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line

Milvus: Building a Large-Scale (opens in new tab)

LINE VOOM transitioned its recommendation system from a batch-based offline process to a real-time infrastructure to solve critical content freshness issues. By adopting Milvus, an open-source vector database, the team enabled the immediate indexing and searching of new video content as soon as it is uploaded. This implementation ensures that time-sensitive posts are recommended to users without the previous 24-hour delay, significantly enhancing user engagement. ### Limitations of the Legacy Recommendation System * The original system relied on daily offline batch processing for embedding generation and similarity searches. * New content, such as holiday greetings or trending sports clips, suffered from a "lack of immediacy," often taking up to a full day to appear in user feeds. * To improve user experience, the team needed to shift from offline candidate pools to an online system capable of real-time Approximate Nearest Neighbor (ANN) searches. ### Selecting Milvus as the Vector Database * The team evaluated Milvus and Qdrant based on performance, open-source status, and on-premise compatibility. * Milvus was selected due to its superior performance, handling 2,406 requests per second compared to Qdrant's 326, with lower query latency (1ms vs 4ms). * Key architectural advantages of Milvus included the separation of storage and computing, support for both stream and batch inserts, and a diverse range of supported in-memory index types. ### Reliability Verification via Chaos Testing * Given the complexity of Milvus clusters, the team performed chaos testing by intentionally injecting failures like pod kills and scaling events. * Tests revealed critical vulnerabilities: killing the `Querycoord` led to collection release and search failure, while losing the `Etcd` quorum caused total metadata loss. * These findings highlighted the need for robust high-availability (HA) configurations to prevent service interruptions during component failures. ### High Availability (HA) Implementation Strategies * **Collection-Level HA:** To prevent search failures during coordinator issues, the team implemented a dual-writing system where embeddings are recorded in two separate collections simultaneously. * **Alias Switching:** Client applications use an "alias" to reference collections; if the primary collection becomes unavailable, the system instantly switches the alias to the backup collection to minimize downtime. * **Coordinator-Level HA:** To eliminate single points of failure, coordinators (such as `Indexcoord`) were configured in an Active-Standby mode, ensuring a backup is always ready to take over management tasks. To successfully deploy a large-scale real-time recommendation engine, it is critical to select a vector database that decouples storage from compute and to implement multi-layered high-availability strategies, such as dual-collection writing and active-standby coordinators, to ensure production stability.

vector-dbhigh-availabilityembeddingmilvus+3
google

LSM-2: Learning from incomplete wearable sensor data (opens in new tab)

LSM-2 introduces a paradigm shift in processing wearable sensor data by treating naturally occurring data gaps as inherent features rather than errors to be corrected. By utilizing the Adaptive and Inherited Masking (AIM) framework, the model learns directly from fragmented, real-world data streams without the need for biased imputation or data-discarding filters. This approach allows LSM-2 to achieve state-of-the-art performance in health-related classification and regression tasks, maintaining robustness even when sensors fail or data is highly interrupted. ## The Challenge of Pervasive Missingness * Real-world wearable data is almost never continuous; factors such as device charging, motion artifacts, and battery-saving modes create frequent "missingness." * Traditional self-supervised learning models require complete data, forcing researchers to use imputation—which can introduce artificial bias—or aggressive filtering that discards over 90% of potentially useful samples. * In a dataset of 1.6 million day-long windows, research found that not a single sample had 0% missingness, highlighting the impracticality of training only on complete datasets. ## Adaptive and Inherited Masking (AIM) * AIM extends the Masked Autoencoder (MAE) framework by treating "inherited" masks (naturally occurring gaps) and "artificial" masks (training objectives) as equivalent. * The framework utilizes a dual masking strategy: it employs token dropout on a fixed ratio of tokens to ensure computational efficiency during encoding. * To handle the unpredictable and variable nature of real-world gaps, AIM uses attention masking within the transformer blocks for any remaining masked tokens. * During evaluation and fine-tuning, the model relies solely on attention masking to navigate naturally occurring gaps, allowing for accurate physiological modeling without filling in missing values. ## Scale and Training Architecture * LSM-2 was trained on a massive dataset comprising 40 million hours of de-identified wearable data from more than 60,000 participants using Fitbit and Google Pixel devices. * The model learns to understand underlying physiological structures by reconstructing masked segments across multimodal inputs, including heart signals, sleep patterns, and activity levels. * Because it is trained on fragmented data, the resulting foundation model is significantly more resilient to sensor dropouts in downstream tasks like hypertension prediction or stress monitoring. LSM-2 demonstrates that foundation models for health should be built to embrace the messiness of real-world environments. By integrating missingness directly into the self-supervised learning objective, developers can bypass the computational and statistical overhead of imputation while building more reliable diagnostic and monitoring tools.

