IDC study says the global workforce engaged in software design is expanding Inside Figma News IDC forecasts that the number of people involved in software design will increase by more than 30% from 2025 to 2029—signaling the rising importance of design as a competitive different…
To enhance user engagement on the LINE OpenChat main screen, LY Corporation developed a system to extract and surface "trending keywords" from real-time message data. By shifting focus from chat room recommendations to content-driven keyword clusters, the team addresses the lack of context in individual messages while providing a more dynamic discovery experience. This approach utilizes a combination of statistical Z-tests to identify frequency spikes and MinHash clustering to eliminate near-duplicate content, ensuring that the trending topics are both relevant and diverse. **The Shift from Chat Rooms to Content-Driven Recommendations** * Traditional recommendations focus on entire chat rooms, which often require significant user effort to investigate and evaluate. * Inspired by micro-blogging services, the team aimed to surface messages as individual content pieces to increase the "main screen visit" KPI. * Because individual chat messages are often fragmented or full of typos, the system groups them by keywords to create meaningful thematic content. **Statistical Detection of Trending Keywords** * Simple frequency counts are ineffective because they capture common social fillers like greetings or expressions of gratitude rather than actual trends. * Trends are defined as keywords showing a sharp increase in frequency compared to a baseline from seven days prior. * The system uses a Z-test for two-sample proportions to assign a score to each word, filtering for terms with at least a 30% frequency growth. * A seven-day comparison window is specifically used to suppress weekly cyclical noise (e.g., mentions of "weekend") and to capture topics whose popularity peaks over several consecutive days. **MinHash-based Message Deduplication** * Redundant messages, such as copy-pasted text, are removed prior to frequency aggregation to prevent skewed results and repetitive user experiences. * The system employs MinHash, a dimensionality reduction technique, to identify near-duplicate messages based on Jaccard similarity. * The process involves "shingling" messages into sets of tokens (primarily nouns) and generating $k$-length signatures; messages with identical signatures are clustered together. * To evaluate the efficiency of these clusters without high computational costs, the team developed a "SetDiv" (Set Diversity) metric that operates in linear time complexity. By combining Z-test statistical modeling with MinHash deduplication, this methodology successfully transforms fragmented chat data into a structured discovery layer. For developers working with high-volume social data, using a rolling weekly baseline and signature-based clustering offers a scalable way to surface high-velocity trends while filtering out both routine social noise and repetitive content.
Hack Week 2025: How these engineers liquid-cooled a GPU server Hack Week 2025 at Dropbox centered on the theme “Keep It Simple,” offering opportunities for innovation, experimentation, and finding smart solutions to complex challenges. With in-person hubs in San Francisco, Seatt…
Effective refactoring often fails when developers focus on the physical structure of code rather than its conceptual meaning. When nested loops for paged data are extracted into separate functions based solely on their technical boundaries, the resulting code can remain difficult to read and maintain. The article argues that true code quality is achieved by aligning function boundaries with logical units, such as abstracting data retrieval into sequences to flatten complex structures. ## Limitations of Naive Extraction - Traditional paged data processing often results in nested loops, where an outer `while` loop manages page indices and an inner `for` loop iterates through items in a chunk. - Simply extracting the inner loop into a private method like `saveMetadataInPage(page)` frequently fails to improve readability because it splits the conceptual task of "fetching all items" into two disconnected locations. - This "mechanical extraction" preserves the underlying implementation complexity, forcing the reader to track the state of pagination and loop conditions across multiple function calls. ## Refactoring Based on Conceptual Boundaries - A more effective approach identifies the high-level semantic units: "retrieving all items" and "processing each item." - In Kotlin, the pagination logic can be encapsulated within a `Sequence<Item>` using the `sequence` builder and `yieldAll` keywords. - By transforming the data source into a sequence, the consumer function can replace a nested loop with a single, clean `for` loop. - This abstraction allows the main business logic to focus on "what" is being done (saving metadata) while hiding the "how" (managing page indices and `hasNext` flags). ## Forest over Trees - When refactoring, developers should prioritize the "forest" (the relationship between operations) over the "trees" (individual functions). - This methodology is not limited to loops; it applies equally to nested conditional branches and complex data structures. - The goal should always be to ensure that the code reflects the meaning of the task, which often requires restructuring the data flow rather than just splitting existing blocks of code.
