News Feed and Timeline Evolution · Meta Feature Trail

Mar 2026Friend Bubbles: Enhancing Social Discovery on Facebook ReelsIntroduced 'Friend Bubbles' to Facebook Reels, a system that highlights content friends have liked or reacted to. This involved developing user-user closeness models (survey-based and on-platform interaction-based) to identify relevant friends, and enhancing video ranking models with friend-bubble interaction signals and new tasks to incorporate relationship strength and learn downstream engagement. The system also optimized client-side performance by integrating friend-bubble metadata retrieval into the existing video prefetch work and conditionally disabling animations to maintain smooth scrolling and low latency.

Sep 2025A New Ranking Framework for Better Notification Quality on InstagramIntroduced a diversity-aware notification ranking framework for Instagram that sits on top of existing ML engagement models. This framework applies multiplicative penalties to candidate notification scores based on similarity to recently sent notifications across dimensions like content, author, and product type. The mathematical formulation uses a demotion factor D(c) = product(1 - w_i * p_i(c)), where p_i(c) is a similarity signal and w_i is a demotion strength weight. Future plans include dynamic demotion strategies and LLM integration for semantic similarity.

Dec 2024Inside Facebook’s video delivery systemThis post details Meta's extensive two-year effort to unify its video delivery systems, consolidating distinct products like Watch and Reels into a single, streamlined experience. It outlines the technical challenges and solutions involved in unifying the ranking, server, and mobile layers, including data models, architectures, and UI elements. The post highlights the benefits of this unification, such as improved developer velocity, consistent user experiences, and better content discovery, and explains the lifecycle of a video feed network request within this new unified system.

Oct 2022Improving Instagram notification management with machine learning and causal inferenceThis post details the application of machine learning and causal inference to optimize Instagram's daily digest push notifications. It moves beyond traditional click-through rate models to identify users who are likely to see content organically, thereby reducing notification volume without impacting user engagement. The post explains the use of uplift modeling and a randomized experiment to estimate incremental value, and an online approach with score transformation to stabilize sending rates, leading to improved user experience and reduced resource usage.

Jul 2021A linear programming approach for optimizing features in ML modelsThis post introduces a linear programming approach to optimize feature selection for ML models, particularly for ranking algorithms like those used in News Feed and ad delivery. It addresses the challenge of balancing model performance gains from new features against finite infrastructure capacity (memory, CPU, storage). The post details how framing this as a linear programming problem allows for maximizing model performance, probing sensitivity to infrastructure constraints, and quantifying trade-offs, thereby automating and scaling the feature approval process.

Jan 2021How machine learning powers Facebook’s News Feed ranking algorithmThis post details the machine learning architecture behind Facebook's News Feed ranking algorithm. It explains how multitask neural networks, embeddings, and offline learning systems are used to predict user engagement (likes, comments, shares) for billions of users in real-time. The system employs a multi-pass approach for scoring and ranking posts, including an initial lightweight model to select relevant candidates, a main scoring pass using powerful neural networks, and a contextual pass for diversity. The post also discusses how user surveys are used to align the ranking objective function with perceived long-term value.

Dec 2020On the value of diversified recommendationsThis post introduces the concept of diversified recommendations as a crucial evolution beyond simple predictive accuracy in recommendation systems. It uses toy models (Polya and balancing processes) to illustrate the trade-offs between positive reinforcement (leading to boredom) and negative reinforcement (leading to lack of engagement). The post then outlines practical methods for achieving recommendation diversity, including author, media type, and semantic diversity, exploring similar semantic nodes, maintaining long and short-term preferences, utilizing explore-exploit algorithms (epsilon-greedy, UCB), qualitative trade-off computation, and negative guardrails. It emphasizes optimizing for long-term user satisfaction over purely metric-driven approaches.

Dec 2020How Instagram suggests new contentThis post introduces 'Suggested Posts' for Instagram, a new feature that aims to provide personalized content recommendations from accounts the user doesn't currently follow, while maintaining a 'Feels Like Home' experience. It details the system's two-stage design (candidate generation and selection), the use of embeddings and co-occurrence for candidate generation, methods for addressing the cold-start problem, and the ranking model based on engagement and aversion factors. The 'Feels Like Home' principle is further elaborated through prioritization of Home Feed-like sources, similar freshness heuristics, and media type mixture.

