Techlist.io - Korean Tech Blog Curator

figmaSep 8, 20252 min readCurated summary

How to Harness Skills That AI Can’t Automate | Figma Blog

Great products require more than AI-generated functionality; they depend on human craft, including curiosity, intuition, taste, and intention. AI accelerates prototyping and iteration, but people must decide what questions to ask, when to expand the scope, and whether an experience truly resonates. The article argues that these uniquely human skills are essential for steering AI toward thoughtful, high-quality outcomes. ## Curiosity: Starting from First Principles - Curiosity means asking “why” and “what if,” exploring multiple directions, and validating ideas through iteration. - Tools such as Figma Make, Claude, and ChatGPT make it faster to generate prototypes, campaign concepts, and alternative approaches. - AI can expand possibilities within a prompt, but it cannot independently challenge the original scope, follow an unexpected hunch, or recognize that a tangent deserves deeper investigation. - Early exploration improves confidence and reduces rework: - Figma designer Natasha Tenggoro used Figma Make to test video-playback concepts for Figma Buzz. - Prototyping exposed edge cases, clarified feasibility, and helped engineers understand the feature before implementation. - Curiosity can also lead to valuable scope expansion: - While designing icons for four new Figma products, Tim Van Damme realized the existing icon family needed a broader redesign. - He explored hundreds of variations and redesigned the suite for greater consistency, distinctiveness, scalability, and a unified Figma identity. ## Intuition: Following a Feeling - AI can help bring products to market quickly, but it cannot determine what will emotionally or practically resonate with customers. - Human designers can recognize when an interaction feels confusing even if it technically works. - Intuition may also identify qualitative improvements—such as adding white space to a crowded layout—that are difficult to express as strict requirements. - Eliel Johnson of CVS Health describes design as an active practice centered on asking whether an experience “feels good,” not merely whether it satisfies functional constraints. The practical lesson is to use AI for speed and breadth while reserving human judgment for direction, exploration, emotional resonance, and quality. Human craft is what turns technically workable output into a product people genuinely value.

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Figma User Experience Prototyping Generative AI+2

figmaSep 8, 20253 min readCurated summary

The Anatomy of a Summer Campaign: How Instacart Staged a Sick ’90s Throwback | Figma Blog

Instacart’s “Summer Like It’s 1999” campaign used nostalgia to recreate the carefree feeling of a late-’90s summer. The campaign combined discounted retro snacks, a cohesive visual identity, immersive advertising, and a free Third Eye Blind concert. Its success depended on close collaboration across creative, marketing, CPG partners, and external agencies, with Figma serving as the shared source of truth. ## Recreating ’90s Nostalgia - Instacart reduced prices on nostalgic products such as Bagel Bites, Capri-Sun, and Otter Pops to their approximate 1999 prices. - The campaign culminated in a free Third Eye Blind concert in New York City. - Its visual language drew on: - Chunky typography - Bright, vivid colors - Fish-eye photography - Oversize clothing and other recognizable ’90s references - The team aimed to evoke nostalgia while keeping the work distinctly recognizable as Instacart. ## Building an All-in-One Creative Toolkit - Instacart’s in-house creative studio held daily meetings to align on: - Typography - Color palettes - Illustration - Photography - Logo and lockup treatments - Designers explored a wide range of ’90s references before reducing them to the essential elements that fit the brand. - The resulting toolkit documented the campaign’s colors, illustrations, typography, and other visual rules. - It became a living Figma file that internal teams, marketers, and external partners could use to create new assets. - The toolkit supported later campaign extensions, including a Venmo partnership and an advertisement inspired by the ’90s movie theater experience. ## Cross-Functional Collaboration - Creative, marketing, and CPG co-marketing teams worked together from the beginning rather than relying on formal handoffs. - Marketers had direct access to Figma, enabling frequent reviews, rapid changes, and visibility into work in progress. - The shared workspace helped maintain consistency across many campaign touchpoints, including: - The app icon - Splash pages - Television commercials - Billboards - Partner executions - The toolkit provided a flexible “north star” while allowing teams to remain expressive and adapt the campaign as new needs emerged. ## Developing a Detailed Photography Brief - The photography brief specified not only the products and scenes to capture, but also the precise visual treatment. - The team planned: - Low camera angles - Fish-eye effects - Idyllic outdoor locations - ’90s-inspired styling - Product placement required careful planning because the brief passed through multiple stakeholders. - Illustrations, typography, and textures were designed to layer over the photography, adding complexity and reinforcing a unified campaign style. Instacart’s campaign demonstrates how a strong, flexible design system can turn a broad creative idea into a coordinated, multi-channel experience. Establishing shared guidelines early—and keeping them accessible throughout production—helped the company move quickly without losing brand consistency.

