Techlist.io - Korean Tech Blog Curator

discordNov 13, 20252 min readCurated summary

How to Share What You’re Playing, Listening to, or Watching as Your Status on Discord

Discord lets users share their current gaming, music, or anime activity as a profile status. The post explains how to connect supported platforms and enable automatic activity sharing, while also showing how to hide individual activities or disable sharing entirely. ## Sharing PC Games - The Discord desktop app can display games or other applications running on Windows, macOS, or Linux. - If a game is not detected automatically, go to **User Settings > Registered Games** and select **“Add it!”** - Use the eye toggles beside registered games to prevent specific titles from appearing as your status. ## Sharing Console Games - Link a PlayStation or Xbox account through **User Settings > Connections**. - Enable **“Display PlayStation Network as your status”** or **“Display Xbox as your status.”** - Supported consoles include PlayStation 5, Xbox Series X|S, and Xbox One. - Xbox Cloud Gaming titles can also appear as Discord activity when streamed from supported Xbox devices. ## Sharing Spotify Music - Connect Spotify through **User Settings > Connections**. - Turn on **“Display Spotify as your status.”** - Discord will show the song currently playing in Spotify when both services are being used on the same computer. ## Sharing Crunchyroll Anime - Link Crunchyroll from **User Settings > Connections**. - After signing in and authorizing the connection, Discord can automatically display the anime being watched. ## Controlling Activity Visibility - Disable all automatic activity sharing through **User Settings > Activity Privacy** by turning off **“Share my activity.”** - To hide only certain platforms, use the individual visibility controls under **User Settings > Connections**. - These settings control whether games, music, and anime appear in the user’s Discord status. Overall, users can make their Discord status more informative by connecting supported services, while the privacy controls allow them to share selectively or remain completely incognito.

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Discord Spotify Playstation Xbox+3

tossNov 13, 2025Original article

Frontend Code That Lasts 1 (opens in new tab)

Toss Payments evolved its Payment SDK to solve the inherent complexities of integrating payment systems, where developers must navigate UI implementation, security flows, and exception handling. By transitioning from V1 to V2, the team moved beyond simply providing a library to building a robust, architecture-driven system that ensures stability and scalability across diverse merchant environments. The core conclusion is that a successful SDK must be treated as a critical infrastructure layer, relying on modular design and deep observability to handle the unpredictable nature of third-party runtimes. ## The Unique Challenges of SDK Development * SDK code lives within the merchant's runtime environment, meaning it shares the same lifecycle and performance constraints as the merchant’s own code. * Internal logging can inadvertently create bottlenecks; for instance, adding network logs to a frequently called method can lead to "self-DDoS" scenarios that crash the merchant's payment page. * Type safety is a major hurdle, as merchants may pass unexpected data types (e.g., a number instead of a string), causing fatal runtime errors like `startsWith is not a function`. * The SDK acts as a bridge for technical communication, requiring it to function as both an API consumer for internal systems and an API provider for external developers. ## Ensuring Stability through Observability * To manage the unpredictable ways merchants use the SDK, Toss implemented over 300 unit tests and 500 E2E integration tests based on real-world use cases. * The team utilizes a "Global Trace ID" to track a single payment journey across both the frontend and backend, allowing for seamless debugging across the entire system. * A custom Monitoring CLI was developed to compare payment success rates before and after deployments, categorized by merchant and runtime environment (e.g., PC Chrome vs. Android WebView). * This observability infrastructure enables the team to quickly identify edge-case failures—such as a specific merchant's checkout failing only on mobile WebViews—which are often missed by standard QA processes. ## Scaling with Modular Architecture * To avoid "if-statement hell" caused by merchant-specific requirements (e.g., fixing installment months or custom validation for a specific store), Toss moved to a "Lego-block" architecture. * The SDK is organized into three distinct layers based on the "reason for change" principle: * **Public Interface Layer:** Manages the contract with the merchant, validating inputs and translating them into internal domain models. * **Domain Layer:** Encapsulates core business logic and payment policies, keeping them isolated from external changes. * **External Service Layer:** Handles dependencies like Server APIs and Web APIs, ensuring technical shifts don't leak into the business logic. * This separation allows the team to implement custom merchant logic by swapping specific blocks without modifying the core codebase, reducing the risk of regressions and lowering maintenance costs. For developers building SDKs or integration tools, the shift from monolithic logic to a layered, observable architecture is essential. Prioritizing the separation of domain logic from public interfaces and investing in environment-specific monitoring allows for a highly flexible product that remains stable even as the client-side environment grows increasingly complex.