Jan 2018Improving Android video on News Feed with LithoThis post details the migration of video rendering within Facebook's Android News Feed to the Litho UI framework. It addresses challenges in efficiently rendering video content, which is more resource-intensive than text or images, by leveraging Litho's asynchronous layout, fine-grained recycling, and pre-allocation features. The introduction of a `CoreVideoComponent` MountSpec and its composition within `AutoplayVideoComponent` and `VideoAttachmentComponent` LayoutSpecs allows for more flexible and maintainable video UI elements. Performance improvements are highlighted, including reduced object allocations and smoother scrolling due to Litho's recycling system and pre-allocation of video views.

Oct 2017Open-sourcing Sections: Declarative data handling for Litho listsIntroduced 'Sections', a declarative and composable API built on top of Litho for writing highly-optimized list surfaces. Sections structures data and translates it into Litho Components, handling data diffing and minimal UI refreshes automatically. It provides building blocks like `GroupSectionSpec`, `SingleComponentSection`, and `DataDiffSection` to manage lists of data and translate them into UI components, significantly improving performance and maintainability for scrollable lists compared to traditional RecyclerView implementations.

Oct 2017Mobile @Scale 2017 recapThis post details the migration of the Facebook Android News Feed codebase to background rendering using Litho and Infer, addressing concurrency and other bugs. It also highlights the technical challenges and approaches to scaling the Recommendations product, insights into high-performance app development through instrumentation and re-architecture, and processes for reducing battery consumption by Facebook applications. Additionally, it touches upon the evolution of WhatsApp within Facebook's data centers, real-time analytics for billions of devices, and mobile backend development.

Sep 2017Multithreaded rendering on Android with Litho and InferThis post details the migration of News Feed's Android UI to Litho for multithreaded rendering, enabling smoother scrolling and richer content. It highlights the challenges of ensuring thread safety in an existing codebase and the crucial role of Infer's new thread-safety analyzer in detecting and fixing concurrency bugs, which was essential for the successful implementation of background layout at scale.

Apr 2017Facebook open source at F8 2017This post announces the open-sourcing of Litho, a declarative UI framework for Android that improves performance by preparing UI components ahead of time and running layout code in background threads. It also details React Fiber, a rewrite of React core enabling sophisticated scheduling of rendering work for improved developer features and future innovation, and Relay Modern, a new version of the GraphQL client framework offering enhanced performance and extensibility. Finally, it introduces React VR for building VR experiences on the web.

Apr 2017Open-sourcing Litho, a declarative UI framework for AndroidThis post introduces Litho, a declarative UI framework for Android, open-sourced from Meta. Litho aims to improve UI performance by decoupling layout operations from Android views using React's declarative model and Yoga layout system, allowing heavy measure and layout operations to be moved to a background thread. It also breaks down complex views into smaller, incrementally rendered pieces and recycles them, reducing memory usage and improving scroll performance by up to 35%.

Dec 2016Made in NY: The engineering behind social recommendationsThis post introduces the 'Recommendations' feature, which automatically identifies and structures place recommendations from free-text comments on Facebook posts. It details the engineering challenges and solutions for extracting place entities from natural language, resolving ambiguity, and dynamically updating feed stories on both Android and iOS. The post highlights the integration with local search, places data graphs, and the Applied Machine Learning team's conversational understanding capabilities, as well as the use of ComponentKit and state management for dynamic UI updates.

Dec 2016Yoga: A cross-platform layout engineIntroduced Yoga, a standalone, cross-platform layout engine implemented in C, derived from css-layout. Yoga aims to unify layout handling across mobile, desktop, and web platforms by providing a single implementation of the Flexbox spec. It offers performance improvements (33% faster on Android compared to Java) and bindings for Java, Objective-C, and C#. The post details its usage with code samples for UIKit and highlights its integration into React Native, Components for Android, Oculus, and plans for Instagram and ComponentKit.

Oct 2016Components for Android: A declarative framework for efficient UIsIntroduced Components for Android (C4A), a declarative UI framework for Android inspired by React. C4A utilizes a unidirectional data flow model, pure functions for UI components, and an annotation processor to generate component builders. It integrates with css-layout for Flexbox-based layout calculations, decoupling layout from Android views. Key features include asynchronous layout on background threads, flatter view hierarchies by optimizing container usage and mounting drawables, incremental mounting of components, and fine-grained recycling of individual UI elements for improved performance and memory efficiency in complex scrollable lists.

