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meta

How AI Is Transforming the Adoption of Secure-by-Default Mobile Frameworks - Engineering at Meta (opens in new tab)

Meta utilizes secure-by-default frameworks to wrap potentially unsafe operating system and third-party functions, ensuring security is integrated into the development process without sacrificing developer velocity. By leveraging generative AI and automation, the company scales the adoption of these frameworks across its massive codebase, effectively mitigating risks such as Android intent hijacking. This approach balances high-level security enforcement with the practical need for friction-free developer experiences. ## Design Principles for Secure-by-Default Frameworks To ensure high adoption and long-term viability, Meta follows specific architectural guidelines when building security wrappers: * **API Mirroring:** Secure framework APIs are designed to closely resemble the existing native APIs they replace (e.g., mirroring the Android Context API). This reduces the cognitive burden on developers and simplifies the use of automated tools for code conversion. * **Reliance on Public Interfaces:** Frameworks are built exclusively on public and stable APIs. Avoiding private or undocumented OS interfaces prevents maintenance "fire drills" and ensures the frameworks remain functional across various OS updates. * **Modularity and Reach:** Rather than creating a single monolithic tool, Meta develops small, modular libraries that target specific security issues while remaining usable across all apps and platform versions. * **Friction Reduction:** Frameworks must avoid introducing excessive complexity or noticeable performance overhead in terms of CPU and RAM, as high friction often leads developers to bypass security measures entirely. ## SecureLinkLauncher: Preventing Android Intent Hijacking SecureLinkLauncher (SLL) is a primary example of a secure-by-default framework designed to stop sensitive data from leaking via the Android intent system. * **Wrapped Execution:** SLL wraps native Android methods such as `startActivity()` and `startActivityForResult()`. Instead of calling `context.startActivity(intent)`, developers use `SecureLinkLauncher.launchInternalActivity(intent, context)`. * **Scope Verification:** The framework enforces scope verification before delegating to the native API. This ensures that intents are directed to intended "family" apps rather than being intercepted by malicious third-party applications. * **Mitigating Implicit Intents:** SLL addresses the risks of untargeted intents, which can be received by any app with a matching intent-filter. By enforcing a developer-specified scope, SLL ensures that data like `SECRET_INFO` is only accessible to authorized packages. ## Scaling Adoption through AI and Automation The transition from legacy, insecure patterns to secure frameworks is managed through a combination of automated tooling and artificial intelligence. * **Automated Migration:** Generative AI identifies insecure usage patterns across Meta’s vast codebase and suggests—or automatically applies—the appropriate secure framework replacements. * **Continuous Monitoring:** Automation tools continuously scan the codebase to ensure compliance with secure-by-default standards, preventing the reintroduction of vulnerable code. * **Scaling Consistency:** By reducing the manual effort required for refactoring, AI enables consistent security enforcement across different teams and applications without slowing down the shipping cycle. For organizations managing large-scale mobile codebases, the recommended approach is to build thin, developer-friendly wrappers around risky platform APIs and utilize automated refactoring tools to drive adoption. This ensures that security becomes an invisible, default component of the development lifecycle rather than a manual checklist.

androidaigen-aiautomation+5
woowahan

We Did Everything from Planning to (opens in new tab)

The 7th Woowacourse crew has successfully launched three distinct services, demonstrating that modern software engineering requires a synergy of technical mastery and "soft skills" like product planning and team communication. By owning the entire lifecycle from ideation to deployment, these developers moved beyond mere coding to solve real-world problems through agile iterations, user feedback, and robust infrastructure management. The program’s focus on the full stack of development—including monitoring, 2-week sprints, and collaborative design—highlights a shift toward producing well-rounded engineers capable of navigating professional environments. ### The Woowacourse Full-Cycle Philosophy * The 10-month curriculum emphasizes soft skills, including speaking and writing, alongside traditional technical tracks like Web Backend, Frontend, and Mobile Android. * During Level 3 and 4, crews transition from fundamental programming to managing team projects where they must handle everything from initial architecture to UI/UX design. * The process mimics real-world industry standards by implementing 2-week development sprints, establishing monitoring environments, and managing automated deployment pipelines. * The core goal is to shift the developer's mindset from simply writing code to understanding why certain features are planned and how architecture choices impact the final user value. ### Pickeat: Collaborative Dining Decisions * This service addresses "decision fatigue" during group meals by providing a collaborative platform to filter restaurants based on dietary constraints and preferences. * Technical challenges included frequent domain restructuring and UI overhauls as the team pivoted based on real-world user feedback during demo days. * The platform utilizes location data for automatic restaurant lookups and supports real-time voting mechanisms to ensure democratic and efficient group decisions. * Development focused on aligning team judgment standards and iterating quickly to validate product-market fit rather than adhering strictly to initial specifications. ### Bottari: Real-Time Synchronized Checklists * Bottari is a checklist service designed for situations like traveling or moving, focusing on "becoming a companion for the user’s memory." * The service features template-based list generation and a "Team Bottari" function that allows multiple users to collaborate on a single list with real-time synchronization. * A major technical focus was placed on the user experience flow, specifically optimizing notification timing and sync states to provide "peace of mind" for users. * The project demonstrates the principle that technology serves as a tool for solving psychological pain points, such as the anxiety of forgetting essential items. ### Coffee Shout: Real-Time Betting and Mini-Games * Designed to gamify office culture, this service replaces simple "rock-paper-scissors" with interactive mini-games and weighted roulette for coffee bets. * The technical stack involved challenging implementations of WebSockets and distributed environments to handle the concurrency required for real-time gaming. * The team focused on algorithm balancing for the weighted roulette system to ensure fairness and excitement during the betting process. * Refinement of the service was driven by direct feedback from other Woowacourse crews, emphasizing the importance of community testing in the development lifecycle. These projects underscore that the transition from a student to a professional developer is defined by the ability to manage shifting requirements and technical complexity while maintaining a focus on the end-user's experience.