Observability TypeScript Software Architecture Elasticsearch+3

lineNov 13, 2025Original article

Pushsphere: The Secret to Fast and (opens in new tab)

LINE developed Pushsphere to overcome the inherent instability and rate-limiting challenges of delivering high-volume push notifications via providers like APNs and FCM. By implementing a sophisticated gateway architecture rather than relying on naive retry logic, the system ensures reliable delivery even during massive traffic spikes or regional emergencies. This approach has successfully stabilized the messaging pipeline, drastically reducing operational overhead and system-wide failures. ## Limitations of Standard Push Architectures * External push providers are frequently unstable, exhibiting misbehaving instances, sudden disconnections, and unpredictable timeouts. * Naive retry strategies often lead to "retry storms," which quickly exhaust rate-limit quotas and result in HTTP 429 (Too Many Requests) errors. * At massive scales, manual management of hundreds of server connections becomes impossible, necessitating automated decisions on when to abandon or switch between faulty nodes. ## Unified Gateway Design and High-Performance Transport * Pushsphere provides a single entry point for all push platforms, abstracting the complexities of mTLS for Apple and OAuth 2.0 for Firebase. * The system is built on the Armeria microservice framework and utilizes Netty for high-performance, non-blocking communication within the Java Virtual Machine. * The architecture includes a client library and gateway server that support zone-aware routing, ensuring low latency and efficient traffic distribution across data centers. ## Intelligent Retry and Load Balancing Strategies * The "retry-aware" load balancer uses a Round Robin base strategy but is designed to skip previously attempted endpoints during a retry cycle to avoid repeated failures on faulty nodes. * Quota-aware logic monitors rate limits in real-time, preventing the system from retrying endpoints that are nearing their capacity. * These smarter traffic distribution rules balance high delivery success rates with the preservation of provider quotas, preventing service-wide blocking. ## Resilient Endpoint Management via Circuit Breakers * Pushsphere assigns a dedicated circuit breaker to every endpoint to report success and failure rates continuously. * When a circuit opens due to frequent failures, the unhealthy endpoint is immediately removed from the active pool and replaced with a fresh candidate from a DNS-refreshed pool. * This automated replacement mechanism maintains a consistent pool of healthy endpoints, allowing the system to remain stable without manual intervention during hardware or network degradations. Pushsphere has transformed LINE's notification infrastructure, reducing annual on-call alerts from over 30 to just four, despite implementing stricter monitoring thresholds. For developers managing high-volume messaging services, adopting a gateway-based approach with automated circuit breaking and quota awareness is a proven path to achieving carrier-grade reliability.

Microservices Push Notifications Armeria Netty+4

googleNov 12, 2025Original article

Separating natural forests from other tree cover with AI for deforestation-free supply chains (opens in new tab)

Researchers from Google DeepMind and Google Research have developed "Natural Forests of the World 2020," an AI-powered global map that distinguishes natural ecosystems from commercial tree plantations. By utilizing high-resolution satellite data and machine learning, the project provides a critical 10-meter resolution baseline to support deforestation-free supply chain regulations like the EUDR. This tool enables governments and companies to monitor biodiversity-rich areas with unprecedented accuracy, ensuring that natural forests are protected from industrial degradation. **The Limitation of Traditional Tree Cover Maps** * Existing maps frequently conflate all woody vegetation into a generic "tree cover" category, leading to "apples-to-oranges" comparisons between different land types. * This lack of distinction makes it difficult to differentiate between the harvesting of short-term plantations and the permanent loss of ancient, biodiversity-rich natural forests. * Precise mapping is now a legal necessity due to regulations like the European Union Regulation on Deforestation-free Products (EUDR), which bans products from land deforested or degraded after December 31, 2020. **The MTSViT Modeling Approach** * To accurately identify forest types, researchers developed the Multi-modal Temporal-Spatial Vision Transformer (MTSViT). * Rather than relying on a single snapshot, the AI "observes" 1280 x 1280 meter patches over the course of a year to identify seasonal, spectral, and textural signatures. * The model integrates multi-modal data, including Sentinel-2 satellite imagery, topographical information (such as elevation and slope), and specific geographical coordinates. * This temporal-spatial analysis allows the AI to recognize the complex patterns of natural forests that distinguish them from the uniform, fast-growing structures of commercial plantations. **Dataset Scale and Global Validation** * The model was trained on a massive dataset comprising over 1.2 million global patches at 10-meter resolution. * The final map provides seamless global coverage, achieving a best-in-class validation accuracy of 92.2% against an independent global dataset. * The research was a collaborative effort involving the World Resources Institute and the International Institute for Applied Systems Analysis to ensure scientific rigor and practical utility. The "Natural Forests of the World 2020" dataset is publicly available via Google Earth Engine and other open repositories. Organizations should leverage this high-resolution baseline to conduct environmental due diligence, support government monitoring, and target conservation efforts in preparation for global climate milestones like COP30.

