We tested the video call quality (opens in new tab)

To optimize the LINE messenger’s communication performance, LY Corporation conducted an on-site call quality assessment in Thailand to analyze local network conditions and compare performance against rising competitors. The study concluded that while LINE offers superior visual clarity and higher bitrates than its rivals, this high-performance strategy requires a careful technical balance to prevent video freezing in unstable network environments. ### High Video Call Adoption in Thailand * Thailand exhibits the highest video call usage among LINE’s major markets, with video calls accounting for 30.43% of all 1:1 sessions—more than double the rate of Japan or Taiwan. * The surge in usage by competitors, specifically "Messenger A," has necessitated frequent benchmarking to maintain LINE’s market leadership and technical edge. * Thailand serves as the primary testing ground for any updates to video modules due to the local user base's preference for high-quality real-time visual communication. ### On-Site Quality Testing Methodology * The assessment was performed over five days by five engineers across high-traffic locations in Bangkok, such as Siam Paragon and Samron Market, using True and AIS 4G/5G networks. * Engineers focused on Quality of Service (QoS) metrics—including packet loss and jitter—to estimate the actual Quality of Experience (QoE) for users. * Baseline performance for LINE in Thailand was recorded at VGA resolution, with frame rates exceeding 20 FPS and an average latency of approximately 150ms. ### Bitrate Strategy and Performance Trade-offs * LINE utilizes a high-bitrate strategy, capping at 1Mbps on 5G and 600kbps on 4G, to deliver sharper, more defined images than Competitor A. * A "start-at-max" approach is used where LINE attempts to find and utilize the highest possible bitrate from the beginning of the call to ensure immediate high quality. * In contrast, competitors adopt a conservative bitrate strategy, starting low and increasing slowly to prioritize connection stability over visual fidelity. * The trade-off for LINE’s higher quality is an increased risk of "freezing"—defined as a single frame persisting for more than 200ms—when the network becomes congested or unstable. ### Technical Implications for Future Development * The relationship between bitrate and network stability remains a zero-sum trade-off; higher bitrates provide better clarity but increase the likelihood of packet delay and loss at the router level. * LINE’s engineering focus is directed toward optimizing the "initial bitrate" detection logic to ensure high quality without triggering network-induced lag in crowded urban environments. * Continuous tuning of the balance between peak visual performance and consistent playback remains the core challenge for maintaining service quality in the Thai market.

Bill Atkinson’s 10 Rules for Making Interfaces More Human | Figma Blog (opens in new tab)

Paula Scher’s 10 rules for play Maker Stories Thought leadership Tips & inspiration Brainstorming Config

Optimizing LLM-based trip planning (opens in new tab)

Google Research has developed a hybrid planning system that combines Large Language Models (LLMs) with traditional optimization algorithms to solve complex trip-planning tasks. While LLMs excel at interpreting qualitative user preferences—such as a desire for "lesser-known museums"—they often struggle with hard quantitative constraints like travel logistics and fluctuating opening hours. By using an LLM to generate an initial draft and a secondary algorithm to refine it against real-world data, the system produces itineraries that are both highly personalized and logistically feasible. ## The Hybrid Planning Architecture * The process begins with a Gemini model generating an initial trip plan based on the user's natural language query, identifying specific activities and their perceived importance. * This draft is grounded using live data, incorporating up-to-date opening hours, transit schedules, and travel times between locations. * Search backends simultaneously retrieve alternative activities to serve as potential substitutes if the LLM's original suggestions prove logistically impossible. ## Two-Stage Optimization Algorithm * The first stage focuses on single-day scheduling, using dynamic programming and exhaustive search to find the most efficient sequence for subsets of activities. * Each potential daily schedule is assigned a quality score based on its feasibility and how closely it aligns with the LLM's original intent. * The second stage addresses the multi-day itinerary as a weighted variant of the "set packing problem," which ensures that activities do not overlap across different days. * Because multi-day optimization is NP-complete, the system employs local search heuristics to swap activities between days, iteratively improving the total score until the plan converges. ## Balancing Intent and Feasibility * In practical testing, the system demonstrated a superior ability to handle nuanced requests, such as finding "lesser-known" museums in NYC, which traditional retrieval systems often fail by suggesting famous landmarks like the Met. * The optimization layer specifically corrects geographical inefficiencies, such as the LLM suggesting a "zig-zag" route across San Francisco, by regrouping activities into logical clusters to minimize travel time. * The system maintains the "spirit" of the LLM's creative suggestions—like visiting a specific scenic viewpoint—while ensuring the user doesn't arrive after the gates have closed. This hybrid approach suggests that the most reliable AI planning tools do not rely on LLMs in isolation. By using LLMs as creative engines for intent interpretation and delegating logistical verification to rigid algorithmic frameworks, developers can create tools that are both imaginative and practically dependable.

