Techlist.io - Korean Tech Blog Curator

lineMay 30, 2025Original article

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

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

User Experience Fraud Detection Identity Verification Sms Authentication+4

figmaMay 28, 20254 min readCurated summary

From Multi-Day Latency to Near Real-Time Insights: Figma’s Data Pipeline Upgrade | Figma Blog

Figma replaced a daily full-table export system that could take hours or days with an incremental synchronization pipeline designed for near real-time analytics. The new architecture combines database snapshots, change data capture (CDC), and Snowflake merge logic to transfer only recent changes. By building the system in-house, Figma gained greater flexibility, lower projected costs, and a design that can scale with continued growth. ## Why the Legacy Pipeline Failed - Since 2020, a daily cron job ran `SELECT * FROM <TABLE>`, exported results to S3, and loaded them into Snowflake. - As Figma’s tables and insert volume grew: - Daily syncs reached roughly six hours by 2023. - The largest tables took several days or longer. - Additional database replicas were required for exports. - Replica maintenance cost millions of dollars annually. - The delays limited access to timely company KPIs and analytical insights. ## Choosing Incremental Synchronization Figma evaluated three options: - Continue using the legacy process, which was increasingly expensive and too slow. - Add parallelism, which might improve throughput temporarily but would not scale sustainably. - Rebuild the synchronization system around incremental updates. Incremental synchronization transfers only new and changed records instead of repeatedly copying entire tables, reducing data movement, processing time, and infrastructure usage. ## Buy vs. Build Figma decided to build the pipeline internally because available proprietary tools did not meet its requirements. - **Flexibility:** Generic SQL tools did not take advantage of capabilities such as Amazon RDS for PostgreSQL snapshot exports. - **Cost:** Commercial solutions were projected to cost five to ten times more than an in-house implementation. - **Scale:** Building internally allowed Figma to optimize the system for its infrastructure and adapt it as the company grows. ## Pipeline Components The bespoke system combines several lower-level technologies: - **Snapshots:** Amazon RDS exports initial table copies to S3. - **Change data capture:** Kafka Connect streams database changes through Amazon MSK. - **Warehouse ingestion:** A Snowflake Connector loads CDC events into Snowflake. - **Incremental merging:** Custom Snowflake stored procedures and scheduled tasks merge changes into base tables. ## Architecture Principles The redesign was guided by four goals: - Reduce end-to-end synchronization latency. - Control costs as data volume increases. - Meet regulatory and compliance requirements. - Preserve data accuracy, completeness, consistency, and trustworthiness. The resulting architecture uses two workflows: a bootstrap workflow for onboarding tables and a validation workflow for checking data correctness. ## Bootstrap Workflow The automated onboarding process includes: - The CDC service begins capturing the new Postgres table and publishes events to a per-table Kafka topic. - Amazon RDS exports the latest database snapshot to S3. - Snowflake’s `COPY INTO <table>` loads the snapshot into a per-entity base table. - An MSK Connect Snowflake Sink Connector streams Kafka events into a separate CDC table, with offsets arranged so changes before the snapshot timestamp are retained. - A scheduled Snowflake task runs a custom `MERGE` procedure to combine the snapshot and CDC data. - Once the process catches up with current changes, Figma creates a lightweight user-facing view over the base table. ## Zero-Downtime Re-Bootstrapping - Bootstrap artifacts are versioned, while the final user-facing view remains stable. - New versions can be built in parallel without interrupting queries. - Promotion is completed through an atomic view update. - This supports schema evolution and other situations requiring a fresh bootstrap without downtime. ## Data Validation - Even well-designed pipelines can suffer corruption from partial failures, configuration errors, software bugs, or unexpected source-data anomalies. - Figma therefore added a validation workflow to verify correctness as data moves through snapshot exports, CDC capture, and incremental merging. Figma’s experience shows that incremental synchronization is a more sustainable alternative to repeated full-table exports. Combining managed infrastructure with custom orchestration can deliver lower latency, better cost control, and stronger operational flexibility than a one-size-fits-all commercial pipeline.

