Techlist.io - Korean Tech Blog Curator

discordAug 21, 20252 min readCurated summary

Transforming Game Discovery with Instant Play Experiences on Discord

Discord and NVIDIA are developing an integration that lets users stream games directly inside Discord through GeForce NOW, eliminating downloads, installations, patches, and separate launchers. Beginning with a closed-door Fortnite demo at gamescom, the initiative aims to make game discovery faster and more social by letting users try games immediately when friends share or play them. Discord presents this as both a premium user experience and a new acquisition channel for game developers. ## Instant Cloud-Streamed Gameplay - The integration is powered by NVIDIA GeForce NOW and the NVIDIA Graphics Delivery Network. - The Fortnite demo supports streaming at up to 60 frames per second and 1440p resolution. - Players without a game installed could launch and play it directly inside the Discord client. - A limited-time trial of the GeForce NOW Performance experience is part of the proposed experience. - Removing launchers, installations, and updates is intended to reduce friction between discovering and playing a game. ## Social Discovery and Player Acquisition - Discord argues that game discovery increasingly happens through friends and shared conversations. - Internal 2025 data cited by Discord shows: - 72% of users play games with friends on Discord weekly. - 50% stream gameplay to friends. - 40% launch the same game within an hour of watching a friend stream it. - Sessions are seven times longer when users play with at least one friend. - Developers could reach players within the communities where game discussions and recommendations already occur. - The integration is designed to turn friend activity—such as watching someone stream a game—into an immediate opportunity to try it. ## Expanding Discord’s Premium Gaming Ecosystem - Discord says it has more than 200 million monthly active users worldwide. - The company is positioning Discord as a central hub connecting players, developers, advertisers, and gaming communities. - The NVIDIA partnership represents a new trial and acquisition pathway for developers. - The Fortnite demonstration is an early look at the concept rather than a broad product launch announcement. The collaboration suggests a future in which Discord becomes not only a place to discuss games, but also an instant gateway for playing them. Developers interested in using this distribution model are encouraged to contact Discord’s business development team.

Read original

Discord Game Streaming Cloud Gaming Nvidia Geforce Now+2

discordAug 21, 20252 min readCurated summary

Discord’s Powerful Cross-Platform Chat: Ready for Your Game

Discord is moving its Social SDK communication features out of closed beta, allowing games to integrate Discord-powered voice and text chat across desktop, console, and mobile. The SDK is designed to strengthen multiplayer connections, improve player retention, and let users communicate even without Discord accounts. New features such as lobby chat history, custom audio processing, and diagnostics make the integration more practical for developers. ## Social Features for Games - **Unified friends lists** let players access Discord friends in-game and connect with friends made through the game. - **Rich Presence** works across desktop, console, and mobile, displaying gameplay activity and supporting one-click joins. - **Game Invites** help players launch games and enter the correct party or lobby quickly. - **Cross-platform chat** supports communication between desktop, mobile, and console players, including users without Discord accounts. - **Discord Voice Chat** brings Discord’s real-time voice infrastructure into game lobbies, guilds, and matches. - **Linked Channels** connect in-game text lobbies with selected Discord channels. ## New Communication Improvements - **Chat history for active lobbies** allows conversations to persist between play sessions. - **Direct-message history** is planned for a future release. - **Custom effect processing** lets developers route Discord voice audio through middleware such as FMOD or Wwise. - **Audio diagnostics** provide debugging information about issues including echo cancellation and noise reduction. - **Mobile audio improvements** increase audio quality and connection stability. - Updated documentation covers voice chat management, moderation, lobby history, and chat-history design. ## Accessing the SDK - Developers must use the Discord Developer Portal. - The setup process involves selecting a platform, creating a Discord Application, and enabling the Social SDK. - Communication features are included in the initial SDK package. - Usage is rate-limited during testing. ## Approval Requirements Before submitting a game for approval, developers must: - Integrate core features such as account linking, Rich Presence with Discord Joins, and Discord Friends. - Ensure all selected features work end-to-end across both the game and Discord clients. - Handle user denials and other failure states correctly. - Make account linking accessible and clearly explained to players. - Provide supporting materials, including integration captures and a development timeline. - Meet Discord’s age-restricted user protection requirements, which are not fully detailed in the provided text. Developers interested in cross-platform social features can begin with the Social SDK now, using Discord’s documentation and developer community for implementation guidance.