Double Click: What Does MCP Mean for Agentic AI? | Figma Blog (opens in new tab)

Introducing our MCP server: Bringing Figma into your workflow Inside Figma Product updates Dev Mode AI Engineering News

Zooming in: Efficient regional environmental risk assessment with generative AI (opens in new tab)

Google Research has introduced a dynamical-generative downscaling method that combines physics-based climate modeling with probabilistic diffusion models to produce high-resolution regional environmental risk assessments. By bridging the resolution gap between global Earth system models and city-level data needs, this approach provides a computationally efficient way to quantify climate uncertainties at a 10 km scale. This hybrid technique significantly reduces error rates compared to traditional statistical methods while remaining far less computationally expensive than full-scale dynamical simulations. ## The Resolution Gap in Climate Modeling * Traditional Earth system models typically operate at a resolution of ~100 km, which is too coarse for city-level planning regarding floods, heatwaves, and wildfires. * Existing "dynamical downscaling" uses regional climate models (RCMs) to provide physically realistic 10 km projections, but the computational cost is too high to apply to large ensembles of climate data. * Statistical downscaling offers a faster alternative but often fails to capture complex local weather patterns or extreme events, and it struggles to generalize to unprecedented future climate conditions. ## A Hybrid Dynamical-Generative Framework * The process begins with a "physics-based first pass," where an RCM downscales global data to an intermediate resolution of 50 km to establish a common physical representation. * A generative AI system called "R2D2" (Regional Residual Diffusion-based Downscaling) then adds fine-scale details, such as the effects of complex topography, to reach the target 10 km resolution. * R2D2 specifically learns the "residual"—the difference between intermediate and high-resolution fields—which simplifies the learning task and improves the model's ability to generalize to unseen environmental conditions. ## Efficiency and Accuracy in Risk Assessment * The model was trained and validated using the Western United States Dynamically Downscaled Dataset (WUS-D3), which utilizes the "gold standard" WRF model. * The dynamical-generative approach reduced fine-scale errors by over 40% compared to popular statistical methods like BCSD and STAR-ESDM. * A key advantage of this method is its scalability; the AI requires training on only one dynamically downscaled model to effectively process outputs from various other Earth system models, allowing for the rapid assessment of large climate ensembles. By combining the physical grounding of traditional regional models with the speed of diffusion-based AI, researchers can now produce granular risk assessments that were previously cost-prohibitive. This method allows for a more robust exploration of future climate scenarios, providing essential data for farming, water management, and community protection.

Code Quality Improvement Techniques Part (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.

Introducing our MCP server: Bringing Figma into your workflow | Figma Blog (opens in new tab)

Introducing our MCP server: Bringing Figma into your workflow Inside Figma Product updates Dev Mode AI Engineering News Today we’re announcing the beta release of the Figma MCP server, which brings Figma directly into the developer workflow to help LLMs achieve design-informed c…

Detecting faulty deployments: Our journey from unlabeled data to supervised learning (opens in new tab)

David Asker Sebastien Levy Deployments are the backbone of software development, but they can be scary: Even when code is well tested, bugs can creep in. In today's fast-paced tech landscape, deployments need to happen more frequently, at a wider scale, and in many different dat…