Oct 2016Client-side ranking to more efficiently show people stories in feedIntroduced a new client-side ranking architecture for News Feed to improve story relevance and rendering efficiency, particularly for users with slower internet connections. This shifts ranking logic from the server to the client, allowing for dynamic re-ranking based on media loading status and network conditions. The system now prioritizes stories with fully loaded media and optimizes content for each session, reducing spinners and gray boxes.

Jun 2016Developing the technical language behind creativity for CanvasThis post introduces Canvas, a new full-screen mobile content creation tool that builds upon existing infrastructure for video autoplay and rendering engines (leveraging AsyncDisplayKit). It highlights how Canvas evolved from observations of user behavior with existing products like carousels and Lead Ads, enabling creators to build interactive stories with a defined set of components and a declarative language. The post details the technical challenges of mobile-first design, loading speed, and real-time creative previews, and explains how Canvas aims to solve limitations in mobile content creation for advertisers.

Jun 2016Safety Check: Streamlining deployment around the worldThis post details the development and technical implementation of Safety Check, a feature that leverages News Feed hooks and the social graph to proactively notify users in affected areas during crises. It highlights the challenges of a 'push' model compared to Facebook's typical 'pull' model, the algorithmic approach using social graph traversal for rapid identification of affected individuals, and the engineering solutions for rate limiting and ensuring system stability during high-volume, infrequent activations. It also introduces automated deployment tools and a Messenger bot for monitoring, showcasing the evolution of internal processes for crisis response features.

Mar 2016Engineering for nostalgia: Building a personalized “On This Day” experienceThis post details the engineering efforts behind the 'On This Day' feature, a personalized memory surfacing experience. It highlights the integration of user experience research, automatic filtering (e.g., for exes, blocked people, dismissed memories), user-controlled preferences (blocklisting people or dates), and a machine-learning ranking model. The ranking model utilizes personalization signals (user interactions, demographics) and content understanding powered by a computer vision platform to identify and surface meaningful memories in the News Feed.

Dec 2015Continuing to build News Feed for all types of connectionsThis post details improvements to News Feed for users on slow or intermittent internet connections. It introduces a mechanism to rank and display previously downloaded, unviewed stories based on relevance and image availability when a user's connection is poor, thus avoiding long loading times. Additionally, it enables users to compose comments on posts while offline, with the comments being posted upon regaining a connection. These changes focus on efficient content delivery and offline user interaction without altering the core News Feed ranking algorithm.

Dec 2015Under the hood: Broadcasting live video to millionsThis post details the engineering challenges and solutions for broadcasting live video to millions of users. It introduces the 'thundering herd' problem and its mitigation through edge caching and request coalescing. It also explains the adoption of RTMP to reduce latency for a more interactive live streaming experience, building upon previous work with Live for Facebook Mentions.

Oct 2015Under the hood: Building 360 videoThis post details the technical challenges and solutions involved in integrating 360 video into the News Feed. It highlights the transformation from equirectangular to cube map projections to address geometry distortion and pixel redundancy, and discusses the backend engineering required to handle large file sizes, optimize encoding through distributed processing, and develop custom video filters for efficient processing at scale. This represents a significant expansion of the News Feed's capabilities to support immersive video content.

Aug 2015Under the hood: Place Tips at LollapaloozaThis post details the engineering behind 'Place Tips', a feature that connects the Facebook experience with the physical world by surfacing social content and business information related to visited locations. It highlights the development of a flexible, server-controlled rendering framework that uses 'styles' (contracts between client and server) and pre-existing client templates to display varied data aggregations (like friends' photos or event details) consistently across mobile applications. The post also showcases the application of this framework for a specific event, Lollapalooza, demonstrating rapid development enabled by the platform and the use of Bluetooth beacons for location-specific content delivery.

Jun 2015Delivering high scroll performanceThis post details a strategy for identifying and fixing scroll performance regressions in Meta's iOS News Feed. It introduces an on-device measurement technique using CADisplayLink to detect dropped frames and a signal-based profiling approach (SIGPROF) to capture main thread call stacks during high utilization periods. A dedicated, high-priority signaling thread is used to ensure timely signal firing. The captured backtraces are then symbolicated and aggregated for analysis.