Read original

Voice Chat Discord Social Sdk Rich Presence Game Invites+4

discordAug 21, 20252 min readCurated summary

Discord for Business Vol. 2: Cannes-worthy ad product updates

Discord’s latest business update positions the platform as a major advertising channel for mainstream gaming audiences. The company introduced Orbs-based rewards, Kantar Brand Lift measurement, and mobile Quests to increase engagement and demonstrate campaign impact. It also highlighted recent brand collaborations and research emphasizing more social, immersive marketing experiences. ## Cannes Lions and Discord’s Advertising Growth - Discord made its Cannes Lions debut as its advertising business continues to expand. - Panels with Xbox, Kantar, and Unilever focused on Discord’s opt-in, community-first approach. - The company argued that gaming is now mainstream, with Discord serving as a place where audiences communicate and influence one another. ## Orbs and Brand Lift Measurement - **Orbs** are a new virtual currency users can earn by completing advertising Quests. - Users can redeem Orbs for Discord rewards such as profile cosmetics and Nitro. - Built-in incentives are intended to improve engagement, completion rates, and interaction time without requiring advertisers to create their own rewards. - **Kantar Brand Lift studies** measure campaign outcomes including awareness, recall, and purchase intent. - Third-party measurement gives advertisers a more credible way to evaluate return on investment. ## Mobile Quests Beta - Quests are expanding to mobile after an alpha featuring campaigns for Marvel Snap, Tank Blitz, and Brawl Stars. - The mobile beta begins in August and targets the fast-growing mobile gaming audience. - AppsFlyer will serve as the first mobile measurement partner. - Discord invited brands to apply for the remaining early-access positions. ## Recent Campaigns and Industry Research - Netflix and Mediahub promoted *The Old Guard 2* through a custom Discord server and a Video Quest featuring Uma Thurman. - The campaign was designed around fan communities and the spaces where audiences already interact. - Dentsu’s 2025 report, *Gaming: Your Marketing Cheatcode*, featured Discord as an example of the shift toward social and immersive gaming marketing. Brands seeking gaming audiences can use Discord’s new reward system, mobile campaigns, and independent measurement tools to create more engaging campaigns while better proving their effectiveness.

Read original

Discord Mobile Apps Mobile Gaming Virtual Currency+4

discordAug 21, 20252 min readCurated summary

Introducing the Community Server Cleanup Report for August 2025

Discord’s August 2025 Community Server Cleanup Report introduces the first wave of updates aimed at giving moderators, administrators, and game developers more control over their servers. The changes improve server management, onboarding, and Forum channels by fixing usability bugs, expanding feature availability, and improving accessibility. Discord says these improvements are part of an ongoing effort to make community management easier. ## Server and Channel Management - Unread mentions in Voice and Stage channels now appear in Suggested Channels for members who have not enabled “Show All Channels.” - The Role list in Permission settings now includes a right-click delete option. - Mod View now works on servers without the Community feature enabled. - Channel description editing has improved Markdown support. - Role changes now update correctly in Settings on Android and iOS. ## Community Onboarding and Server Guide - Disabling the Community feature now also disables the Server Guide if it was previously active. - The Server Guide supports screen readers for better accessibility. - A mobile bug that trapped users in an infinite Server Onboarding exit loop has been fixed. ## Forum Channel Improvements - Forum channels are now available on all server types, without requiring the Community feature. - Forum post editing on iOS has been improved. - Members can no longer bypass required Forum post tags. - Shift, Home, and End key behavior is now consistent when browsing Forums. - Users are no longer incorrectly affected by Slowmode when a Forum is full and their post cannot be created. - Closing Threads works correctly in Gallery View. - Posts from blocked users are hidden in Forums, as they are in regular channels. - Discord also made general cleanup improvements to Forum sorting and tagging. Discord presents these fixes as the beginning of a longer series of community-focused updates. Moderators and game developers can expect further improvements targeting server administration and member experiences.