Detecting faulty deployments: Our journey from unlabeled data to supervised learning | Datadog (opens in new tab)

Detect human names in logs with ML in Sensitive Data Scanner

Thank You for Ten Years (opens in new tab)

Discord is celebrating its tenth anniversary, marking a decade of evolution from a niche gaming communication tool into a global social platform for 200 million monthly active users. The milestone report highlights how the platform has shifted the social media paradigm away from algorithmic feeds toward intimate, "digital living room" environments. Ultimately, the data shows that integrated voice and video features are the primary drivers for long-form engagement, significantly increasing both session duration and user retention. ## Gaming Ecosystem and Engagement Metrics * Discord’s reach has expanded to 200 million monthly active users, with over 90% of the user base having played a PC, console, or mobile game within the last 30 days. * The platform supports a massive variety of content, with users engaging in more than 8,000 unique titles per month on PC alone. * Total monthly gaming time on the platform exceeds 2 billion hours, highlighting its role as a central hub for the global gaming community. * Technical integration of voice chat acts as a force multiplier for engagement; users stay in gaming sessions three times longer when connected via Discord voice. ## Social Dynamics and Multimedia Co-consumption * Social influence drives discovery and play, as 28% of users launch a specific game within one hour of watching a friend stream it via the platform. * The presence of a social circle dramatically impacts performance and endurance, with gameplay sessions lasting seven times longer when users play with friends. * The platform has successfully transitioned into a general-purpose hangout space; after gaming ends, 66% of users remain to watch videos, 59% listen to music, and 49% watch movies or shows together. * 92% of users utilize voice channels simultaneously while gaming, indicating that the platform functions as a secondary layer to the primary gaming experience. ## The Architecture of Small-Scale Socializing * Discord has redefined digital interaction by prioritizing "micro-communities" over mass broadcasting, with 90% of all activity occurring in small, intimate servers. * Communication remains focused and personal, evidenced by the fact that the average voice call consists of only four participants. * Users are increasingly tribal but focused, typically rotating their time between three different friend-based servers per month. * This structure replaces traditional social media "doomscrolling" with active participation, mimicking the feeling of physical presence through low-latency voice and video communication. As Discord enters its second decade, its trajectory suggests that the future of social tech lies in facilitating high-quality, small-group interactions rather than massive public feeds. For developers and creators, the takeaway is clear: community stickiness is best achieved by building tools that allow users to seamlessly transition between active tasks, like gaming, and passive co-consumption of media.

How to Use the Discord Soundboard & Add More Sounds (opens in new tab)

Discord’s Soundboard feature enables users to play immediate audio clips during voice calls to react to live events or social cues. By providing a collection of pre-set and customizable soundbites, the tool adds a layer of expressive, real-time engagement to the platform’s communication suite. This integrated system streamlines the use of audio reactions, removing the need for external software to trigger sound effects during conversations. ### Soundboard Functionality and Usage * Trigger specific audio reactions like airhorns or audience cheers instantly during voice calls. * Utilize contextual sounds, such as crickets, to fill silences or react to specific in-game moments. * Access the feature through a dedicated interface within the call window for rapid selection. ### Customization and Audio Control * Expand the library by uploading custom sound files to personalize the collection for specific servers or friend groups. * Manage the playback experience using dedicated volume controls to ensure sounds are audible without being disruptive. * Navigate settings to find where to add, remove, and organize sounds for easier access during high-energy moments. To enhance your group interactions, explore adding custom sounds that reflect your community's inside jokes, but be sure to use the individual volume sliders to maintain a comfortable balance for all participants in the call.

