Techlist.io - Korean Tech Blog Curator

lineOriginal article

Code Quality Improvement Techniques Part 2 (opens in new tab)

The quality of code documentation depends heavily on the hierarchy of information, specifically prioritizing high-level intent in the very first sentence. By focusing on abstract summaries rather than implementation details at the start, developers can ensure that readers understand a function's purpose instantly without parsing through sequential logic. This principle of "summary-first" communication enhances readability and developer productivity across documentation comments, inline explanations, and TODO tasks. ### Strategies for Effective Documentation * **Prioritize the first sentence:** Documentation comments should be written so that the overview is understandable from the first sentence alone. * **Increase abstraction levels:** Avoid simply repeating what the code does (e.g., "split by period and remove empty strings"). Instead, describe the result in domain terms, such as "returns a list of words grouped by sentences." * **Identify the most important element:** Since the primary goal of most functions is to produce a result, the summary should lead with what is being returned rather than how it is calculated. * **Layer the details:** Technical specifics—such as specific delimiters like `SENTENCE_SEPARATOR` ('.') or `WORD_SEPARATOR_REGEX` ([ ,]+)—and exclusion rules for empty strings should follow the initial summary. * **Use concrete examples:** For complex transformations or edge cases, include a sample input and output (e.g., showing how `" a bc. .d,,."` maps to `[["a", "bc"], ["d"]]`) to clarify boundary conditions. ### Prioritizing Intent in Non-Documentation Comments * **Focus on the "Why" for workarounds:** In inline comments, especially for "temporary fixes" or bug workarounds, the reason for the code's existence is more important than the action it performs. For instance, leading with "This is to avoid a bug in Device X" is more helpful than "Resetting the value to its previous state." * **Lead with the goal in TODOs:** When writing TODO comments, state the ideal state or the required change first. Explanations regarding current limitations or why the change cannot be made immediately should be relegated to the following sentences. * **Improve scannability:** Structuring comments this way allows developers to scan the codebase and understand the motivation behind complex logic without needing to read the entire comment block. To maintain a clean and maintainable codebase, always choose the most critical piece of information—whether it is the function's return value, a bug's context, or a future goal—and place it at the very beginning of your comments.

kakaoOriginal article

Korean and Images at Once (opens in new tab)

Kakao has developed Kanana-v-embedding, a specialized multimodal embedding model designed to bridge the gap between Korean text and visual data within a unified semantic space. By leveraging a Vision-Language Model (VLM) framework, the model enables seamless search and recommendation across various combinations of text and images, offering a significant performance boost over existing English-centric models like CLIP. This development provides a robust technical foundation for enhancing Kakao’s services, including RAG-based systems and localized content discovery. ### Unified Multimodal Meaning Space * The model maps text and images into a single vector space where semantic similarity is measured via cosine similarity. * Unlike traditional CLIP models that use independent encoders, this architecture treats text and images as a single sequence, allowing for "text + image" combined queries. * It supports four primary interaction modes: Text-to-Text, Text-to-Image, Image-to-Image, and (Text+Image)-to-(Text+Image). ### VLM-Based Architecture and Instruction Tuning * The system utilizes a VLM consisting of an LLM and an image encoder, extracting embeddings from the final hidden state of the [EOS] token. * It employs instruction-based query embedding, where specific prompts (e.g., "Find an image matching this caption") guide the model to generate embeddings tailored to the specific task, such as retrieval or classification. * The model is optimized for the Korean language and cultural context, addressing the limitations of previous models that struggled with non-English data. ### Advanced Training for Scalability and Precision * **Gradient Caching:** To overcome GPU memory limitations, this technique allows the model to train with effectively large batch sizes, which is critical for the InfoNCE loss used in contrastive learning. * **Matryoshka Representation Learning (MRL):** The model supports flexible embedding sizes ranging from 64 to 2,048 dimensions. This allows services to choose between low-latency (smaller dimensions) or high-precision (larger dimensions) without retraining. * **Hard Negative Mining:** The training process incorporates "hard negatives"—items that are similar but incorrect—to sharpen the model’s ability to distinguish between subtle differences in data. ### Performance Benchmarks and Efficiency * Kanana-v-embedding significantly outperforms CLIP and VLM2Vec on the KoEmbed benchmark, particularly in Korean Text-to-Image and Image-to-Text retrieval tasks. * In the M-BEIR (Multimodal Benchmark for Retrieval), the model demonstrated superior performance in multimodal document retrieval and image-to-text tasks compared to established open-source models. * Evaluation of MRL showed that the model retains high accuracy even when dimensions are reduced to 256 or 512, providing a 4x to 8x improvement in storage and search efficiency with minimal loss in quality. For organizations looking to implement multimodal RAG or advanced recommendation systems in Korean-language environments, Kanana-v-embedding offers a highly adaptable solution. Its ability to balance computational cost and retrieval quality through Matryoshka learning makes it particularly suitable for large-scale production environments where latency is a primary concern.