AI Machine Learning Computer Vision Remote Sensing+3

googleNov 12, 2025Original article

A new quantum toolkit for optimization (opens in new tab)

Researchers at Google Quantum AI have introduced Decoded Quantum Interferometry (DQI), a new quantum algorithm designed to tackle optimization problems that remain intractable for classical supercomputers. By leveraging the wavelike nature of quantum mechanics to create specific interference patterns, the algorithm converts complex optimization tasks into high-dimensional lattice decoding problems. This breakthrough provides a theoretical framework where large-scale, error-corrected quantum computers could eventually outperform classical methods by several orders of magnitude on commercially relevant tasks. ### Linking Optimization to Lattice Decoding * The DQI algorithm functions by mapping the cost landscape of an optimization problem onto a periodic lattice structure. * The "decoding" aspect involves identifying the nearest lattice element to a specific point in space, a task that becomes exponentially difficult for classical computers as dimensions increase into the hundreds or thousands. * By using quantum interference to bridge these fields, researchers can apply decades of sophisticated classical decoding research—originally developed for data storage and transmission—to solve optimization challenges. * This approach is unique because it requires a quantum computer to leverage these classical decoding algorithms in a way that conventional hardware cannot. ### Solving the Optimal Polynomial Intersection (OPI) Problem * The most significant application of DQI is for the OPI problem, where the goal is to find a low-degree polynomial that intersects the maximum number of given target points. * OPI is a foundational task in data science (polynomial regression), cryptography, and digital error correction, yet it remains "hopelessly difficult" for classical algorithms in many scenarios. * DQI transforms the OPI problem into a task of decoding Reed-Solomon codes, a family of codes widely used in technologies like QR codes and DVDs. * Technical analysis indicates a massive performance gap: certain OPI instances could be solved by a quantum computer in approximately a few million operations, while the most efficient classical algorithms would require over $10^{23}$ (one hundred sextillion) operations. ### Practical Conclusion As quantum hardware moves toward the era of error correction, Decoded Quantum Interferometry identifies a specific class of "NP-hard" problems where quantum machines can provide a clear win. Researchers and industries focusing on cryptography and complex data regression should monitor DQI as a primary candidate for demonstrating the first generation of commercially viable quantum advantage in optimization.

