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Meet the innovative startups from the Facebook Accelerator: Business Solutions program

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With more than 200 million companies using one of our apps every month, Facebook plays a key role in helping small businesses stay connected with customers, innovate and grow. Facebook has been supporting small businesses with free education, resources and tools for a number of years and, in 2020, we accelerated our investments through the launch of Shops on Facebook and Instagram, Facebook Business Suite, and – more recently – Facebook Business App Store.

Our next investment in this space is the launch of Facebook Accelerator: Business Solutions – a 12-week virtual program supporting 61 startups globally that offer small and medium-sized businesses solutions in four categories:

  • Customer Data Platforms (CDPs)
  • System Integrators
  • Service apps
  • Creative apps

Over the course of three months, selected startups will have an opportunity to benefit from comprehensive training on Facebook’s suite of products and technologies, receive advice from Facebook product and partnership experts, and build a global network of fellow founders and innovators.

Today, we are excited to announce the 61 startups that are invited to join the Facebook Accelerator: Business Solutions program. These startups were evaluated by an international panel of Facebook experts through our application process and were selected for having a product focused on driving value to small and medium sized businesses, diverse and focused leadership team, groundbreaking technology and evidence of business growth.

22 companies joining the program from Asia Pacific include:

Customer Data Platforms (CDPs)

Analytist (Thailand), whose CDP product GrowthAI provides predictive analytics and marketing automation.

APEX Technologies (China), a customer data technology that combines big data, AI, and blockchain technology.

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ConvertLab (China), providing an integrated Marketing Cloud for customer-centric digitalization as a marketing solution.

Deeply Digital (Australia / India), providing real-time customer insights and hyper-personalization.

Landscape (Japan), a B2B database company to assist customer acquisition.

NHN DATA / Dighty (South Korea), discovering potential customers in data and helping to increase the value of available data.

ORDERLY / Ezorderly (Taiwan), offering e-commerce businesses data aggregation, analytics and application in one platform.

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PrimeData (Vietnam), offering CDP-driven data and automation that brings unified customer data and personalisation experience management to all business units.

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Realmax (Vietnam), a digital transformation consultant that focuses on using agile and lean development principles to solve business problems.

Reddoor MarTech / Eagle Eye (Taiwan), a CDP that helps clients to manage all customer information in one platform.

Sensors Data (China), a professional service provider of big data analysis and platform for organizations with consulting services and industrial solutions.

TechSun Incorporation (China), a global customer engagement platform consisting of CDP and CRM modules that help brands build a 360° view of customers and their engagement on a personalised level.

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Business Apps

Convo (Australia), helping small businesses to create effective marketing campaigns.

Featuring (South Korea), an AI-based influencer matchmaking platform.

GliaCloud (Taiwan), an AI technology company empowering the media and advertising industry to turn stories into quality videos at scale.

Mish Guru (New Zealand), helping brands source, moderate and publish high-performing Story content for Snapchat and Instagram.

Now Book It (Australia), a platform that enables restaurants to take bookings online, manage tables, sell gift cards, take online orders and build a customer database.

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Ovatu (Australia), a smart scheduling software that helps businesses simplify, automate and scale their booking process.

Quickwork (India), a leading integration platform to build workflows, publish APIs and manage conversations.

ShowHue (Taiwan), an online tool that helps businesses create stunning visuals to market their products with an AI-powered algorithm.

Simple Salon (Australia), a leading provider of proven, cutting-edge, cloud-based salon software.

Stunning (South Korea), whose creative platform LoudSourcing provides a design crowdsourcing service for SMBs.

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22 companies joining the program from Europe, Middle East and Africa include:

Customer Data Platforms (CDPs)

Africa’s Talking (Kenya), an API provider that developed a Customer Engagement Platform.

CrossEngage (Germany), software that collects data and makes predictions through a CDP and a Customer Prediction Platform.

Custimy (Denmark), no-code CDP that is ready to be used in less than 15 minutes.

Data Talks (Sweden), a CDP that gathers data to build a data pool and run automated campaigns.