kakaoOriginal article

The Evolution of Kanana-o Toward (opens in new tab)

Kakao has significantly advanced its integrated multimodal model, Kanana-o, by enhancing its ability to process complex instructions across text, image, and audio inputs while enriching its emotional vocal expression. By developing specialized datasets and sophisticated training techniques for prosody, the team has bridged the performance gap between text and audio modalities. The result is a more natural, human-like AI capable of nuanced interaction and high-performance instruction following, particularly within the Korean linguistic context. ## Advancing Multimodal Instruction Following * Addressed the "modality gap" where multimodal models often show decreased reasoning and reasoning performance when processing audio inputs compared to text. * Constructed a structured, high-quality dataset featuring complex, multi-step instructions such as summarizing a context and then translating it into a specific language or style. * Leveraged the Speech-KoMT-Bench to evaluate performance, showing that Kanana-o significantly outperforms global competitors of similar scale in Korean-specific tasks. * Focused on "Domain-generalization" to ensure the model's core intelligence remains stable regardless of whether the input is text, audio, or a combination of both. ## Image-Audio-Text Modality Alignment * Developed integrated datasets to ensure that reasoning capabilities learned in text-image or text-audio contexts generalize to complex image-audio scenarios. * Trained the model to handle tasks where users ask questions about visual information via voice, requiring the simultaneous alignment of three different data types. * Prioritized the maintenance of "World Knowledge" during multimodal training so that the addition of new modalities does not degrade the model’s factual accuracy. ## Enhancing Vocal Expressiveness and Prosody * Focused on "prosody"—the rhythm, pitch, and stress of speech—to move beyond robotic, flat text-to-speech (TTS) outputs. * Implemented a system of descriptive tokens and emotion tags (e.g., "warm voice," "excited tone") during training to give the model fine-grained control over its vocal persona. * Incorporated natural human speech elements, such as realistic breathing patterns and contextual variations in speech speed, to make interactions feel more intuitive and less synthetic. * Refined the model's ability to interpret the user's emotional state from their voice and respond with a matching emotional intensity. The evolution of Kanana-o highlights a shift from simply maximizing generic benchmarks to optimizing real-world user experiences through multimodal alignment and emotional intelligence. The success of this model underscores the necessity of high-quality, structured instruction data and fine-grained control over output styles to create truly conversational AI that feels natural to the user.

googleOriginal article

Spotlight on innovation: Google-sponsored Data Science for Health Ideathon across Africa (opens in new tab)

Google Research, in partnership with several pan-African machine learning communities, recently concluded the Africa-wide Data Science for Health Ideathon to address regional medical challenges. By providing access to specialized open-source health models and technical mentorship, the initiative empowered local researchers to develop tailored solutions for issues ranging from maternal health to oncology. The event demonstrated that localized innovation, supported by high-performance AI foundations, can effectively bridge healthcare gaps in resource-constrained environments. ## Collaborative Framework and Objectives * The Ideathon was launched at the 2025 Deep Learning Indaba in Kigali, Rwanda, in collaboration with SisonkeBiotik, Ro’ya, and DS-I Africa. * The primary goal was to foster capacity building within the African AI community, moving beyond theoretical research toward the execution of practical healthcare tools. * Participants received hands-on training on Google’s specialized health models and were supported with Google Cloud Vertex AI compute credits and mentorship from global experts. * Submissions were evaluated based on their innovation, technical feasibility, and contextual relevance to African health systems. ## Technical Foundations and Google Health Models * Developers focused on a suite of open health AI models, including MedGemma for clinical reasoning, TxGemma for therapeutics, and MedSigLIP for medical vision-language tasks. * The competition utilized a two-phase journey: an initial "Idea Development" stage where teams defined clinical problems and outlined AI approaches, followed by a "Prototype & Pitch" phase. * Technical implementations frequently involved advanced techniques such as Retrieval-Augmented Generation (RAG) to ensure alignment with local medical protocols and WHO guidelines. * Fine-tuning methods, specifically Low-Rank Adaptation (LoRA), were utilized by teams to specialize large-scale models like MedGemma-27B-IT for niche datasets. ## Innovative Solutions for Regional Health * **Dawa Health:** This first-place winner developed an AI-powered cervical cancer screening tool that uses MedSigLIP to identify abnormalities in colposcopy images uploaded via WhatsApp, combined with Gemini RAG for clinical guidance. * **Solver (CerviScreen AI):** This team built a web application for automated cervical-cytology screening by fine-tuning MedGemma-27B-IT on the CRIC dataset to assist cytopathologists with annotated images. * **Mkunga:** A maternal health call center that adapts MedGemma and Gemini to provide advice in Swahili using Speech-to-Text (STT) and Text-to-Speech (TTS) technologies. * **HexAI (DermaDetect):** Recognized for the best proof-of-concept, this offline-first mobile app allows community health workers to triage skin conditions using on-device versions of MedSigLIP, specifically designed for low-connectivity areas. The success of the Ideathon underscores the importance of "local solutions for local priorities." By making sophisticated models like MedGemma and MedSigLIP openly available, the technical barrier to entry is lowered, allowing African developers to build high-impact, culturally and linguistically relevant medical tools. For organizations looking to implement AI in global health, this model of providing foundational tools and cloud resources to local experts remains a highly effective strategy for sustainable innovation.

