new ‘SapFix’ AI automatically debugs your code

Facebook has quietly built and deployed an artificial intelligence programming tool called SapFix that scans code, automatically identifies bugs, tests different patches and suggests the best ones that engineers can choose to implement. Revealed today at Facebook’s @Scale engineering conference, SapFix is already running on Facebook’s massive code base and the company plans to eventually […]

Facebook has quietly built and deployed an artificial intelligence programming tool called SapFix that scans code, automatically identifies bugs, tests different patches and suggests the best ones that engineers can choose to implement. Revealed today at Facebook’s @Scale engineering conference, SapFix is already running on Facebook’s massive code base and the company plans to eventually share it with the developer community.

“To our knowledge, this marks the first time that a machine-generated fix — with automated end-to-end testing and repair — has been deployed into a codebase of Facebook’s scale,” writes Facebook’s developer tool team. “It’s an important milestone for AI hybrids and offers further evidence that search-based software engineering can reduce friction in software development.” SapFix can run with or without Sapienz, Facebook’s previous automated bug spotter. It uses it in conjunction with SapFix, suggesting solutions to problems Sapienz discovers.

These types of tools could allow smaller teams to build more powerful products, or let big corporations save a ton on wasted engineering time. That’s critical for Facebook as it has so many other problems to worry about.

Glow AI hardware partners

Meanwhile, Facebook is pressing forward with its strategy of reorienting the computing hardware ecosystem around its own machine learning software. Today it announced that its Glow compiler for machine learning hardware acceleration has signed up the top silicon manufacturers, like Cadence, Esperanto, Intel, Marvell, and Qualcomm, to support Glow. The plan mirrors Facebook’s Open Compute Project for open sourcing server designs and Telecom Infra Project for connectivity technology.

Glow works with a wide array of machine learning frameworks and hardware accelerators to speed up how they perform deep learning processes. It was open sourced earlier this year at Facebook’s F8 conference.

“Hardware accelerators are specialized to solve the task of machine learning execution. They typically contain a large number of execution units, on-chip memory banks, and application-specific circuits that make the execution of ML workloads very efficient,” Facebook’s team writes. “To execute machine learning programs on specialized hardware, compilers are used to orchestrate the different parts and make them work together . . . Hardware partners that use Glow can reduce the time it takes to bring their product to market.”

Essentially, Facebook needs help in the silicon department. Instead of isolating itself and building its own chips like Apple and Google, it’s effectively outsourcing the hardware development to the experts. That means it might forego a competitive advantage from this infrastructure, but it also allows it to save money and focus on its core strengths.

The technologies aside, the Scale conference was evidence that Facebook will keep hacking, policy scandals be damned. There was nary a mention of Cambridge Analytica or election interference as a packed room of engineers chuckled to nerdy jokes during keynotes packed with enough coding jargon to make the unindoctrinated assume it was in another language. If Facebook is burning, you couldn’t tell from here.

 

Starting March 2019, iPhone & Watch apps must support Xs Max and Series 4 hardware

Starting March 2019, all new iPhone and universal iOS apps and their updates must be built with the iOS 12 SDK and support the new iPhone XS Max, Apple’s highest-resolution iPhone yet. All new watch apps and updates will need to be built with the watchOS 5 SDK and support the new Apple Watch Series 4.

To increase support for the latest screen sizes and technologies, Apple will soon stop accepting iPhone apps that don’t take full advantage the native display resolution of iPhone Xs Max. Likewise, Apple Watch apps must support the new Apple Watch Series 4 hardware.... Read the rest of this post here


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Nvidia launches the Tesla T4, its fastest data center inferencing platform yet

Nvidia today announced its new GPU for machine learning and inferencing in the data center. The new Tesla T4 GPUs (where the ‘T’ stands for Nvidia’s new Turing architecture) are the successors to the current batch of P4 GPUs that virtually every major cloud computing provider now offers. Google, Nvidia said, will be among the […]

Nvidia today announced its new GPU for machine learning and inferencing in the data center. The new Tesla T4 GPUs (where the ‘T’ stands for Nvidia’s new Turing architecture) are the successors to the current batch of P4 GPUs that virtually every major cloud computing provider now offers. Google, Nvidia said, will be among the first to bring the new T4 GPUs to its Cloud Platform.

Nvidia argues that the T4s are significantly faster than the P4s. For language inferencing, for example, the T4 is 34 times faster than using a CPU and more than 3.5 times faster than the P4. Peak performance for the P4 is 260 TOPS for 4-bit integer operations and 65 TOPS for floating point operations. The T4 sits on a standard low-profile 75 watt PCI-e card.

What’s most important, though, is that Nvidia designed these chips specifically for AI inferencing. “What makes Tesla T4 such an efficient GPU for inferencing is the new Turing tensor core,” said Ian Buck, Nvidia’s VP and GM of its Tesla data center business. “[Nvidia CEO] Jensen [Huang] already talked about the Tensor core and what it can do for gaming and rendering and for AI, but for inferencing — that’s what it’s designed for.” In total, the chip features 320 Turing Tensor cores and 2,560 CUDA cores.

