meta·Mar 18, 2026·3 min readCurated summary

Friend Bubbles: Enhancing Social Discovery on Facebook Reels

Friend bubbles in Facebook Reels surface videos that friends have liked or interacted with, combining content discovery with opportunities for conversation. The system uses machine-learning models to estimate viewer-friend closeness, retrieve relevant friend-interacted videos, and rank them alongside conventional recommendation signals. Its goal is not to show the most bubbles possible, but to identify meaningful connections and content that can drive both engagement and social interaction. ## System Architecture - The recommendation system combines: - **Viewer-friend closeness**, determining whose interactions matter most. - **Video relevance**, determining which friend-interacted videos best fit the viewer. - Multiple friends interacting with the same video can indicate stronger shared interest. - Social discovery and engagement reinforce one another: relevant friend content encourages interaction, which improves the system’s understanding of the social graph. ## Modeling Viewer-Friend Closeness - Facebook uses two complementary models: - A survey-based model estimating real-world relationship strength. - An activity-based model estimating closeness from on-platform behavior. - The survey model considers: - Mutual friends and interaction patterns. - User-provided attributes such as location. - Number of friends and posts shared. - Communication frequency and other survey proxies for offline closeness. - Users are periodically asked whether they feel close to a randomly selected connection. - The model is refreshed regularly and performs weekly inference across trillions of friend relationships. - The activity-based model learns from likes, comments, reshares, and interactions occurring after bubbles are shown. - Facebook prioritizes connection quality over quantity: larger friend networks may create more opportunities, but the system aims to surface only relationships likely to make recommendations meaningful. ## Retrieving and Ranking Friend Content ### Expanding Candidate Retrieval - The retrieval stage explicitly sources videos interacted with by close friends. - This expands the recommendation funnel so high-quality friend content can reach downstream ranking systems. - Without dedicated retrieval, relevant friend videos might never become candidates. ### Adding Social Context to Ranking Models - Friend-interacted videos could rank poorly when models lacked viewer-friend closeness information. - The system added bubble interaction signals and relationship-strength features to early- and late-stage multi-task, multi-label ranking models. - These features help models distinguish social relevance from ordinary content-interest signals. - Feedback from bubble impressions and resulting interactions continuously flows back into model training. - Ranking objectives consider: - Watch time. - Likes and comments. - The probability of engagement after a bubble impression: `P(video engagement | bubble impression)`. - Tunable weights balance entertainment and video quality against social goals such as discovering friends’ interests and encouraging conversation. ## Client Infrastructure and Reels Performance - Friend-bubble metadata had to be integrated without harming Reels’ core experience. - The implementation targeted: - Smooth scrolling. - No additional loading latency. - Low CPU usage during metadata retrieval and processing. - Facebook aligned bubble metadata retrieval with the existing video prefetch window, which already loads metadata, thumbnails, and buffered content before playback. - This allows the system to reuse cached results and avoid adding unnecessary work during scrolling. Friend bubbles work best when social relevance and content quality are optimized together. By combining relationship models, friend-aware retrieval and ranking, feedback-driven learning, and performance-conscious client infrastructure, Facebook turns shared video interests into lightweight opportunities for discovery and conversation.

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Feedback LoopDatabase DesignMachine LearningRecommendation Systems+3

grammarly·Jan 29, 2026Original article

How to Use AI Agents: A Simple Guide to Getting Started (opens in new tab)

AI agents represent a shift from reactive, prompt-based AI to proactive, goal-oriented systems capable of planning and executing multi-step tasks with minimal oversight. By operating in a continuous loop of gathering context, selecting tools, and evaluating results, these agents can manage complex workflows that previously required manual follow-up. The most effective implementation strategy involves starting with small, repeatable processes and gradually increasing agent autonomy as reliability is proven through feedback and testing. ### The Mechanism of Agentic AI * Unlike traditional generative AI that responds to isolated instructions, agents possess "agency," allowing them to decide the next best action to reach a defined objective. * Agents function through an iterative operational cycle: they analyze relevant context, select an action, utilize available tools, and evaluate the outcome to determine if the goal is met. * Advanced writing agents, such as those integrated into workplace tools, can proactively suggest revisions for tone, logical progression, and specificity by maintaining contextual awareness across a document's lifecycle. ### Deploying Agents via Repeatable Workflows * Initial use cases should focus on contained, well-understood tasks rather than end-to-end process overhauls to ensure the agent’s logic can be easily monitored. * In research and organization, agents can be tasked with continuously gathering and categorizing sources, updating citations as new data becomes available. * Communication workflows benefit from agents that can reference historical conversation threads to draft follow-ups, summarize long discussions, and adjust meeting agendas dynamically. * Content creation agents can manage the transition from rough notes to structured outlines, applying specific tone and clarity feedback across multiple versions of a draft. ### Integration and Tool Selection * Effective deployment often requires no coding experience, as agentic capabilities are increasingly built into existing word processors, email clients, and project management platforms. * Using familiar software ecosystems reduces the technical barrier to entry and allows for easier scaling of the agent’s behavior over time. * Project management agents can be utilized to monitor task progress, adjust timelines based on changing conditions, and surface high-priority items automatically. ### Establishing Goals and Ownership * Success depends on defining specific end states rather than vague instructions; for example, asking an agent to "flag logical gaps and suggest supporting evidence" is more effective than asking it to "improve writing." * Defining clear ownership ensures the agent knows which parameters to prioritize, such as maintaining a consistent brand voice while revising for conciseness. * Testing should begin with small-scale scenarios, like a single recurring email update, to allow for the refinement of instructions and priorities based on real-world performance. ### Scaling Autonomy and Oversight * Once an agent demonstrates consistent accuracy in a narrow task, its scope can be broadened to include related steps, such as tracking data throughout the week to prepare a draft before being prompted. * Increased autonomy does not mean a lack of control; humans should remain in the loop to provide feedback, which the agent uses to refine its future decision-making logic. * The transition from prompts to progress is achieved by allowing agents to work across different tools and contexts as they prove their ability to handle more complex judgment calls. To get the most out of AI agents, treat them as collaborative partners by starting with a narrow focus and providing specific, goal-oriented feedback. Rather than handing off entire processes immediately, focus on delegating repeatable tasks where the agent’s ability to plan and adapt can yield the highest immediate value.

Feedback LoopAI AgentsAutomationProject Management+2