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PostgreSQL Apache Kafka Amazon S3 Data Pipeline+4

lineMay 28, 2025Original article

Code Quality Improvement Techniques Part (opens in new tab)

The "Clone Family" anti-pattern occurs when two parallel inheritance hierarchies—such as a data model tree and a provider tree—share an implicit relationship that is not enforced by the type system. This structure often leads to type-safety issues and requires risky downcasting to access specific data types, increasing the likelihood of runtime errors during code modifications. To resolve this, developers should replace rigid inheritance with composition or utilize parametric polymorphism to explicitly link related types. ## The Risks of Implicit Correspondence Maintaining two separate inheritance trees where individual subclasses are meant to correspond to one another creates several technical hurdles. * **Downcasting Requirements:** Because a base provider typically returns a base data model type, developers must manually cast the result to a specific subclass (e.g., `as FooDataModel`), which bypasses compiler safety. * **Lack of Type Enforcement:** The constraint that a specific provider always returns a specific model is purely implicit; the compiler cannot prevent a provider from returning the wrong model type. * **Fragile Architecture:** As the system grows, ensuring that "Provider A" always maps to "Model A" becomes difficult to audit, leading to potential bugs when new developers join the project or when the hierarchy is extended. ## Substituting Inheritance with Composition When the primary goal of inheritance is simply to share common logic, such as fetching raw data, using composition or aggregation is often a superior alternative. * **Logic Extraction:** Shared functionality can be moved into a standalone class, such as an `OriginalDataProvider`, which is then held as a private property within specific provider classes. * **Direct Type Returns:** By removing the shared parent class, each provider can explicitly return its specific data model type without needing a common interface. * **Decoupling:** This approach eliminates the "Clone Family" entirely by removing the need for parallel trees, resulting in cleaner and more modular code. ## Leveraging Parametric Polymorphism In scenarios where a common parent class is necessary—for example, to manage a collection of providers within a shared lifecycle—generics can be used to bridge the two hierarchies safely. * **Generic Type Parameters:** By defining the parent as `ParentProvider<T>`, the base class can use a type parameter for its return values rather than a generic base model. * **Subclass Specification:** Each implementation (e.g., `FooProvider : ParentProvider<FooDataModel>`) explicitly defines its return type, allowing the compiler to enforce the relationship. * **Flexible Constraints:** Developers can still utilize type bounds, such as `ParentProvider<T : CommonDataModel>`, to ensure that the generics adhere to a specific interface while maintaining type safety for callers. When designing data providers and models, avoid creating parallel structures that rely on implicit assumptions. Prioritize composition to simplify the architecture, or use generics if inheritance is required, ensuring that the relationships between classes remain explicit and verifiable by the compiler.

Kotlin Code Quality Refactoring Oop+3

lineMay 23, 2025Original article

Implementing a RAG-based (opens in new tab)

To address the operational burden of handling repetitive user inquiries for the AWX automation platform, LY Corporation developed a support bot utilizing Retrieval-Augmented Generation (RAG). By combining internal documentation with historical Slack thread data, the system provides automated, context-aware answers that significantly reduce manual SRE intervention. This approach enhances service reliability by ensuring users receive immediate assistance while allowing engineers to focus on high-priority development tasks. ### Technical Infrastructure and Stack * **Slack Integration**: The bot is built using the **Bolt for Python** framework to handle real-time interactions within the company’s communication channels. * **LLM Orchestration**: **LangChain** is used to manage the RAG pipeline; the developers suggest transitioning to LangGraph for teams requiring more complex multi-agent workflows. * **Embedding Model**: The **paraphrase-multilingual-mpnet-base-v2** (SBERT) model was selected to support multi-language inquiries from LY Corporation’s global workforce. * **Vector Database**: **OpenSearch** serves as the vector store, chosen for its availability as an internal PaaS and its efficiency in handling high-dimensional data. * **Large Language Model**: The system utilizes **OpenAI (ChatGPT) Enterprise**, which ensures business data privacy by preventing the model from training on internal inputs. ### Enhancing LLM Accuracy through RAG and Vector Search * **Overcoming LLM Limits**: Traditional LLMs suffer from "hallucinations," lack of up-to-date info, and opaque sourcing; RAG fixes this by providing the model with specific, trusted context during the prompt phase. * **Embedding and Vectorization**: Textual data from wikis and chats are converted into high-dimensional vectors, where semantically similar phrases (e.g., "Buy" and "Purchase") are stored in close proximity. * **k-NN Retrieval**: When a user asks a question, the bot uses **k-Nearest Neighbors (k-NN)** algorithms to retrieve the top *k* most relevant snippets of information from the vector database. * **Contextual Generation**: Rather than relying on its internal training data, the LLM generates a response based specifically on the retrieved snippets, leading to higher accuracy and domain-specific relevance. ### AWX Support Bot Workflow and Data Sources * **Multi-Source Indexing**: The bot references two main data streams: the official internal AWX guide wiki and historical Slack inquiry threads where previous solutions were discussed. * **Automated First Response**: The workflow begins when a user submits a query via a Slack workflow; the bot immediately processes the request and provides an initial AI-generated answer. * **Human-in-the-Loop Validation**: After receiving an answer, users can click "Issue Resolved" to close the ticket or "Call AWX Admin" if the AI's response was insufficient. * **Efficiency Gains**: This tiered approach filters out "RTFM" (Read The F***ing Manual) style questions, ensuring that human administrators only spend time on unique or complex technical issues. Implementing a RAG-based support bot is a highly effective strategy for SRE teams looking to scale their internal support without increasing headcount. For the best results, organizations should focus on maintaining clean internal documentation and selecting embedding models that reflect the linguistic diversity of their specific workforce.