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Figma Design Systems Typography Photography+3

figmaSep 4, 20252 min readCurated summary

How To Build A Resilient Design Team | Figma Blog

Design teams must build resilience by creating psychological safety, encouraging experimentation, and preserving high standards of craft. The article argues that leaders should prepare teams to adapt to changing technologies, roles, and constraints rather than rely on a fixed playbook. Resilience comes from trust, flexibility, thoughtful capacity planning, and a willingness to question established assumptions. ## Start with Team Health - Stress, burnout, overwork, and fear prevent people from doing their best creative work. - Leaders should build trust and a shared mission while encouraging candid, non-personal critique. - Provide both synchronous and asynchronous ways for people to contribute so the loudest voices do not dominate. - Make it safe to share work at every stage, from rough sketches to polished designs. - Use regular 1:1s, career conversations, and feedback surveys to identify problems early. - Plan realistically around team bandwidth instead of overcommitting. - Model vulnerability by openly acknowledging uncertainty and personal challenges. ## Encourage Experimentation and Shifting Roles - Designers have different strengths, so teams should combine complementary skills and create opportunities for mutual learning. - Boundaries between design, research, product, and engineering are becoming more fluid. - Encourage designers to work closer to production through AI prototyping and code experimentation. - Sharing unfinished work and lessons learned—such as through Figma’s `#design-wip` channel—helps normalize experimentation. - Invite cross-functional partners into design critiques to bring broader perspectives into the process. - Balance shipping current work with inventing and testing new approaches as technology changes. ## Treat Craft as a Differentiator - High-quality, carefully executed products can distinguish a company in crowded markets, strengthen user loyalty, and drive growth. - Craft is especially valuable in enterprise software, where design quality is often neglected. - Strong execution requires attention to detail and may involve trade-offs, such as refining the user experience instead of adding more features. - Fast-moving teams must deliberately protect time for visual consistency, clarity, and overall product quality. ## Practical Recommendation Build resilience as an ongoing operating practice: protect team health, make experimentation routine, support fluid collaboration across roles, and treat quality as a strategic advantage rather than an optional extra.

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Figma Design Systems User Experience Product Design+2

slackSep 4, 20253 min readCurated summary

Building Slack’s Anomaly Event Response

Slack’s Anomaly Event Response (AER) is designed to close the gap between detecting suspicious activity and stopping it. By combining real-time monitoring, adaptive analytics, and automated session termination, AER can disrupt high-confidence attacks within minutes rather than hours or days. Slack presents it as a built-in security capability for Enterprise Grid customers that works without additional tools or security staff. ## Shared Responsibility for Securing Slack - Slack processes billions of daily interactions from tens of millions of weekly users. - Enterprise customers receive audit logs covering hundreds of platform actions. - Specialized anomaly logs flag activity such as: - Irregular logins - Malware uploads - Unexpected data transfers - Audit logs provide early warning but traditionally require security personnel or third-party systems to interpret and act on them. - AER provides automated response for customers that lack the resources or infrastructure to build those integrations. - Advanced customers can still combine AER with customized security controls. ## Configurable Threat Detection AER focuses on common indicators of account compromise, data exfiltration, and automated abuse: - Access from Tor exit nodes - Excessive downloading - Data scraping through non-native automation tools - Session fingerprint mismatches - Unexpected API-call volumes or patterns - Unusual user agents, including virtual or non-standard clients Organizations can choose which anomaly types should terminate sessions and which should only be logged. Notification settings are also configurable, with alerts available for organization owners and security administrators through email or Slack. ## Detection Engine - The detection engine analyzes billions of Slack events each day. - It combines rule-based heuristics with dynamic thresholds. - Thresholds are calibrated to each enterprise’s historical usage patterns. - This prevents normal high-volume activity in one organization from being treated as anomalous in another. - Adaptive thresholds help reduce false positives while allowing Slack to refine detection sensitivity over time. ## AER Architecture AER consists of three main components: - **Detection engine:** Identifies suspicious activity and creates anomaly audit payloads. - **Decision framework:** Validates detected behavior and determines whether it qualifies for automated response. - **Response orchestrator:** Carries out the configured response, including terminating user sessions. The overall flow is: 1. Suspicious user activity is analyzed. 2. An anomaly is detected. 3. The AER controller determines whether it is a supported anomaly and whether the organization’s settings require action. 4. Associated user sessions may be terminated. 5. The event is always recorded in audit logs. 6. Customer notifications are sent according to configured preferences. AER’s practical value is that it turns anomaly detection into immediate containment, helping organizations interrupt attack chains before attackers can complete data theft or compromise.

