Zoomer: Powering AI Performance at Meta's Scale Through Intelligent Debugging and Optimization (opens in new tab)

Zoomer is Meta’s centralized, automated platform designed to solve performance bottlenecks and GPU underutilization across its massive AI training and inference infrastructure. By integrating deep analytics with scalable data collection, the tool has become the internal standard for optimizing workloads ranging from Llama 3 training to large-scale ads recommendation engines. Ultimately, Zoomer enables significant energy savings and hardware efficiency gains, allowing Meta to accelerate model iteration and increase throughput across its global fleet of GPUs. ### The Three-Layered Architecture * **Infrastructure and Platform Layer:** This foundation utilizes Meta’s Manifold blob storage for trace data and employs fault-tolerant processing pipelines to manage massive trace files across thousands of hosts. * **Analytics and Insights Engine:** This layer performs deep analysis using specialized tools such as Kineto for GPU traces, NVIDIA DCGM for hardware metrics, and StrobeLight for CPU profiling. It automatically detects performance anti-patterns and provides actionable optimization recommendations. * **Visualization and User Interface Layer:** The presentation layer transforms complex data into interactive timelines and heat maps. It integrates with Perfetto for kernel-level inspection and provides drill-down dashboards that highlight outliers across distributed GPU deployments. ### Automated Profiling and Data Capture * **Trigger Mechanisms:** To ensure data accuracy, Zoomer automatically triggers profiling for training workloads during stable states (typically around iteration 550) to avoid startup noise, while inference workloads use on-demand or benchmark-integrated triggers. * **Comprehensive Metrics:** The platform simultaneously collects GPU SM utilization, Tensor Core usage, memory bandwidth, and power consumption via DCGM. * **System-Level Telemetry:** Beyond the GPU, Zoomer captures host-level data including CPU utilization, storage access patterns, and network I/O through dyno telemetry. * **Distributed Communication:** For large-scale training, the tool analyzes NCCL collective operations and inter-node communication patterns to identify stragglers and network bottlenecks. ### Inference and Training Optimization * **Inference Performance:** Zoomer tracks request/response latency, GPU memory allocation patterns, and Thrift request-level profiling to identify bottlenecks in serving user requests at scale. * **Workflow Acceleration:** By correlating application-level annotations—such as forward/backward passes and optimizer steps—with hardware performance, developers can pinpoint exactly which part of a model's execution is inefficient. * **Operational Impact:** These insights have led to significant improvements in Queries Per Second (QPS) for recommendation models and reduced training times for generative AI features by eliminating resource waste. For organizations managing large-scale AI clusters, the Zoomer model suggests that the key to efficiency is moving away from manual, reactive debugging toward an "always-on" automated profiling system. Correlating high-level software phases with low-level hardware telemetry is essential for maximizing the return on investment for expensive GPU resources and maintaining rapid iteration cycles.

Code Quality Improvement Techniques Part 24: The Value of Legacy (opens in new tab)

The LY Corporation Review Committee advocates for simplifying code by avoiding unnecessary inheritance when differences between classes are limited to static data rather than dynamic logic. By replacing complex interfaces and subclasses with simple data models and specific instances, developers can reduce architectural overhead and improve code readability. This approach ensures that configurations, such as UI themes, remain predictable and easier to maintain without the baggage of a type hierarchy. ### Limitations of Inheritance-Based Configuration * The initial implementation used a `FooScreenThemeStrategy` interface to define UI elements like background colors, text colors, and icons. * Specific themes (Light and Dark) were implemented as separate classes that overridden the interface properties. * This pattern creates an unnecessary proliferation of types when the only difference between the themes is the specific value of the constants being returned. * Using inheritance for simple value changes makes the code harder to follow and can lead to over-engineering. ### Valid Scenarios for Inheritance * **Dynamic Logic:** When behavior needs to change dynamically at runtime via dynamic dispatch. * **Sum Types:** Implementing restricted class hierarchies, such as Kotlin `sealed` classes or Java's equivalent. * **Decoupling:** Separating interface from implementation to satisfy DI container requirements or to improve build speeds. * **Dependency Inversion:** Applying architectural patterns to resolve circular dependencies or to enforce one-way dependency flows. ### Transitioning to Data Models and Instantiation * Instead of an interface, a single "final" class or data class (e.g., `FooScreenThemeModel`) should be defined to hold the required properties. * Individual themes are created as simple instances of this model rather than unique subclasses. * In Kotlin, defining a class without the `open` keyword ensures that the properties are not dynamically altered and that no hidden, instance-specific logic is introduced. * This "instantiation over inheritance" strategy guarantees that properties remain static and the code remains concise. To maintain a clean codebase, prioritize data-driven instantiation over class-based inheritance whenever logic remains constant. This practice reduces the complexity of the type system and makes the code more resilient to unintended side effects.