Read original

Discord Android iOS Markdown+4

googleAug 20, 2025Original article

From massive models to mobile magic: The tech behind YouTube real-time generative AI effects (opens in new tab)

YouTube has successfully deployed over 20 real-time generative AI effects by distilling the capabilities of massive cloud-based models into compact, mobile-ready architectures. By utilizing a "teacher-student" training paradigm, the system overcomes the computational bottlenecks of high-fidelity generative AI while ensuring the output remains responsive on mobile hardware. This approach allows for complex transformations, such as cartoon style transfer and makeup application, to run frame-by-frame on-device without sacrificing the user’s identity. ### Data Curation and Diversity * The foundation of the effects pipeline relies on high-quality, properly licensed face datasets. * Datasets are meticulously filtered to ensure a uniform distribution across different ages, genders, and skin tones. * The Monk Skin Tone Scale is used as a benchmark to ensure the effects work equitably for all users. ### The Teacher-Student Framework * **The Teacher:** A large, powerful pre-trained model (initially StyleGAN2 with StyleCLIP, later transitioning to Google DeepMind’s Imagen) acts as the "expert" that generates high-fidelity visual effects. * **The Student:** A lightweight UNet-based architecture designed for mobile efficiency. It utilizes a MobileNet backbone for both the encoder and decoder to ensure fast frame-by-frame processing. * The distillation process narrows the scope of the massive teacher model into a student model focused on a single, specific task. ### Iterative Distillation and Training * **Data Generation:** The teacher model processes thousands of images to create "before and after" pairs. These are augmented with synthetic elements like AR glasses, sunglasses, and hand occlusions to improve real-world robustness. * **Optimization:** The student model is trained using a sophisticated combination of loss functions, including L1, LPIPS, Adaptive, and Adversarial loss, to balance numerical accuracy with aesthetic quality. * **Architecture Search:** Neural architecture search is employed to tune "depth" and "width" multipliers, identifying the most efficient model structure for different mobile hardware constraints. ### Addressing the Inversion Problem * A major challenge in real-time effects is the "inversion problem," where the model struggles to represent a real face in latent space, leading to a loss of the user's identity (e.g., changes in skin tone or clothing). * YouTube uses Pivotal Tuning Inversion (PTI) to ensure that the user's specific features are preserved during the generative process. * By editing images in the latent space—a compressed numerical representation—the system can apply stylistic changes while maintaining the core characteristics of the original video stream. By combining advanced model distillation with on-device optimization via MediaPipe, YouTube demonstrates a practical path for bringing heavy generative AI research into consumer-facing mobile applications.

AI Generative AI Computer Vision Knowledge Distillation+5

figmaAug 20, 20252 min readCurated summary

Forrester Analyzes The ROI Of Dev Mode | Figma Blog

Dev Mode is presented as a way to reduce friction between designers and developers, especially during handoff and implementation. A Forrester Total Economic Impact study found that it can increase developer output by 20–30%, save more than 90 minutes per developer each week, and generate significant financial benefits over three years. The central conclusion is that a shared, inspectable source of truth helps teams ship faster without sacrificing product quality. ## The Business Case for Dev Mode - Figma commissioned Forrester two years after Dev Mode launched. - Forrester interviewed four decision-makers and modeled a composite organization with 100–1,000 designers and developers. - The study identified inefficient handoffs and redundant work as major sources of lost time. - Its modeled benefits include: - 20–30% higher developer output - More than 90 minutes saved per developer each week - Approximately $10 million in developer-efficiency time savings over three years - Approximately $2 million in increased profit from faster time to market ## One Shared Source of Truth - Teams working in a shared space can collaborate continuously instead of waiting for a formal design handoff. - One organization reportedly moved from design completion to release in six to eight months, compared with an earlier process that could take two or three years. - A survey of 200 developers at another company found average savings of 98 minutes per week. - Developers can inspect design files directly to find: - Variables and specifications - Exact assets - Documentation - Code snippets - This reduces dependence on Slack messages, meetings, time-zone coordination, and manually maintained annotations. ## More Autonomous Developers - Dev Mode allows developers to resolve many implementation questions independently. - By seeing design intent and technical details directly in Figma, developers spend less time clarifying requirements with designers. - Reduced context switching and fewer back-and-forth conversations let developers focus more of their time on building products. - The broader benefit is not simply faster execution, but lower mental overhead and smoother collaboration between disciplines. The study supports adopting Dev Mode—or similar workflow improvements—when teams need to accelerate delivery while preserving design quality. Its strongest value comes from making design information immediately accessible and enabling designers and developers to collaborate before either side has finished their work.