Quantum Computing Quantum Algorithms Optimization Error Correction+4

figmaNov 11, 20252 min readCurated summary

Figma Opens a New Hub in India | Figma Blog

Figma has opened a new hub in Bengaluru to support India’s rapidly expanding design and product-development community. India is now Figma’s second-largest market by monthly active users, with strong adoption among major companies and growing interest in AI-assisted design and development. The Bengaluru office is intended to bring Figma closer to local users while helping shape the future of product creation in India. ## Bengaluru Hub and India’s Growing Community - The office opened on November 12, 2025, after more than 7,000 people registered for the launch event. - Figma says India has nearly 25,000 Friends of Figma community members. - More than 40% of Bombay Stock Exchange 100 companies used Figma as of September 2025. - Major customers include Airtel, Flipkart, Swiggy, Zomato, Myntra, Groww, and TCS. ## AI’s Impact on Design and Development Research surveying 730 design, engineering, and product leaders found that: - 93% of designers’ companies already use AI in design. - 85% of designers believe AI makes writing code easier. - 81% of developers say design is increasingly important to successful AI-powered products. These findings support Figma’s broader strategy of connecting design, code, collaboration, and AI in one platform. ## Figma’s Expanding Product Platform At the Bengaluru launch, Figma highlighted tools across its product ecosystem: - **Figma Design:** Create digital products. - **Figma Make:** Turn prompts or designs into prototypes and applications. - **Dev Mode:** Help developers translate designs into code. - **Figma Sites:** Design and publish websites. - **FigJam:** Collaborate through online whiteboards. - **Figma Slides:** Build interactive presentations. - **Figma Draw:** Create advanced vector illustrations. - **Figma Buzz:** Produce branded marketing assets at scale. Figma also introduced **Figma Weave**, based on its acquisition of Weavy, to add AI-native capabilities for image, video, animation, motion design, and VFX creation. ## Figma’s Global Expansion - 85% of Figma users were outside the United States in Q1 2025. - About half of Figma’s 2024 revenue came from international markets. - Bengaluru joins Figma offices in cities including Tokyo, Singapore, London, Paris, Berlin, Sydney, São Paulo, and New York. The new hub reflects Figma’s shift from a standalone design tool toward a global, AI-powered platform supporting the full journey from idea to product.

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Figma AI Design Systems User Experience+4

googleNov 11, 2025Original article

Differentially private machine learning at scale with JAX-Privacy (opens in new tab)

Google DeepMind and Google Research have announced the release of JAX-Privacy 1.0, a high-performance library designed to scale differentially private (DP) machine learning. By leveraging JAX’s native parallelization and functional programming model, the toolkit enables researchers to train large-scale foundation models while maintaining rigorous privacy guarantees. This version introduces modular components for advanced algorithms and empirical auditing, making private training both computationally efficient and verifiable across distributed environments. ### Scaling Differential Privacy with JAX * The library is built directly on the JAX ecosystem, integrating seamlessly with Flax for neural network architectures and Optax for optimization. * It utilizes JAX’s `vmap` for automatic vectorization and `shard_map` for single-program multiple-data (SPMD) parallelization, allowing DP primitives to scale across multiple accelerators. * By using just-in-time (JIT) compilation, the library mitigates the traditional performance overhead associated with per-example gradient clipping and noise addition. ### Core Components and Advanced Algorithms * The toolkit provides fundamental building blocks for implementing standard DP algorithms like DP-SGD and DP-FTRL, including specialized modules for data batch construction. * It supports state-of-the-art methods such as DP matrix factorization, which improves performance by injecting correlated noise across training iterations. * Features like micro-batching and padding are included to handle the massive, variable-sized batches often required to achieve an optimal balance between privacy and model utility. ### Verification and Privacy Auditing * JAX-Privacy incorporates rigorous privacy accounting based on Rényi Differential Privacy to provide precise tracking of privacy budgets. * The library includes tools for empirical auditing, allowing developers to validate their privacy guarantees through techniques like membership inference attacks and data poisoning. * The design ensures correctness in distributed settings, specifically focusing on consistent noise generation and gradient synchronization across clusters. JAX-Privacy 1.0 is a robust solution for researchers and engineers who need to deploy production-grade private models. Its modular architecture and integration with high-performance computing primitives make it a primary choice for training foundation models on sensitive datasets without compromising on scalability or security.