figma2 min readCurated summary

Hard Problems Are Still Hard: A Story About the Tools That Change and the Work That Doesn't | Figma Blog

Nikolas Klein reflects on seven years building Figma’s prototyping tools and how AI transformed the design landscape. While AI changes the tools and accelerates execution, it does not eliminate the difficult work of framing problems, making judgments, iterating, and collaborating. The central conclusion is that AI is another tool in the designer’s toolbox—not a replacement for human insight. ## A Career in Prototyping - Klein describes moving from Figma designer to product manager. - His work centered on building interactive prototyping tools over several years. - Prototyping involved continual experimentation, iteration, and collaboration between designers and engineers. ## AI Changes the Tools - The arrival of AI prompts fundamental questions about the future role of designers and prototyping. - AI can generate ideas and help produce outputs more quickly. - The shift can feel overwhelming because it changes familiar workflows and raises uncertainty about which skills remain valuable. - Klein ultimately frames AI as an addition to the existing toolbox rather than a complete replacement for traditional design tools. ## The Work That Remains Difficult - AI does not remove the need to understand the underlying problem. - Designers still need to exercise judgment, evaluate alternatives, and decide what should be built. - Complex work depends on iteration, animation, interaction design, and close collaboration. - Human expertise remains important for turning vague ideas into coherent, useful experiences. ## Collaboration and the Future - Designers and engineers continue to contribute complementary perspectives. - AI can work alongside people, supporting exploration while humans provide direction and context. - The tools may evolve rapidly, but the challenge of solving meaningful, complex problems remains. The practical recommendation is to adopt AI as part of a broader creative workflow while continuing to develop the judgment, collaboration, and problem-solving skills that difficult design work requires.

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stripe2 min readCurated summary

Introducing the Agentic Commerce Suite: A complete solution for selling on AI agents

Stripe’s Agentic Commerce Suite is designed to help businesses sell through AI agents without building separate integrations for every platform. It provides product discovery, checkout, payments, fraud protection, and order-event handling through a single, modular integration while allowing merchants to retain their existing commerce systems. The suite is rolling out through Stripe, ecommerce platforms, and omnichannel commerce providers. ## The Integration Challenge - Supporting each AI agent can require up to six months of work. - Businesses otherwise need to maintain: - Public, versioned ACP endpoints - Agent-specific catalogs and APIs - Access controls and commerce-stack integrations - The Agentic Commerce Suite aims to standardize these requirements. ## Making Products Discoverable - Merchants connect their product catalog to Stripe or import it from supported product syndicators. - Stripe provides a hosted Agentic Commerce Protocol (ACP) endpoint. - Product, pricing, and availability data can be shared with AI agents in near real time. - Merchants can select supported AI agents in the Stripe Dashboard and enable payments with minimal additional work. ## Simplifying Checkout and Order Management - Stripe Checkout Sessions API supports agentic checkout, including taxes and shipping. - Businesses can use Stripe Tax and other Stripe products or continue using their existing systems for: - Tax codes - Inventory checks - Dynamic shipping rates - Existing order management and fulfillment workflows remain in place. - Merchants stay responsible for customer relationships, refunds, and disputes as the merchant of record. ## Agentic Payments and Fraud Protection - AI-agent transactions introduce different fraud risks because automated traffic can resemble suspicious activity or be manipulated by attackers. - The suite supports Shared Payment Tokens (SPTs), which let agents use a buyer’s saved payment method without exposing payment credentials. - SPTs can be restricted by: - Seller - Time period - Transaction amount - Tokens are observable throughout their lifecycle to help prevent unauthorized actions and disputes. - Stripe Radar can evaluate risk signals such as card testing, stolen cards, issuer declines, and likely fraudulent disputes. ## Availability and Adoption - Early participants include Etsy, URBN brands, Ashley Furniture, Coach, Kate Spade, Revolve, and others. - The suite will be available through: - Stripe Dashboard and APIs - Wix, WooCommerce, BigCommerce, Squarespace, and commercetools - Omnichannel platforms including Akeneo, Mirakl, Pipe17, and Rithum - Businesses can join the waitlist and consult Stripe’s integration guides. Businesses seeking to sell through AI agents can use the suite to avoid bespoke integrations while preserving their existing catalog, checkout, fulfillment, and customer-service operations.

