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Weekly Bytes | Ask Alexa for COVID-19 vaccine info, Facebook’s tool for transferring posts, and more

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Here’s our curated list of important tech news from this week in byte size.

(Subscribe to our Today’s Cache newsletter for a quick snapshot of top 5 tech stories. Click here to subscribe for free.)

Ask Alexa for COVID-19 vaccine information

Amazon’s virtual assistant, Alexa, can help people by answering questions about vaccine availability and eligibility requirements for more than 85 countries, the company said in a blog post, and added that it can also help them find and connect with nearby vaccination sites. People can just say, “Alexa, where can I get tested for COVID-19?” to find their nearest COVID-19 testing locations, or just ask, “Alexa, where can I get a COVID vaccine?” to locate the nearby vaccination sites. They can also say, “Alexa, call the first one,” to connect to a particular site directly. In another update, Amazon launched Fire TV Cube that offers hands-free Alexa voice control in India for ₹12,999.

Facebook users can now transfer their posts, notes

Facebook has expanded data portability options enabling users to transfer their Facebook posts and notes to Google Docs, Blogger and WordPress.com, the company noted in a release. Facebook’s Data Portability Tool now renamed as “Transfer Your Information,” already allows people to transfer their photos and videos to Dropbox, Google Photos and Koofr. Users can access the tool in Facebook settings under ‘Your Facebook Information’, and follow the on-screen prompts to transfer their data. Users will be asked to re-enter their password before the transfer starts and encrypt their data as it moves between services, the firm noted. Facebook said it plans to continue expanding the data types and partners in the future. This week, Facebook also said it has made changes to its advertising tools, a week ahead of Apple’s iOS 14.5 software update.

Xbox cloud gaming on iOS, PC

Microsoft said on Monday it has started testing Xbox cloud gaming limited beta for Windows 10 PCs and Apple phones and tablets via web browsers. The company has begun sending out invites to select Xbox Game Pass Ultimate members, and will send additional invites on a continuous basis to players in all 22 supported countries, it added. “Our plan is to iterate quickly and open up to all Xbox Game Pass Ultimate members in the coming months,” Catherine Gluckstein, VP, Project xCloud at Microsoft, said in a blog post. Invitees will be able to play over 100 Xbox Game Pass titles on xbox.com/play using a compatible Bluetooth or USB-connected controller via Edge, Chrome, or Safari browser. In another gaming update, with lockdowns brewing, here are all the latest games to keep you indoors, safe and sane — as well as games you can look forward to in the next few months.

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New Zoom Meetings, Chat features

Zoom has introduced some new features on its platform to help people collaborate, and manage their communications better. People can use the Vanishing Pen feature to highlight text or objects on the shared screen without having to undo or erase highlight annotations in Zoom Meetings and Video Webinars, according to a company blog post. Additionally, the Whiteboard Auto-shapes feature allows users to create straight lines and perfect shapes when annotating on the Zoom mobile app. The firm has also added more emoji reactions to Meetings. Further, the platform now provides an option to account owners and admins to add a user’s manager to appear on an employee’s Zoom profile, the firm noted. Finally, using InformaCast, Zoom Phone users can trigger and receive mass notifications. Recently, Microsoft had introduced several new enhancements to Teams to improve the meeting, chat and collaboration experience of its users.

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Convert old Galaxy smartphones into smart home devices

Samsung has announced Galaxy Upcycling at Home, an initiative that gives new life to older Galaxy smartphones by converting them into a variety of Internet of Things (IoT) devices through a simple software update. The programme enables users to easily turn their old Galaxy devices, into smart home devices like a childcare monitor, a pet care solution and other tools that meet individual lifestyle needs. Users can transform their old Galaxy devices, including all S, Note, and Z series released from 2018 running Android 9 and above, to provide enhanced sound and light-control features, by repurposing built-in sensors through SmartThings Labs, a feature within the SmartThings app. Samsung has implemented the beta service for Galaxy Upcycling at Home in the US, the UK and Korea, it said in a statement. In another update, Samsung is recycling its old Galaxy smartphones into healthcare equipment for underserved people in India, Morocco, Vietnam and Papua New Guinea.