AI Python RAG Vector Db+5

googleMay 22, 2025Original article

Fine-tuning LLMs with user-level differential privacy (opens in new tab)

Researchers from Google investigated scaling user-level differential privacy (DP) to the fine-tuning of large language models in datacenter environments. While traditional example-level DP protects individual data points, user-level DP provides a stronger guarantee by masking the presence of an entire user's dataset, which is critical for privacy-sensitive, domain-specific tasks. The study explores how the flexibility of datacenter training can be used to optimize sampling strategies and contribution bounds to minimize the noise typically required for these stringent privacy guarantees. ## Limitations of Example-Level Privacy * Standard differential privacy focuses on "example-level" protection, which prevents attackers from learning about specific individual data points. * In many real-world scenarios, a single user contributes many examples to a dataset; if an attacker can analyze these multiple points together, they may still learn private information about the user even under example-level DP. * User-level DP addresses this by ensuring a model remains essentially the same whether or not a specific user’s entire data collection was used during training. * While more robust, user-level DP is "strictly harder" to implement because it requires injecting significantly more noise into the training process, a problem that scales with the size of the model. ## Methodologies for User-Level DP Fine-Tuning * Both primary algorithms require a "contribution bound" during pre-processing, which strictly limits the number of examples any single user can provide to the training set. * Example-Level Sampling (ELS) involves sampling random individual examples for a batch and then applying a modified version of DP-SGD with high noise to compensate for the potential presence of multiple examples from the same user. * User-Level Sampling (ULS) involves sampling random users and including all of their (bounded) examples in a batch, which more closely resembles the structure of federated learning. * The datacenter environment offers a unique advantage over federated learning because researchers can perform precise queries on both individual examples and whole users, allowing for better optimization of the noise-to-utility ratio. ## Optimization and Datacenter Flexibility * The researchers focused on fine-tuning rather than full training because DP requires additional computation that is often unaffordable for base model training. * A central challenge in this research is determining the optimal "contribution bound"—if the bound is too low, valuable data is discarded, but if it is too high, more noise must be added to maintain privacy. * Because the datacenter allows for random sampling of any user at any time (unlike federated learning where devices must be online), the ULS algorithm can be tuned more effectively to achieve quality gains in the final model. To maximize the utility of LLMs fine-tuned on private data, developers should prioritize User-Level Sampling (ULS) strategies and carefully calibrate the contribution bounds of their datasets. By leveraging the controlled environment of a datacenter to optimize these parameters, it is possible to achieve high-performance models that respect user privacy more effectively than traditional example-level methods.

AI Large Language Models Differential Privacy Federated Learning+4

lineMay 22, 2025Original article

Code Quality Improvement Techniques Part (opens in new tab)

The "Set Discount" technique improves code quality by grouping related mutable properties into a single state object rather than allowing them to be updated individually. By restricting state changes through a controlled interface, developers can prevent inconsistent configurations and simplify the lifecycle management of complex classes. This approach ensures that dependent values are updated atomically, significantly reducing bugs caused by race conditions or stale data. ### The Risks of Fragmented Mutability When a class exposes multiple independent mutable properties, such as `isActive`, `minImportanceToRecord`, and `dataCountPerSampling`, it creates several maintenance challenges: * **Order Dependency:** Developers might accidentally set `isActive` to true before updating the configuration properties, causing the system to briefly run with stale or incorrect settings. * **Inconsistent Logic:** Internal state resets (like clearing a counter) may be tied to one property but forgotten when another related property changes, leading to unpredictable behavior. * **Concurrency Issues:** Even in single-threaded environments, asynchronous updates to individual properties can create race conditions that are difficult to debug. ### Consolidating State with SamplingPolicy To resolve these issues, the post recommends refactoring individual properties into a dedicated configuration class and using a single reference to manage the state: * **Atomic Updates:** By wrapping configuration values into a `SamplingPolicy` class, the system ensures that the minimum importance level and sampling interval are always updated together. * **Representing "Inactive" with Nulls:** Instead of a separate boolean flag, the `policy` property can be made nullable. An `inactive` state is naturally represented by `null`, making it impossible to "activate" the recorder without providing a valid policy. * **Explicit Lifecycle Methods:** Replacing property setters with methods like `startRecording()` and `finishRecording()` forces a clear transition of state and ensures that counters are reset consistently every time a new session begins. ### Advantages of Restricting State Transitions Moving from individual property mutation to a consolidated interface offers several technical benefits: * **Guaranteed Consistency:** It eliminates the possibility of "half-configured" states because the policy is replaced as a whole. * **Simplified Thread Safety:** If the class needs to be thread-safe, developers only need to synchronize a single reference update rather than coordinating multiple volatile variables. * **Improved Readability:** The intent of the code becomes clearer to future maintainers because the valid combinations of state are explicitly defined by the API. When designing components where properties are interdependent or must change simultaneously, you should avoid providing public setters for every field. Instead, provide a focused interface that limits updates to valid combinations, ensuring the object remains in a predictable state throughout its lifecycle.