aitransformerfoundation-modelswearable-technology+3
discord

*FLAILS AROUND* SUMMER SPECIAL! JOIN NITRO, GET AN EXTRA MONTH OF NITRO ON US! (opens in new tab)

Discord is launching a limited-time "Subscriber Speedway" promotion to incentivize new users to join its premium Nitro service. From now until July 15th, 2025, eligible individuals who sign up for a monthly Nitro membership will receive a second month at no additional cost. This "buy one, get one free" offer effectively provides 60 days of premium features for the price of a standard 30-day subscription. ### Discord Nitro Feature Set Discord Nitro offers a suite of cosmetic and functional upgrades designed to enhance the standard user experience. Key technical and social benefits include: * **Enhanced Expression:** Access to a wider array of custom emojis and stickers across all servers. * **Profile Personalization:** Additional tools and assets for customizing user profiles. * **Performance Upgrades:** Higher-quality gameplay streaming capabilities for sharing screens with friends. * **Increased Data Limits:** Expanded file-sharing capacities, allowing for the transmission of larger assets and media. ### Summer Promotion Terms The current "Subscriber Speedway" deal is structured to attract users who are not currently enrolled in a Nitro plan. Specific details include: * **Duration:** The promotion is active through July 15th, 2025. * **Eligibility:** The offer is targeted at users who do not have an active Nitro membership at the time of purchase. * **Subscription Model:** The deal applies specifically to those starting a new monthly Nitro membership, granting a full second month as a bonus. Users interested in testing Discord’s premium features should initiate their monthly membership before the July 15th deadline to maximize the value of the 60-day promotional window.

discorddiscord-nitrostreamingfile-sharing+2
line

Making the Most of Flutter (opens in new tab)