Jun 2015Building “add a link”This post details the rapid development of a new feature on iPhone that allows users to easily find and share links within Facebook posts. The feature leverages an existing index of shared posts and was developed from an idea to a hackathon prototype. Key technical contributions include: balancing social and global ranking signals for search results, optimizing search result ranking and page size for faster loading, implementing client-side caching, and turning off non-essential ranking features. The post also highlights the collaborative process involving engineers, designers, data analysts, and UX researchers.

Jun 2015Recommending items to more than a billion peopleThis post details the challenges and solutions for scaling collaborative filtering (CF) for recommendation systems at Facebook's massive scale. It introduces the problem of handling billions of ratings and users, discusses the limitations of standard matrix factorization approaches (SGD and ALS) due to network traffic and data skew, and presents a novel 'rotational hybrid approach' leveraging Apache Giraph and worker-to-worker messaging to significantly reduce network traffic and improve processing efficiency for recommending items like pages, groups, and events.

Mar 2015Introducing ComponentKit: Functional and declarative UI on iOSIntroduced ComponentKit, a native Objective-C++ library for iOS inspired by React, to provide a functional and declarative approach to building UI. ComponentKit abstracts UI rendering, performs layout on background threads, creates minimal view hierarchies, and implements intelligent view reuse, leading to a 70% reduction in rendering code for News Feed, significantly improved scroll performance, and better test coverage through snapshot tests.

Mar 2015Serving Facebook Multifeed: Efficiency, performance gains through redesignThis post details the disaggregation of the Multifeed system, a backend component of Facebook's News Feed. The original aggregated design co-located CPU-intensive aggregators with memory-intensive leaf servers, leading to reliability issues, hardware scalability challenges, resource waste, and performance contention. The disaggregated design separates these components into specialized server pools (CPU-focused for aggregators, memory-focused for leaves). This resulted in a 40% efficiency improvement, 10% aggregator latency reduction, independent scaling of components, and increased resilience to traffic spikes and component failures. The post also introduces the concept of disaggregated flash sleds for other services.

Mar 2015Shimmer for AndroidIntroduced Shimmer for Android, a reusable UI component implemented as a ShimmerFrameLayout. This component provides a shimmering loading indicator effect by rendering nested views with an animated alpha mask. It allows for customization of animation duration and is used in Facebook applications for feed loading status.

Jan 2015Fast Rendering News Feed on AndroidThis post details the architectural changes made to the Facebook for Android News Feed to improve scrolling performance. The previous approach used custom view classes with a `bindModel` method, which led to issues with view recycling, complicated hierarchies, deep view hierarchies, and computationally intensive binding. The new architecture splits each story into multiple items in the ListView, enabling effective view recycling and amortizing binding time over several frames. It also decouples binding logic from views by introducing 'dumb' custom views and Binder classes with `prepare`, `bind`, and `unbind` methods. A `PartDefinition` class was introduced to manage the hierarchy of story parts and generate binders. These changes resulted in a 17% reduction in out-of-memory errors, an 8% reduction in total errors, and a 10% reduction in maximum frame render time.

Oct 2014Making News Feed nearly 50% faster on iOSReplaced Core Data with a new data model layer for News Feed on iOS. The new layer uses immutable PONSOs (plain ol' NSObjects) with denormalized storage and asynchronous, opt-in consistency. This change resulted in a nearly 50% performance improvement by addressing Core Data's performance issues with a large number of entities and fields. A protocol-based approach was used for gradual migration and A/B testing.

Oct 2014Introducing AsyncDisplayKit: For smooth and responsive apps on iOSIntroduced AsyncDisplayKit, a framework for iOS that enables asynchronous creation and rendering of complex view hierarchies on background threads. The core abstraction is the 'node', a thread-safe alternative to UIView, allowing for image and text rendering, as well as complex view hierarchies, to be initialized and laid out on background threads. This addresses the main thread bottleneck in iOS UI development, which can cause stuttering and unresponsiveness.

Apr 2014Large-scale graph partitioning with Apache GiraphThis post introduces a novel approach to large-scale graph partitioning using Apache Giraph to optimize data distribution in sharded systems. It addresses the challenge of high latency in queries that require fetching data across multiple machines by co-locating related data (e.g., friends of a person) within the same partition. The post details a heuristic algorithm inspired by balanced label propagation, adapted for Giraph's distributed model, which iteratively swaps vertices to maximize local edges while maintaining partition balance. It demonstrates significant improvements in iteration time for graph processing algorithms like PageRank and a 2x improvement in bandwidth utilization for a specific service by improving data locality.

