Techlist.io - Korean Tech Blog Curator

figmaJun 9, 20253 min readCurated summary

8 Essential Tips for Using Figma Make | Figma Blog

Figma Make works best when users provide clear context, prepare clean design files, and refine complex projects incrementally. Detailed initial prompts reduce revisions, while organized Figma layers and Auto Layout help designs translate into functional prototypes. For ambitious builds, breaking work into focused prompts and separate code folders improves control, maintainability, and debugging. ## Provide Detailed Initial Prompts - Include: - The task Figma Make should perform - Product or flow context - Essential design elements - Expected interactions and behaviors - Device, layout, and visual constraints - Front-loading requirements helps produce a stronger first version with fewer follow-up prompts. - Use precise, measurable instructions instead of vague requests: - “Move this element down 20 pixels” - “Add 16px of space between these buttons” - If repeated adjustments are not working, restart with a new file and use lessons from the first attempt. - Effective project prompts can include an overview, platform, purpose, features, visual direction, technical details, and an explicit first implementation step. ## Clean Up Figma Files Before Importing Them - Figma Make can either create new designs or turn existing Figma frames into interactive prototypes. - Before copying a frame into Figma Make: - Organize the file - Apply appropriate constraints - Use Auto Layout correctly - Name layers according to their purpose - Figma tools such as Suggest Auto Layout and Rename Layers with AI, along with plugins like Clean Document, can help prepare files. - If the result is too large or not responsive, use prompts such as: - “Scale this to the size of my screen and make it responsive.” - “Keep this mobile-sized.” - A well-structured Auto Layout design can enable complex interactions from a single prompt, such as making a CD spin when a music player starts. ## Build Complex Projects Incrementally - Use a detailed first prompt to establish the overall foundation, then make smaller, focused changes. - Smaller requests allow the model to respond more precisely and reduce the risk of unwanted changes elsewhere. - Incremental prompting is useful for: - Building complex interfaces - Creating multi-page flows - Adding individual features - Maintaining the intended visual direction - Ask Figma Make to place separate elements in separate code folders to improve organization, maintainability, and error isolation. - Large projects may require many prompts; one financial dashboard and onboarding flow took more than 150 focused iterations. - Example follow-ups included adding journal post-its, inserting a detailed finance table, and adding a currency-selection checkbox. - Separating 3D landmarks into individual coded files similarly makes it easier to refine components without affecting the wider environment. Use Figma Make as an iterative design-and-development tool: prepare the source file carefully, describe the desired result precisely, and make complex changes one manageable step at a time.

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Figma User Experience Prototyping Generative AI+4

lineJun 9, 2025Original article

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.

Real Time Communication Voip Quality Of Service Network Performance+3

figmaJun 8, 20252 min readCurated summary

Bill Atkinson’s 10 Rules for Making Interfaces More Human | Figma Blog

Bill Atkinson’s work at Apple showed that powerful technology can feel natural, accessible, and even joyful. Through QuickDraw, MacPaint, and HyperCard, he transformed complex computing tasks into intuitive creative experiences. The post distills his approach into principles centered on designing within limits, democratizing creation, challenging assumptions, and making interfaces disappear into the user’s work. ## Designing Within Constraints - Atkinson developed QuickDraw for the original Macintosh, which had only 128KB of RAM and limited processing power. - Instead of lowering the ambition for smooth graphics, he created highly efficient algorithms for drawing shapes and curves. - His approach favored elegant solutions that respected constraints rather than brute-force hardware improvements. ## Democratizing Creativity - HyperCard aimed to let people create interactive software without years of programming experience. - Its visual tools and natural-language scripting enabled teachers, artists, and businesses to build their own applications. - Atkinson believed transformative tools should serve everyone, not only technical specialists. ## Making Interfaces Feel Inevitable - Atkinson helped establish interface conventions such as the menu bar, double-clicking, and smooth graphical interactions. - These features made drag-and-drop and point-and-click computing accessible to non-programmers. - Great interface design, in this view, lets users focus on their work instead of the software. ## Questioning Conventional Wisdom - Atkinson challenged assumptions that computing had to be text-based or that bitmap editing was too difficult for ordinary users. - He later acknowledged that HyperCard’s architecture was limited by Apple’s “box-centric” worldview and might have become an early web browser in a network-oriented environment. - Designers should scrutinize ideas that seem obviously correct or impossible. ## Optimizing for Delight - MacPaint was designed to be immediately understandable, even to young children. - Tools such as the brush and paint bucket behaved like their real-world counterparts. - The goal was not merely task completion, but encouraging experimentation, play, and creative exploration. Atkinson’s legacy suggests that human-centered software combines technical rigor with empathy: work within limitations, remove unnecessary complexity, and make creation feel inviting.