Connecting thousands of LY Corporation services (opens in new tab)

LY Corporation developed a centralized control plane using Central Dogma to manage service-to-service communication across its vast, heterogeneous infrastructure of physical machines, virtual machines, and Kubernetes clusters. By adopting the industry-standard xDS protocol, the new system resolves the interoperability and scaling limitations of their legacy platform while providing a robust GitOps-based workflow. This architecture enables the company to connect thousands of services with high reliability and sophisticated traffic control capabilities. ## Limitations of the Legacy System The previous control plane environment faced several architectural bottlenecks that hindered developer productivity and system flexibility: * **Tight Coupling:** The system was heavily dependent on a specific internal project management tool (PMC), making it difficult to support modern containerized environments like Kubernetes. * **Proprietary Schemas:** Communication relied on custom message schemas, which created interoperability issues between different clients and versions. * **Lack of Dynamic Registration:** The legacy setup could not handle dynamic endpoint registration effectively, functioning more as a static registry than a functional service mesh control plane. * **Limited Traffic Control:** It lacked the ability to perform complex routing tasks, such as canary releases or advanced client-side load balancing, across diverse infrastructures. ## Central Dogma as a Control Plane To solve these issues, the team leveraged Central Dogma, a Git-based repository service for textual configuration, to act as the foundation for a new control plane: * **xDS Protocol Integration:** The new control plane implements the industry-standard xDS protocol, ensuring seamless compatibility with Envoy and other modern data plane proxies. * **GitOps Workflow:** By utilizing Central Dogma’s mirroring features, developers can manage service configurations and traffic policies safely through Pull Requests in external Git repositories. * **High Reliability:** The system inherits Central Dogma’s native strengths, including multi-datacenter replication, high availability, and a robust authorization system. * **Schema Evolution:** The control plane automatically transforms legacy metadata into standard xDS resources, allowing for a smooth transition from old infrastructure to the new service mesh. ## Dynamic Service Discovery and Registration The architecture provides automated ways to manage service endpoints across different environments: * **Kubernetes Endpoint Plugin:** A dedicated plugin watches for changes in Kubernetes services and automatically updates the xDS resource tree in Central Dogma. * **Automated API Registration:** The system provides gRPC and HTTP APIs (e.g., `RegisterLocalityLbEndpoint`) that allow services to register themselves dynamically during the startup process. * **Advanced Traffic Features:** The new control plane supports sophisticated features like zone-aware routing, circuit breakers, automatic retries, and "slow start" mechanisms for new endpoints. ## Evolution Toward Sidecar-less Service Mesh A major focus of the project is improving the developer experience by reducing the operational overhead of the data plane: * **Sidecar-less Options:** The team is working toward providing service mesh benefits without requiring a sidecar proxy for every pod, which reduces resource consumption and simplifies debugging. * **Unified Control:** Central Dogma acts as a single source of truth for both proxy-based and proxyless service mesh configurations, ensuring consistent policy enforcement across the entire organization. For organizations managing large-scale, heterogeneous infrastructure, transitioning to an xDS-compliant control plane backed by a reliable Git-based configuration store is highly recommended. This approach balances the need for high-speed dynamic updates with the safety and auditability of GitOps, ultimately allowing for a more scalable and developer-friendly service mesh.

Key Transparency Comes to Messenger (opens in new tab)