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woowahanOriginal article

Enhancing the “Frequently Bought (opens in new tab)

Baedal Minjok (Baemin) has significantly improved its cart recommendation system by transitioning from a basic Item2Vec model to a sophisticated two-stage architecture that combines graph-based embeddings with Transformer sequence modeling. This evolution addresses the "substitutability bias" and lack of sequential context found in previous methods, allowing the system to understand the specific intent behind a user's shopping journey. By moving beyond simple item similarity, the new model effectively identifies cross-selling opportunities that align with the logical flow of a customer's purchase behavior. ### Limitations of the Item2Vec Approach * **Substitutability Bias:** The original Item2Vec model, based on the Skip-gram architecture, tended to map items from the same category into similar vector spaces. This resulted in recommending alternative brands of the same product (e.g., suggesting another brand of milk) rather than complementary goods (e.g., cereal or bread). * **Loss of Sequential Context:** Because Item2Vec treats a basket of goods as a "bag of words," it ignores the order in which items are added. This prevents the model from distinguishing between different user intents, such as a user starting with meat to grill versus a user starting with ingredients for a stew. * **Failure in Cross-Selling:** The primary goal of cart recommendations is to encourage cross-selling, but the reliance on embedding similarity alone limited the diversity of suggestions, often trapping users within a single product category. ### Stage 1: Graph-Based Product and Category Embeddings * **Node2Vec Implementation:** To combat data sparsity and the "long-tail" problem where many items have low purchase frequency, the team utilized Node2Vec. This method uses random walks to generate sequences that help the model learn structural relationships even when direct transaction data is thin. * **Heterogeneous Graph Construction:** The graph consists of both "Item Nodes" and "Category Nodes." Connecting items to their respective categories allows the system to generate initial vectors for new or low-volume products that lack sufficient historical purchase data. * **Association Rule Weighting:** Rather than using simple co-occurrence counts for edge weights, the team applied Association Rules. This ensures that weights reflect the actual strength of the complementary relationship, preventing popular "mega-hit" items from dominating all recommendation results. ### Stage 2: Transformer-Based Sequence Recommendation * **Capturing Purchase Context:** The second stage employs a Transformer model to analyze the sequence of items currently in the user's cart. This architecture is specifically designed to understand how the meaning of an item changes based on what preceded it. * **Next Item Prediction:** Using the pre-trained embeddings from Stage 1 as inputs, the Transformer predicts the most likely "next item" a user will add. This allows the system to provide dynamic recommendations that evolve as the user continues to shop. * **Integration of Category Data:** By feeding both item-level and category-level embeddings into the Transformer, the model maintains a high level of accuracy even when a user interacts with niche products, as the category context provides a fallback for the recommendation logic. ### Practical Conclusion For production-scale recommendation systems, relying solely on item similarity often leads to redundant suggestions that do not drive incremental sales. By decoupling the learning of structural relationships (via graphs) from the learning of temporal intent (via Transformers), engineers can build a system that is robust against data sparsity while remaining highly sensitive to the immediate context of a user's session. This two-stage approach is recommended for e-commerce environments where cross-category discovery is a key business metric.