Riverpod is a powerful state management library for Flutter designed to overcome the limitations of its predecessor, Provider, by offering a more flexible and robust framework. By decoupling state from the widget tree and providing built-in support for asynchronous data, it significantly reduces boilerplate code and improves application reliability. Ultimately, it allows developers to focus on logic rather than the complexities of manual state synchronization and resource management. ### Modern State Management Architecture Riverpod introduces a streamlined approach to state by separating the logic into Models, Providers, and Views. Unlike the standard `setState` approach, Riverpod manages the lifecycle of state automatically, ensuring resources are allocated and disposed of efficiently. * **Providers as Logic Hubs:** Providers define how state is built and updated, supporting synchronous data, Futures, and Streams. * **Consumer Widgets:** Views use `ref.watch` to subscribe to data and `ref.read` to trigger actions, creating a clear reactive loop. * **Global Access:** Because providers are not tied to the widget hierarchy, they can be accessed from anywhere in the app without passing context through multiple layers. ### Optimization for Server Data and Asynchronous Logic One of Riverpod's strongest advantages is its native handling of server-side data, which typically requires manual logic in other libraries. It simplifies the user experience during network requests by providing built-in states for loading and error handling. * **Resource Cleanup:** Using `ref.onDispose`, developers can automatically cancel active API calls when a provider is no longer needed, preventing memory leaks and unnecessary network usage. * **State Management Utilities:** It natively supports "pull-to-refresh" functionality through `ref.refresh` and allows for custom data expiration settings. * **AsyncValue Integration:** Riverpod wraps asynchronous data in an `AsyncValue` object, making it easy to check if a provider `hasValue`, `hasError`, or `isLoading` directly within the UI. ### Advanced State Interactions and Caching Beyond basic data fetching, Riverpod allows providers to interact with each other to create complex, reactive workflows. This is particularly useful for features like search filters or multi-layered data displays. * **Cross-Provider Subscriptions:** A provider can "watch" another provider; for example, a `PostList` provider can automatically rebuild itself whenever a `Filter` provider's state changes. * **Strategic Caching:** Developers can implement "instant" page transitions by yielding cached data from a list provider to a detail provider immediately, then updating the UI once the full network request completes. * **Offline-First Capabilities:** By combining local database streams with server-side Futures, Riverpod can display local data first to ensure a seamless user experience regardless of network connectivity. ### Seamless Data Synchronization Maintaining consistency across different screens is simplified through Riverpod's centralized state. When a user interacts with a data point on one screen—such as "starring" a post on a detail page—the change can be propagated globally so that the main list view is updated instantly without additional manual refreshes. This synchronization ensures the UI remains a "single source of truth" across the entire application. For developers building data-intensive Flutter applications, Riverpod is a highly recommended choice. Its ability to handle complex asynchronous states and inter-provider dependencies with minimal code makes it an essential tool for creating scalable, maintainable, and high-performance mobile apps.

state-managementfluttercachingdependency-injection+3
google

Measuring heart rate with consumer ultra-wideband radar (opens in new tab)

Google Research has demonstrated that ultra-wideband (UWB) radar technology, which is already integrated into many modern smartphones for tasks like precise location and vehicle unlocking, can be repurposed for contactless heart rate monitoring. By employing a transfer learning approach, researchers successfully applied models trained on large datasets from Frequency Modulated Continuous Wave (FMCW) radar to the newer UWB systems. This development suggests that everyday consumer electronics could soon provide accurate vital sign measurements without the need for additional specialized sensors or physical contact. ## Leveraging Existing Consumer Hardware While Google previously used Soli radar (FMCW) for sleep sensing in the Nest Hub, UWB technology represents a more widely available hardware platform in the mobile market. * UWB is currently used primarily for non-radar applications like digital car keys and item tracking (e.g., Apple AirTags). * The technology is increasingly standard in high-end mobile phones, providing a ready-made infrastructure for health sensing. * Utilizing existing UWB chips eliminates the need for manufacturers to add dedicated medical sensors to devices. ## Overcoming Signal Interference in Vital Sensing The primary challenge in radar-based heart rate monitoring is that the micro-movements of the chest wall caused by a heartbeat are significantly smaller than movements caused by breathing or general body shifts. * The system utilizes three-dimensional spatial resolution to create a "measurement zone" focused specifically on the user's torso. * High temporal resolution, sampling at speeds up to 200Hz, allows the radar to capture the rapid, subtle pulses of a heartbeat. * By isolating reflections from the chest area, the radar can ignore stationary background objects and external movements that would otherwise corrupt the data. ## Cross-Radar Transfer Learning Because the researchers possessed extensive datasets for FMCW radar but very limited data for UWB, they developed a method to transfer learned features between different radar types despite their different physical principles. * FMCW radar transmits continuous sinusoidal waves, whereas UWB radar transmits extremely short pulses (picoseconds to nanoseconds). * The study used a large 980-hour FMCW dataset to "teach" the model the characteristics of human vitals. * This pre-trained knowledge was then applied to a smaller 37.3-hour UWB dataset, proving that heart rate features are consistent enough across hardware types for effective transfer learning. ## A Novel Spatio-Temporal Deep Learning Model The researchers designed a custom neural network architecture to process the complex multidimensional data generated by radar sensors. * The framework uses a 2D ResNet to analyze the input data across two axes: time and spatial measurements. * Following the initial analysis, the model uses average pooling to collapse the spatial dimension, focusing purely on the temporal signal. * A 1D ResNet then identifies long-range periodic patterns to estimate the heart rate. * The model achieved a mean absolute error (MAE) of 0.85 beats per minute (bpm), which is a 50% reduction in error compared to previous state-of-the-art methods. This research indicates that high-precision health monitoring can be integrated into the mobile devices users already carry. By transforming smartphones into passive health sensors, UWB technology could allow for continuous heart rate tracking during routine activities, such as sitting at a desk or holding a phone in one's lap.