Formaloo (Estonia), a platform that helps businesses to collect data and profile their customers.

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Mindbox (Russia), a CDP that gathers siloed data and segments the audience.

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Nessie (Belgium), no-code CDP that collects customer data and builds data pools.

Segmentify (United Kingdom), an eCommerce personalisation platform that helps online retailers to personalize their website through data collection.

SegmentStream (United Kingdom), a marketing analytics and optimization platform that unifies data into a data warehouse and generates insights.

White Rabbit (Italy), an easy-to-use platform specifically designed for small businesses.

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Zeotap (Germany), a Customer Intelligence Platform that uses its universal ID solution to track customers without cookies.

Business Apps:

Appointedd (United Kingdom), a cloud-based appointment booking solution that helps businesses with customer data management and scheduling.

ContentCal (United Kingdom), a content marketing planning tool that helps marketing agencies to manage social media posting from one platform.

DIKIDI (Russia), offering small businesses an app that helps them manage their bookings online, and their customers – make a booking online.

Hospitality Digital (Germany), offering an appointment booking platform for businesses in the hospitality industry.

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Minta (Israel), offering an AI-powered software that analyses an online store’s real-time data and automatically creates the most effective video ads to promote a product.

Optios (Belgium), a SaaS for salon owners that manages the accounting side of the business, keeps track of the inventory and increases the salon’s visibility.

Photoslurp (Spain), uses AI to collect user-generated content and publish it on the brand’s eCommerce platform.

Pudding.ai (Israel), analyzes the creative content published on an ad account and makes reports, insights and suggestions.

SheerME (Portugal), an online appointment booking system for merchants in the wellness, beauty and fitness industry.

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ZEG (United Kingdom), a platform that turns 2D photos into 3D rendering.

Zyda (Egypt), a platform that helps restaurants to set up their own site to accept orders

17 companies joining the program from North America include:

Business Apps

Ambassador.ai (Canada), empowers small businesses to manage their take-out and self-managed delivery orders without third party apps or marketplaces.

Bbot (United States of America), a food ordering solution that does not require account creation or app download.

BigVu (United States of America), a destination app for short business videos.

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BLKBOX (United States of America), enables companies to create data-driven campaigns in less than a minute and automate their creative testing processes.

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Cameraah (United States of America), a no-code tool to create AR experiences for social and commerce in minutes.

ConvertEvent (Canada), helps brands capture more customer data through Facebook Conversions API.

CreatorKit (United States of America) that enables companies to create animated videos, stories, and ads, in minutes.

Crello (United States of America / Cyprus), an online platform for easily creating videos and graphic designs.

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GlamAfric (United States of America), a beauty appointment scheduling and business platform, making it easier for African beauty businesses globally to manage bookings, payments, marketing, product sales.

Lunchbox (United States of America), offers a collection of enterprise solutions that redefine the digital restaurant experience.

PandaFlow (United States of America), a low-code / no-code automation and integration platform.

Phyllo (United States of America), a data gateway to access creator data directly from the source platforms.

ROAR (United States of America), a content management platform for augmented reality that creates immersive shopping experiences, drives consumer engagement and provides invaluable data and ROI for brands and retailers.

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Setka (United States of America), combining design and data science to help distributed teams grow their business by elevating content experiences.

Tailwind (United States of America), a platform that enables companies to create, schedule and optimize marketing campaigns.

UNUM (United States of America), a developer of a customisable platform aimed at powering the connection between content and creators.

Vcita (United States of America), a scheduling software and calendar for small businesses.

Facebook Accelerator: Business Solutions program is delivered in partnership with Plug and Play, an open innovation platform with over 2,000 accelerated startups last year alone and 500+ partners. Plug and Play is also one of the most active VC globally in the early stage segment, with an average of 200 investments each year, and more than 20+ unicorns in its portfolio. Today Plug and Play is present in more than 40 cities, among America, Europe, Africa and Asia.

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If you are interested in joining a future Facebook startup program, please visit developers.facebook.com/startups.

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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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