Kotlin Code Quality Software Design Refactoring+2

googleMay 21, 2025Original article

Google Research at Google I/O 2025 (opens in new tab)

Google Research at I/O 2025 showcases the "research to reality" transition, highlighting how years of foundational breakthroughs are now being integrated into Gemini models and specialized products. By focusing on multimodal capabilities, pedagogy, and extreme model efficiency, Google aims to democratize access to advanced AI while ensuring it remains grounded and useful across global contexts. ## Specialized Healthcare Models: MedGemma and AMIE * **MedGemma:** This new open model, based on Gemma 3, is optimized for multimodal medical tasks such as radiology image analysis and clinical data summarization. It is available in 4B and 27B sizes, performing similarly to much larger models on the MedQA benchmark while remaining small enough for efficient local fine-tuning. * **AMIE (Articulate Medical Intelligence Explorer):** A research AI agent designed for diagnostic medical reasoning. Its latest multimodal version can now interpret and reason about visual medical information, such as skin lesions or medical imaging, to assist clinicians in diagnostic accuracy. ## Educational Optimization through LearnLM * **Gemini 2.5 Pro Integration:** The LearnLM family of models, developed with educational experts, is now integrated into Gemini 2.5 Pro. This fine-tuning enhances STEM reasoning, multimodal understanding, and pedagogical feedback. * **Interactive Learning Tools:** A new research-optimized quiz experience allows students to generate custom assessments from their own notes, providing specific feedback on right and wrong answers rather than just providing solutions. * **Global Assessment Pilots:** Through partnerships like the one with Kayma, Google is testing the automatic assessment of short and long-form content in regions like Ghana to scale quality educational tools. ## Multilingual Expansion and On-Device Gemma Models * **Gemma 3 and 3n:** Research breakthroughs have expanded Gemma 3’s support to over 140 languages. The introduction of **Gemma 3n** targets extreme efficiency, capable of running on devices with as little as 2GB of RAM while maintaining low latency and low energy consumption. * **ECLeKTic Benchmark:** To assist the developer community, Google introduced this novel benchmark specifically for evaluating how well large language models transfer knowledge across different languages. ## Model Efficiency and Factuality in Search * **Inference Techniques:** Google Research continues to set industry standards for model speed and accessibility through technical innovations like **speculative decoding** and **cascades**, which reduce the computational cost of generating high-quality responses. * **Grounded Outputs:** Significant focus remains on factual consistency, ensuring that the AI models powering features like AI Overviews in Search provide reliable and grounded information to users. As Google continues to shrink the gap between laboratory breakthroughs and consumer products, the emphasis remains on making high-performance AI accessible on low-cost hardware and across diverse linguistic landscapes. Developers and researchers can now leverage these specialized tools via platforms like HuggingFace and Vertex AI to build more targeted, efficient applications.

AI Generative AI Gemini Multimodal AI+5

lineMay 21, 2025Original article

How to evaluate AI-generated images? (opens in new tab)