AI Machine Learning Differential Privacy Jax+5

stripeNov 11, 20253 min readCurated summary

Create new monetization opportunities with Stripe Billing’s recent upgrades

Stripe Billing is expanding into a more flexible platform for complex revenue models, supporting multiple payment processors, adaptable invoicing, hybrid pricing, and AI-specific billing. The updates aim to help businesses manage recurring revenue across diverse payment infrastructure while responding quickly to changing customer needs and AI costs. Stripe says Billing now serves more than 300,000 users and has been recognized by Gartner and Forrester. ## Expanded Support for Off-Stripe Payments - Billing can now track successful, failed, refunded, and canceled payments processed outside Stripe. - Businesses can attach off-Stripe payment records to invoices and report failed transactions. - Scheduled retries and dunning workflows are available for those transactions. - Uploaded payment method names and logos can appear in the customer portal. - Paid support for unified reporting and revenue recognition covers both Stripe and non-Stripe volume. - Future additions include Stripe Sigma reporting, dispute recording, and customer-portal subscription updates for other processors. ## More Flexible Billing and Invoicing - Businesses can prebill future subscription periods at any time, including: - Specific subscription items - Arbitrary date ranges - Partial periods - Multiple months in advance - Invoicing improvements include: - Local-currency payments in more than 150 countries through Adaptive Pricing - Installment payments via Klarna, Affirm, and Afterpay/Clearpay - Partial invoice payments - Unapplying payments to correct reconciliation errors - These capabilities help companies offer incentives, improve cash-flow predictability, and reduce payment friction. ## Hybrid Pricing for AI Products - New pricing plans combine: - Usage-based rates - Dimensional pricing - Recurring fees - Credits - Companies can issue recurring credits, define how credits are consumed, and consolidate charges into one bill. - Real-time usage tracking and Usage Analytics API dashboards show consumption and remaining balances. - Automatic credit top-ups can prevent service interruptions. - Lovable reportedly built and launched its complete system with two engineers in under two weeks. ## LLM Proxy for Protecting Margins - Stripe’s LLM proxy, currently in private preview, tracks token usage from API requests and applies predefined pricing automatically. - Stripe calculates usage, adds the company’s markup, and produces unified invoices. - This allows businesses to update underlying AI costs without manually redesigning their billing systems. - Integrations with OpenRouter, Cloudflare, Vercel, and Helicone support routing through third-party LLM proxies while recording usage events. Stripe’s latest Billing features are designed for companies whose pricing, payment infrastructure, and cost structures are constantly changing. Businesses with multi-processor setups, flexible invoicing needs, or AI-driven usage models can use these tools to centralize billing while adapting prices and protecting margins more quickly.

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Fintech Recurring Billing Usage Based Pricing Subscription Management+4

lineNov 7, 2025Original article

Code Quality Improvement Techniques Part 22: To equal, or not to equal (opens in new tab)

The post argues that developers should avoid overriding the `equals` method to compare only a subset of an object’s properties, as this violates the fundamental principles of identity and structural equivalence. Implementing "partial equality" often leads to subtle, hard-to-trace bugs in reactive programming environments where UI updates depend on detecting changes through equality checks. To ensure system reliability, `equals` must strictly represent either referential identity or total structural equivalence. ### Risks of Partial Equality in Reactive UI * Reactive frameworks such as Kotlin’s `StateFlow`, `Flow`, and Android’s `LiveData` utilize `distinctUntilChanged` logic to optimize performance. * These "observable" patterns compare the new object instance with the previous one using `equals`; if the result is `true`, the update is ignored to prevent unnecessary re-rendering. * If a `UserProfileViewData` object only compares a `userId` field, the UI will fail to reflect changes to a user's nickname or profile image because the framework incorrectly assumes the data has not changed. * To avoid this, any comparison logic that only checks specific fields should be moved to a uniquely named function, such as `hasSameIdWith()`, instead of hijacking the standard `equals` method. ### Defining Identity vs. Equivalence * **Identity (Referential Equality):** This indicates that two references point to the exact same object instance, which is the default behavior of `Object.equals()` in Java or `Any.equals()` in Kotlin. * **Equivalence (Structural Equality):** This indicates that two objects are logically the same because all their properties match. In Kotlin, `data class` implementations provide this by default for all parameters defined in the primary constructor. * Proper implementation of equivalence requires that all fields within the object also have clearly defined equality logic. ### Nuances and Implementation Exceptions * **Kotlin Data Class Limitations:** Only properties declared in the primary constructor are included in the compiler-generated `equals` and `hashCode` methods; properties declared in the class body are ignored by default. * **Calculated Caches:** It is acceptable to exclude certain fields from an equality check if they do not change the logical state of the object, such as a `cachedValue` used to store the results of a heavy mathematical operation. * **Context-Dependent Equality:** The definition of equality can change based on the model's purpose. For example, a mathematical model might treat 1/2 and 2/4 as equal, whereas a UI display model might treat them as different because they represent different strings of text. When implementing `equals`, prioritize full structural equivalence to prevent data-stale bugs in reactive systems. If you only need to compare a unique identifier, create a dedicated method instead of repurposing the standard equality check.