figma2 min readCurated summary

Figma Make Is Now Available in Figma for Government | Figma Blog

Figma Make is now available in Figma for Government, giving federal agencies a secure way to create and test interactive prototypes. Figma argues that faster, collaborative design can help modernize public services, improve accessibility, and build public trust. The feature is available within Figma’s FedRAMP Moderate–authorized environment, although some cloud infrastructure will not reach full compliance until 2026. ## Modernizing Public Services - Government teams are improving services such as public portals, operational systems, benefits platforms, and research tools. - The online passport renewal system demonstrates the potential impact: by 2025, more than two million people had renewed passports online, with 97% reporting a positive experience. - Public-sector projects are difficult because policymakers, technologists, and operational leaders must coordinate under changing requirements and limited resources. - A 2025 executive order highlighted usability, accessibility, and design consistency as essential to citizen-facing services. ## Faster Prototyping and Collaboration - Figma Make enables teams to turn ideas into interactive prototypes in hours rather than weeks. - Designers, policy leaders, researchers, engineers, and IT staff can collaborate in a shared workspace. - Teams can test workflows and validate assumptions with citizens before development begins. - Shared libraries, integrations, accessibility standards, and design patterns help agencies maintain consistency. - Prototyping supports a wide range of initiatives, including federal benefits, financial communications, education platforms, space systems, and internal agency tools. ## Security and Compliance - Figma Make is included in Figma for Government and operates within its authorized environment. - Figma for Government is FedRAMP Moderate authorized, providing agencies with a compliant foundation for design and prototyping. - Figma notes that part of its cloud infrastructure is expected to achieve full FedRAMP Moderate compliance in 2026, while Make is usable today within the authorized environment. ## Broader Government Design Efforts - Federal agencies are already using Figma to modernize complex public services. - Shared design systems can improve accessibility and consistency across agencies and products. - Examples such as the National Park Service’s digital app illustrate how small teams can transform large, paper-based systems into accessible digital experiences. Figma positions Make as a practical tool for helping agencies adopt faster, citizen-first design processes. Government teams can use it to explore ideas early, collaborate across disciplines, and test accessible services before committing development resources.

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tossOriginal article

Legacy Settlement Modernization: From the (opens in new tab)

Toss Payments recently overhauled its 20-year-old legacy settlement system to overcome deep-seated technical debt and prepare for massive transaction growth. By shifting from monolithic SQL queries and aggregated data to a granular, object-oriented architecture, the team significantly improved system maintainability, traceability, and batch processing performance. The transition focused on breaking down complex dependencies and ensuring that every transaction is verifiable and reproducible. ### Replacing Monolithic SQL with Object-Oriented Logic * The legacy system relied on a "giant common query" filled with nested `DECODE`, `CASE WHEN`, and complex joins, making it nearly impossible to identify the impact of small changes. * The team applied a "Divide and Conquer" strategy, splitting the massive query into distinct domains and refined sub-functions. * Business logic was moved from the database layer into Kotlin-based objects (e.g., `SettlementFeeCalculator`), making business rules explicit and easier to test. * This modular approach allowed for "Incremental Migration," where specific features (like exchange rate conversions) could be upgraded to the new system independently. ### Improving Traceability through Granular Data Modeling * The old system stored data in an aggregated state (Sum), which prevented developers from tracing errors back to specific transactions or reusing data for different reporting needs. * The new architecture manages data at the minimum transaction unit (1:1), ensuring that every settlement result corresponds to a specific transaction. * "Setting Snapshots" were introduced to store the exact contract conditions (fee rates, VAT status) at the time of calculation, allowing the system to reconstruct the context of past settlements. * A state-based processing model was implemented to enable selective retries for failed transactions, significantly reducing recovery time compared to the previous "all-or-nothing" transaction approach. ### Optimizing High-Resolution Data and Query Performance * Managing data at the transaction level led to an explosion in data volume, necessitating specialized database strategies. * The team implemented date-based Range Partitioning and composite indexing on settlement dates to maintain high query speeds despite the increased scale. * To balance write performance and read needs, they created "Query-specific tables" that offload the processing burden from the main batch system. * Complex administrative queries were delegated to a separate high-performance data serving platform, maintaining a clean separation between core settlement logic and flexible data analysis. ### Resolving Batch Performance and I/O Bottlenecks * The legacy batch system struggled with long processing times that scaled poorly with transaction growth due to heavy I/O and single-threaded processing. * I/O was minimized by caching merchant contract information in memory at the start of a batch step, eliminating millions of redundant database lookups. * The team optimized the `ItemProcessor` in Spring Batch by implementing bulk lookups (using a Wrapper structure) to handle multiple records at once rather than querying the database for every individual item. This modernization demonstrates that scaling a financial system requires moving beyond "convenient" aggregations toward a granular, state-driven architecture. By decoupling business logic from the database and prioritizing data traceability, Toss Payments has built a foundation capable of handling the next generation of transaction volumes.

figma2 min readCurated summary

Turn Your Prompts Into Visual Assets and Slide Decks With the Figma App in ChatGPT | Figma Blog