aimachine-learningdeep-learningtransfer-learning+5
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

LY's Tech Conference, ' (opens in new tab)

LY Corporation’s Tech-Verse 2025 conference highlighted the company's strategic pivot toward becoming an AI-centric organization through the "Catalyst One Platform" initiative. By integrating the disparate infrastructures of LINE and Yahoo! JAPAN into a unified private cloud, the company aims to achieve massive cost efficiencies while accelerating the deployment of AI agents across its entire service ecosystem. This transformation focuses on empowering engineers with AI-driven development tools to foster rapid innovation and deliver a seamless, "WOW" experience for global users. ### Infrastructure Integration and the Catalyst One Platform To address the redundancies following the merger of LINE and Yahoo! JAPAN, LY Corporation is consolidating its technical foundations into a single internal ecosystem known as the Catalyst One Platform. * **Private Cloud Advantage:** The company maintains its own private cloud to achieve a four-fold cost reduction compared to public cloud alternatives, managed by a lean team of 700 people supporting 500,000 servers. * **Unified Architecture:** The integration spans several layers, including Infrastructure (Project "DC-Hub"), Cloud (Project "Flava"), and specialized Data and AI platforms. * **Next-Generation Cloud "Flava":** This platform integrates existing services to enhance VM specifications, VPC networking, and high-performance object storage (Ceph and Dragon). * **Information Security:** A dedicated "SafeOps" framework is being implemented to provide governance and security across all integrated services, ensuring a safer environment for user data. ### AI Strategy and Service Agentization A core pillar of LY’s strategy is the "AI Agentization" of all its services, moving beyond simple features to proactive, personalized assistance. * **Scaling GenAI:** Generative AI has already been integrated into 44 different services within the group. * **Personalized Agents:** The company is developing the capacity to generate millions of specialized agents that can be linked together to support the unique needs of individual users. * **Agent Ecosystem:** The goal is to move from a standard platform model to one where every user interaction is mediated by an intelligent agent. ### AI-Driven Development Transformation Beyond user-facing services, LY is fundamentally changing how its engineers work by deploying internal AI development solutions to all staff starting in July. * **Code and Test Automation:** Proof of Concept (PoC) results showed a 96% accuracy rate for "Code Assist" and a 97% reduction in time for "Auto Test" procedures. * **RAG Integration:** The system utilizes Retrieval-Augmented Generation (RAG) to leverage internal company knowledge and guidelines, ensuring high-quality, context-aware development support. * **Efficiency Gains:** By automating repetitive tasks, the company intends for engineers to shift their focus from maintenance to creative service improvement and innovation. The successful integration of these platforms and the aggressive adoption of AI-driven development tools suggest that LY Corporation is positioning itself to be a leader in the "AI-agent" era. For technical organizations, LY's model serves as a case study in how large-scale mergers can leverage private cloud infrastructure to fund and accelerate a company-wide AI transition.

aigen-aiai-agentrag+4
discord

Get More From Your Boosts With New Server Perks (opens in new tab)