Messenger has enhanced the security of its end-to-end encrypted chats by launching key transparency, a system that provides an automated, verifiable record of public encryption keys. By moving beyond manual key comparisons, this feature ensures that users can verify their contacts' identities without technical friction, even when those contacts use multiple devices. This implementation allows Messenger to provide a higher level of assurance that no third party, including Meta, has tampered with or swapped the keys used to secure a conversation. ## The Role of Key Transparency in Encrypted Messaging * Provides a verifiable and auditable record of public keys, ensuring that messages are always encrypted with the correct keys for the intended recipient. * Prevents "man-in-the-middle" attacks by a compromised server by making any unauthorized key changes visible to the system. * Simplifies the user experience by automating the verification process, which previously required users to manually compare long strings of characters across every device their contact owned. ## Architecture and Third-Party Auditing * Built upon the open-source Auditable Key Directory (AKD) library, which was previously used to implement similar security properties for WhatsApp. * Partners with Cloudflare to act as a third-party auditor, maintaining a public Key Transparency Dashboard that allows anyone to verify the integrity of the directory. * Leverages an "epoch" system where the directory is updated and published frequently to ensure that the global log of keys remains current and immutable. ## Scaling for Global Messenger Traffic * Manages a massive database that has already grown to billions of entries, reflecting the high volume of users and the fact that Messenger indexes keys for every individual device a user logs into. * Operates at a high frequency, publishing a new epoch approximately every two minutes, with each update containing hundreds of thousands of new key entries. * Optimized the algorithmic efficiency of the AKD library to ensure that cryptographic proof sizes remain small and manageable, even as the number of updates for a single key grows over time. ## Infrastructure Resilience and Recovery * Improved the system's ability to handle temporary outages and long delays in key sequencing, drawing on two years of operational data from the WhatsApp implementation. * Replaced older proof methods that grew linearly with the height of the transparency tree with more efficient operations to maintain high availability and real-time verification speeds. * Established a robust recovery process to ensure that the transparency log remains consistent even after infrastructure disruptions. By automating the verification of encryption keys through a transparent, audited directory, Messenger has made sophisticated cryptographic security accessible to billions of users. This rollout represents a significant shift in how trust is managed in digital communications, replacing manual user checks with a seamless, background-level guarantee of privacy.

Naver TV (opens in new tab)

The Apache Kafka ecosystem is undergoing a significant architectural shift with the introduction of Consumer Group Protocol v2, as outlined in KIP-848. This update addresses long-standing performance bottlenecks and stability issues inherent in the original client-side rebalancing logic by moving the responsibility of partition assignment to the broker. This change effectively eliminates the "stop-the-world" effect during rebalances and significantly improves the scalability of large-scale consumer groups. ### Limitations of the Legacy Consumer Group Protocol (v1) * **Heavy Client-Side Logic:** In v1, the "Group Leader" (a specific consumer instance) is responsible for calculating partition assignments, which creates a heavy burden on the client and leads to inconsistent behavior across different programming language implementations. * **Stop-the-World Rebalancing:** Whenever a member joins or leaves the group, all consumers must stop processing data until the new assignment is synchronized, leading to significant latency spikes. * **Sensitivity to Processing Delays:** Because heartbeats and data processing often share the same thread, a slow consumer can trigger a session timeout, causing an unnecessary and disruptive group rebalance. ### Architectural Improvements in Protocol v2 * **Server-Side Reconciliation:** The reconciliation logic is moved to the Group Coordinator on the broker, simplifying the client and ensuring that partition assignment is managed centrally and consistently. * **Incremental Rebalancing:** Unlike the "eager" rebalancing of v1, the new protocol allows consumers to keep their existing partitions while negotiating new ones, ensuring continuous data processing. * **Decoupled Heartbeats:** The heartbeat mechanism is separated from the main processing loop, preventing "zombie member" scenarios where a busy consumer is incorrectly marked as dead. ### Performance and Scalability Gains * **Reduced Rebalance Latency:** By offloading the assignment logic to the broker, the time required to stabilize a group after a membership change is reduced from seconds to milliseconds. * **Large-Scale Group Support:** The new protocol is designed to handle thousands of partitions and hundreds of consumers within a single group without the exponential performance degradation seen in v1. * **Stable Deployments:** During rolling restarts or deployments, the group remains stable and avoids the "rebalance storms" that typically occur when multiple instances cycle at once. ### Migration and Practical Implementation * **Configuration Requirements:** Users can opt-in to the new protocol by setting the `group.protocol` configuration to `consumer` (introduced as early access in Kafka 3.7 and standard in 4.0). * **Compatibility:** While the new protocol requires updated brokers and clients, it is designed to support a transition phase to allow organizations to migrate their workloads gradually. * **New Tooling:** Updated command-line tools and metrics are provided to monitor the server-side assignment process and track group state more granularly. Organizations experiencing frequent rebalance issues or managing high-throughput Kafka clusters should plan for a migration to Consumer Group Protocol v2. Transitioning to this server-side assignment model is highly recommended for stabilizing production environments and reducing the operational overhead associated with consumer group management.