androiddistributed-systemsmonitoringfrontend+4
netflix

AV1 — Now Powering 30% of Netflix Streaming | by Netflix Technology Blog | Dec, 2025 | Netflix TechBlog (opens in new tab)

Netflix has successfully integrated the AV1 codec into its streaming infrastructure, where it now accounts for 30% of all viewing traffic and is on track to become the platform's primary format. This transition from legacy standards like H.264/AVC is driven by AV1's superior compression efficiency, which allows for higher visual quality at significantly lower bitrates. By leveraging this open-source technology, Netflix has enhanced the user experience across a diverse range of devices while simultaneously optimizing global network bandwidth. ### Evolution of AV1 Adoption The journey to 30% adoption began with a strategic rollout across different device ecosystems, balancing software flexibility with hardware requirements. * **Mobile Origins:** The rollout started in 2020 on Android using the "dav1d" software decoder, which was specifically optimized for ARM chipsets to provide better quality for data-conscious mobile users. * **Large Screen Integration:** In 2021, Netflix expanded AV1 to Smart TVs and streaming sticks, working closely with SoC vendors to certify hardware decoders capable of handling 4K and high frame rate (HFR) content. * **Ecosystem Expansion:** Support was extended to web browsers in 2022 and eventually to the Apple ecosystem in 2023 following the introduction of hardware AV1 support in M3 and A17 Pro chips. ### Quantifiable Performance Gains The shift to AV1 has resulted in measurable improvements in video fidelity and streaming stability compared to previous standards. * **Visual Quality:** On average, AV1 streaming sessions achieve VMAF scores that are 4.3 points higher than AVC and 0.9 points higher than HEVC. * **Bandwidth Efficiency:** AV1 sessions require approximately one-third less bandwidth than both AVC and HEVC to maintain the same level of quality. * **Reliability:** The increased efficiency has led to a 45% reduction in buffering interruptions, making high-quality 4K streaming more accessible in regions with limited network infrastructure. ### Live Streaming and Spatial Video Beyond standard video-on-demand, Netflix is utilizing AV1 to power its latest innovations in live broadcasting and immersive media. * **Live Events:** For major live events, such as the Jake Paul vs. Mike Tyson fight, Netflix utilized 10-bit AV1 to provide better resilience against packet loss and lower latency compared to traditional codecs. * **Immersive Content:** AV1 serves as the backbone for spatial video on devices like the Apple Vision Pro, delivering high-bitrate HDR content necessary for a convincing "cinema-grade" experience. As AV1 continues to displace older codecs, the industry is already looking toward the next milestone with the upcoming release of AV2. For developers and hardware manufacturers, the rapid success of AV1 underscores the importance of supporting open-source media standards to meet the increasing consumer demand for high-fidelity, low-latency streaming.

androidvideo-streamingav1video-codecs+4
line

Code Quality Improvement Techniques Part 22 (opens in new tab)