Read original

AI Design Systems Code Snippets Cross Platform Development+3

googleAug 19, 2025Original article

Securing private data at scale with differentially private partition selection (opens in new tab)

Google Research has introduced a novel parallel algorithm called MaxAdaptiveDegree (MAD) to enhance differentially private (DP) partition selection, a critical process for identifying common data items in massive datasets without compromising individual privacy. By utilizing an adaptive weighting mechanism, the algorithm optimizes the utility-privacy trade-off, allowing researchers to safely release significantly more data than previous non-adaptive methods. This breakthrough enables privacy-preserving analysis on datasets containing hundreds of billions of items, scaling up to three orders of magnitude larger than existing sequential approaches. ## The Role of DP Partition Selection * DP partition selection identifies a meaningful subset of unique items from large collections based on their frequency across multiple users. * The process ensures that no single individual's data can be identified in the final list by adding controlled noise and filtering out items that are not sufficiently common. * This technique is a foundational step for various machine learning tasks, including extracting n-gram vocabularies for language models, analyzing private data streams, and increasing efficiency in private model fine-tuning. ## The Weight, Noise, and Filter Paradigm * The standard approach to private partition selection begins by computing a "weight" for each item, typically representing its frequency, while ensuring "low sensitivity" so no single user has an outsized impact. * Random Gaussian noise is added to these weights to obfuscate exact counts, preventing attackers from inferring the presence of specific individuals. * A threshold determined by DP parameters is then applied; only items whose noisy weights exceed this threshold are included in the final output. ## Improving Utility via Adaptive Weighting * Traditional non-adaptive methods often result in "wastage," where highly popular items receive significantly more weight than necessary to cross the selection threshold. * The MaxAdaptiveDegree (MAD) algorithm introduces adaptivity by identifying items with excess weight and rerouting that weight to "under-allocated" items sitting just below the threshold. * This strategic reallocation allows a larger number of less-frequent items to be safely released, significantly increasing the utility of the dataset without compromising privacy or computational efficiency. ## Scalability and Parallelization * Unlike sequential algorithms that process data one piece at a time, MAD is designed as a parallel algorithm to handle the scale of modern user-based datasets. * The algorithm can process datasets with hundreds of billions of items by breaking the problem down into smaller parts computed simultaneously across multiple processors. * Google has open-sourced the implementation on GitHub to provide the research community with a tool that maintains robust privacy guarantees even at a massive scale. Researchers and data scientists working with large-scale sensitive datasets should consider implementing the MaxAdaptiveDegree algorithm to maximize the amount of shareable data while strictly adhering to user-level differential privacy standards.

AI Machine Learning Differential Privacy Data Privacy+4

lineAug 14, 2025Original article

Case Study: Improving Video (opens in new tab)

Engineers at LINE identified a recurring monthly degradation in video call quality, specifically in Japan, where packet loss increased and frames per second (FPS) dropped toward the end of each month. Investigation revealed that this pattern was caused by mobile ISP bitrate throttling once users exhausted their monthly data caps, which the existing congestion control mechanisms were failing to handle efficiently. To resolve this, the team improved their proprietary CCFS (Congestion Control based on Forward path Status) algorithm to more accurately detect these specific network constraints and maintain stable playback. ### Analysis of Monthly Quality Degradation * Data analysis showed a "monthly cycle" where video decoding FPS was highest at the start of the month and progressively declined toward the end. * This quality drop was specifically tied to an increase in video packet loss, which prevents normal decoding and results in stuttering or frozen frames. * Statistical segmentation revealed the issue occurred almost exclusively on 4G mobile networks rather than Wi-Fi, and was more pronounced in high-bitrate video calls than in voice calls. * The root cause was identified as mobile data plan policies; as users hit their monthly data limits, ISPs impose speed restrictions that create network congestion if the application continues to send high-bitrate data. ### Limitations of Standard Congestion Control * While the IETF RMCAT working group has standardized algorithms like NADA (RFC8698) and SCReAM (RFC8298), real-time two-way communication requires more sensitive response times than one-way streaming. * In two-way calls, even a one-second delay makes natural conversation difficult, meaning the system cannot rely on large buffers to smooth out network instability. * Existing mechanisms were not reacting fast enough to the rigid throughput limits imposed by carrier throttling, leading to packet accumulation in network queues and subsequent loss. ### The CCFS Proprietary Algorithm * LINE utilizes a custom-developed, sender-based algorithm called CCFS (Congestion Control based on Forward path Status). * Unlike older algorithms that rely on Round Trip Time (RTT), CCFS focuses on the "forward path"—the actual path packets take to the receiver—by analyzing feedback on packet arrival times and loss. * CCFS categorizes network status into four distinct states: Default, Probing, Throttled, and Competing. * The system monitors "delay variation"; when it detects a continuous increase in delay exceeding a specific threshold, it transitions to the "Throttled" state to proactively reduce bitrate before the queue overflows. ### Strategies for Quality Improvement * The team focused on refining how CCFS handles the transition into the Throttled state to better align with the artificial bandwidth ceilings created by ISPs. * By improving the sensitivity of forward path status monitoring, the application can more rapidly adjust its transmission rate to stay within the user's current data plan limits. * This technical adaptation ensures that even when a user's mobile speed is restricted, the video remains smooth, albeit at a lower resolution, rather than breaking up due to packet loss. To provide a high-quality communication experience, developers must account for external factors like regional ISP policies. Refining proprietary congestion control algorithms to detect specific patterns, such as monthly data-cap throttling, allows for a more resilient service that maintains stability across diverse mobile environments.