Reducing EV range anxiety: How a simple AI model predicts port availability (opens in new tab)

Google Research has developed a lightweight AI model designed to predict the probability of EV charging port availability at specific future intervals, directly addressing the "range anxiety" experienced by electric vehicle drivers. By co-designing the model with deployment infrastructure, researchers found that a simple linear regression approach outperformed more complex architectures like neural networks and decision trees. The resulting system effectively predicts availability changes during high-turnover periods, providing more reliable navigation and planning data than traditional "no-change" assumptions. ### Model Architecture and Feature Selection * The development team prioritized a minimal feature set to ensure low-latency deployment and high speed in real-world navigational applications. * After testing various architectures, a straightforward linear regression model was selected for its robustness and superior performance in this specific predictive task. * The model was trained using real-time availability data from diverse geographical regions, specifically California and Germany, with an emphasis on larger charging stations that reflect high-traffic usage patterns. ### Temporal Feature Weights and Occupancy Trends * The model uses the hour of the day as a primary feature, treating each hour as an independent variable to capture specific daily cycles. * Learned numerical "weights" dictate the predicted rate of occupancy change: positive weights indicate ports are becoming occupied (e.g., during morning rush), while negative weights indicate ports are being freed up (e.g., during evening hours). * The system is designed to only deviate from the current occupancy state when the change rate is statistically significant or when a station's large size amplifies the likelihood of a status change. ### Performance Benchmarking and Validation * The model was evaluated against a "Keep Current State" baseline, which assumes future availability will be identical to the present status—a difficult baseline to beat since port status remains unchanged roughly 90% of the time over 30-minute windows. * Accuracy was measured using Mean Squared Error (MSE) and Mean Absolute Error (MAE) over 30-minute and 60-minute time horizons across 100 randomly selected stations. * Testing confirmed that the linear regression model provides its greatest value during infrequent but critical moments of high turnover, successfully identifying when a station is likely to become full or available. The success of this model demonstrates that sophisticated deep learning is not always the optimal solution for infrastructure challenges. By combining intuitive real-world logic—such as driver schedules and station capacity—with simple machine learning techniques, developers can create highly efficient tools that significantly improve the EV user experience without requiring massive computational overhead.

Creativity meets precision with Gemini 3 Pro Image Pro | Figma Blog (opens in new tab)

Gemini 3 Pro is now available in Figma Make Inside Figma Figma Make AI Product updates Design Prototyping News

How the POPM Course Became a (opens in new tab)

Kakao developed its internal POPM (Product Owner/Product Manager) training program by treating the curriculum itself as an evolving product rather than a static lecture series. By applying agile methodologies such as data-driven prioritization and iterative versioning, the program successfully moved from a generic pilot to a structured framework that aligns teams through a shared language of problem-solving. This approach demonstrates that internal capability building is most effective when managed with the same rigor and experimentation used in software development. ## Strategic Motivation for POPM Training * Addressed the inherent ambiguity of the PO/PM role, where non-visible tasks often make it difficult for practitioners to define their own growth or impact. * Sought to resolve the disconnect between strategic problem definition (PO) and tactical execution (PM) within Kakao’s teams. * Prioritized the creation of a "common language" to allow cross-functional team members to define problems, analyze metrics, and design experiments under a unified structure. ## Iterative Design and Versioning * The program transitioned through multiple "versions," starting with an 8-session pilot that covered the entire lifecycle from bottleneck exploration to execution review. * Based on participant feedback regarding high fatigue and low efficiency in long presentations, the curriculum was condensed into 5 core modules: Strategy, Metrics, Experiment, Design, and Execution. * The instructional design shifted from "delivering information" to "designing a rhythm," utilizing a "one slide, one question, one example" rule to maintain engagement. ## Data-Driven Program Refinement * Applied a "Product Metaphor" to education by calculating "Opportunity Scores" using a matrix of Importance vs. Satisfaction for each session. * Identified "Data/Metrics" as the highest priority for redesign because it scored high in importance but low in satisfaction, indicating a structural gap in the teaching method. * Refined the "features" of the training by redesigning worksheets to focus on execution routines and converting mandatory practice tasks into selective, flexible modules. ## Structural Insights for Organizational Growth * Focused on accumulating "structure" rather than just training individuals, ensuring that even as participants change, the framework for defining problems remains consistent within the organization. * Designed practice sessions to function as "thinking structures" rather than "answer-seeking" exercises, encouraging teams to bring their training insights directly into actual team meetings. * Prioritized scalability and simplicity in the curriculum to ensure the structure can be adopted across different departments with varying product needs. To build effective internal capabilities, organizations should treat training as a product that requires constant maintenance and versioning. Instead of focusing on one-off lectures, leaders should design structural "rhythms" and feedback loops that allow the curriculum to evolve based on the actual pain points of the practitioners.