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Cybersecurity Anomaly Detection Threat Detection Session Management+3

figmaSep 3, 20253 min readCurated summary

Why Is Corpcore Suddenly Such a Thing? | Figma Blog

Software merchandise has shifted from ordinary corporate swag to desirable streetwear, a trend the article calls “corpcore.” Vintage Apple and Microsoft shirts now command high resale prices, while new collections from companies like Figma attract crowds comparable to major fashion drops. The appeal combines nostalgia, brand loyalty, irony, and a renewed interest in technology’s cultural influence. ## The Rise of Tech Merchandise - Vintage Apple T-shirts, many with simple designs commemorating teams or projects, can sell for hundreds of dollars on Depop and Grailed. - Even the 1997 book *Apple T-Shirts: A Yearbook of History at Apple Computer* can cost up to $850. - Contemporary company merchandise is also attracting serious demand: - Figma’s 2025 Config conference featured a pop-up store with long lines. - The collection included apparel and Otto plush toys. - Its development involved eight months of work between Figma’s Brand Studio and Garrett Elizabeth Office. - Designer Jeff Staple compared Figma’s release to a Supreme drop, highlighting how tech merchandise now operates within streetwear culture. ## Nostalgia for Early Technology - Vintage corpcore appeals to people who remember the early web and their first encounters with companies such as Apple and Microsoft. - The merchandise evokes a period when technology seemed rebellious and disruptive—young programmers challenging established corporate power. - Wearing an old company shirt signals affiliation with a successful or influential group, much like a bumper sticker or sports jersey. - Vintage tech apparel also allows people to celebrate that history from a deliberately ironic distance. ## A Post-Ironic Corporate Style - Modern corpcore blends sincere brand enthusiasm with absurdity and irony. - Examples include: - Cash App’s 2023 club-kid fashion line designed by Marshall Columbia and modeled by Julia Fox. - Overtime’s limited-edition Dunkin’ Donuts streetwear. - Fashion items featuring Lockheed Martin branding, including polo shirts and tactical tracksuits. - Designers must balance making merchandise feel genuinely desirable without losing the humor or self-awareness associated with corporate branding. ## From Company Swag to Cultural Identity - People increasingly wear app and software logos on shirts, hats, totes, and water bottles as they would wear band merchandise or sports jerseys. - The trend reflects both affection for technology brands and the growing influence of software companies on everyday culture. - “Corpcore” succeeds because it turns corporate identity into a form of personal expression rather than merely advertising an employer. The broader lesson is that thoughtful, culturally aware merchandise can transform a company logo into a fashion symbol. Brands that understand nostalgia, scarcity, design quality, and the tension between sincerity and irony can make corporate swag feel genuinely collectible.

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Figma Github Microsoft Apple+3

lineSep 3, 2025Original article

Code Quality Improvement Techniques Part 19: Child Lock (opens in new tab)