In addition to the new chip, Nvidia is also launching a refresh of its TensorRT software for optimizing deep learning models. This new version also includes the TensorRT inference server, a fully containerized microservice for data center inferencing that plugs seamlessly into an existing Kubernetes infrastructure.

 

 

Twilio’s contact center products just got more analytical with Ytica acquisition

Twilio, a company best known for supplying a communications APIs for developers has a product called Twilio Flex for building sophisticated customer service applications on top of Twilio’s APIs. Today, it announced it was acquiring Ytica (pronounced Why-tica) to provide an operational and analytical layer on top of the customer service solution. The companies would […]

Twilio, a company best known for supplying a communications APIs for developers has a product called Twilio Flex for building sophisticated customer service applications on top of Twilio’s APIs. Today, it announced it was acquiring Ytica (pronounced Why-tica) to provide an operational and analytical layer on top of the customer service solution.

The companies would not discuss the purchase price, but Twilio indicated it does not expect the acquisition to have a material impact on its “results, operations or financial condition.” In other words, it probably didn’t cost much.

Ytica, which is based in Prague, has actually been a partner with Twilio for some time, so coming together in this fashion really made a lot of sense, especially as Twilio has been developing Flex.

Twilio Flex is an app platform for contact centers, which offers a full stack of applications and allows users to deliver customer support over multiple channels, Al Cook, general manager of Twilio Flex explained. “Flex deploys like SaaS, but because it’s built on top of APIs, you can reach in and change how Flex works,” he said. That is very appealing, especially for larger operations looking for a flexible, cloud-based solution without the baggage of on-prem legacy products.

What the product was lacking, however, was a native way to manage customer service representatives from within the application, and understand through analytics and dashboards, how well or poorly the team was doing. Having that ability to measure the effectiveness of the team becomes even more critical the larger the group becomes, and Cook indicated some Flex users are managing enormous groups with 10,000-20,000 employees.

Ytica provides a way to measure the performance of customer service staff, allowing management to monitor and intervene and coach when necessary. “It made so much sense to join together as one team. They have huge experience in the contact center, and a similar philosophy to build something customizable and programmable in the cloud,” Cook said.

While Ytica works with other vendors beyond Twilio, CEO Simon Vostrý says that they will continue to support those customers, even as they join the Twilio family. “We can run Flex and can continue to run this separately. We have customers running on other SaaS platforms, and we will continue to support them,” he said.

The company will remain in Prague and become a Twilio satellite office. All 14 employees are expected to join the Twilio team and Cook says plans are already in the works to expand the Prague team.

Anaxi brings more visibility to the development process

Anaxi‘s mission is to bring more transparency to the software development process. The tool, which is now live for iOS, with web and Android versions planned for the near future, connects to GitHub to give you actionable insights about the state of your projects and manage your projects and issues. Support for Atlassian’s Jira is […]

Anaxi‘s mission is to bring more transparency to the software development process. The tool, which is now live for iOS, with web and Android versions planned for the near future, connects to GitHub to give you actionable insights about the state of your projects and manage your projects and issues. Support for Atlassian’s Jira is also in the works.

The new company was founded by former Apple engineering manager and Docker EVP of product development Marc Verstaen and former CodinGame CEO John Lafleur. Unsurprisingly, this new tool is all about fixing the issues these two have seen in their daily lives as developers.

“I’ve been doing software for 40 years,” Verstaen told me.” And every time is the same. You start with a small team and it’s fine. Then you grow and you don’t know what’s going on. It’s a black box.” While the rest of the business world now focuses on data and analytics, software development never quite reached that point. Verstaen argues that this was acceptable until 10 or 15 years ago because only software companies were doing software. But now that every company is becoming a software company, that’s not acceptable anymore.

Using Anaxi, you can easily see all issue reports and pull requests from your GitHub repositories, both public and private. But you also get visual status indicators that tell you when a project has too many blockers, for example, as well as the ability to define your own labels. You also can define due dates for issues.

One interesting aspect of Anaxi is that it doesn’t store all of this information on your phone or on a proprietary server. Instead, it only caches as little information as necessary (including your handles) and then pulls the rest of the information from GitHub as needed. That cache is encrypted on the phone, but for the most part, Anaxi simply relies on the GitHub API to pull in data when needed. There’s a bit of a trade-off here in terms of speed, but Verstaen noted that this also means you always get the most recent data and that GitHub’s API is quite fast and easy to work with.

The service is currently available for free. The company plans to introduce pricing plans in the future, with prices based on the number of developers that use the product inside a company.

New Developer Insights video pushes devs to adopt auto-renewable subscriptions

Auto-renewable subscriptions bring value throughout the entire subscription lifecycle while helping developers earn predictable revenue to fund app development.

Eager to continue growing its booming Services business, Apple would prefer its registered developers embrace an in-app subscription model and has created a little video highlighting the benefits of recurring payments within apps.... Read the rest of this post here


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