The post argues that developers should avoid overriding the `equals` method to compare only a subset of an object’s properties, as this violates the fundamental principles of identity and structural equivalence. Implementing "partial equality" often leads to subtle, hard-to-trace bugs in reactive programming environments where UI updates depend on detecting changes through equality checks. To ensure system reliability, `equals` must strictly represent either referential identity or total structural equivalence. ### Risks of Partial Equality in Reactive UI * Reactive frameworks such as Kotlin’s `StateFlow`, `Flow`, and Android’s `LiveData` utilize `distinctUntilChanged` logic to optimize performance. * These "observable" patterns compare the new object instance with the previous one using `equals`; if the result is `true`, the update is ignored to prevent unnecessary re-rendering. * If a `UserProfileViewData` object only compares a `userId` field, the UI will fail to reflect changes to a user's nickname or profile image because the framework incorrectly assumes the data has not changed. * To avoid this, any comparison logic that only checks specific fields should be moved to a uniquely named function, such as `hasSameIdWith()`, instead of hijacking the standard `equals` method. ### Defining Identity vs. Equivalence * **Identity (Referential Equality):** This indicates that two references point to the exact same object instance, which is the default behavior of `Object.equals()` in Java or `Any.equals()` in Kotlin. * **Equivalence (Structural Equality):** This indicates that two objects are logically the same because all their properties match. In Kotlin, `data class` implementations provide this by default for all parameters defined in the primary constructor. * Proper implementation of equivalence requires that all fields within the object also have clearly defined equality logic. ### Nuances and Implementation Exceptions * **Kotlin Data Class Limitations:** Only properties declared in the primary constructor are included in the compiler-generated `equals` and `hashCode` methods; properties declared in the class body are ignored by default. * **Calculated Caches:** It is acceptable to exclude certain fields from an equality check if they do not change the logical state of the object, such as a `cachedValue` used to store the results of a heavy mathematical operation. * **Context-Dependent Equality:** The definition of equality can change based on the model's purpose. For example, a mathematical model might treat 1/2 and 2/4 as equal, whereas a UI display model might treat them as different because they represent different strings of text. When implementing `equals`, prioritize full structural equivalence to prevent data-stale bugs in reactive systems. If you only need to compare a unique identifier, create a dedicated method instead of repurposing the standard equality check.

androidcode-qualitykotlinsoftware-design+3
google

Android Earthquake Alerts: A global system for early warning (opens in new tab)

Google’s Android Earthquake Alerts system utilizes the onboard accelerometers of billions of smartphones to create a global, crowdsourced seismic network. By detecting the initial P-waves of an earthquake and rapidly processing aggregate data, the system provides critical early warnings to regions that often lack traditional, expensive seismic infrastructure. This technological shift has expanded earthquake early warning access from roughly 250 million people to over 2.5 billion worldwide. ### Leveraging On-Device Accelerometers * Mobile accelerometers, typically used for screen orientation, function as mini-seismometers capable of detecting the initial, fast-moving P-waves of an earthquake. * When a stationary phone detects these vibrations, it sends a signal along with a coarse location to a centralized detection server. * The system aggregates these signals to confirm the event and estimate its magnitude before the slower, more destructive S-waves reach the population. ### Global Reach and Implementation * Active in nearly 100 countries, the system has detected over 18,000 earthquakes ranging from M1.9 to M7.8. * The system has issued alerts for over 2,000 significant earthquakes, resulting in approximately 790 million alerts sent to users globally. * By utilizing existing consumer hardware, the system serves as a "global safety net" for earthquake-prone regions that cannot afford traditional ground-based sensor networks. ### Magnitude Estimation and Accuracy * A primary technical challenge is the trade-off between speed and accuracy; the first few seconds of data are limited but essential for providing early warnings. * Over three years, the system's median absolute error for initial magnitude estimates has been reduced from 0.50 to 0.25. * The accuracy of these smartphone-based detections is now comparable to—and in some cases exceeds—the performance of established traditional seismic networks. ### User Alerts and Real-World Impact * The system delivers two tiers of notifications: "BeAware" alerts for light shaking and "TakeAction" alerts, which use full-screen takeovers and loud sounds for intense shaking. * During a magnitude 6.7 earthquake in the Philippines, the system issued alerts 18.3 seconds after the quake began, providing users further from the epicenter up to 60 seconds of lead time. * To maintain privacy, the system relies on coarse location data and requires users to have Wi-Fi or cellular connectivity and location settings enabled. For users in seismic zones, ensuring that Android Earthquake Alerts and location services are enabled provides a vital layer of protection. This crowdsourced model demonstrates how ubiquitous consumer technology can be repurposed to provide essential public safety infrastructure on a planetary scale.

androidreal-time-data-processingearthquake-early-warningaccelerometers+2
line

Code Quality Improvement Techniques Part 14 (opens in new tab)