Figma has expanded its ChatGPT app beyond FigJam diagrams, allowing users to generate editable visual assets and presentation decks. ChatGPT provides initial creative directions, while Figma Buzz and Figma Slides support detailed editing, branding, resizing, and collaboration. The workflow is designed to move quickly from an idea or outline to a polished, team-ready result. ## Creating Visual Assets with Figma Buzz - Users can prompt ChatGPT to generate starting concepts for posters, invitations, digital ads, and video collages. - The app can produce multiple variations of a visual identity, layout, and copy. - Assets can then be opened in Figma Buzz for refinement, including: - Editing text and typography - Adjusting color palettes with blend modes - Applying brand-specific changes - Resizing designs for LinkedIn, X, Instagram, and other platforms - Figma Buzz is intended to make polished marketing assets accessible even to people without specialized design skills. ## Building Presentations in Figma Slides - ChatGPT can generate a Figma Slides deck from a user-provided outline or suggest a presentation flow. - Example decks include “year in review” templates organized around goals, priorities, highlights, and completed projects. - Users can select a generated option and continue editing collaboratively in Figma Slides. - Teams can replace generated images, adjust templates, and refine colors and typography to match brand guidelines. - Interactive features such as live polls, stamps, and alignment scales support feedback and discussion during presentations. ## Availability - The Figma app is available to ChatGPT users outside the European Union. - Figma Buzz and Figma Slides features are currently in beta within the app. The recommended workflow is to use ChatGPT for rapid ideation and first drafts, then move into Figma Buzz or Figma Slides for brand alignment, detailed editing, and collaborative refinement.

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woowahanOriginal article

We refactor culture just like code. (opens in new tab)

The Commerce Web Frontend Development team at Woowa Brothers recently underwent a significant organizational "refactoring" to manage the increasing complexity of their expanding commerce platform. By moving away from rigid, siloed roles and adopting a flexible "boundary-less" part system, the team successfully synchronized disparate services like B Mart and Baemin Store. This cultural shift demonstrates that treating organizational structure with the same iterative mindset as code can eliminate operational bottlenecks and foster a more resilient engineering environment. ### Transitioning to Boundary-less Parts * The team abandoned traditional division methods—such as project-based, funnel-based, or service-vs-backoffice splits—because they created resource imbalances and restricted developers' understanding of the overall service flow. * Traditional project-based splits often led to specific teams being overwhelmed during peak periods while others remained underutilized, creating significant delivery bottlenecks. * To solve these inefficiencies, the team introduced "boundary-less parts," where developers are not strictly tied to a single domain but are encouraged to work across the entire commerce ecosystem. * This structure allows the organization to remain agile, moving resources fluidly to address high-priority business needs without being hindered by departmental "walls." ### From R&R to Responsibility and Expandability (R&E) * The team replaced the traditional R&R (Role & Responsibility) model with "R&E" (Responsibility & Expandability), focusing on the core principle of "owning" a problem until it is fully resolved. * This shift encourages developers to expand their expertise beyond their immediate tasks, fostering a culture where helping colleagues and understanding neighboring domains is the standard. * Work is distributed through a strategic sync between team and part leaders, but team members maintain the flexibility to jump into different domains as project requirements evolve. * Regular "part shuffling" is utilized to ensure that domain knowledge is distributed across the entire 20-person frontend team, preventing the formation of information silos. ### Impact on Technical Integration and Team Resilience * The flexible structure was instrumental in the "ONE COMMERCE" initiative, which required integrating the technical stacks and user experiences of B Mart and Baemin Store. * Because developers had broad domain context, they were able to identify redundant logic across different services and abstract them into shared, common modules, ensuring architectural consistency. * The organization significantly improved its "Bus Factor"—the number of people who can leave before a project stalls—by ensuring multiple engineers understand the context of any given system. * Developers evolved into "domain-wide engineers" who understand the full lifecycle of a transaction, from the customer-facing UI to the backend administrative and logistics data flows. To prevent today's organizational solutions from becoming tomorrow's cultural legacy debt, engineering teams should proactively refactor their workflows. Moving from rigid role definitions to a model based on shared responsibility and cross-domain mobility is essential for maintaining velocity and technical excellence in large-scale platform environments.

daangnOriginal article

Drawing a Karrot Data Map: (opens in new tab)