Mar 2014Looking back on “Look Back” videosThis post details the engineering challenges and solutions behind the 'Look Back' video feature, a large-scale, time-bound initiative to deliver personalized video content to users for Facebook's 10th anniversary. It highlights the rapid infrastructure scaling required for storage (25 petabytes estimated, 11 petabytes used), compute, and network bandwidth (187 Gbps planned, 450 Gbps peak achieved), as well as the agile development process to deliver the feature in under a month. It also underscores the importance of cross-team collaboration and empowered engineers in achieving ambitious goals.

Oct 2013Under the Hood: Building posts searchThis post details the engineering challenges and solutions for building the posts search feature within Graph Search. It highlights the difficulties in collecting and indexing data from diverse post types stored in MySQL, the use of HBase and Hadoop for index building, and the implementation of Wormhole for real-time index updates. It also discusses the innovative approach to serving the massive posts index by using a combination of solid-state flash memory and RAM, and the sophisticated ranking techniques employed to deliver relevant results from a trillion-post index. The post also notes the project's origin as a Hackathon initiative.

Jun 2013TAO: The power of the graphThis post introduces TAO (The Associations and Objects), a new distributed data storage system designed specifically to handle the demanding workload of serving the social graph. It addresses the limitations of using Memcache and MySQL for complex graph data by providing a dedicated graph data model (objects and associations) and API. TAO optimizes for creation-time locality, handles high read volumes, and simplifies data access for product engineers, directly supporting the on-the-fly rendering of personalized user views like News Feed stories.

Apr 2013Under the Hood: The natural language interface of Graph SearchThis post details the technical underpinnings of Graph Search's natural language interface. It explains how Graph Search moves beyond keyword limitations by utilizing a structured graph of nodes and edges, and how it employs entity recognition, lexical analysis, and semantic parsing with a weighted context-free grammar to interpret complex user queries. The post highlights the iterative process of developing this interface, emphasizing the shift towards natural language for more precise and efficient data querying.

Mar 2013Under the Hood: Indexing and ranking in Graph SearchThis post details the technical implementation of search ranking within Meta's Unicorn search engine, specifically for Graph Search. It explains how Unicorn was extended to handle search ranking by separating entity types into distinct verticals, enhancing retrieval operations, implementing query rewriting and scoring mechanisms (including forward index and result set scoring), blending results from different verticals, and supporting nested queries. The post also outlines the A/B testing framework used to measure ranking changes and describes the life cycle of a Graph Search query through both the query suggestion and search phases, highlighting the role of NLP and the Top Aggregator in processing natural language queries and constructing complex search plans.

Mar 2013Under the Hood: Building out the infrastructure for Graph SearchThis post details the infrastructure built for Graph Search, introducing the Unicorn system as a unified, scalable inverted-index search engine. It explains how Unicorn consolidates previous search systems (PPS, Typeahead) and enables natural language queries over the Facebook graph's entities and relationships. The post elaborates on Unicorn's architecture, indexing mechanisms, multi-hop query capabilities, and the real-time update pipeline, highlighting its role in enabling richer, more connected search experiences.

Dec 2012Under the Hood: Rebuilding Facebook for AndroidThis post details the complete native rewrite of the Facebook for Android app, moving from a hybrid native/webview approach to pure native code. Key technical contributions include: 1. Reducing Garbage Collection by minimizing, deferring, or moving allocations in performance-critical code off the UI thread. 2. Writing a custom, lightweight event bus that avoids reflection and object iterators to reduce operations and memory allocations. 3. Moving bitmaps to the native heap using `inPurgeable` to reduce GC events and out-of-memory errors. 4. Developing a custom ListView recycler to improve scrolling performance for lists with varying row heights by detaching heavy content views and recycling substories.

Oct 2012Interning at Facebook: Building the new mobile advertising interfaceThis post details the development of the first mobile advertising interface for Facebook, specifically for 'Promoted Posts' within the Pages Manager for iOS application. It highlights the strategic importance of mobile advertising, the integration of desktop features like Promoted Posts into mobile, and the technical implementation challenges faced by an intern, including client-side UI development, API scaling, and internationalization. The post also showcases the collaborative effort across teams and the impact of the new interface on Page admin experience.