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Figma Human Computer Interaction Interface Design Graphical User Interface+3

googleJun 5, 2025Original article

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.

AI Large Language Models Gemini Dynamic Programming+4

googleJun 4, 2025Original article

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.

AI Generative AI Diffusion Models Climate Modeling+5

figmaJun 4, 20252 min readCurated summary

Double Click: What Does MCP Mean for Agentic AI? | Figma Blog

MCP is emerging as a common interoperability layer between AI assistants and external tools or data sources. By allowing models to discover and invoke tools during a conversation, it can make agentic systems faster, more scalable, and less dependent on bespoke integrations. The growing ecosystem—including Figma’s own MCP server—suggests MCP could become foundational infrastructure for the agentic web. ## What MCP Is - Anthropic introduced the Model Context Protocol in November, 2024; support from OpenAI helped drive widespread adoption. - MCP standardizes how assistants such as Claude, Copilot, and Cursor communicate with tools and data. - Developers can avoid building separate custom integrations for every AI assistant and service. - The protocol is compared to: - **USB-C**, as a universal connection for AI applications. - **HTTP**, because it is lightweight, composable, interoperable, and largely unconcerned with the payload. - Microsoft CTO Kevin Scott described MCP as a potential backbone for agent communication and evolution. ## MCP and Agentic AI - MCP gives large language models real-time access to tools and information. - This enables AI agents to act on a user’s behalf rather than merely generate text or code. - The number of available MCP servers is growing rapidly. - Figma created an MCP server that connects design information directly to developer workflows, supporting design-informed code generation. ## MCP as an Accelerant - Traditional API-based workflows often require an LLM to generate code and execute it whenever it needs to interact with a tool. - With MCP, the model can recognize available tools and invoke them directly during a conversation. - This reduces friction and improves speed, efficiency, and scalability. - The resulting experience makes complex actions feel more immediate—users can increasingly “just do things.” ## Early Examples of MCP Use - An automated AI travel agency used four agents across Google Maps, Airbnb, Google Calendar, and Weather. - A Blender MCP server generated a 3D scene of a low-poly dragon guarding treasure from a few natural-language instructions. - Y Combinator hosted a large MCP hackathon, demonstrating the rapid growth of experimentation around the protocol. MCP’s main promise is not simply connecting AI to APIs, but creating a shared, extensible standard that lets agents use many tools dynamically. Its long-term impact will depend on how reliably and responsibly these increasingly capable systems operate.

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Figma Large Language Models AI Agents Model Context Protocol+1

lineJun 4, 2025Original article

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.

Android Kotlin Code Quality Software Design+3

figmaJun 3, 20253 min readCurated summary

Introducing our MCP server: Bringing Figma into your workflow | Figma Blog

Figma’s beta MCP server connects Figma to AI coding tools such as Cursor, Copilot in VS Code, Windsurf, and Claude Code. It gives LLMs richer design context than screenshots or API responses alone, helping them generate code that reflects a team’s design system, codebase patterns, and intended behavior. Figma argues that accurate design-to-code work requires a holistic understanding of both visual design and implementation context. ## Why Design Context Matters - LLMs can produce functional code without additional context, but it may not match a team’s: - Architecture and file structure - Framework and terminology - Existing components and workflows - Evolving codebase conventions - These team-specific patterns form a unique “fingerprint” that models cannot reliably infer from training data. - MCP provides a standardized way for applications such as Figma to supply targeted context to agentic AI tools. ## Translating Design Intent for LLMs - Human developers typically: - Zoom out to understand overall structure and layout - Examine screen sequences and application flows - Infer how designs should map to code files and components - Interpret placeholder content as real data or backend requirements - Move between high-level patterns and low-level implementation details - The Figma MCP server aims to give LLMs the same broad perspective. - Its tools expose different kinds of context, allowing users to control which information is included and avoid wasting context-window space. ## Pattern Metadata - Figma can provide references to specific: - Components - Variables and design tokens - Styles - Code files - This is especially useful when design and code are already aligned through a design system. - Code Connect can identify the exact code component associated with a Figma component, reducing unnecessary codebase searches and preventing duplicate implementations. - For design tokens, Figma can identify the precise variable used—even when several tokens share the same visual value. - If code syntax is defined for a variable, the MCP server can pass the exact implementation syntax to the LLM. - Supplying this metadata improves precision and reduces token usage. ## Screenshots - Screenshots supplement metadata when visual or interactive meaning is difficult to express structurally. - They can communicate: - Embedded or interactive content represented by imagery - Relationships between sections and nodes - Sequences of screens - Mobile and desktop layouts - Overall application flow - Screenshots are not intended as pixel-perfect specifications. - Figma emphasizes that generated code should reflect design intent rather than merely reproduce pixels. - Combining screenshots with Figma’s code-oriented outputs is more effective than relying on either alone. ## Interactivity and Behavior - Code examples and pseudocode can express behavior more effectively than raw design metadata. - They are useful for: - Stateful components - Encapsulated functionality - UI sequences and transitions - Differences between related states or screens - Pseudocode becomes more valuable when it incorporates codebase context, such as variable syntax and Code Connect component mappings. ## Beta Roadmap - The MCP server is an early beta release. - Figma plans to add remote server capabilities and deeper integrations with codebases. - The company is seeking feedback while continuing to expand the design-to-code workflow. Figma’s MCP server is most valuable when teams maintain strong alignment between their design systems and codebases. Combining structured metadata, visual context, and behavioral examples gives AI coding agents a better foundation for producing implementation-ready code.