Daangn’s data governance team addressed the lack of transparency in their data pipelines by building a column-level lineage system using SQL parsing. By analyzing BigQuery query logs with specialized parsing tools, they successfully mapped intricate data dependencies that standard table-level tracking could not capture. This system now enables precise impact analysis and significantly improves data reliability and troubleshooting speed across the organization. **The Necessity of Column-Level Visibility** * Table-level lineage, while easily accessible via BigQuery’s `JOBS` view, fails to identify how specific fields—such as PII or calculated metrics—propagate through downstream systems. * Without granular lineage, the team faced "cascading failures" where a single pipeline error triggered a chain of broken tables that were difficult to trace manually. * Schema migrations, such as modifying a source MySQL column, were historically high-risk because the impact on derivative BigQuery tables and columns was unknown. **Evaluating Extraction Strategies** * BigQuery’s native `INFORMATION_SCHEMA` was found to be insufficient because it does not support column-level detail and often obscures original source tables when Views are involved. * Frameworks like OpenLineage were considered but rejected due to high operational costs; requiring every team to instrument their own Airflow jobs or notebooks was deemed impractical for a central governance team. * The team chose a centralized SQL parsing approach, leveraging the fact that nearly all data transformations within the company are executed as SQL queries within BigQuery. **Technical Implementation and Tech Stack** * **sqlglot:** This library serves as the core engine, parsing SQL strings into Abstract Syntax Trees (AST) to programmatically identify source and destination columns. * **Data Collection:** The system pulls raw query text from `INFORMATION_SCHEMA.JOBS` across all Google Cloud projects to ensure comprehensive coverage. * **Processing and Orchestration:** Spark is utilized to handle the parallel processing of massive query logs, while Airflow schedules regular updates to the lineage data. * **Storage:** The resulting mappings are stored in a centralized BigQuery table (`data_catalog.lineage`), making the dependency map easily accessible for impact analysis and data cataloging. By centralizing lineage extraction through SQL parsing rather than per-job instrumentation, organizations can achieve comprehensive visibility without placing an integration burden on individual developers. This approach is highly effective for BigQuery-centric environments where SQL is the primary language for data movement and transformation.

lineOriginal article

Why an Athenz Engineer Took (opens in new tab)

Security platform engineer Jung-woo Kim details his transition from a specialized Athenz developer to a "Kubestronaut," a prestigious CNCF designation awarded to those who master the entire Kubernetes ecosystem. By systematically obtaining five distinct certifications, he argues that deep, practical knowledge of container orchestration is essential for building secure, scalable access control systems in private cloud environments. His journey demonstrates that moving beyond application-level expertise to master cluster administration and security directly improves architectural design and operational troubleshooting. ## The Kubestronaut Framework * The title is awarded by the Cloud Native Computing Foundation (CNCF) to individuals who pass five specific certification exams: CKA, CKAD, CKS, KCNA, and KCSA. * The CKA (Administrator), CKAD (Application Developer), and CKS (Security Specialist) exams are performance-based, requiring candidates to solve real-world technical problems in a live terminal environment rather than answering multiple-choice questions. * Success in these exams demands a combination of deep technical knowledge, speed, and accuracy, as practitioners must configure clusters and resolve failures under strict time constraints. * The remaining Associate-level exams (KCNA and KCSA) provide a theoretical foundation in cloud-native security and ecosystem standards. ## A Progressive Path to Technical Mastery * **CKAD (Application Developer):** The initial focus was on mastering the deployment of Athenz—an open-source auth system—ensuring it runs efficiently from a developer's perspective. Preparation involved rigorous use of tools like killer.sh to simulate high-pressure environments. * **CKA (Administrator):** To manage multi-cluster environments and understand the underlying components that make Kubernetes function, the author moved to the administrator level, gaining insight into how various services interact within the cluster. * **CKS (Security Specialist):** Given his background in security, this was the most critical and difficult stage, focusing on cluster hardening, vulnerability analysis, and implementing strict network policies to ensure the entire infrastructure remains resilient. ## Organizational Impact and Open Source Governance * Obtaining these certifications provided a clearer understanding of open-source governance, specifically how Special Interest Groups (SIGs) and pull request (PR) workflows drive massive projects like Kubernetes. * This technical depth was applied to a high-stakes project providing Athenz services in a Bare Metal as a Service (BMaaS) environment, allowing for more stable and efficient architecture design. * The learning process was supported by corporate initiatives, including access to Udemy Business for technical training and a hybrid work culture that allowed for consistent, early-morning study habits. To achieve expert-level proficiency in complex systems like Kubernetes, engineers should adopt the "Ubo-cheonri" philosophy—making slow but steady progress. Starting with even one minute of study or a single GitHub commit per day can eventually lead to mastering the highest levels of cloud-native architecture. For those managing enterprise-grade infrastructure, pursuing the Kubestronaut path is highly recommended as it transforms theoretical knowledge into a broad, practical vision for system design.

