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Facebook Tests New ‘Spotlight’ Conversation Option to Facilitate More Focused In-App Chats

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Facebook’s testing out a new way to host more focused public chats in the app, via a new ‘Spotlight Conversation’ post option, which, when enabled, separates replies from specific users into a new chat thread.

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As Facebook explains in these intro screens (shared by social media expert Matt Navarra), Spotlight Conversation enables you to invite specific people to respond to a post, with their responses then separates into ‘a dedicated spaces without interruptions’. Other users can still see your post and comment on the chat, but their replies are placed in another tab within the reply stream.

Facebook Spotlight conversation

As you can see here, the original post shows the invited contributors to the chat, and the discussion between the host and guest/s is then displayed in a dedicated ‘Spotlight’ chat stream, with all other replies on an ‘Other Comments’ tab.

Which is actually very similar to Twitter’s reply control options, which it added last August, enabling users to limit who can reply to their tweets to only those they @mention.

Twitter reply controls

Part of the impetus behind that update was the case of people using tweets to conduct live-stream interviews, which were often difficult to follow along with, because anyone could reply to a tweet thread. By limiting who can actually reply, that makes it much easier to stay on topic, and for viewers to read through a dedicated stream of back and forth responses between the focus profiles within a tweet thread.

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Facebook looks to be honing in on similar here, which could provide more means to share real-time chats in the app for popular users, and could expand the utility of Facebook reply threads to Q and A, celebrity interviews, product info sessions, etc.

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It seems like a fairly minor addition, but one that could be of specific benefit – and it may well be that by sharing one-on-one interviews and discussions within the app, that could open up new possibilities to reach a larger audience with such, and maximize response to your Facebook posts and discussions.

I mean, Facebook users, of course, generally want to reply for themselves, and participate where they can. But maybe, there’s also value in restricting your reply threads in certain cases, and facilitating more direct chats.

There could also a level of risk there. Say ‘Antivaxxer 1’ wants to interview an ‘expert’ on vaccines, and keep that reply chain separate from doubters. That could facilitate new forms of misinformation and reinforcement, which could be problematic.

It’s also somewhat similar to another Facebook test in threaded replies, which doesn’t enable participation, as such, but does add another angle to post reply threads.

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Facebook threaded replies

It seems that Facebook is looking for more ways to utilize posts and replies to expand your potential options, each of which will have specific use cases and benefits, without major technical implications on Facebook’s end.

That could open up new opportunities – there’s no word on the full scope of this new test, but both Spotlight Conversations and threads are now available so some users in the app.   

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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

First seen at developers.facebook.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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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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