Android Kotlin Java Code Quality+3

discordNov 6, 20252 min readCurated summary

During October, Treat a Friend to Nitro and Trick Out Your Profile for Halloween 🎃

Discord’s October 2025 Halloween promotion adds a limited-time “Trick or Treat” collection of profile items, each offering spooky and cute variations. Users can also earn Halloween avatar decorations by gifting Nitro to friends, with gifting available on both desktop and mobile. The collection and promotion end on November 3, 2025. ## Trick or Treat Profile Items - The Discord Shop features Halloween-themed decorations with separate “Trick” and “Treat” versions. - Some Shop elements may change appearance as users browse, reinforcing the Halloween theme. - The collection is available only until **November 3, 2025**. ## Nitro Gifting Rewards - Users who gift Nitro to a friend between October 8 and November 3 can claim one of **eight new Halloween avatar decorations**. - Nitro gifts can now be sent from both desktop and mobile. - Gifting Nitro to additional friends unlocks more decorations. - Users can potentially collect all eight decorations by sending enough gifts. - Discord directs users to its Nitro Gifting support guide for promotion details. ## More Halloween Events - Discord hints at additional Halloween treats and giveaways through its social media channels. - The campaign encourages users to customize their profiles and involve friends through Nitro gifting. Discord users interested in the seasonal items or avatar decorations should act before **November 3, 2025**, when the promotion ends.

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Discord Desktop App Mobile App Nitro+1

googleNov 6, 2025Original article

Introducing Nested Learning: A new ML paradigm for continual learning (opens in new tab)

Google Research has introduced Nested Learning, a paradigm that treats machine learning models as systems of interconnected, multi-level optimization problems rather than separate architectures and training rules. By unifying structure and optimization through varying update frequencies, this approach aims to mitigate "catastrophic forgetting," the tendency for models to lose old knowledge when acquiring new skills. The researchers validated this framework through "Hope," a self-modifying architecture that outperforms current state-of-the-art models in long-context memory and language modeling. ### The Nested Learning Paradigm This framework shifts the view of machine learning from a single continuous process to a set of coherent, nested optimization problems. Each component within a model is characterized by its own "context flow"—the specific set of information it learns from—and its own update frequency. * The paradigm argues that architecture (structure) and optimization (training rules) are fundamentally the same concept, differing only by their level of computational depth and update rates. * Associative memory is used as a core illustrative concept, where the training process (backpropagation) is modeled as a system mapping data points to local error values. * By defining an update frequency rate for each component, researchers can order these problems into "levels," allowing for a more unified and efficient learning system inspired by the human brain's neuroplasticity. ### Deep Optimizers and Refined Objectives Nested Learning provides a principled way to improve standard optimization algorithms by viewing them through the lens of associative memory modules. * Existing momentum-based optimizers often rely on simple dot-product similarity, which fails to account for how different data samples relate to one another. * By replacing these simple similarities with standard loss metrics, such as L2 regression loss, the researchers derived new formulations for momentum that are more resilient to imperfect or noisy data. * This approach turns the optimizer itself into a deeper learning component with its own internal optimization objective. ### Continuum Memory Systems and the "Hope" Architecture The paradigm addresses the limitations of Large Language Models (LLMs), which are often restricted to either their immediate input window or static pre-trained knowledge. * The researchers developed "Hope," a proof-of-concept architecture that utilizes multi-time-scale updates for its internal components. * While standard Transformers act primarily as short-term memory, the Nested Learning approach allows for "continuum memory" that manages long-context information more effectively. * Experimental results show that this self-modifying architecture achieves superior performance in language modeling compared to existing state-of-the-art models. By recognizing that every part of a model is essentially an optimizer operating at a different frequency, Nested Learning offers a path toward AI that can adapt to new experiences in real-time. This structural shift moves away from the "static pre-training" bottleneck and toward systems capable of true human-like neuroplasticity and lifelong learning.