The "child lock" technique focuses on improving code robustness by restricting the scope of what child classes can override in an inheritance hierarchy. By moving away from broad, overridable functions that rely on manual `super` calls, developers can prevent common implementation errors and ensure that core logic remains intact across all subclasses. This approach shifts the responsibility of maintaining the execution flow to the parent class, making the codebase more predictable and easier to maintain. ## Problems with Open Functions and Manual Super Calls Providing an `open` function in a parent class that requires child classes to call `super` creates several risks: * **Missing `super` calls:** If a developer forgets to call `super.bind()`, the essential logic in the parent class (such as updating headers or footers) is skipped, often leading to silent bugs that are difficult to track. * **Implicit requirements:** Relying on inline comments to tell developers they must override a function is brittle. If the method isn't `abstract`, the compiler cannot enforce that the child class implements necessary logic. * **Mismatched responsibilities:** When a single function handles both shared logic and specific implementations, the responsibility of the code becomes blurred, making it easier for child classes to introduce side effects or incorrect behavior. ## Implementing the "Child Lock" with Template Methods To resolve these issues, the post recommends a pattern often referred to as the Template Method pattern: * **Seal the execution flow:** Remove the `open` modifier from the primary entry point (e.g., the `bind` method). This prevents child classes from changing the overall sequence of operations. * **Separate concerns:** Move the customizable portion of the logic into a new `protected abstract` function. * **Enforced implementation:** Because the new function is `abstract`, the compiler forces every child class to provide an implementation, ensuring that specific logic is never accidentally omitted. * **Guaranteed execution:** The parent class calls the abstract method from within its non-overridable method, ensuring that shared logic (like UI updates) always runs regardless of how the child is implemented. ## Refining Overridability and Language Considerations Designing for inheritance requires careful control over how child classes interact with parent logic: * **Avoid "super" dependency:** Generally, if a child class must explicitly call a parent function to work correctly, the inheritance structure is too loose. Exceptions are usually limited to lifecycle methods like `onCreate` in Android or constructors/destructors. * **C++ Private Virtuals:** In C++, developers can use `private virtual` functions. These allow a parent class to define a rigid flow in a public method while still allowing subclasses to provide specific implementations for the private virtual components, even though the child cannot call those functions directly. To ensure long-term code quality, the range of overridability should be limited as much as possible. By narrowing the interface between parent and child classes, you create a more rigid "contract" that prevents accidental bugs and clarifies the intent of the code.

Kotlin Code Quality Software Design Cpp+3

figmaSep 2, 20252 min readCurated summary

IDC Study Says the Global Workforce Engaged in Software Design Is Expanding | Figma Blog

IDC forecasts that the global workforce involved in software design and development will grow by more than 30%, reaching 144 million people by 2029. This expansion reflects the growing role of design as a competitive differentiator and the increasing demand for design talent. Generative AI is expected to accelerate product development while raising expectations for usability and visual quality. ## Workforce Growth in Software Design - The population of knowledge workers and developers designing digital products and interfaces is projected to grow from **107 million in 2025 to 144 million in 2029**. - The research covers multiple industries, company sizes, and roles involved in software design and product development. - IDC based its model on quantitative research involving more than 1,000 IT and business leaders. ## UX Design Leads Growth - UX professionals are expected to grow at a **7.6% compound annual growth rate** between 2025 and 2029. - This growth is expected to outpace other knowledge-worker categories. - IDC links the trend to rising demand for design-intensive digital products and solutions. ## Generative AI Raises the Bar - Generative AI will increase the speed and volume of software development. - Faster development may give users more product choices and enable better-designed digital experiences. - Designers and developers will face greater pressure to make AI-powered features highly usable, appealing, and intuitive. Overall, the study suggests that companies should treat design as a strategic capability and invest in UX talent as software development becomes faster and more AI-driven.

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Figma User Experience Generative AI User Interface+3