Real Time Communication Video Streaming Congestion Control Network Adaptation+4

googleAug 13, 2025Original article

Beyond billion-parameter burdens: Unlocking data synthesis with a conditional generator (opens in new tab)

The CTCL (Data Synthesis with ConTrollability and CLustering) framework provides a lightweight alternative to the computationally expensive process of fine-tuning billion-parameter models for differentially private synthetic data generation. By utilizing a 140-million parameter generator and a universal topic model, the system achieves high-quality distribution matching while remaining accessible for resource-constrained applications. This approach allows for the generation of unlimited synthetic samples without incurring additional privacy costs, consistently outperforming existing API-based and large-scale baselines under strict privacy guarantees. ### Pre-training Universal Components The framework relies on two core components developed using large-scale public corpora, which can be reused across different private domains: * **CTCL-Topic:** A universal topic model derived from Wikipedia documents. It uses BERTopic to embed and cluster data into approximately 1,000 distinct topics, each represented by 10 descriptive keywords. * **CTCL-Generator:** A conditional language model based on the 140M-parameter BART-base architecture. It was pre-trained on 430 million description–document pairs from the SlimPajama dataset, with descriptions generated by Gemma-2-2B to ensure the model can generate text based on specific input conditions. ### Learning the Private Domain Once the universal components are established, the framework learns the specific characteristics of a private dataset through a two-step process: * **Differentially Private (DP) Histograms:** The system captures high-level distributional information by creating a DP-protected histogram that represents the percentage of each topic present in the private corpus. * **DP Fine-Tuning:** Each document in the private dataset is associated with its corresponding keywords from the CTCL-Topic model. The CTCL-Generator is then fine-tuned on these keyword-document pairs using differential privacy to ensure individual data points are protected. ### Controllable Data Generation The final stage involves producing the synthetic dataset by sampling from the fine-tuned generator: * **Proportional Sampling:** The system generates data by targeting the exact topic proportions found in the private domain histogram. * **Keyword Conditioning:** For each topic, the model uses the associated 10 keywords as input to prompt the DP fine-tuned generator to produce relevant documents. * **Post-Processing Efficiency:** Because the generator is already fine-tuned with DP, the framework can generate an unlimited number of synthetic samples without further privacy budget expenditure, a significant advantage over iterative selection algorithms. CTCL offers a highly scalable and efficient solution for organizations needing to synthesize private text data without the infrastructure requirements of massive LLMs. Its ability to maintain topic-wise distribution through keyword conditioning makes it an ideal choice for specialized domains where maintaining the statistical utility of the data is as critical as protecting user privacy.