Applying the Single Responsibility Principle is a fundamental practice for maintaining high code quality, but over-fragmenting logic can inadvertently lead to architectural complexity. While splitting classes aims to increase cohesion, it can also scatter business constraints and force callers to manage an overwhelming number of dependencies. This post explores the "responsibility of assigning responsibility," arguing that sometimes maintaining a slightly larger, consolidated class is preferable to creating fragmented "Ravioli code." ### Initial Implementation and the Refactoring Drive The scenario involves a dynamic "Launch Button" that can fire rockets, fireworks, or products depending on its mode. * The initial design used a single `LaunchButtonBinder` that held references to all possible `Launcher` types and an internal enum to select the active one. * To strictly follow the Single Responsibility Principle, developers often attempt to split this into two parts: a binder for the button logic and a selector for choosing the mode. * The refactored approach utilized a `LaunchBinderSelector` to manage multiple `LaunchButtonBinder` instances, using an `isEnabled` flag to toggle which logic was active. ### The Problem of Scattered Constraints and State While the refactored classes are individually simpler, the overall system becomes harder to reason about due to fragmented logic. * **Verification Difficulty:** In the original code, the constraint that "only one thing launches at a time" was obvious in a single file; in the refactored version, a developer must trace multiple classes and loops to verify this behavior. * **State Redundancy:** Adding an `isEnabled` property to binders creates a risk of state synchronization issues between the selector’s current mode and the binders' internal flags. * **Information Hiding Trade-offs:** Attempting to hide implementation details often forces the caller to resolve all dependencies (binders, buttons, and launchers) manually, which can turn the caller into a bloated "God class." ### Avoiding "Ravioli Code" Through Balanced Design The pursuit of granular responsibilities can lead to "Ravioli code," where the system consists of many small, independent components but lacks a clear, cohesive structure. * The original implementation’s advantage was that it encapsulated all logic related to the launch button's constraints in one place. * When deciding to split a class, developers must evaluate if the move improves the overall system or simply shifts the burden of complexity to the caller. * Effective design requires balancing individual class cohesion with the overhead of inter-module coupling and dependency management. When refactoring for code quality, prioritize the clarity of the overall system over the dogmatic pursuit of small classes. If splitting a class makes it harder to verify business constraints or complicates the caller's logic significantly, it may be better to keep those related responsibilities together.

androidcode-qualitykotlinrefactoring+3
google

Amplify Initiative: Localized data for globalized AI (opens in new tab)

The Amplify Initiative by Google Research addresses the critical lack of linguistic and cultural diversity in generative AI training data by establishing an open, community-based platform for localized data collection. By partnering with regional experts to co-create structured, high-quality datasets, the initiative aims to ensure AI models are both representative and effective in solving local challenges across health, finance, and education. This approach shifts data collection from a top-down model to a participatory framework that prioritizes responsible, locally respectful practices in the Global South. ## The Amplify Platform Framework The initiative is designed to bridge the gap between global AI capabilities and local needs through three core pillars: * **Participatory Co-creation:** Researchers and local communities collaborate to define specific data needs, ensuring the resulting datasets address region-specific problems like financial literacy or localized health misinformation. * **Open Access for Innovation:** The platform provides high-quality, multilingual datasets suitable for fine-tuning and evaluating models, specifically empowering developers in the Global South to build tools for their own communities. * **Author Recognition:** Contributors receive tangible rewards, including professional certificates, research acknowledgments, and data authorship attribution, creating a sustainable ecosystem for expert participation. ## Pilot Implementation in Sub-Saharan Africa To test the methodology, Google Research partnered with Makerere University’s AI Lab in Uganda to conduct an on-the-ground pilot program. * **Expert Onboarding:** The program trained 259 experts across Ghana, Kenya, Malawi, Nigeria, and Uganda through a combination of in-person workshops and app-based modules. * **Dataset Composition:** The pilot resulted in 8,091 annotated adversarial queries across seven languages, covering salient domains such as education and finance. * **Adversarial Focus:** By focusing on adversarial queries, the team captured localized nuances of potential AI harms, including regional stereotypes and specialized advice that generic models often miss. ## Technical Workflow and App-Based Methodology The initiative utilizes a structured technical pipeline to scale data collection while maintaining high quality and privacy. * **Privacy-Preserving Android App:** A dedicated app serves as the primary interface for training, data creation, and annotation, allowing experts to contribute from their own environments. * **Automated Validation:** The app includes built-in feedback loops that use automated checks to ensure queries are relevant and to prevent the submission of semantically similar or duplicate entries. * **Domain-Specific Annotation:** Experts are provided with specialized annotation topics tailored to their professional backgrounds, ensuring that the metadata for each query is technically accurate and contextually relevant. The Amplify Initiative provides a scalable blueprint for building inclusive AI by empowering experts in the Global South to define their own data needs. As the project expands to India and Brazil, it offers a vital resource for developers seeking to fine-tune models for local contexts and improve the safety and relevance of AI on a global scale.

androidaillmgen-ai+5