discordSep 1, 20253 min readCurated summary

Bringing DAVE to All Discord Platforms

Discord is making DAVE, its end-to-end encryption protocol for audio and video calls, mandatory across all platforms. Browser support required solving WebRTC compatibility issues, designing an efficient Web Worker architecture, and reusing proven C++ cryptography through WebAssembly. Clients without DAVE support will be unable to join calls starting March 1, 2026. ## DAVE Becomes the Standard - DAVE already protects tens of millions of Discord calls daily. - Support is expanding to browsers, consoles, and the Social SDK. - Non-DAVE clients and applications will lose access to Discord calls on March 1, 2026. ## Browser Support and Firefox Compatibility - Discord uses the WebRTC Encoded Transform API to encrypt audio and video inside the WebRTC pipeline. - Firefox initially failed during real calls because its encryption Web Worker received no media data. - Discord engineers identified a recursive mutex deadlock in Firefox’s `FrameTransformerProxy`, triggered when video arrived too early. - Mozilla merged Discord’s fix, which is available in Firefox 142.0—the minimum Firefox version required for DAVE. ## Web Workers and Call State - Dedicated Web Workers encrypt and decrypt media: - One worker handles call audio and camera video. - Separate workers handle screenshare and game-stream audio and video. - Each media stream has a unique SSRC, allowing workers to select the correct symmetric encryption key for each frame. - Workers retain only essential call state, including SSRC-to-user mappings and encryption keys. - The main thread manages WebRTC connections, participants, and media tracks. - MLS membership changes are also handled on the main thread, preventing encryption work from delaying users joining or leaving calls. - Cryptographic state changes are sent asynchronously to workers. ## WebAssembly for Proven Cryptography - Discord compiled its existing, battle-tested C++ DAVE implementation to WebAssembly. - Reusing the same implementation across platforms reduces platform-specific security and reliability risks. - DAVE must selectively encrypt media while preserving metadata needed by WebRTC packetization and depacketization. - Since encrypted output cannot be modified in transit, byte-level parsing must be precise. - WebAssembly provides near-native performance while avoiding a more error-prone JavaScript reimplementation. ## WebAssembly Versus Browser Cryptography APIs - WebAssembly introduces a small performance cost compared with native browser APIs such as `SubtleCrypto`. - Discord’s benchmarks evaluate this trade-off against the benefits of shared, mature cryptographic code. - The post indicates that WebAssembly remains practical because frame parsing and selective encryption are computationally complex, while the security and portability benefits outweigh the minor cryptographic overhead. Discord’s platform transition means developers maintaining Discord clients, integrations, or SDK-based applications should add DAVE support before March 1, 2026. Browser users must also use Firefox 142.0 or newer when connecting through Firefox.

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Webassembly End To End Encryption Webrtc Cpp+2

lineAug 29, 2025Original article

Extracting Trending Keywords from OpenChat (opens in new tab)

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.

AI Machine Learning Natural Language Processing Statistical Analysis+5

dropboxAug 27, 20252 min readCurated summary

Hack Week 2025: How these engineers liquid-cooled a GPU server

Dropbox engineers used Hack Week 2025 to build a liquid-cooling system for GPU servers, anticipating the rising heat and power demands of AI workloads. Their prototype, assembled from radiators, fans, a pump, reservoir, tubing, manifolds, and sensors, reduced temperatures by 20–30°C during stress tests compared with air cooling. The project also enabled quieter operation and may help Dropbox use less data-center space and energy as GPU requirements increase. ## Building a Custom Liquid-Cooling System - The team built the system from scratch after failing to source a complete commercial setup in time. - It replicated key data-center cooling components: - Radiators and fans for heat dissipation - A pump and reservoir to circulate coolant - Tubing and manifolds - Sensors to verify flow and monitor performance - Engineers tested the cooling loop before connecting expensive GPUs, then integrated it with a GPU server. ## Thermal and Noise Improvements - Liquid cooling lowered CPU and GPU temperatures by approximately 20–30°C during demanding torture tests. - Because the liquid loop handled the primary heat-generating components, the team could: - Remove some fans - Run others at lower speeds - Reduce noise and potentially power consumption - Remaining airflow was directed toward lower-heat components such as DIMMs and network cards. - The team considered using an airflow baffle to cool those components more precisely. ## Preparing for AI-Era Infrastructure - High-end GPUs increasingly consume more power and generate more heat, potentially making air cooling insufficient. - Liquid cooling could allow Dropbox to: - Fit more powerful servers into existing data-center footprints - Avoid spreading servers across additional space - Reduce cooling energy and operating costs - Although vendors do not yet universally require liquid cooling for top-tier GPUs, the engineers expect that requirement to become more common. - Dropbox’s growing focus on AI workloads provided additional motivation to investigate the technology early. ## Hack Week as an Experimentation Platform - The project received funding and organizational support from Dropbox’s infrastructure teams. - Hack Week gave engineers time to explore a long-term infrastructure problem outside their normal work. - The team’s Learn Fast award recognized the project’s emphasis on experimentation and rapid learning. - Working in person helped the engineers exchange ideas, troubleshoot quickly, and collaborate with colleagues across the company. Dropbox plans to expand testing with additional liquid-cooling labs in multiple data centers. The prototype is an early step toward infrastructure capable of supporting increasingly powerful, GPU-intensive AI systems.

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Ai Infrastructure Gpu Computing Liquid Cooling Thermal Management+3

lineAug 27, 2025Original article

Code Quality Improvement Techniques Part 18 (opens in new tab)

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.