To optimize the Background Person Removal (BPR) feature in image editing services, the LY Corporation AMD team evaluated various generative AI inpainting models to determine which automated metrics best align with human judgment. While traditional research benchmarks often fail to reflect performance in high-resolution, real-world scenarios, this study identifies a framework for selecting models that produce the most natural results. The research highlights that as the complexity and size of the masked area increase, the gap between model performance becomes more pronounced, requiring more sophisticated evaluation strategies. ### Background Person Removal Workflow * **Instance Segmentation:** The process begins by identifying individual pixels to classify objects such as people, buildings, or trees within the input image. * **Salient Object Detection:** This step distinguishes the main subjects of the photo from background elements to ensure only unwanted figures are targeted for removal. * **Inpainting Execution:** Once the background figures are removed, inpainting technology is used to reconstruct the empty space so it blends seamlessly with the surrounding environment. ### Comparison of Inpainting Technologies * **Diffusion-based Models:** These models, such as FLUX.1-Fill-dev, restore damaged areas by gradually removing noise. While they excel at restoring complex details, they are generally slower than GANs and can occasionally generate artifacts. * **GAN-based Models:** Using a generator-discriminator architecture, models like LaMa and HINT offer faster generation speeds and competitive performance for lower-resolution or smaller inpainting tasks. * **Performance Discrepancy:** Experiments showed that while most models perform well on small areas, high-resolution images with large missing sections reveal significant quality differences that are not always captured in standard academic benchmarks. ### Evaluation Methodology and Metrics * **BPR Evaluation Dataset:** The team curated a specific dataset of 10 images with high quality-variance to test 11 different inpainting models released between 2022 and 2024. * **Single Image Quality Metrics:** Evaluated models using LAION Aesthetics score-v2, CLIP-IQA, and Q-Align to measure the aesthetic quality of individual generated frames. * **Preference and Reward Models:** Utilized PickScore, ImageReward, and HPS v2 to determine which generated images would be most preferred by human users. * **Objective:** The goal of these tests was to find an automated evaluation method that minimizes the need for expensive and time-consuming human reviews while maintaining high reliability. Selecting an inpainting model based solely on paper-presented metrics is insufficient for production-level services. For features like BPR, it is critical to implement an evaluation pipeline that combines both aesthetic scoring and human preference models to ensure consistent quality across diverse, high-resolution user photos.

AI Generative AI Computer Vision Diffusion Models+5

discordMay 20, 20252 min readCurated summary

Introducing the Discord for Business Newsletter, Vol. 1

Discord’s first *Discord for Business Newsletter* highlights the company’s expanding advertising, developer, and entertainment initiatives. It reports strong results from Quests, introduces mobile video advertising and the Social SDK, and marks Discord’s tenth anniversary with a new CEO. Overall, Discord is positioning itself as a business platform built around authentic gaming communities and player engagement. ## Quests Advertising Reaches Its First Anniversary - Discord launched its first advertising product, Quests, one year ago. - **Video Quests** deliver branded video content and average more than 80% view-through completion. - **Play Quests** reward users for playing or streaming games, emphasizing active engagement over passive impressions. - More than 70 Quests have launched, generating millions of rewards. - **Mobile Video Quests** will begin a pilot in June 2025, expanding Discord’s advertising reach beyond desktop. ## Discord Social SDK for Game Developers - Announced at GDC 2025, the Social SDK lets developers add Discord-powered features directly to games. - Supported features include: - Friend lists - Game invites - Messaging - Voice chat - The SDK is free and can support players who do not have Discord accounts. - Discord says the tools can improve game discoverability and encourage longer play sessions. - Early users include Theorycraft Games, Facepunch Studios, and Scopely; the SDK is now available to all developers. ## Discord Turns Ten Under New Leadership - Discord now reports more than 200 million monthly active users and over 1.5 billion hours of monthly gaming activity. - Co-founder Jason Citron appointed **Humam Sakhnini** as CEO. - Sakhnini brings more than 15 years of gaming-industry experience, including leadership roles at Activision Blizzard and King Digital Entertainment. - The transition is intended to support Discord’s next stage of growth. ## Partnerships, Press, and New Promotions - Recent coverage has focused on Discord’s mobile advertising plans, Quests, gaming discovery, and its emphasis on authenticity. - Entertainment partnerships include: - A *Mission: Impossible* Quest offering a day of Nitro for completing a puzzle. - A Star Wars collection in the Discord Shop released around May 4. - Discord directs businesses to its Quest success stories and advertising solutions for further examples. Discord’s business strategy centers on rewarding users and integrating brands into existing gaming communities rather than relying solely on conventional ad impressions. Businesses interested in reaching gamers can explore Quests, mobile video campaigns, and Discord’s developer tools.