Were We Solving the Real Problem (opens in new tab)

The POPM (Product Owner/Product Manager) training course at Kakao focuses on restructuring existing professional knowledge into a cohesive framework for solving real-world business problems. Rather than simply delivering new information, the program emphasizes aligning strategy with execution, transforming "strategy" from a vague concept into a practical set of decision-making criteria. The ultimate goal is to move teams away from a "release-only" mindset toward a cycle of continuous hypothesis verification and learning. ### Strategic Thinking and Metric Modeling * **Strategic Decision Criteria**: Strategy is redefined as the standard for team judgment, utilizing frameworks like MECE, MVP, and priority-setting models to align daily tasks with long-term goals. * **Metrics as Problem-Solving Language**: Key indicators such as Funnel, Retention, Cohort, and LTV are treated not just as data points, but as a language used to define and reveal underlying product issues. * **Context-Based Design**: UX design is approached through "context-based logic" rather than intuition, encouraging teams to ask which specific design fits the current user journey. ### Systematic Experimentation and A/B Testing * **The MASS Framework**: Experiments are designed and evaluated based on being Measurable, Attributable, Sensitive, and having a Short-term cycle. * **Failure Analysis Routines**: The curriculum emphasizes the importance of establishing a routine for interpreting failed experiments, ensuring that every test contributes to the team's institutional knowledge. * **Incremental Testing**: Encourages a culture of "starting small," giving teams the confidence to run experiments without requiring massive resource allocation. ### Building Repeatable Execution Loops * **Metric-Based Retrospectives**: Teams transition from simply finishing a release to a structured loop of "Problem Definition → Hypothesis → Metric → Verification → Retrospective." * **Formalizing Problem Definitions**: Using templates to 명문화 (formally document) the problem, expected behavior, and success metrics ensures that the entire team—not just the PO—understands the "why" behind every task. * **Operational Rhythms**: Teams are adopting fixed weekly or bi-weekly cycles for sharing insights and adjusting priorities, turning data-driven execution into a natural habit. The most critical takeaway for product teams is to constantly ask: "Is the work we are doing right now actually a solution to a defined problem, or are we just busy releasing features?" Success lies in moving beyond the sense of accomplishment from a launch and establishing a repeatable rhythm that validates whether those efforts truly move the needle.

Beyond LTV: MTV (opens in new tab)

Toss has developed MTVi (Mid-term Value - incremental) to quantify the financial impact of specific services within its platform, moving beyond the limitations of traditional LifeTime Value (LTV). By focusing on the incremental value generated over a one-year period, the metric allows the company to justify services that may lose money individually but drive significant ecosystem-wide growth. This framework provides a data-driven standard for prioritizing features and setting marketing budgets based on actual financial contributions. ### Limitations of Traditional LTV * **Time Horizon Mismatch:** Traditional LTV projects value over 3 to 5 years, which is too slow for Toss’s rapid iteration cycles and fails to reflect the immediate impact of service improvements. * **Investment Recovery Gaps:** Standard LTV models often benchmark marketing costs (CAC) against long-term projections, making it difficult to evaluate the efficiency of short-term experiments. * **Lack of Incrementality:** LTV measures average user value but cannot isolate the specific "extra" value created by a single service, making it impossible to distinguish between a service's impact and natural user growth. ### Defining MTVi and DID Methodology * **Incremental Focus:** MTVi is defined as the net financial value generated over one year specifically because a user experienced a new service, rather than just the average revenue of a user. * **Quasi-Experimental Design:** Since A/B testing every service combination is impossible, Toss uses the Difference-in-Difference (DID) method to compare "Newly Activated Users" (NAU) against "Never" users. * **Segment-Based Analysis:** To prevent bias—such as highly active users naturally gravitating toward more services—Toss segments users by age and historical activity (e.g., app open frequency) to ensure "apples-to-apples" comparisons within identical cohorts. ### Organizational Impact and Strategy * **Unified Decision Metric:** MTVi provides a "common language" for different product teams (silos), allowing them to compare the value of disparate services—like pedometers versus remittances—on a single financial scale. * **Efficiency Benchmarking:** The metric establishes a hard ceiling for investment; for example, Customer Acquisition Cost (CAC) is strictly managed so it does not exceed the calculated MTVi. * **Platform-Wide Valuation:** By calculating both direct revenue and indirect spillover effects, Toss can prove the financial viability of "loss-leader" services that provide user benefits but increase overall app engagement and cross-service usage. For organizations operating complex multi-service platforms, adopting an incremental value metric like MTVi is essential for moving beyond isolated P&L statements. Data teams should prioritize quasi-experimental methods like DID and rigorous user segmentation to accurately map how individual features influence the broader financial health of the ecosystem.