AI Large Language Models Fine Tuning Differential Privacy+5

airbnbAug 13, 20254 min readCurated summary

Migrating Airbnb’s JVM Monorepo to Bazel

Airbnb migrated its tens-of-millions-of-lines JVM monorepo from Gradle to Bazel over 4.5 years, achieving faster builds, testing, IntelliJ syncs, and development deployments. The move was driven by Bazel’s scalable remote execution, hermetic builds, and ability to provide shared infrastructure across Airbnb’s language-specific repositories. A gradual rollout, extensive automation, and close collaboration with service teams were central to making the migration successful. ## Results of the Migration - Build CSAT increased from 38% to 68%. - Local build and test times became 3–5 times faster. - IntelliJ syncs became 2–3 times faster. - Development-environment deployments became 2–3 times faster. ## Why Airbnb Chose Bazel ### Faster Builds Through Remote Execution - Large Gradle builds frequently took more than 20 minutes locally, while pre-merge CI builds had a p90 of 35 minutes. - Gradle had already been optimized with powerful machines and build sharding, but sharding caused underutilization and duplicated shared work. - Bazel’s cacheable actions and remote build execution enabled thousands of actions to run in parallel on short-lived workers. - “Build without the Bytes” reduced the amount of build output developers needed to download. - Bazel analysis runs in parallel, unlike the often single-threaded configuration phase of large Gradle projects. - Remote execution also improved local build performance, not just CI performance. ### More Reliable and Reproducible Builds - Gradle tasks could access the entire filesystem, creating accidental dependencies and race conditions. - Bazel sandboxes expose only declared inputs to each action, preventing undeclared files from affecting builds. - Bazel’s remote execution runs actions in identical containers with strict resource limits. - Using remote execution for both local and CI builds reduced differences between developer and CI environments. ### A Shared Build Infrastructure Layer Because Airbnb’s web, iOS, Python, Go, and JVM repositories all use Bazel, the company could standardize infrastructure for: - Remote caching - Remote build execution - Affected-target calculation - Build Event Protocol instrumentation and logging ## Starting with a Proof of Concept - Airbnb first migrated Viaduct, a large GraphQL monolith platform. - Viaduct was selected because it was complex, had slow builds, affected roughly 300 product engineers monthly, and had an infrastructure team willing to collaborate. - Bazel and Gradle initially coexisted, allowing developers to choose either system. - The team ported Viaduct’s build logic and created an automated Bazel build-file generator because the Gradle dependency graph continued to change. - Although Bazel was initially 2–4 times faster locally, developers did not adopt it immediately. - The team spent several additional months fixing missing integrations and bugs before Viaduct engineers voluntarily switched. ## Scaling Across the JVM Monorepo - Airbnb expanded breadth-first, aiming to make the entire repository compile and test under Bazel. - Gradle and Bazel continued to coexist during the migration. - This allowed developers to use Bazel locally while deployments still relied on Gradle. - Gradle provided a fallback when Bazel infrastructure, such as remote caching or execution, experienced incidents. - Maintaining two build graphs was costly, so Airbnb invested heavily in automation rather than requiring developers to maintain Bazel files manually. ## Automated Build-File Generation - The generator was inspired by Gazelle but was built internally to meet stricter performance requirements and handle dependency cycles. - It parses Java, Kotlin, and Scala source files to identify packages, imports, and symbol declarations. - These relationships are used to construct a file-level dependency graph. - Since generation ran on every commit before merging, Airbnb added external caching to keep it fast. - CI publishes a cached repository index for each mainline commit, allowing the generator to rescan only directories changed since that commit. Airbnb’s experience suggests that a large build-system migration is most effective when introduced incrementally: prove the benefits on a representative service, automate maintenance, preserve a fallback during rollout, and address developer workflow issues before expanding across the organization.

Read original

Kotlin Java Monorepo Jvm+4

figmaAug 12, 20253 min readCurated summary

The Anatomy of an Activation | Figma Blog

Figma marked its 2025 public-market debut by turning the New York Stock Exchange into a public celebration of design and community. Rather than follow a conventional IPO event, the company created “Figma Commons,” supported by citywide murals, posters, billboards, and street experiences. The activation aimed to make the milestone accessible and enjoyable for both longtime users and people encountering Figma for the first time. ## A Citywide Celebration of Design - The July 31 NYSE event centered on the message “Design is everyone’s business.” - Figma’s events and community teams expanded the celebration beyond the exchange with: - A large Brooklyn mural - Posters across New York City - A Times Square digital billboard - The Figma Commons outdoor event - The initiative reflected Figma’s belief that its community is central to the brand and should share in major milestones. ## Building Figma Commons - Community and events leads Jordan Scott and Kelley Sauer shaped the experience over weeks of planning. - The project required approvals from the NYC Department of Buildings, NYPD, and the mayor’s office. - Temporary structures had to be mounted on wheels to comply with emergency-access regulations. - Storms complicated installation, with work stopping for 30 minutes after each lightning strike. - The team deliberately chose a larger, more ambitious event than a standard NYSE listing ceremony. ## Extending the Invitation - Because communications were embargoed until a press release was issued, Figma had fewer than 15 hours to promote the event. - The team relied on grassroots and physical marketing: - Flyers at subway stations and ferry terminals - Wildpostings around Manhattan and Brooklyn - Direct outreach to longtime local customers - Posters included QR codes linking to free invitations, reinforcing the idea that the event was open to everyone. ## Designing the Experience - Figma paid close attention to visual and physical details because its community values design craft. - The event included: - Bespoke signage from Figma’s brand team - Limited-edition merchandise designed to feel fresh rather than excessively branded - Food and drinks from popular New York eateries - A live mural by graffiti artist Greg Lamarche featuring the word “make” - Additional citywide creative work promoted Figma Make, including Times Square billboards and a Brooklyn mural built from more than 200 hand-drawn glyphs. Figma Commons demonstrates how a corporate milestone can become a participatory public experience. By combining strong visual design, local culture, grassroots outreach, and careful logistical planning, Figma made its IPO celebration feel like an invitation to join the company’s broader design community.