Machine Learning Deep Learning Transformer Optimization Algorithms+4

tossNov 6, 2025Original article

Creating the worst experience at Toss (opens in new tab)

Toss designer Lee Hyeon-jeong argues that business goals and user experience are not mutually exclusive, even when integrating controversial elements like advertising. By identifying the intersection between monetization and usability, her team transformed intrusive ads into value-driven features that maintain user trust while driving significant revenue. The ultimate conclusion is that transparency and appropriate rewards can mitigate negative feedback and even increase user engagement. ### Reducing Friction through Predictability and Placement * Addressed "surprise" ads by introducing clear labeling, such as "Watch Ad" buttons or specifying ad durations (e.g., "30-second ad"), which reduced negative sentiment without decreasing revenue. * Discovered that when users are given a choice and clear expectations, their anxiety decreases and their willingness to engage with the content increases. * Eliminated "flow-breaking" ads that mimicked functional UI elements, such as banners placed inside transaction histories that users frequently mistook for personal bank records. * Established a design principle to place advertisements only in areas that do not interfere with information discovery or core user navigation tasks. ### Transforming Advertisements into User Benefits * Developed a dedicated B2B ad platform to scale the variety of available advertisements, ensuring that users receive ads relevant to their specific life stages, such as car insurance or new credit cards. * Shifted the internal perception of ads from "noise" to "benefits" by focusing on the right timing and high-quality matching between the advertiser and the user's needs. * Institutionalized regular "creative ideation sessions" to explore interactive formats, including advertisements that respond to phone movement (gyroscope), quizzes, and mini-games. * Leveraged long-term internal experiments to ensure that even if an idea cannot be implemented immediately, it remains in the team's "creative bank" for future product opportunities. ### Optimizing Value Exchange through Rewards * Conducted over a year of A/B testing on reward thresholds, comparing small cash amounts (1 KRW to 200 KRW), non-monetary items (gifticons), and high-stakes lottery-style prizes. * Analyzed the "labor intensity" of ads by adjusting lengths (10 to 30 seconds) to find the psychological tipping point where users felt the reward was worth their time. * Implemented a high-value lottery system within the Toss Pedometer service, which successfully transitioned a loss-making feature into a profitable revenue stream. * Maintained user activity and satisfaction levels despite the increased presence of ads by ensuring the "worst-case experience"—viewing ads for no gain—was entirely avoided. Product teams should stop viewing business requirements and UX as a zero-sum game. By focusing on user psychology—specifically transparency, non-disruption, and fair value exchange—it is possible to achieve aggressive business targets while maintaining a sustainable and trusted user environment.

User Experience Product Design A B Testing Gamification+3

slackNov 6, 20253 min readCurated summary

Build better software to build software better

Slack’s backend build pipeline for Quip and Slack Canvas once took 60 minutes, delaying feedback and slowing delivery. The team improved build performance by applying familiar software-engineering techniques—caching, parallelization, precise interfaces, and careful decomposition—using Bazel. The central argument is that build systems should be designed like high-performance programs: do less work, distribute unavoidable work, and define work units rigorously. ## Modeling Builds as Dependency Graphs - Applications can be represented as directed acyclic graphs of source files, intermediate artifacts, and deployable outputs. - A backend artifact depends on Python files, while a frontend artifact depends on TypeScript files. - Changing a Python file should rebuild the backend but not unrelated frontend components. - Clearly defined graph nodes allow build systems to optimize work rather than rebuilding everything. ## Caching and Hermetic Work - Caching avoids repeating expensive operations by storing outputs for known inputs. - The article uses a cached recursive `factorial()` function as an analogy: - The input is the cache key. - The return value is the cached artifact. - Effective caching requires work to be: - **Hermetic:** dependent only on explicitly provided inputs. - **Idempotent:** producing the same output for the same inputs. - Cache hit rate matters: poorly defined work units produce more cache misses. ## Granular Cache Units - Caching an entire `process_images(images, transforms)` operation is inefficient because changing one image invalidates the result for every image. - A more granular design caches `process_image(image, transform)` independently. - The higher-level operation can then reuse cached results and process only new image-transform combinations. - Smaller, well-defined units generally improve cache reuse and reduce rebuild time. ## Parallelizing Independent Work - Image processing can also be distributed across CPU threads using `ThreadPoolExecutor`. - Parallel work requires: - Completely specified inputs and outputs. - The ability to transfer data across thread, process, or network boundaries. - Handling completion and failure in any order. - APIs must document ordering guarantees; the threaded example returns images in completion order rather than input order. - Work-unit granularity affects scalability: - Too few large tasks limit available parallelism. - Too many tiny tasks may introduce coordination overhead. - The appropriate balance depends on the workload. ## Applying These Principles to Bazel - Bazel represents builds as directed acyclic graphs made of targets. - Each target defines: - Its input or dependency files. - Its output files. - The commands that transform inputs into outputs. - This structure provides the foundation for caching and parallel execution, just as explicit function inputs and outputs enable those optimizations in application code. The practical recommendation is to design build steps as small, hermetic, idempotent, and independently executable units. Combined with Bazel’s dependency graph, this lets teams avoid unnecessary work, maximize cache hits, and run independent tasks concurrently—turning slow build pipelines into faster sources of developer feedback.