Managing friend groups through standard Group DMs often leads to redundant chat lists and disorganized conversation streams that are difficult to navigate. Discord servers offer a more structured alternative, providing a centralized hub for multi-faceted communication and specific events like game nights. This guide introduces the transition from temporary, cluttered chats to a permanent and organized server environment. ### The Inefficiency of Traditional Group Chats * Group DMs frequently proliferate whenever a new participant is added to a temporary event, resulting in multiple overlapping chat threads with the same core members. * High-velocity conversations within a single DM stream make it labor-intensive for users to parse through history and catch up on missed context. * The lack of organizational depth in standard messaging forces users to manage disparate conversations across a fragmented interface. ### Benefits of the Discord Server Model * Servers act as a consolidated infrastructure where various sub-topics and social circles can be managed under one digital roof. * The platform allows for a more persistent social space compared to the ephemeral and often repetitive nature of group direct messages. * Creating a server provides a scalable solution for friend groups, whether they are migrating a single chat or organizing a large-scale community. For groups experiencing "chat fatigue" from disjointed DMs, migrating to a dedicated Discord server is the most practical way to streamline communication and ensure all members stay connected without the clutter.
To address a projected global deficit of 11 million healthcare workers by 2030, Google Research is exploring how generative AI can provide personalized, competency-based education for medical professionals. By combining qualitative user-centered design with quantitative benchmarking of the pedagogically fine-tuned LearnLM model, researchers have demonstrated that AI can effectively mimic the behaviors of high-quality human tutors. The studies conclude that specialized models, now integrated into Gemini 2.5 Pro, can significantly enhance clinical reasoning and adapt to the individual learning styles of medical students. ## Learner-Centered Design and Participatory Research * Researchers conducted interdisciplinary co-design workshops featuring medical students, clinicians, and AI researchers to identify specific educational needs. * The team developed a rapid prototype of an AI tutor designed to guide learners through clinical reasoning exercises anchored in synthetic clinical vignettes. * Qualitative feedback from medical residents and students highlighted a demand for "preceptor-like" behaviors, such as the ability to manage cognitive load, provide constructive feedback, and encourage active reflection. * Analysis revealed that learners specifically value AI tools that can identify and bridge individual knowledge gaps rather than providing generic information. ## Quantitative Benchmarking via LearnLM * The study utilized LearnLM, a version of Gemini fine-tuned specifically for educational pedagogy, and compared its performance against Gemini 1.5 Pro. * Evaluations were conducted using 50 synthetic scenarios covering a spectrum of medical education, ranging from preclinical topics like platelet activation to clinical subjects such as neonatal jaundice. * Medical students engaged in 290 role-playing conversations, which were then evaluated based on four primary metrics: overall experience, meeting learning needs, enjoyability, and understandability. * Physician educators performed blinded reviews of conversation transcripts to assess whether the AI adhered to medical education standards and core competencies. ## Pedagogical Performance and Expert Evaluation * LearnLM was consistently rated higher than the base model by both students and educators, with experts noting it behaved "more like a very good human tutor." * The fine-tuned model demonstrated a superior ability to maintain a conversation plan and use grounding materials to provide accurate, context-aware instruction. * Findings suggest that pedagogical fine-tuning is essential for AI to move beyond simple fact-delivery and toward true interactive tutoring. * These specialized learning capabilities have been transitioned from the research phase into Gemini 2.5 Pro to support broader educational applications. By integrating these specialized AI behaviors into medical training pipelines, institutions can provide scalable, individualized support to students. The transition of LearnLM’s pedagogical features into Gemini 2.5 Pro provides a practical framework for developers to create tools that not only provide medical information but actively foster the critical thinking skills required for clinical practice.