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Figma Large Language Models AI Agents Model Context Protocol+3

datadogJun 3, 20251 min readCurated summary

Detecting faulty deployments: Our journey from unlabeled data to supervised learning | Datadog

The supplied content does not include the blog post itself; it contains Datadog’s navigation menu and a link titled “Detecting Faulty Deployments.” As a result, there is not enough information to accurately summarize the article’s arguments, implementation details, or conclusions. ## Available context - The linked article appears to concern identifying deployments that introduce faults or regressions. - Datadog’s platform covers related capabilities such as: - Application Performance Monitoring - Metrics and infrastructure monitoring - Logs and error tracking - CI Visibility and software delivery monitoring - Service-level objectives and incident response - The page also promotes Datadog’s recognition as a Leader in the Gartner Magic Quadrant for Observability Platforms. ## Missing information - The article’s detection methodology - Metrics, queries, or deployment signals used - Alerting, rollback, or remediation procedures - Technical examples and conclusions Please provide the article text or a page extract containing the post body for an accurate summary.

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Kubernetes Datadog Observability Application Performance Monitoring+4

datadogJun 3, 20253 min readCurated summary

Detecting faulty deployments: Our journey from unlabeled data to supervised learning

Deployments are a major source of software incidents, making rapid detection of faulty releases essential. Datadog developed Automatic Faulty Deployment Detection to identify releases associated with significant, deployment-related increases in error rates, despite having no reliable labeled dataset. Their solution evolved into an iterative, unsupervised ensemble of statistical checks designed to balance precision, recall, and the diverse behavior of customer applications. ## Challenges in Detecting Faulty Deployments - No universal ground truth exists because teams define “faulty” differently depending on their applications. - Faulty deployments are rare, creating severe class imbalance: - Random manual labeling would produce few useful examples. - Even a low false-positive rate could result in poor precision. - Applications have widely varying traffic and error patterns: - Seasonal applications naturally experience periodic changes. - Low-traffic services need longer observation periods. - Frequent deployments can make it difficult to identify which release caused an incident. ## Defining a Faulty Deployment Datadog focused on deployments that caused a significant and sustained increase in error rate. The definition relied on three attributes: - **Impact** - The total number of errors must be meaningfully higher than the baseline. - The increase must be significantly worse than in previous versions. - **Temporal correlation** - The error increase should align with the introduction of the new version. - **Persistence** - The elevated error rate must continue over time rather than reflecting temporary deployment noise. ## Building an Iterative Detection Framework - The initial system applied simple statistical rules to the first 60 minutes after each deployment. - Manual annotation was used to estimate precision, but this required substantial effort and did not reveal recall. - Datadog created an iterative framework composed of checks for different deployment requirements. - Checks included: - Comparing error rates before and after deployment. - Comparing a release with previous versions. - Accounting for periodic traffic and errors. - Handling sparse traffic patterns. - The checks were combined into a unanimous-voting ensemble: a deployment was flagged only when every check classified it as faulty. - The process began with a high-recall model, then: - Manually reviewed predicted faults. - Analyzed false positives. - Added new checks and adjusted thresholds to improve precision and recall. - Incident data and version rollbacks provided additional signals for finding faulty deployments the model had missed. ## Balancing Detection Speed and Recall - The model used the first hour after deployment to gather enough data to determine whether increased errors were persistent. - Increasing the observation period can improve confidence but delays detection. - The framework became progressively more sophisticated, adapting to: - Periodic error and traffic patterns. - Sparse traffic. - Multiple concurrent application versions. The practical recommendation is to begin with simple, high-recall statistical rules, then iteratively improve them through targeted manual review, false-positive analysis, and additional operational signals such as incidents and rollbacks. This approach can support other anomaly-detection problems where labels are scarce, failures are rare, and application behavior varies significantly.

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Database Design Application Performance Monitoring Incident Management Statistical Analysis+4

discordJun 2, 2025Original article

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.