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TypeScript Python Caching Bazel+4

figmaNov 5, 20252 min readCurated summary

Issue No.13: All Systems Go | Figma Blog

Design systems are becoming essential infrastructure as teams move faster and adopt AI-assisted design and coding tools. Figma argues that well-structured systems extend quality and design intent across human and machine-generated work. The issue highlights how Figma’s products and customer practices are helping teams design, build, and collaborate more reliably in this new environment. ## Unlocking Design Systems - Schema by Figma emphasized that design systems now serve AI as well as human designers. - Figma is adding design-system context to Figma Make to improve generated results. - Its MCP server supports more effective agentic coding workflows. - The broader goal is to produce more consistent, polished, and reliable outputs as teams scale. ## Forging New Paths with Figma Make - Figma Make enables teams to move beyond traditional design workflows by working more directly in code. - Companies including Maven Clinic, Pendo, ServiceNow, and LinkedIn are using it to rethink: - Product roadmaps - Product requirements documents - Starter templates - Design-system deployment - Designers can use prompts to explore ideas, prototype faster, and create new paths for collaboration. ## Clarifying Design Context - As developers increasingly rely on AI coding agents, design files need to communicate more than visual appearance. - Clear file organization, meaningful structure, and annotations help developers—and AI tools—understand design intent. - Providing strong context allows coding tools to implement designs more accurately and efficiently. - Designers should treat their files and systems as shared sources of information, not merely presentation artifacts. ## Further Reading - Figma points readers to foundational design-system material covering basic principles through AI-driven automation. - It also recommends guidance for design leaders building resilient teams and helping designers adapt to rapid change. Strong design systems, combined with clear context and modern tools like Figma Make and MCP, can help teams preserve craft while increasing speed. The practical recommendation is to build systems that are understandable, reusable, and structured for both people and AI.

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AI Design Systems Model Context Protocol Figma Make+1

figmaNov 5, 20252 min readCurated summary

ServiceNow and Figma Launch Strategic Collaboration to Turn Design Vision Into Enterprise Transformation | Figma Blog

ServiceNow and Figma have launched an MCP-powered integration that turns Figma designs directly into enterprise applications. By using a Figma design link as a prompt for ServiceNow’s Build Agent, teams can automate the transition from visual concept to secure, scalable software. The collaboration aims to combine Figma’s design context with ServiceNow’s AI workflows, governance, and platform intelligence. ## From Design to Enterprise Application - Developers can provide a Figma design link directly to the ServiceNow integrated development environment. - ServiceNow’s Build Agent interprets layouts, components, styles, and other design details. - The agent generates a functional enterprise application rather than merely reproducing an image. - The process is intended to reduce manual coding, improve consistency, and accelerate development from minutes-long design-to-build workflows. ## Powered by Figma’s MCP Server - Figma’s Model Context Protocol (MCP) server gives ServiceNow structured design context. - This deeper understanding supports higher-fidelity translations of designs into working applications. - The integration connects design intent with production code, helping designers, product builders, and professional developers collaborate more effectively. ## Security and Governance - The integration uses OAuth 2.0 authentication and secure server-to-server communication. - Access tokens are stored within the customer’s ServiceNow instance to support privacy and compliance. - Applications created through Build Agent inherit ServiceNow capabilities such as permissions, audit trails, version control, and enterprise governance. ## Availability and Broader Impact - The integration is available in the latest ServiceNow Build Agent release through the ServiceNow Store. - Customers must request access after installation. - ServiceNow and Figma position the collaboration as a way to deliver AI-powered experiences faster while preserving human-centered design and enterprise-scale reliability. - Figma’s CTO emphasizes that design quality will remain a key differentiator as AI-generated software becomes more common. Organizations using both platforms can now shorten the path from prototype to production while maintaining security, governance, and design fidelity. The integration is especially suited to teams that want to accelerate enterprise application development without losing the original design intent.

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Figma Generative AI Model Context Protocol Automation+4