Researchers at Google have developed a scalable framework for evaluating health-focused language models by replacing subjective, high-complexity rubrics with granular, binary criteria. This "Adaptive Precise Boolean" approach addresses the high costs and low inter-rater reliability typically associated with expert-led evaluation in specialized medical domains. By dynamically filtering rubric questions based on context, the framework significantly improves both the speed and precision of model assessments. ## Limitations of Traditional Evaluation * Current evaluation practices for health LLMs rely heavily on human experts, making them cost-prohibitive and difficult to scale. * Standard tools, such as Likert scales (e.g., 1-5 ratings) or open-ended text, often lead to subjective interpretations and low inter-rater consistency. * Evaluating complex, personalized health data requires a level of detail that traditional broad-scale rubrics fail to capture accurately. ## Precise Boolean Rubrics * The framework "granularizes" complex evaluation targets into a larger set of focused, binary (Yes/No) questions. * This format reduces ambiguity by forcing raters to make definitive judgments on specific aspects of a model's response. * By removing the middle ground found in multi-point scales, the framework produces a more robust and actionable signal for programmatic model refinement. ## The Adaptive Filtering Mechanism * To prevent the high volume of binary questions from overwhelming human raters, the researchers introduced an "Adaptive" layer. * The framework uses the Gemini model as a zero-shot classifier to analyze the user query and LLM response, identifying only the most relevant rubric questions. * This data-driven adaptation ensures that human experts only spend time on pertinent criteria, resulting in "Human-Adaptive Precise Boolean" rubrics. ## Performance and Reliability Gains * The methodology was validated in the domain of metabolic health, covering topics like diabetes, obesity, and cardiovascular disease. * The Adaptive Precise Boolean approach reduced human evaluation time by over 50% compared to traditional Likert-scale methods. * Inter-rater reliability, measured through intra-class correlation coefficients (ICC), was significantly higher than the baseline, proving that simpler scoring can provide a higher quality signal. This framework demonstrates that breaking down complex medical evaluations into simple, machine-filtered binary questions is a more efficient path toward safe and accurate health AI. Organizations developing domain-specific models should consider adopting adaptive binary rubrics to balance the need for expert oversight with the requirements of large-scale model iteration.
Version control: How a UX writer weighs one word against another Inside Figma Writing UI/UX Prototyping
Discord has officially moved its Social SDK communication features out of closed beta, making integrated voice and text chat available to all game developers. By bringing these native Discord features directly into the game environment, the SDK aims to foster deeper player connections and increase session lengths through improved multiplayer interactions. This release marks a significant step in streamlining social connectivity, allowing studios to leverage Discord’s infrastructure without forcing players to leave the game client. ### Expanding In-Game Communication * Developers can now fully implement Discord-powered voice and text chat features within their titles. * The SDK is designed to enhance the multiplayer experience by providing high-quality, reliable communication tools that are synonymous with the Discord platform. * Initially introduced at GDC, these features are intended to maximize player engagement by making social interaction a core part of the gameplay loop. ### Frictionless Player Connectivity * The SDK allows players to connect with friends and join multiplayer sessions even if they do not currently have a Discord account. * By removing barriers to entry, the tools help players find new teammates and build communities more easily within the game. * Integration focuses on creating "meaningful multiplayer interactions" that contribute to higher player retention and longer-term interest in the game. For developers seeking to build a robust social layer into their games, the Discord Social SDK offers a proven communication stack that functions independently of external account requirements, ensuring a broader reach for community-building efforts.