Fitbit announces ‘Luxe’ fitness tracker

Fitbit has announced a fashion-forward fitness and wellness tracker, dubbed ‘Luxe’. The device has a slim form factor and offers a wide range of health and wellness features, including automatic activity and sleep tracking, as well as wellbeing insights in the Fitbit app with the Health Metrics dashboard which tracks breathing rate, heart rate variability, resting heart rate, and skin temperature variation. The sleek tracker with a colour touchscreen can help people stay connected with customisable call, text and smartphone notifications across a broad range of Android and iOS devices. Luxe provides up to five days of battery life and can be paired with a wide range of stylish accessories. Luxe is available for pre-order for $149.95 (about ₹11,200), with worldwide availability later this year, the Google-owned firm said. This week, we delved into the intricacies of Apple’s AirTag; from design to the ultra wideband technology to the inevitable privacy concerns.

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NASA picks SpaceX to land humans back on Moon

NASA has selected SpaceX to continue the development of the first commercial human lander that will safely carry the next two American astronauts to the lunar surface as part of the Artemis programme. The space agency plans to launch four astronauts aboard the Orion spacecraft for a multi-day journey to lunar orbit. There, two crew members will transfer to the SpaceX human landing system (HLS) to travel to the surface of the Moon. After approximately a week-long lunar exploration the astronauts will use the HLS to come back to orbit where they will return to Orion before heading back to Earth. SpaceX’s HLS Starship leans on the company’s tested Raptor engines and flight heritage of the Falcon and Dragon vehicles. Starship includes a spacious cabin and two airlocks for astronaut moonwalks. In another update, SpaceX launched four astronauts into orbit on Friday using a recycled rocket and capsule, the third crew flight in less than a year for Elon Musk’s rapidly expanding company.

AWS, DISH team up to build cloud-based 5G O-RAN

Amazon Web Services (AWS) and DISH Network Corporation have partnered to reinvent 5G connectivity and innovation. DISH will leverage AWS infrastructure and services to build a cloud-based, 5G Open Radio Access Network (O-RAN) in the U.S. that delivers consistent, cost-effective performance from core to the edge, according to an Amazon release. Both companies will also work to transform how organisations and customers, including AWS and Amazon, order and consume 5G services or create their own private 5G networks. AWS will also power DISH’s fully automated Operation and Business Support Systems that will enable the company to provision and operate its customers’ 5G workloads and monetise its network. “Together, we’re opening the door to new technologies that will transform factories, workplaces, entertainment, and transportation in ways people have only dreamed,” AWS CEO Andy Jassy, said in a statement. This week, Amazon said it has secured an agreement with space launch firm, United Launch Alliance, to support the deployment of its Project Kuiper satellites.

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.

Facebook SDK

To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.

App Ads, Facebook Analytics, & App Events

Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.

With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.

When developers send Facebook custom events, these events could include data types outside of standard events. Developers control sending these events to Facebook either directly via application code or in Events Manager for codeless app events. Developers can review their code and Events Manager to determine which data types they are sending to Facebook. It’s the developer’s responsibility to ensure this is reflected in their application’s privacy policy.

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Advanced Matching

Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:

  • email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
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Facebook Login

There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.

Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.

Device Information

We may also receive and process the following information if your app is integrated with the Facebook SDK:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
  • Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.

Audience Network SDK

We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
  • Ad performance information, such as impressions, clicks, placement, and viewability.

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Enabling Faster Python Authoring With Wasabi

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This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.


Motivation

At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.

Introducing Wasabi

Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:

  • Lints and diagnostics: These are available as the user types.
  • Auto import quick fix: This is available for undefined-variable lint.
  • Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
  • Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
  • Python snippets: Snippet suggestions are available as the user types for common code patterns.

Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.

Figure 1: Example for global symbols autocomplete, one of Wasabi’s features

Language Services at Meta Scale

A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.

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At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.

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The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.

For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.

While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.

For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.

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How Wasabi Works

Offline + Online Processing:

In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):

  1. A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
  2. Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
  3. Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
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As a result, all Wasabi features are low latency and available to the user seamlessly as they type.

Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.

Figure 2: Wasabi’s high level architecture

Ranking the Results

In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.

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For example, auto import ranking is done by taking into account:

  • Locality:
    • The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
    • The distance of the suggested module directory path from the current directory path of the local file.
    • Whether the file has been locally changed (“dirty” files are ranked higher).
  • Usage: The number of occurrences the import statement was used by other files in the repository.

To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.

Positive feedbacks from developers

After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:

“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”

“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”

As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.

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The metric to measure authoring velocity

In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.

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Figure 3: Authoring Velocity Metric Formula

The result

With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.

Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.

Summary

With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.

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To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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