Real Time Communication Voip Streaming Instant Messaging+3

discordJun 2, 20252 min readCurated summary

Checkpoint 3: Leveling Up Discord Quests with Orbs and Advanced Measurement

Discord is expanding Quests into a more flexible, measurable advertising platform. It is testing Discord Orbs, a virtual reward users can exchange for Nitro credits, profile cosmetics, and other Shop items, while partnering with Kantar to measure campaign effects on awareness, recall, and purchase intent. The company also plans to broaden game-focused Play Quests to support more brands and games. ## Discord Orbs as Flexible Rewards - A limited group of users can earn Orbs by completing Quests. - Orbs can be spent in Discord’s Shop without requiring a payment method. - Rewards include: - Nitro credits - Profile cosmetics - Other first-party Shop items - The system gives users more choice, including a way for non-subscribers to try Nitro. - Advertisers no longer need to provide their own rewards or predict which incentives will appeal to their audience. - Discord plans to add more Orb-based rewards and offer enhanced deals to Nitro subscribers. ## Kantar-Powered Brand Measurement - Discord is partnering with Kantar to provide brand lift studies for Quests campaigns. - Advertisers will be able to measure: - Brand awareness - Ad recall - Purchase or brand intent - The partnership is intended to show whether Quests produce meaningful changes in brand perception, not just engagement. - Kantar describes Quests as a natural fit for gaming audiences because they connect advertising with how users already play. ## Expanding Advertising Opportunities - Discord positions itself as a social space where gaming communities build relationships and engage with friends. - The company plans to expand its Play Quest format, which rewards users for playing selected games. - Future expansion will support more brands and advertisers while encouraging discovery and growth for participating games. - Discord says these efforts are still early and that additional details will follow. Discord’s strategy combines user-controlled rewards with stronger advertising analytics. By making rewards more relevant through Orbs and improving campaign measurement through Kantar, Quests could become more attractive to both users and advertisers while preserving a game-centered experience.

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Discord Gaming Digital Advertising Discord Quests+2

discordJun 2, 20253 min readCurated summary

How to Use the Discord Soundboard & Add More Sounds

Discord’s Soundboard lets users play short audio clips during voice calls, with built-in sounds available everywhere they have permission. Users can upload custom MP3 clips to servers, favorite frequently used sounds, adjust playback volume, and—if they have Nitro—use accessible sounds across servers and DMs. Server moderators can also restrict who may use the Soundboard or sounds from outside the server. ## Using the Soundboard - **Desktop:** Join a voice channel or DM call and click the 🎉 button in the voice control bar. - **Mobile:** Join a voice call, swipe up on the call panel, and select **🎉 Soundboard**. - Soundboard access depends on server permissions, but it is also available in direct and group direct calls. - Everyone receives six default sounds: - Quack - Airhorn - Cricket - Golf Clap - Sad Horn - Da Bum Diss ## Adding and Removing Custom Sounds Custom sounds are stored at the server level, similarly to emojis and stickers. - Go to **Server Settings > Soundboard** and select **Upload Sound**. - Uploaded sounds must meet these requirements: - **Format:** `.MP3` - **Maximum size:** 512 KB - **Maximum length:** 5 seconds - **Name:** Describes the spoken phrase or sound - **Related emoji:** A default or custom community emoji - **Volume:** An optional default playback level - The post explains how to add sounds but does not provide further removal instructions in the supplied text. ## Favoriting Sounds Favorites make frequently used clips easier to access. - **Desktop:** Hover over a sound and click the ⭐️ button. - **Mobile:** Long-press a sound and choose **Add to Favorites**. - Favorites appear in a dedicated category at the top of the Soundboard. - Favorites synchronize across platforms. ## Adjusting or Muting Soundboard Volume Users can control how loud Soundboard clips are personally. - Navigate to **User Settings > Voice & Video > Soundboard**. - Use the volume slider to adjust all Soundboard sounds. - Setting the volume to **0%** mutes the audio completely. - Even when muted, associated emojis still appear when others play sounds. ## Nitro Features Discord Nitro provides additional Soundboard capabilities. - Nitro members can use any Soundboard sounds they have access to in other servers and DM calls. - Nitro users can configure a favorite sound to play automatically when joining a server voice channel. - Different entrance sounds can be selected for different servers. ## Moderation and Permissions Large communities may want to limit Soundboard usage. - Server administrators can restrict roles from: - Using sounds from outside the server - Using the Soundboard entirely - These controls are accessed through the server’s role settings. Discord’s Soundboard is designed for quick reactions in voice calls, while its upload, favorite, volume, Nitro, and moderation controls let communities tailor the experience to their needs.

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Discord Voice Chat Discord Nitro Soundboard+2

googleJun 2, 2025Original article

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.

Large Language Models Reinforcement Learning Supervised Fine Tuning Action Based Contrastive Self Training+4

discordJun 1, 2025Original article

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.

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