Discord is addressing long-standing community management challenges by launching a series of updates designed to empower server moderators and game developers. Through the formation of a new dedicated engineering team, the platform aims to provide more granular control and resolve common pain points for high-growth community builders. These improvements represent the initial phase of a broader commitment to enhancing server health and administrative efficiency. **Strategic Focus on Community Control** * Discord has established a specialized team focused exclusively on providing server leaders with more power and reducing administrative friction. * The development roadmap is currently prioritizing a backlog of legacy requests from community managers and game developers. * The initiative focuses on creating a more stable environment for "healthy and active" servers through improved backend support and feature sets. **Empowering Developers and Moderators** * New updates are being released in "waves," with this first installment focusing on the core tools needed to spin up and maintain large communities. * The platform aims to reduce the necessity for external workarounds by integrating requested fixes directly into the Discord interface. * Special attention is being given to game developer-led communities to ensure they have the specific tools required to manage official brand spaces. Community administrators should stay tuned for subsequent update waves as Discord continues to roll out features from their dedicated community-power backlog. Keeping an eye on these native tool improvements will likely reduce the reliance on third-party moderation bots and manual administrative overhead.
Discord's debut at the Cannes Lions festival marks a significant strategic milestone in the expansion of its advertising business and brand partnership ecosystem. By championing an opt-in, community-first model, the platform aims to redefine how advertisers engage with a digital-native audience that prioritizes privacy and authentic interaction. The central conclusion from their industry showcase is that gaming has moved into the cultural mainstream, positioning Discord as the primary hub for audience influence and peer-to-peer communication. ### Strategic Advertising and Industry Partnerships * Discord is actively scaling its ad business by moving away from traditional intrusive formats toward a model based on user consent and community integration. * High-level panels featuring leadership from Xbox, Kantar, and Unilever underscore Discord's growing legitimacy as a critical platform for global brand strategy. * The platform’s evolution focuses on providing businesses with measurable opportunities to connect with high-intent users within their own social environments. ### The Mainstreaming of Gaming Communities * Gaming has transcended its niche origins to become a dominant cultural force, requiring brands to adapt to new methods of digital social interaction. * Discord serves as the "digital third place" where modern gamers talk, share content, and influence one another’s purchasing decisions in real-time. * The community-first approach allows brands to move past broad-reach tactics and instead foster deeper, more meaningful connections within specific interest groups. To effectively reach modern gaming audiences, brands should transition from traditional broadcast advertising toward community-centric engagement. Leveraging Discord’s opt-in framework allows companies to build long-term loyalty by participating in the authentic conversations already happening within these influential digital spaces.
Discord and NVIDIA have partnered to integrate GeForce NOW cloud streaming directly into the Discord platform, aiming to eliminate traditional barriers to game discovery. By removing the need for downloads, installations, and patches, this collaboration allows users to launch and play high-fidelity titles instantly within their social workspace. This move positions Discord as a streamlined gateway for gaming, facilitating immediate engagement between players and developers. ### Instant Play via NVIDIA Graphics Delivery Network * The integration leverages the NVIDIA Graphics Delivery Network (GDN) and GeForce NOW infrastructure to provide a "click-to-play" experience without external launchers. * By bypassing the friction of large file downloads and software updates, the service allows users to join games as easily as they join voice channels. * For developers, this creates a frictionless path to showcase titles to Discord’s highly engaged community, potentially increasing conversion rates for new game discovery. ### High-Performance Streaming Specifications * The cloud-streamed experience is capable of delivering gameplay at up to 1440p resolution and 60 frames per second. * Initial demonstrations feature *Fortnite*, serving as a proof-of-concept for how competitive, resource-intensive games can perform within the Discord interface. * Users who do not have a game installed can access a limited-time trial of the GeForce NOW Performance experience, allowing for immediate social gaming without hardware constraints. This partnership marks a significant shift in the gaming ecosystem, moving away from local hardware reliance toward a more accessible, social-first distribution model. For players, it offers a seamless way to test new titles with friends, while for developers, it provides a powerful tool to reduce the "time-to-fun" for their audience.
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.
Figma's 2025 AI report: Perspectives from designers and developers Insights AI Research Report Thought leadership
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.