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Voice Chat Game Development Discord Social Sdk Mobile Advertising+2

discordMay 20, 20253 min readCurated summary

Staff Picks, May 2025: The Games That Brought Us to Discord

Discord’s May 2025 Staff Picks reflects on the games that first brought several employees to Discord and the titles they currently enjoy with friends. The stories show Discord’s role as a social bridge: it helped college students connect during quarantine, friends maintain relationships across time zones, and console players transition to PC gaming. Across the examples, shared play and conversation matter more than winning. ## Christina: Among Us, R.E.P.O., and VALORANT - Christina, a Product Marketing team member and former college ambassador, first used Discord extensively during the 2020 quarantine period. - **Among Us** became her entry point because it was easy to organize for casual hangouts and college club events. - She created a server connecting friends from different colleges, eventually hosting multiple full lobbies and forming new friendships. - Her current favorite is **R.E.P.O.**, a cooperative horror game featuring proximity chat and chaotic physics. - She also regularly plays **VALORANT**, mainly in five-person groups. She began as a Neon player and has recently played more Clove, valuing the group’s humor and camaraderie even during losses. ## Emi: PUBG, Baldur’s Gate 3, and Warzone - Emi recalls playing **PlayerUnknown’s Battlegrounds (PUBG)** with friends in 2017 after moving back from Japan. - Discord helped the group coordinate across Japan and other time zones, supporting weekend gaming sessions that continue roughly every other week. - Emi’s friends are revisiting **Baldur’s Gate 3**, taking advantage of additions such as mod support and new subclasses. - The game has become a relaxed “coffee time” activity, with players either starting new campaigns or returning to unfinished saves. - The group has also returned to **Call of Duty: Warzone’s Verdansk map**, enjoying its mixture of social downtime, exploration, and combat. - The release of Ranked Play is expected to encourage even more Warzone sessions. ## Jeremy: League of Legends and the Move to PC - Jeremy originally played exclusively on console before friends introduced him to **League of Legends** and Discord in 2018. - Watching friends stream normal matches in their shared server helped him become part of the group. - The experience shifted him almost entirely toward PC gaming. - He previously specialized in Brand support but now plays champions such as Nasus and Illaoi in the top lane. - The shared Discord server remains an active weekend gathering place for the group. The post’s examples suggest that Discord’s lasting appeal comes from making games more social and accessible. Whether through quarantine-era party games, long-distance battle royales, cooperative horror, or persistent gaming communities, the platform helps friends keep playing and staying connected over many years.

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Discord Video Games Online Gaming Proximity Chat+2

figmaMay 18, 20253 min readCurated summary

Andrew “Boz” Bosworth's 10 Rules for Navigating the Next Design Paradigm | Figma Blog

Andrew “Boz” Bosworth argues that designers entering the next era of AI and spatial computing must challenge inherited assumptions rather than merely improve existing interfaces. His approach begins with real human problems, favors experimentation and intuition, and treats interactions as complete systems. The goal is to create technology that understands users and ultimately becomes nearly invisible. ## Start with real human problems - Identify a specific person with a genuine problem. - Let users determine whether a product is useful through their behavior. - Avoid prioritizing abstract ideas over practical human needs. ## Question inherited design assumptions - Recognize that familiar constraints and interaction patterns are man-made paradigms. - Ask whether the current approach is actually appropriate. - Treat seemingly fixed limitations as potentially changeable. ## Reimagine the interaction paradigm - Current computing often forces users to translate simple intentions into complicated sequences of apps and services. - After decades of desktop and mobile conventions, designers should reconsider the entire model of interaction. - New technology should simplify intentions rather than expose underlying complexity. ## Distinguish invention from optimization - “Zero-to-one” invention happens without established customers or constraints. - Optimization improves and refines an existing product with audience feedback. - AI and spatial interfaces provide greenfield opportunities where old assumptions may not apply. ## Use taste and intuition to choose the right direction - Product development resembles climbing through a difficult problem space. - Intuition helps teams choose promising “terrain” before investing in execution. - Good judgment does not remove the hard work, but it improves the odds of pursuing the right problem. ## Prototype aggressively - Build rough, unconventional prototypes to test whether an idea is worth pursuing. - Meta has used crude physical setups, including tracked hats and mesh-walled rooms, to explore spatial computing. - Direct experimentation reveals possibilities that discussion alone cannot. ## Design the complete system - Spatial products cannot be designed by changing one isolated component. - Gestures, visual/audio/haptic feedback, and resulting functionality must evolve together. - Iteration should happen across the whole interaction system. ## Give tools an appropriate theory of mind - Future tools should understand users’ intentions and goals. - They need enough agency to assist meaningfully, but not so much that they become intrusive or unpredictable. - Effective assistance depends on balancing automation with user control. ## Treat products and people as works in progress - Products should be viewed as successive versions rather than finished objects. - Bugs and improvements are a normal part of continued development. - Designers should apply the same iterative mindset to their own growth. ## Make interfaces disappear - Interfaces should be as seamless and minimal as possible. - Their value lies in enabling an experience, not in drawing attention to themselves. - The ideal interaction removes unnecessary friction between a person and their intention. Bosworth’s practical recommendation is to stay close to human needs, challenge conventional assumptions, prototype early, and design the entire experience. In emerging fields such as AI and spatial computing, success depends less on polishing familiar interfaces than on inventing simpler, more natural ways for people to accomplish things.