Naver TV (opens in new tab)

JVM applications often suffer from initial latency spikes because the Just-In-Time (JIT) compiler requires a "warm-up" period to optimize frequently executed code into machine language. While traditional strategies rely on simulated API calls to trigger this optimization, these methods often introduce side effects like data pollution, log noise, and increased maintenance overhead. This new approach advocates for a library-centric warm-up that targets core execution paths and dependencies directly, ensuring high performance from the first real request without the risks of full-scale API simulation. ### Limitations of Traditional API-Based Warm-up * **Data and State Pollution:** Simulated API calls can inadvertently trigger database writes, send notifications, or pollute analytics data, requiring complex logic to bypass these side effects. * **Maintenance Burden:** As business logic and API signatures change, developers must constantly update the warm-up scripts or "dummy" requests to match the current application state. * **Operational Risk:** Relying on external dependencies or complex internal services during the warm-up phase can lead to deployment failures if the mock environment is not perfectly aligned with production. ### The Library-Centric Warm-up Strategy * **Targeted Optimization:** Instead of hitting the entry-point controllers, the focus shifts to warming up heavy third-party libraries and internal utility classes (e.g., JSON parsers, encryption modules, and DB drivers). * **Internal Execution Path:** By directly invoking methods within the application's service or infrastructure layer during the startup phase, the JIT compiler can reach "Tier 4" (C2) optimization for critical code blocks. * **Decoupled Logic:** Because the warm-up targets underlying libraries rather than specific business endpoints, the logic remains stable even when the high-level API changes. ### Implementation and Performance Verification * **Reflection and Hooks:** The implementation uses application startup hooks to execute intensive code paths, ensuring the JVM is "hot" before the load balancer begins directing traffic to the instance. * **JIT Compilation Monitoring:** Success is measured by tracking the number of JIT-compiled methods and the time taken to reach a stable state, specifically targeting the reduction of "cold" execution time. * **Latency Improvements:** Empirical data shows a significant reduction in P99 latency during the first few minutes of deployment, as the most CPU-intensive library functions are already pre-optimized. ### Advantages and Practical Constraints * **Safer Deployments:** Removing the need for simulated network requests makes the deployment process more robust and prevents accidental side effects in downstream systems. * **Granular Control:** Developers can selectively warm up only the most performance-sensitive parts of the application, saving startup time compared to a full-system simulation. * **Incomplete Path Coverage:** A primary limitation is that library-only warming may miss specific branch optimizations that occur only during full end-to-end request processing. To achieve the best balance between safety and performance, engineering teams should prioritize warming up shared infrastructure libraries and high-overhead utilities. While it may not cover 100% of the application's execution paths, a library-based approach provides a more maintainable and lower-risk foundation for JVM performance tuning than traditional request-based methods.

Efficient Optimization With Ax, an Open Platform for Adaptive Experimentation (opens in new tab)