Learning to clarify: Multi-turn conversations with Action-Based Contrastive Self-Training (opens in new tab)

Action-Based Contrastive Self-Training (ACT) is a novel approach designed to enhance the multi-turn conversational capabilities of large language models, specifically their ability to ask clarifying questions when faced with ambiguity. While standard models often default to guessing a user's intent or overhedging, ACT optimizes conversational action planning as an implicit subtask of response generation. This method demonstrates that data-efficient tuning can significantly improve dialogue policy learning and reasoning in complex, mixed-initiative interactive scenarios. ## Implicit Action Planning * Traditional conversational agents use separate modules for dialogue planning (deciding when to clarify) and response generation. * ACT introduces "implicit action planning," which integrates these steps by teaching the model to perform planning as an inherent part of the end-to-end generation process. * This approach addresses the limitations of standard Direct Preference Optimization (DPO), which often fails to account for the long-term, multi-turn consequences of specific dialogue actions. ## Action-Based Contrastive Data Generation * The first phase involves building a preference dataset by identifying "winning" and "losing" actions for specific conversation turns. * Using an existing dataset, the system identifies a successful turn (e.g., a clarifying question) as the winning response. * A synthetic "rejected" response is then generated to represent a converse, less-optimal action (e.g., attempting to answer despite ambiguity). * This creates a pairwise dataset that contrastively defines successful versus unsuccessful conversational strategies. ## Quasi-Online Contrastive Self-Training * Instead of relying solely on static, offline pairs, ACT employs on-policy sampling to simulate the multi-turn trajectory of a response. * The model evaluates whether a sampled response (such as a clarifying question) leads to a successful final outcome based on the user's original intent. * If the simulated trajectory is successful, it replaces the winning response in the DPO update; if it fails, it is used to refine the losing response. * This quasi-online feedback loop ensures the model is optimized based on the actual outcomes of its conversational decisions rather than just single-turn labels. ## Evaluation and the AmbigSQL Benchmark * The researchers introduced AmbigSQL, a new benchmark task focusing on disambiguating information-seeking requests for complex SQL code generation. * ACT was also tested on real-world tasks including tabular-grounded question-answering and machine reading comprehension. * Experimental results show that ACT substantially outperforms standard Supervised Fine-Tuning (SFT) and standard DPO in multi-turn conversation modeling. By focusing on the downstream consequences of dialogue actions, ACT provides a practical framework for developers to build more "mixed-initiative" agents that know when to stop and ask for clarification, ultimately leading to higher accuracy in complex data-seeking tasks.

Checkpoint 3: Leveling Up Discord Quests with Orbs and Advanced Measurement (opens in new tab)

Discord is scaling its Quests advertising platform by introducing a new virtual currency and expanding its measurement capabilities through a strategic analytics partnership. These updates aim to deepen user engagement while providing brand partners with more granular data on the return on investment for their campaigns. **Introduction of Discord Orbs** * Discord Orbs serve as a new virtual reward that users can earn by participating in sponsored Quests. * The currency is redeemable in the Discord Shop for a variety of digital goods, including Nitro credits and profile cosmetics. * Some Shop items will be designated as Orbs exclusives to drive participation within the Quests ecosystem. * The feature is currently rolling out to a select group of users to test integration before a broader release. **Enhanced Advertising Analytics with Kantar** * Discord has established a new partnership with Kantar to bolster the measurement framework of its advertising products. * This collaboration provides advertisers with advanced analytics tools to better track campaign performance and ROI. * The partnership is designed to validate the effectiveness of Quests as an advertising medium by offering third-party performance insights. These updates represent a strategic shift for Discord, transforming Quests from a simple engagement tool into a robust advertising product that rewards user participation with tangible platform value. Brands looking to reach gaming audiences should monitor the rollout of Orbs as a potential benchmark for gamified digital advertising.

Go Beyond, Plus Ultra! with the My Hero Academia Collection (opens in new tab)

