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5 things to know for October 4: Congress, Covid-19, Facebook, Brazil, oil spill – ABC17NEWS

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By AJ Willingham, CNN

The Supreme Court today begins a new term that could usher in landmark decisions on big issues like gun legislation and abortion.

Here’s what you need to know to Get Up to Speed and On with Your Day.

(You can also get “5 Things You Need to Know Today” delivered to your inbox daily. Sign up here.)

1. Congress

Liberal and moderate Democrats are still at odds over crucial, high-dollar parts of Joe Biden’s legislative agenda. Remember, there are two bills at play here: an infrastructure bill and one focused on social spending and climate. The latter is causing particular concern, with a proposed price tag of $3.5 trillion. Moderate Democrats have balked at the figure and suggested a pared-down version of as little as $1.5 trillion. That’s been rejected by progressives, who are also vowing to withhold their votes on the roughly $1 trillion infrastructure bill if details of the other bill aren’t ironed out first. The disagreements led House Speaker Nancy Pelosi to delay bringing the infrastructure bill to the floor for a vote late last week and will undoubtedly continue this week. Though Dems are facing criticism for how long it’s taking to agree on these key issues, Senate Majority Whip Dick Durbin assured that the party won’t let the US default on its debt on October 18.

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2. Coronavirus

A new oral medicine that fights viral infection could give doctors a big leg-up in fighting Covid-19, but experts agree the best way to curb deaths and end the pandemic is still vaccination. Drug makers Merck and Ridgeback Biotherapeutics say their pill, molnupiravir, can reduce risk of Covid-19 hospitalization and death by 50%. They will seek emergency authorization for the antiviral medication from the FDA. Meanwhile, vaccination rates are still holding at about 56% among eligible Americans. Covid-19 has taken the lives of more than 700,000 people in the US, and about 200,000 of those deaths have occurred since vaccinations became widely available. The CDC has also updated its guidance on holiday celebrations as we enter a potentially treacherous winter. It recommends getting vaccinated before gatherings, wearing masks and celebrating virtually.

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3. Facebook

The Facebook whistleblower who released tens of thousands of pages of internal research and documents from the company has revealed herself as Frances Haugen, a former Facebook product manager who worked on civic integrity issues at the company. The documents have sparked a firestorm at the company and have even led to the Senate grilling a Facebook exec over the platform’s effect on young users. On “60 Minutes,” Haugen said the company knows its platforms are used to spread hate, violence and misinformation and has tried to hide that evidence. Facebook has aggressively pushed back against the reports, calling many of the claims “misleading.” Facebook’s vice president of global affairs, Nick Clegg, has pushed back on some related claims, saying for instance that it is “ludicrous” to assign blame to Facebook over the January 6 Capitol riot. Clegg also said the company will never be able to control all content on its site but may be open to more regulation.

4. Brazil

Protesters gathered across Brazil this weekend to call for the impeachment of President Jair Bolsonaro amid worsening economic conditions, hunger, unemployment and other persistent effects of the pandemic. This is not the first time Brazilians have organized to protest Bolsonaro, who has remained defiant in his handling of Covid-19 even as nearly a staggering 600,000 Brazilians have lost their lives to the virus. Bolsonaro’s approval rating has been on a continuous decline, and polls show more than half the country now considers his presidency to be bad or awful. This is especially important because Brazil is holding a presidential election next year, and there’s a possibility Bolsonaro could be ousted in favor of a more popular politician, like former President Luiz Inacio Lula da Silva.

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5. Oil spill

A pipeline breach in the Pacific Ocean has dumped more than 3,000 barrels of oil — equal to about 126,000 gallons of post-production crude — along the Southern California coast. Dead birds and wildlife have been washing up on Huntington Beach in Orange County, and experts worry the spill could infiltrate wetlands and other vulnerable areas, creating an ecological disaster.  The National Transportation Safety Board is sending investigators to gather information and assess the source of the oil spill, which as of yesterday morning had not fully stopped. The pipeline is owned by the oil and gas company Amplify Energy, which has pledged to participate in the recovery.

BREAKFAST BROWSE

‘Venom: Let There Be Carnage’ becomes biggest opening of the pandemic

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Everyone’s favorite ravenous alien symbiote is gobbling up the box office.

Why everyone loves Fat Bear Week

What’s not to love? (Voting ends tomorrow, if you still want to cast your lot on some delightfully zaftig bears.)

A California billionaire is in trouble for tormenting his neighbors with loud music — again

“Hey man, why don’t you go spend some money or something?”

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Plant-based meat was all the rage. Now plant-based seafood is taking the spotlight

“Plant-based fish” is a bizarre phrase, but let’s be honest: It also sounds pretty delicious.

Florida woman wins $2 million with Mega Millions tickets — twice

At what point do you stop being excited about how lucky you are and start getting a little mystified about how lucky you are?

THIS JUST IN …

Nobel Prize in Physiology or Medicine

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The 2021 award goes to David Julius and Ardem Patapoutian for identifying the receptors that allow humans to perceive temperature and touch.

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TODAY’S NUMBER

3,200

That’s about how many pedophiles are estimated to have worked in the French Catholic Church since 1950, according to an independent commission on sexual abuse.

TODAY’S QUOTE

“North Korea has no reasons to provoke or hurt the South.”

North Korean leader Kim Jong Un, who vowed last week to restart communication with South Korea. Indeed, lines of communication between the two countries were restored today for the first time in months.

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TODAY’S WEATHER

Check your local forecast here>>>

AND FINALLY

Relax 

Start off the week with a little zen … and some little zen gardens, created by artist Yuki Kawae. (Click here to view.)

The-CNN-Wire

™ & © 2021 Cable News Network, Inc., a WarnerMedia Company. All rights reserved.

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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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