Meta has released Ax 1.0, an open-source platform designed to automate and optimize complex, resource-intensive experimentation through machine learning. By utilizing Bayesian optimization, the platform helps researchers navigate vast configuration spaces to improve AI models, infrastructure, and hardware design efficiently. The release aims to bridge the gap between sophisticated mathematical theory and the practical requirements of production-scale engineering. ## Real-World Experimentation and Utility * Ax is used extensively at Meta for diverse tasks, including tuning hyperparameter configurations, discovering optimal data mixtures for Generative AI, and optimizing compiler flags. * The platform is built to handle the logistical "overhead" of experimentation, such as managing experiment states, automating orchestration, and providing diagnostic tools. * It supports multi-objective optimization, allowing users to balance competing metrics and enforce "guardrail" constraints rather than just maximizing a single value. * Applications extend beyond software to physical engineering, such as optimizing design parameters for AR/VR hardware. ## System Insight and Analysis * Beyond finding optimal points, Ax serves as a diagnostic tool to help researchers understand the underlying behavior of their systems. * It includes built-in visualizations for Pareto frontiers, which illustrate the trade-offs between different metrics. * Sensitivity analysis tools identify which specific input parameters have the greatest impact on the final results. * The platform provides automated plots and tables to track optimization progress and visualize the effect of parameters across the entire input space. ## Technical Methodology and Architecture * Ax utilizes Bayesian optimization, an iterative approach that balances "exploration" (sampling new areas) with "exploitation" (refining known good areas). * The platform relies on **BoTorch** for its underlying Bayesian components and typically employs **Gaussian processes (GP)** as surrogate models. * GPs are preferred because they can make accurate predictions and quantify uncertainty even when provided with very few data points. * The system uses an **Expected Improvement (EI)** acquisition function to calculate the potential value of new configurations compared to the current best-known result. * This surrogate-based approach is designed to scale to high-dimensional settings involving hundreds of tunable parameters where traditional search methods are too costly. To begin implementing these methods, developers can install the platform via `pip install ax-platform`. Ax 1.0 provides a robust framework for moving cutting-edge optimization research directly into production environments.

How to Link Discord to Battlefield 6, Marvel Rivals & More (opens in new tab)

Discord is enhancing the multiplayer experience by allowing users to link their accounts directly to supported games, bridging the gap between external social platforms and in-game environments. This native integration provides players with more seamless communication tools and matchmaking capabilities without needing to switch between applications or use secondary overlays. ### Native Social Features and Messaging * **Integrated Friend Lists:** Discord contacts now appear directly within the game’s internal friends list, making it easier to see who is online across platforms. * **Streamlined Matchmaking:** Players can invite Discord friends to game sessions with a single click from the in-game menu. * **Cross-Platform Chat:** Bidirectional messaging allows players to use in-game chat to communicate with friends on the Discord app, with replies appearing directly within the game interface. ### Advanced Rich Presence * **Granular Status Updates:** The integration displays specific details about a player's current activity, such as whether they are pushing a specific objective or playing a casual game mode. * **Enhanced Visibility:** These detailed statuses allow friends to see exactly what is happening in a match before deciding to join or send a message. ### Implementation and Supported Titles * **Featured Games:** These Discord-powered features are currently available for major multiplayer titles including *Battlefield 6* and *Marvel Rivals*. * **Account Linking:** To access these capabilities, players must manually link their Discord accounts within the settings menu of the specific game. While these features are live for the specified titles at the time of publication, the available functionality may evolve as developers continue to refine the integration. Players looking for a more unified social experience should check their game settings to enable these Discord-powered tools.

5 Shifts Redefining Design Systems in the AI Era | Figma Blog (opens in new tab)

Schema 2025: Design systems for a new era Inside Figma Design systems Product updates Events News

Reward Your Play: Complete Quests. Earn Orbs. Get Sweet Stuff. (opens in new tab)

Discord has introduced Discord Orbs, a new virtual currency earned by completing specific Quests across both desktop and mobile platforms. These Orbs serve as a reward mechanism that allows users to accumulate a balance through platform engagement and redeem it for various digital goods. By integrating these rewards directly into the Discord Shop, the platform provides a clear path for users to earn premium features through active participation. ### Earning Discord Orbs * Users can acquire Orbs by participating in and successfully finishing designated Quests found on the platform’s Quest page. * The currency is available to users on both the desktop client and mobile applications. * The availability of Orb-earning opportunities varies based on the specific Quests currently active in a user’s region or account. ### Redemption and Shop Integration * Earned Orbs are stored in a "spherical stash" and can be spent exclusively within the Discord Shop. * Rewards include Orb-themed profile items and cosmetic decorations to customize user presence. * A notable high-value redemption option is the 3-Day Nitro credit, allowing users to access premium features for a limited time. * The currency can also be applied toward many existing favorite items already available in the standard Shop rotation. To begin collecting this new currency, users should navigate to their Quests page to identify which active challenges currently offer Orbs as a reward. This system offers a practical way for non-subscribers to test Nitro features or collect profile cosmetics through gameplay and platform activity.