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Facebook’s Good News – The New York Times

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The tech giant has changed its playbook this year to rebut criticism, using its algorithm to promote positive stories about itself.

Image Have you seen the good news?

Credit…Dado Ruvic/Reuters

Whether on privacy, misinformation, hate speech or changes to its News Feed, Facebook has been embroiled in controversy for most of its existence. Its playbook, no matter the perceived misstep, has remained relatively consistent: Apologize and promise to do better.

In January, though, the company’s executives decided to be more aggressive. That has included using its own powerful information-spreading algorithms to respond to criticism, The Times’s Ryan Mac and Sheera Frenkel report. This is part of a multipronged effort to change the narrative about the company by distancing the founder Mark Zuckerberg from scandals, reducing outsiders’ access to internal data, burying a potentially negative report about its content and increasing its own advertising to showcase its brand.

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One of the most visible actions is code-named “Project Amplify.” The initiative promotes positive stories about Facebook on users’ feeds. These articles, like one about ​​“Facebook’s Latest Innovations for 2021,” are displayed with a Facebook logo and in some cases written by Facebook itself. A Facebook spokesman said that Project Amplify was “similar to corporate responsibility initiatives people see in other technology and consumer products.”

Facebook has also cut back on apologies in recent months. Its executives have said, for example, that the storming of the U.S. Capitol had little to do with Facebook and that misinformation on the platform was not the reason for missed vaccination goals. “They’re realizing that no one else is going to come to their defense, so they need to do it and say it themselves,” said Katie Harbath, a former Facebook public policy director.

The company may find it hard to go on the offensive. On Sunday, Facebook responded to an investigative series by The Wall Street Journal, which reported that the company knew more about the harms caused by its platform than it had acknowledged in public. In a blog post, Facebook called the paper’s reporting a mischaracterization of the facts. Late yesterday, the Facebook Oversight Board announced it would review Facebook’s special treatment of V.I.P. users in enforcement actions, one of the policies surfaced by the report.

The House approves a bill raising the debt limit. The legislation would lift the federal debt limit until the end of 2022, fund the government through early December and provide money for Afghan refugees and natural disaster recovery. The measure now heads to the Senate, where Republicans have warned they will block any increase to the debt ceiling. Government funding lapses next week, and the Treasury Department could reach the limits of its borrowing authority next month.

China’s Evergrande says it can repay at least some of its debts, noting in a filing today that a $36 million interest payment due this week was “settled through negotiations.” But the cash-crunched property developer, which owes creditors $300 billion, could miss other payments this week, with prospects for a bailout unclear. Hedge funds have been buying Evergrande’s bonds and hiring advisers in an attempt to make money off the company’s potential collapse.

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The Fed updates its bond-buying plans. The U.S. central bank, which wraps up its latest policy meeting today, is expected to signal that it will soon slow its bond-buying program, a first step in reducing its emergency pandemic support. The Fed chair, Jay Powell, is also likely to face questions about the personal stock and bond trading of top central bank officials, after calling for a review of those trades last week.

Google spends $2.1 billion on a Manhattan office building. It’s one of the highest prices paid for an office building in the U.S. in recent years, and a psychological boost for New York City’s commercial property market, which is struggling with record-high vacancy rates. The tech giant has 12,000 employees in the city, and plans to hire 2,000 more.

The Treasury Department targets cryptocurrency’s role in ransomware attacks. As part of a series of actions to prevent cybercrime, the department placed sanctions on Suex, a crypto exchange based in Russia that it said facilitated payments in multiple attacks. In 2020, ransomware payments topped $400 million, four times larger than the year before, according to officials.

The Justice Department filed an antitrust lawsuit yesterday against American Airlines and JetBlue, arguing that a growing alliance between the two carriers hurts consumers. In bringing the suit, officials called the cooperation a “de facto merger” between the carriers in the New York and Boston markets. Attorneys general in six states and the District of Columbia joined the action. The airlines said they planned to fight the suit in court.

It’s the latest effort by the Biden administration to limit corporate power through antitrust actions. The airline industry’s troubles during the pandemic, which crushed carriers’ revenue, didn’t appear to factor into the decision to sue. “Neither airline is failing; they received billions of dollars in subsidies from American taxpayers over the course of the pandemic,” the charge noted, underlining that playing the failing-firm card would not lower the antitrust standards set by the White House. (Propping up the industry with more than $50 billion in grants was itself contentious.)

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“American has relentlessly pursued a strategy of industry consolidation,” the suit said. “Unable to combine with foreign airlines through formal mergers, American has instead pursued consolidation through a series of international joint ventures.” American is the world’s largest airline and it, along with Delta, United and Southwest, controls over 80 percent of domestic U.S. air travel. JetBlue’s reputation for challenging bigger rivals, forcing them to lower their fares from hubs like Boston, is a “critical source of competition” eliminated by its partnership with American, according to Richard Powers, an acting assistant attorney general in the Justice Department’s antitrust division.