Kotlin Code Quality Refactoring Kotlin Sequences+1

discordAug 26, 20252 min readCurated summary

Starting Your First Discord Server

Discord servers provide a single, organized space for friends to chat, play games, and discuss shared interests instead of splitting conversations across multiple group DMs. The post explains how to create a server, invite friends, and use text and voice channels. Its central recommendation is to create a server when a group needs a persistent, flexible home for communication. ## What a Discord Server Is - A server is an organized online space where friends can gather. - It can support gaming, casual conversation, hobbies, and shared media discussions. - Unlike temporary group DMs, a server keeps everyone and their conversations together. ## Creating a Server - Download Discord from [discord.com/download](https://discord.com/download), use the web app at [discord.com/login](https://discord.com/login), or create an account at [discord.com/register](https://discord.com/register). - Click the circular **+** button on the left side of the app. - Choose a server template, such as **Gaming**, which automatically creates relevant channels. - Give the server a name and optionally choose an icon. - The server name and icon can be changed later. ## Inviting Friends - New servers display an **Invite Your Friends** button. - Invitations can be sent directly to Discord friends or shared as an invite link. - If the button is no longer visible, click the server name and select **Invite People**. - Once shared, the server becomes a central gathering place for the group. ## Text Channels - Text channels work like persistent group chats. - Members can send messages, photos, files, and links. - Typing `@` followed by a username notifies a specific person. - By default, everyone in the server can view and post in a newly created text channel. - Separate channels prevent unrelated conversations from becoming mixed together. ## Voice Channels - Voice channels let members join an ongoing audio conversation without triggering a loud incoming-call ringtone. - Users can enter or leave freely, similar to walking into or out of a room. - Voice channels support unlimited participants for voice-only calls and up to 25 people in video calls. - Members can use cameras, stream games, and share their screens. - Each voice channel also includes a small text space for posting messages, memes, or links relevant to that conversation. A Discord server is a practical upgrade from scattered group chats: create one, customize its channels, and invite friends so conversations and activities can stay organized in one persistent space.

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Discord Voice Chat Screen Sharing Text Chat+2

googleAug 26, 2025Original article

How Google’s AI can help transform health professions education (opens in new tab)

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.

AI Generative AI Gemini Fine Tuning+5

googleAug 25, 2025Original article

A scalable framework for evaluating health language models (opens in new tab)

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.

AI Large Language Models Gemini Natural Language Processing+4

figmaAug 24, 20252 min readCurated summary

Version Control: How Figma Make Helped Us Figure Out Video Playback | Figma Blog

Figma’s team used Figma Make to rapidly prototype video playback for Figma Buzz under a two-month deadline. The tool helped translate complex interaction ideas into working code, clarify feasibility with engineers, and expose edge cases before implementation. Although the project began with support for one video, technical progress expanded the scope to multiple videos and required revisiting the original playback design. ## The Challenge of Video Playback - Figma Buzz lets teams create branded assets containing video, text, images, and other layered elements. - Playback controls could not simply be placed in the center of the canvas because they might obscure important design content. - The team needed to account for videos being: - Moved within or outside their parent frame - Partially visible or completely off-canvas - Enlarged and cropped - Used alongside other assets in grid view - Designer Natasha Tenggoro used Figma Make because the interaction logic was difficult to communicate with static Figma designs and would have taken too long to build in another tool. ## Version One: Single-Video Playback - The initial release focused on the common case: assets containing one video. - Natasha imported an existing Figma Design frame and prompted Figma Make to create: - A draggable media frame - Media that could extend beyond a fixed parent without clipping - Reduced opacity for media outside the parent - Playback controls pinned to the media’s top-right corner - Automatic repositioning of controls when they would leave the parent frame - Control fading when less than 20% of the video was visible - The working prototype gave engineers a concrete demonstration of the intended behavior. - This helped establish technical feasibility, clarify scope, and turn an uncertain idea into a prioritized feature. ## Expanding to Multiple Videos - Further engineering investigation showed that multiple videos could potentially be supported within the original release timeline. - The expanded capability forced the team to reconsider earlier decisions about playback controls. - For a collage containing several videos, the team chose a single overall playback control rather than separate controls for each video. - This approach reduced interface clutter, avoided covering design elements, and provided a more unified preview experience. ## Practical Conclusion Figma Make served as a bridge between design intent and engineering implementation, enabling fast exploration of complex motion and interaction behavior. The experience suggests that functional prototypes are especially valuable when requirements involve dynamic positioning, visibility rules, and changing technical scope.

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Prototyping Figma Make Interaction Design User Interface Design+2