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AI Human Computer Interaction Interface Design Virtual Reality+4

figmaMay 14, 20252 min readCurated summary

Double Click: Does Efficiency Kill Love? | Figma Blog

Config 2025 framed AI less as a tool for maximizing efficiency and more as a way to deepen connection, care, and creative purpose. The article contrasts the slogan “Efficiency kills love” with the idea that “Design is chasing a feeling,” arguing that craft and intentionality should remain central as technology accelerates. AI’s role, therefore, depends on the values people choose to embed in it. ## From Efficiency to Care - Figma’s community expressed renewed enthusiasm for craft, emotion, and meaningful creative work. - Smith & Diction’s presentation, “The Wild West of Collaborative Brand Design,” captured the mood with the declaration that “Efficiency kills love.” - Figma CEO Dylan Field’s keynote offered a complementary idea: “Design is chasing a feeling.” - Together, these statements suggest a shift away from treating productivity and speed as the sole measures of successful design. ## Craft as the Conduit - Yang You, Head of Design at Paradigm, summarized the emerging attitude: “Craft is king, and care is the conduit.” - Designers are looking beyond target metrics and faster workflows toward: - Storytelling - Purposeful making - Emotional resonance - A sense of magic and meaning - Microsoft design leader Brittany Mederos similarly described Config’s theme as making meaningful things with purpose. ## Technology Should Reflect Human Values - Madeline Gannon argued that automation is not inevitable; it is intentional and reflects collective cultural choices. - This reframes AI from an unstoppable force into a technology shaped by human decisions about what should—and should not—be automated. - The central question becomes not simply what AI can make faster, but what people want to create with it and why. ## Slowing Down in an Automated World - Polaroid’s approach illustrates how technology can support, rather than replace, human creativity. - Its app provides AI-powered photography advice while preserving the company’s emphasis on slowing down, making physical things, and creating objects with personal meaning. - The example suggests that even advanced tools can reinforce deliberate, hands-on experiences instead of eliminating them. Ultimately, the article recommends treating AI as an augmentation of creative practice rather than an automatic substitute for it. Efficiency is valuable, but designers should protect the care, craft, and emotional intent that make creative work meaningful.

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Figma AI Product Design Collaborative Design+2

lineMay 14, 2025Original article

Code Quality Improvement Techniques Part (opens in new tab)

Effective code design often involves shifting the responsibility of state verification from the caller to the receiving object. By internalizing "if-checks" within the function that performs the action, developers can reduce boilerplate, prevent bugs caused by missing preconditions, and simplify state transitions. This encapsulation ensures that objects maintain their own integrity while providing a cleaner, more intuitive API for the rest of the system. ### Internalizing State Verification * Instead of the caller using a pattern like `if (!receiver.isState()) { receiver.doAction() }`, the check should be moved inside the `doAction` method. * Moving the check inside the function prevents bugs that occur when a caller forgets to verify the state, which could otherwise lead to crashes or invalid data transitions. * This approach hides internal state details from the caller, simplifying the object's interface and focusing on the desired outcome rather than the prerequisite checks. * If "doing nothing" when a condition isn't met is non-obvious, developers should use descriptive naming (e.g., `markAsFriendIfNotYet`) or clear documentation to signal this behavior. ### Leveraging Return Values for Conditional Logic * When a caller needs to trigger a secondary effect—such as showing a UI popup—only if an action was successful, it is better to return a status value (like a `Boolean`) rather than using higher-order functions. * Passing callbacks like `onSucceeded` into a use case can create unnecessary dependency cycles and makes it difficult for the caller to discern if the execution is synchronous or asynchronous. * Returning a `Boolean` to indicate if a state change actually occurred allows the caller to handle side effects cleanly and sequentially. * To ensure the caller doesn't ignore these results, developers can use documentation or specific compiler annotations to force the verification of the returned value. To improve overall code quality, prioritize "telling" an object what to do rather than "asking" about its state and then acting. Centralizing state logic within the receiver not only makes the code more robust against future changes but also makes the intent of the calling code much easier to follow.