Read original

Figma Figma Make Graphic Design Qr Code+1

lineAug 12, 2025Original article

The Current State of LY Corporation (opens in new tab)

Tech-Verse 2025 showcased LY Corporation’s strategic shift toward an AI-integrated ecosystem following the merger of LINE and Yahoo Japan. The event focused on the practical hurdles of deploying generative AI, concluding that the transition from experimental models to production-ready services requires sophisticated evaluation frameworks and deep contextual integration into developer workflows. ## AI-Driven Engineering with Ark Developer LY Corporation’s internal "Ark Developer" solution demonstrates how AI can be embedded directly into the software development life cycle. * The system utilizes a Retrieval-Augmented Generation (RAG) based code assistant to handle tasks such as code completion, security reviews, and automated test generation. * Rather than treating codebases as simple text documents, the tool performs graph analysis on directory structures to maintain structural context during code synthesis. * Real-world application includes a seamless integration with GitHub for automated Pull Request (PR) creation, with internal users reporting higher satisfaction compared to off-the-shelf tools like GitHub Copilot. ## Quantifying Quality in Generative AI A significant portion of the technical discussion centered on moving away from subjective "vibes-based" assessments toward rigorous, multi-faceted evaluation of AI outputs. * To measure the quality of generated images, developers utilized traditional metrics like Fréchet Inception Distance (FID) and Inception Score (IS) alongside LAION’s Aesthetic Score. * Advanced evaluation techniques were introduced, including CLIP-IQA, Q-Align, and Visual Question Answering (VQA) based on video-language models to analyze image accuracy. * Technical challenges in image translation and inpainting were highlighted, specifically the difficulty of restoring layout and text structures naturally after optical character recognition (OCR) and translation. ## Global Technical Exchange and Implementation The conference served as a collaborative hub for engineers across Japan, Taiwan, and Korea to discuss the implementation of emerging standards like the Model Context Protocol (MCP). * Sessions emphasized the "how-to" of overcoming deployment hurdles rather than just following technical trends. * Poster sessions (Product Street) and interactive Q&A segments allowed developers to share localized insights on LLM agent performance and agentic workflows. * The recurring theme across diverse teams was that the "evaluation and verification" stage is now the primary driver of quality in generative AI services. For organizations looking to scale AI, the key recommendation is to move beyond simple implementation and invest in "evaluation-driven development." By building internal tools that leverage graph-based context and quantitative metrics like Aesthetic Scores and VQA, teams can ensure that generative outputs meet professional service standards.

AI Large Language Models Model Context Protocol RAG+5

datadogAug 12, 20253 min readCurated summary

Scaling down to speed up: How we improved efficiency of live process metrics by 100x

Datadog redesigned its real-time Processes and Containers pipeline to avoid collecting high-frequency metrics that users never see. By limiting 2-second collection to hosts actively viewed and using standard 10-second data for sorting, the company reduced real-time traffic by over 100x, cut infrastructure costs by 98%, and lowered Agent resource usage. The approach also improved scalability without sacrificing the live investigation experience. ## Original Real-Time Collection Model - Datadog Agents normally collect process and container metrics every 10 seconds. - When a user opened a live Processes or Containers view, all hosts in that tenant switched to 2-second collection. - This supported near-real-time monitoring similar to `htop`, but across distributed infrastructure. - As tenants grew, the pipeline had to process millions of processes per second, even though users typically viewed only around 50 processes or containers. - Live sorting required keeping all tenant data in memory on a single server, limiting horizontal scaling and forcing vertical scaling. ## Refocusing on User-Visible Data - Most collected metrics were never displayed to users. - Datadog determined that real-time collection only needed to be enabled for hosts running the processes or containers currently in view—up to roughly 50 hosts per user. - Internal telemetry suggested this could reduce traffic by more than 100x. - This required tracking active host subscriptions and updating them as users navigated the product. - Because sorting occurred every 10 seconds, it did not need 2-second data. Datadog switched live views to use the existing 10-second metrics, aligning live and historical sorting logic. ## Host Subscription Filtering - A proof of concept added host subscriptions to the live data servers. - Servers filtered Kafka payloads and discarded data for hosts without active subscriptions. - This immediately reduced: - Memory usage by 85% - CPU usage by 33% - The improvement came from storing fewer live metrics and processing fewer incoming payloads. - The prototype confirmed that filtering preserved product behavior while simplifying sorting. ## Moving Filtering Earlier in the Pipeline - Late filtering improved live data servers but still left unnecessary work for the rest of the system and customer-side Datadog Agents. - Datadog therefore planned to propagate subscription state to the intake service. - Live data servers publish users’ active host sets over Kafka once per second. - The intake service consumes this information and decides which hosts should activate 2-second process and container metric collection. - This allows real-time collection to be restricted to hosts users are actively investigating while maintaining responsive live views. Datadog’s redesign demonstrates that real-time systems scale more effectively when they prioritize data users can actually see. Filtering at intake, limiting high-frequency collection to subscribed hosts, and reusing standard-resolution data for sorting provide a simpler and more economical architecture without eliminating live functionality.