Discord has officially launched its first anime-themed collection in collaboration with Crunchyroll, featuring the popular series *My Hero Academia*. Released in anticipation of the 2025 Anime Awards, the collection introduces eleven new customization items that leverage character-specific "Quirks" and iconic gear. This partnership represents a direct response to high user demand for anime-centric profile aesthetics and immersive digital collectibles. ### Hero Gear and Avatar Decorations * The creative team focused on "Hero Gear" as the primary design element to ensure decorations remain instantly recognizable while avoiding excessive obstruction of the user’s avatar. * The production process followed a three-step workflow: conceptualizing the gear, applying color to establish mood and richness, and adding custom animations to bring the characters' unique Quirks to life. * The collection features eight distinct decorations, including Izuku Midoriya, Katsuki Bakugo, Ochaco Uraraka, Shoto Todoroki, Endeavor, Hawks, All Might, and Tomura Shigaraki. ### Dynamic Profile Storytelling * Designers utilized the larger surface area of profile effects to move beyond simple gear, focusing instead on "signature moves" and iconic moments from the anime. * The effects are designed for immediate impact, aiming to tell a story in seconds through high-energy animations like Deku’s electrifying "Full Cowling" and Bakugo’s "Cluster" explosions. * Three specific profile effects were created for this launch: Full Cowling, Cluster, and a dedicated League of Villains theme. Fans can now access the *My Hero Academia* collection through the Shop on both desktop and mobile platforms to personalize their digital identity with these limited-edition hero and villain aesthetics.

Complex user authentication processes are easy (opens in new tab)

Designing a robust membership authentication system is a critical early-stage requirement that prevents long-term technical debt and protects a platform’s integrity. By analyzing the renewal of the Demaecan delivery service, it is evident that choosing the right authentication mechanism depends heavily on regional infrastructure and a balance between security costs and user friction. Ultimately, a well-structured authentication flow can simultaneously reduce fraud rates and significantly lower user drop-off during registration. ### The Consequences of Weak Authentication Neglecting authentication design during the initial stages of a project often leads to "ghost members" and operational hurdles that are difficult to rectify later. * **Data Integrity Issues:** Without verification, databases fill with unreachable or fake contact information, such as invalid phone numbers. * **Onboarding Blockers:** Legitimate new users may be prevented from signing up if their recycled phone numbers are already linked to unverified legacy accounts. * **Marketing Abuse:** A lack of unique identifiers makes it impossible to prevent bad actors from creating multiple accounts to exploit promotional coupons or events. ### Regional Differences in Verification Authentication strategies must be tailored to the specific digital infrastructure of the target market, as "identity verification" varies globally. * **Domestic (Korea) Standards:** Highly integrated systems allow for "Identity Verification," which combines possession (OTP) and real-name data through telecommunications companies or banking systems. * **Global and Japanese Standards:** Most regions lack a centralized government-linked identity system, relying instead on "Possession Authentication" via email or SMS, or simple two-factor authentication (2FA). * **Verification Expiration:** High-security services must define clear validity periods for authentication data and determine how long to retain data after a user withdraws to prevent immediate re-abuse. ### Strategic Fraud Prevention via IVR When SMS-based possession authentication becomes insufficient to stop determined abusers, shifting the economic cost for the fraudster is an effective solution. * **SMS vs. Voice (IVR):** In Japan, acquiring phone numbers capable of receiving voice calls is more expensive than acquiring SMS-only numbers. * **IVR Implementation:** By switching to call-based (Inbound Voice Response) authentication, Demaecan increased the barrier to entry for abusers. * **Impact:** This strategic shift in authentication type reduced the fraudulent user rate from over 20% to just 1.5%. ### Optimizing Sign-up UX and Retention A complex authentication process does not have to result in high churn if the UI flow is logically organized and user-friendly. * **Logical Grouping:** Grouping similar tasks—such as placing phone and email verification sequentially—helps users understand the progression of the sign-up flow. * **Streamlined Data Entry:** Integrating social login buttons early in the process allows for email auto-fill, reducing the number of manual input fields for the user. * **Safety Nets:** Implementing simple "back" buttons for correcting typos during email verification and adding warning dialogs when a user tries to close the window significantly reduces accidental exits. * **Performance Metrics:** These UX improvements led to a 30% decrease in user attrition, proving that structured flows can mitigate the friction of multi-step verification. To build a successful authentication system, planners should prioritize the most cost-effective verification method for their specific market and focus on grouping steps logically to maintain a smooth user experience. Monitoring conversion logs is essential to identify and fix specific points in the flow where users might struggle.