Kotlin Code Quality Software Design Refactoring+1

googleMay 13, 2025Original article

Deeper insights into retrieval augmented generation: The role of sufficient context (opens in new tab)

Google Research has introduced "sufficient context" as a critical new metric for evaluating Retrieval Augmented Generation (RAG) systems, arguing that simple relevance is an inadequate measure of performance. By focusing on whether a retrieved context contains all the necessary information to definitively answer a query, researchers developed an LLM-based autorater that classifies context sufficiency with 93% accuracy. This framework reveals that many RAG failures, specifically hallucinations, occur because models fail to abstain from answering when information is incomplete or contradictory. ## Defining and Measuring Sufficient Context * Sufficient context is defined as containing all information necessary to provide a definitive answer, while insufficient context is relevant but incomplete, inconclusive, or contradictory. * The researchers developed an "autorater" using Gemini 1.5 Pro, utilizing chain-of-thought prompting and 1-shot examples to evaluate query-context pairs. * In benchmarks against human expert "gold standard" labels, the autorater achieved 93% accuracy, outperforming specialized models like FLAMe (fine-tuned PaLM 24B) and NLI-based methods. * Unlike traditional metrics, this approach does not require ground-truth answers to evaluate the quality of the retrieved information. ## RAG Failure Modes and Abstention Challenges * State-of-the-art models (Gemini, GPT, Claude) perform exceptionally well when provided with sufficient context but struggle when context is lacking. * The primary driver of hallucinations in RAG systems is the "abstention" problem, where a model attempts to answer a query based on insufficient context rather than stating "I don't know." * Analyzing model responses through the lens of sufficiency allows developers to distinguish between "knowledge" (the model knows the answer internally) and "grounding" (the model correctly uses the provided context). ## Implementation in Vertex AI * The insights from this research have been integrated into the Vertex AI RAG Engine via a new LLM Re-Ranker feature. * The re-ranker prioritizes retrieved snippets based on their likelihood of providing a sufficient answer, significantly improving retrieval metrics such as normalized Discounted Cumulative Gain (nDCG). * By filtering for sufficiency during the retrieval phase, the system reduces the likelihood that the LLM will be forced to process misleading or incomplete data. To minimize hallucinations and improve the reliability of RAG applications, developers should move beyond keyword-based relevance and implement re-ranking stages that specifically evaluate context sufficiency. Ensuring that an LLM has the "right" to answer based on the provided data—and training it to abstain when that data is missing—is essential for building production-grade generative AI tools.

AI Large Language Models Gemini Prompt Engineering+5

googleMay 12, 2025Original article

Differential privacy on trust graphs (opens in new tab)

Researchers from Google have introduced Trust Graph Differential Privacy (TGDP), a framework that models privacy based on varying trust relationships between users represented as vertices in a graph. By allowing users to share data with trusted neighbors who then aggregate and privatize the information, TGDP bridges the gap between the highly accurate central DP model and the high-privacy local DP model. This approach enables more practical and accurate data analysis in scenarios where users exhibit nuanced privacy preferences rather than binary trust assumptions. ## Defining Trust Graph DP * The model represents users as vertices and mutual trust as edges, ensuring that a user’s data remains statistically indistinguishable to any party they do not trust. * This guarantee holds even if non-trusted parties pool their data or collaborate with a user's trusted neighbors to attempt re-identification. * TGDP serves as a mathematical interpolation: a "star graph" topology corresponds to the central DP model, while a fully unconnected graph corresponds to the local DP model. ## Private Aggregation and Error Metrics * The research evaluates TGDP through the fundamental task of private aggregation, where the goal is to estimate the sum of all users' private values ($\Sigma x_i$). * Accuracy is quantified using mean-squared error, allowing researchers to establish theoretical upper and lower bounds for algorithm performance. * These bounds demonstrate that the utility of a privacy-preserving algorithm is directly tied to the specific structure of the trust relationships within the network. ## The Dominating Set Algorithm * The proposed algorithm utilizes the concept of a "dominating set"—a subset of users $T$ such that every user in the graph is either in $T$ or adjacent to someone in $T$. * In this mechanism, each user sends their raw data to a trusted neighbor within the dominating set. * The members of the dominating set aggregate the data they receive and add specific statistical noise to satisfy differential privacy before sharing the results. * This method reduces the total noise required compared to the local model, as the number of noise-adding entities is limited to the size of the dominating set rather than the entire population. By leveraging existing trust networks, TGDP provides a rigorous way to optimize the trade-off between privacy and utility. This framework suggests that identifying small dominating sets within a community can significantly improve the accuracy of data analytics and machine learning without requiring a single, universally trusted central curator.

AI Machine Learning Differential Privacy Graph Theory+5