Read original

Database Design Datadog Apache Kafka Docker & Containers+4

datadogAug 12, 20251 min readCurated summary

Scaling down to speed up: How we improved efficiency of live process metrics by 100x | Datadog

The provided content does not include the blog post itself. It contains Datadog’s navigation menu and a promotional link announcing its recognition as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms, so there is insufficient technical material to summarize the article. ### Content Included - A link to Datadog’s Gartner announcement. - Navigation categories covering: - Infrastructure and application monitoring - Logs, databases, and data observability - Security - Digital experience monitoring - Software delivery - Service management - AI capabilities - The URL suggests the intended article may concern scaling process or pipeline efficiency, but its body is not present. Please provide the full blog post text for a substantive summary.

Read original

Kubernetes Datadog Observability Application Performance Monitoring+4

googleAug 11, 2025Original article

Enabling physician-centered oversight for AMIE (opens in new tab)

Guardrailed-AMIE (g-AMIE) is a diagnostic AI framework designed to perform patient history-taking while strictly adhering to safety guardrails that prevent it from providing direct medical advice. By decoupling data collection from clinical decision-making, the system enables an asynchronous oversight model where primary care physicians (PCPs) review and finalize AI-generated medical summaries. In virtual clinical trials, g-AMIE’s diagnostic outputs and patient communications were preferred by overseeing physicians and patient actors over human-led control groups. ## Multi-Agent Architecture and Guardrails * The system utilizes a multi-agent setup powered by Gemini 2.0 Flash, consisting of a dialogue agent, a guardrail agent, and a SOAP note agent. * The dialogue agent conducts history-taking in three distinct phases: general information gathering, targeted validation of a differential diagnosis, and a conclusion phase for patient questions. * A dedicated guardrail agent monitors and rephrases responses in real-time to ensure the AI abstains from sharing individualized diagnoses or treatment plans directly with the patient. * The SOAP note agent employs sequential multi-step generation to separate summarization tasks (Subjective and Objective) from more complex inferential tasks (Assessment and Plan). ## The Clinician Cockpit and Asynchronous Oversight * To facilitate human review, researchers developed the "clinician cockpit," a web interface co-designed with outpatient physicians through semi-structured interviews. * The interface is structured around the standard SOAP note format, presenting the patient’s perspective, measurable data, differential diagnosis, and proposed management strategy. * This framework allows overseeing PCPs to review cases asynchronously, editing the AI’s proposed differential diagnoses and management plans before sharing a final message with the patient. * The separation of history-taking from decision-making ensures that licensed medical professionals retain ultimate accountability for patient care. ## Performance Evaluation via Virtual OSCE * The system was evaluated in a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) involving 60 case scenarios. * g-AMIE’s performance was compared against primary care physicians, nurse practitioners, and physician assistants who were required to operate under the same restrictive guardrails. * Overseeing PCPs and independent physician raters preferred g-AMIE’s diagnostic accuracy and management plans over those of the human control groups. * Patient actors reported a preference for the messages generated by g-AMIE compared to those drafted by human clinicians in the study. While g-AMIE demonstrates high potential for human-AI collaboration in diagnostics, the researchers emphasize that results should be interpreted with caution. The workflow was specifically optimized for AI characteristics, and human clinicians may require specialized training to perform effectively within such highly regulated guardrail frameworks.

AI Generative AI Natural Language Processing Multi Agent Systems+4