Discord is introducing a fundamental shift in how Server Boosting works, moving away from a strictly linear leveling system to a more flexible, perk-based allocation model. This update allows server owners to spend community-contributed Boosts directly on specific features rather than waiting to hit the next milestone tier. By decoupling high-value perks from the traditional level structure, communities can now customize their server experience with greater precision and immediate results. ### Direct Perk Allocation * Servers can now utilize community Boosts in two distinct ways: contributing toward a global Server Level or bypassing levels to unlock specific features directly. * Perks remain active as long as the server maintains the required number of Boosts allocated to that specific feature. * This system allows servers with "extra" Boosts—those sitting between established Level tiers—to immediately put that value to use rather than waiting for the next level to be reached. ### Enhanced Role Styles * Unlocking this feature requires an allocation of 3 Server Boosts. * It provides administrators with the ability to apply animated, vibrant gradients to specific roles within the server hierarchy. * These styles are designed to add visual flair and help specific community roles or staff members stand out more prominently than standard color options. ### Server Tags * This feature requires a separate allocation of 3 Server Boosts to enable. * Server Tags give members a way to represent their server community across the broader Discord platform, extending the server's identity beyond its own channels. * If a community wishes to utilize both Server Tags and Enhanced Role Styles simultaneously, a total of 6 Boosts must be dedicated to these perks. To maximize the impact of these changes, server administrators should audit their current Boost counts to see if they can immediately activate these visual upgrades without losing current level-based benefits. For servers just short of the requirement, highlighting these specific, tangible rewards can be an effective way to rally the community for additional Boost contributions.

discordgamificationcommunity-managementserver-boosting+3
line

Code Quality Improvement Techniques Part (opens in new tab)

The builder pattern is frequently overused in modern development, often leading to code that is less robust than it appears. While it provides a fluent API, it frequently moves the detection of missing mandatory fields from compile-time to runtime, creating a "house of sand" that can collapse unexpectedly. By prioritizing constructors and factory functions, developers can leverage the compiler to ensure data integrity and build more stable applications. ### Limitations of the Standard Builder Pattern * In a typical builder implementation, mandatory fields are often initialized as nullable types and checked for nullity only when the `.build()` method is called. * This reliance on runtime checks like `checkNotNull` means that a developer might forget to set a required property, leading to an `IllegalStateException` during execution rather than a compiler error. * Unless the platform or a specific library (like an ORM) requires it, the boilerplate of a builder often hides these structural weaknesses without providing significant benefits. ### Strengthening Foundations with Constructors and Defaults * Using a class constructor or a factory function is often the simplest and most effective way to prevent bugs related to missing data. * In languages like Kotlin, the need for builders is further reduced by the availability of default parameters and named arguments, allowing for concise instantiation even with many optional fields. * If a builder must be used, mandatory arguments should be required in the builder's own constructor (e.g., `Builder(userName, emailAddress)`) to ensure the object is never in an invalid state. ### Managing Creation State and Pipelines * Developers sometimes pass a builder as an "out parameter" to other functions to populate data, which can obscure the flow of data and reduce readability. * A better approach is to use functions that return specific values, which are then passed into a final constructor, keeping the logic functional and transparent. * For complex, multi-stage creation logic, defining distinct types for each stage—such as moving from a `UserAccountModel` to a `UserProfileViewComponent`—can ensure that only valid, fully-formed data moves through the pipeline. ### Appropriate Use of Terminal Operations * The builder-like syntax is highly effective when implementing "terminal operations," where various transformations are applied in an arbitrary order before a final execution. * This pattern is particularly useful in image processing or UI styling (e.g., `.crop().fitIn().colorFilter()`), where it serves as a more readable alternative to deeply nested decorator patterns. * In these specific cases, the pattern facilitates a clear sequence of operations while maintaining a "last step" (like `.createBitmap()`) that signals the end of the configuration phase. Prioritize the use of constructors and factory functions to catch as many errors as possible during compilation. Reserve the builder pattern for scenarios involving complex terminal operations or when dealing with restrictive library requirements that demand a specific instantiation style.

code-qualitykotlinrefactoringdesign-patterns+3