The government’s move could dash any plans for future airline deals. Shares of one of the last remaining targets of a takeover that might pass muster, Alaska Airlines, closed more than 1 percent down yesterday and dropped a bit more in after-hours trading.

In other news, the Justice Department is investigating Zoom’s $15 billion deal to buy Five9, citing potential risks to national security.


► “My generation was promised colonies on the moon. Instead we got Facebook.”

— Peter Thiel, a venture capitalist and an early investor in Facebook, told employees of the social media company in what was supposed to be a motivational speech but turned into a critique of the company, according to “The Contrarian,” a new book about Thiel out this week.

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► “We’ve found that eliminating pre-employment testing for cannabis allows us to expand our applicant pool.”

— Beth Galetti, the head of human resources at Amazon, which announced yesterday that it was lobbying the federal government to legalize marijuana.

► “The future of gender equality hangs in the balance, putting our families, communities, businesses and economy at risk.”

— More than 50 companies, including Yelp and Lyft, signed a group letter saying that a new law in Texas that severely restricts abortions made it hard to do business in the state.


Change.org, the tech company known for hosting online petitions, will announce today that it’s transferring its ownership to a nonprofit foundation. As part of the change, more than 90 percent of the company’s investors are donating their equity, including Reid Hoffman, Bill Gates, Richard Branson, Ray Dalio and Arianna Huffington. (Hoffman, Change.org’s largest individual investor, made a $30 million investment in 2017.)

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Change.org’s C.E.O., Ben Rattray, will become the executive chairman of the Change.org Foundation, while the former chief product officer, Nick Allardice, will become C.E.O.

Change.org was already registered as a B Corp, a designation for businesses that focus on social and environmental goals, in addition to financial ones. It will stay as such, and the new structure will allow the organization to focus on Change.org’s mission exclusively, Rattray said. That means it can make new investments that might take longer to play out, like election-focused products between election seasons and initiatives in new markets like Asia. Change.org has been evaluating its corporate governance structure for the past 18 months.

Change.org generates about $70 million annually and is “fully covering” its expenses, Rattray said. The company makes money in two main ways: promoted petitions, where users can pay to get their campaigns highlighted, and monthly subscriptions. Rattray said a “slight majority” of revenue comes from petitions. Change.org won’t be looking for philanthropic donations for its current platform, though it is possible that it will run separate philanthropic projects in the future.

Deals

  • Toast, the restaurant technology vendor, priced its I.P.O. above its recently raised range, valuing the company at $20 billion. (CNBC)

  • Netflix bought the Roald Dahl Story Company, acquiring the full catalog of works by the author of “Charlie and the Chocolate Factory” and “Matilda.” (Deadline)

  • The sports betting firm DraftKings made an offer of more than $20 billion to acquire the gambling company Entain. (Reuters)

  • JPMorgan Chase has acquired Frank, a platform that helps college students with financial planning. (Reuters)

  • SoftBank is among the investors in the former Treasury secretary Steven Mnuchin’s $2.5 billion private equity fund. (FT)

Policy

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  • President Xi Jinping of China told the U.N. General Assembly that his country won’t help build any more coal plants abroad. (NYT)

  • The Secret Service, the F.B.I. and the Defense Department all bought surveillance drones from DJI, a Chinese company the Pentagon deemed a security threat. (Axios)

  • More than 30 companies, including Amazon and UPS, joined a coalition founded by the Chobani C.E.O., Hamdi Ulukaya, to hire and train Afghan refugees in the U.S. (AP)

  • Rohit Chopra’s nomination to lead the C.F.P.B. moved out of committee, where it has been stuck for months, and he now faces a full Senate vote. (WSJ)

  • “Republicans, Don’t Skip Out on America’s Bills,” writes Michael Bloomberg. (Bloomberg Opinion)

Best of the rest

  • One of the junior bankers at Goldman Sachs who created a widely circulated slide deck about brutal working conditions is the son of the vice chairman of TPG, a major Goldman client. (Bloomberg)

  • “Forever C.E.O.s” are dominating Wall Street. (FT)

  • Six workers who didn’t have the option to work from home during the pandemic share their stories. (NYT)

  • John and Jenny Paulson are divorcing after more than 20 years of marriage, the latest split of a couple with a multibillion-dollar fortune. (NY Post)

  • “Apple iPhone 13 Review: The Most Incremental Upgrade Ever” (NYT)

Will international freight make a comeback, or is the era of cheap mobility over? Join The Times on Sept. 23, at 1:30 p.m. Eastern, as our climate reporter Brad Plumer is joined by experts from FedEx, Ikea and more to explore the ways in which business models are changing as people, goods and data move toward a net-zero world. R.S.V.P. here.

We’d like your feedback! Please email thoughts and suggestions to dealbook@nytimes.com.

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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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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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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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