Oct 2012Under the Hood: Building the App Center recommendation engineThis post details the development of a dedicated recommendation engine for the App Center, a key discovery channel for apps. It introduces the aggregator-leaf architecture, quality determination based on user ratings and active users, and algorithmic elements including candidate selection (demographic, social, past interactions), scoring with explicit, dynamic, and learned latent features, and real-time updates to incorporate user feedback and new data.

Aug 2012Under the hood: Rebuilding Facebook for iOSThis post details the complete rewrite of Facebook for iOS from the ground up, moving away from the Three20 framework and leveraging native iOS development for improved performance and reliability. Key technical advancements include offloading computationally expensive tasks (networking, JSON parsing, NSManagedObject creation, disk saving) from the main thread to background threads, asynchronous string layout calculations using Core Text, and optimizing startup performance by caching story heights in Core Data and fetching full story data asynchronously. The app also adopted a modular architecture, incorporating the codebase from Facebook Messenger, and introduced a "fallback" renderer for new story types to allow for server-side updates without requiring app updates. This represents a significant shift from HTML5-based mobile experiences to a native-first approach for depth and quality.

Jul 2012Under the Hood: Timeline apps behind Facebook engineeringThis post details the integration of Open Graph actions into several internal Facebook tools (Phabricator, Pixelcloud, Scuba, SIOG, StayFitFB, Pokemon) to make them more social and to track user activity. It highlights how Open Graph actions can be used to represent events like code commits, file uploads, data analysis queries, machine learning rule updates, workout tracking, and task completion. The post also discusses the technical aspects of modeling objects and actions for Open Graph, the challenges encountered, and the benefits of using Open Graph for internal dogfooding and developer feedback.

Mar 2012Under the Hood: Building the Location APIThis post details the development of Facebook's Location API, focusing on making location tagging universal across all content types (photos, statuses, app updates). It describes the evolution from the initial Places feature to a global places directory with improved search capabilities, the integration of location data into the Timeline map, the algorithms used for aggregating map pins, and the launch of location APIs for third-party applications to leverage this data. This post significantly expands the 'News Feed and Timeline Evolution' thread by detailing a major new dimension of content aggregation and user experience: location-based context.

Jan 2012Under the Hood: Mobile TimelineThis post details the engineering challenges and solutions for adapting the Facebook Timeline to mobile devices, including touch interfaces, varying device capabilities, and limited screen real estate. Key technical achievements include adaptive hardware acceleration for smooth scrolling, a JavaScript swiping component for horizontal navigation, optimized image loading and sizing for bandwidth efficiency, and design adjustments for better content density and user interaction. The implementation leverages HTML5, device detection, a JavaScript native bridge, and embedded webviews within native applications to achieve a consistent experience across m.facebook.com, Android, and iPhone.

Jan 2012Building Timeline: Scaling up to hold your life storyThis post details the engineering effort behind building Facebook's Timeline feature. Key contributions include: a massive denormalization process for data storage to support ranking over years of activity, custom language and compiler for data format conversion, use of flash-only databases and parallelizing query proxies for join tables, and adoption of a data model compatible with Multifeed. The Timeline aggregator was developed using C++ for performance, and advanced caching strategies (memcached, Flashcache) were implemented. The development process highlighted parallel development through staged prototyping.

Sep 2009Reducing markup sizeThis post details the optimization of the Universal Feedback Interface (UFI) for the Like feature within Facebook's News Feed. The primary technical contribution was reducing the markup size by eliminating the transmission of redundant HTML for the Like/Unlike states. Instead of sending both sets of markup and using CSS to hide one, the system now sends only the necessary markup and uses JavaScript to dynamically update the UI after an asynchronous server call. While an initial attempt to send only the updated sentence markup degraded user experience due to a delay in visual feedback, a hybrid approach was adopted: sending both Like and Unlike links initially, but only updating the sentence asynchronously. This resulted in a significant reduction in the UFI's markup size (from 1.7KB to under 100 bytes for an empty UFI, and 1.7KB to ~900 bytes for a UFI with a like sentence), contributing to a 20KB reduction in the average home.php page size, an 8% improvement.

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