pinterest3 min readCurated summary

LLM-Powered Relevance Assessment for Pinterest Search

Pinterest Search uses fine-tuned multilingual LLMs to assess search-result relevance at a much larger scale than human labeling allows. The approach combines five-level relevance classification, stratified query sampling, and paired A/B-test evaluation to detect smaller overall effects and differences across query types. XLM-RoBERTa-large provides a practical balance of accuracy and cost, achieving strong agreement with human judgments while enabling substantially faster labeling. ## Relevance Measurement Challenges - Search relevance measures how well Pins satisfy a user’s query, rather than merely reflecting past engagement. - Human annotations are expensive and limited in volume. - Previous sampling designs could detect only relatively large topline changes, with minimum detectable effects (MDEs) around 1.3%–1.5%. - Limited labels also made it difficult to measure heterogeneous effects across query interests or popularity segments. ## Fine-Tuned LLM Relevance Model - Pinterest defines relevance using five labels: - L5: Highly Relevant - L4: Relevant - L3: Marginally Relevant - L2: Irrelevant - L1: Highly Irrelevant - A cross-encoder model predicts the relevance of each Pin for a query. - Open-source multilingual models are fine-tuned on human-annotated examples using multiclass cross-entropy loss. - Pin representations include: - Titles and descriptions - BLIP-generated image captions - Linked-page titles and descriptions - Board titles where Pins were saved - Highly engaged query tokens associated with the Pin - Models tested included multilingual BERT, T5, mDeBERTa, XLM-RoBERTa, and Llama 3. - The final relevance label is selected from the model’s five output scores using argmax. ## Stratified Query Sampling - Lower LLM labeling costs allow Pinterest to use much larger and more detailed samples. - Queries are stratified using: - A DistilBERT-based query-to-interest model - Query popularity, based on how many users issue each query - Stratification improves representativeness and reduces variance by grouping similar queries. - Pinterest moved from simple random sampling to stratified sampling with optimal allocation across strata. - Most of the MDE improvement came from variance reduction through stratification. - The redesigned process reduced MDEs from approximately 1.3%–1.5% to 0.25% or less. ## LLM-Based A/B-Test Measurement - Pinterest samples paired queries from control and treatment groups. - Pairing controls for differences between queries, which are a major source of relevance variance. - For each query, the top 25 results are retained and labeled by the LLM. - Query-level relevance is measured using sDCG@25, a variant of nDCG that assumes an unlimited supply of highly relevant L5 results. - Results are aggregated into topline experiment metrics. - Heterogeneous effects are analyzed by query popularity and interest categories such as beauty, fashion, and art. - The Benjamini–Hochberg procedure controls the false discovery rate when testing multiple segments. ## Model Choice and Validation - XLM-RoBERTa-large was selected for its balance of quality and efficiency. - On a single A10G GPU, it can label 150,000 rows in about 30 minutes. - Llama 3–8B produced slightly better accuracy but required roughly six times the inference time and cost. - LLM labels matched human labels exactly for 73.7% of Pins. - A total of 91.7% of predictions differed from human ratings by no more than one relevance point. Pinterest’s approach makes relevance evaluation cheaper, faster, and more statistically sensitive. Fine-tuned LLMs paired with stratified sampling are recommended for search experimentation when human labeling cannot provide enough coverage to detect small or heterogeneous ranking effects.

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Improving Work Efficiency: Revisiting the Small (opens in new tab)

The Toss Research Platform team argues that operational efficiency is best achieved by decomposing complex workflows into granular, atomic actions rather than attempting massive systemic overhauls. By systematically questioning the necessity of each step and prioritizing improvements based on stakeholder impact rather than personal workload, teams can eliminate significant waste through incremental automation. This approach demonstrates that even minor reductions in repetitive manual tasks can lead to substantial gains in team-wide productivity as an organization scales. ### Granular Action Mapping * Break down workflows into specific physical or digital actions—such as clicks, data entries, and channel switches—rather than high-level phases. * Document the "Who, Where, What, and Why" for every individual step to identify exactly where friction occurs. * Include exception cases and edge scenarios in the process map to uncover hidden gaps in the current operating model. ### Questioning Necessity and Identifying Automation Targets * Apply a critical filter to every mapped action by asking, "Why is this necessary?" to eliminate redundant tasks like manual cross-platform notifications. * Distinguish between essential human-centric tasks and mechanical actions, such as calendar entry creation, that are ripe for automation. * Address "micro-inefficiencies" that appear insignificant in isolation but aggregate into major resource drains when repeated multiple times daily across a large team. ### Stakeholder-Centric Prioritization * Shift the criteria for optimization from personal convenience to the impact on the broader organization. * Rank improvements based on three specific metrics: the number of people affected, the downstream influence on other workflows, and the total cumulative time consumed. * Recognize that automating a "small" task for an operator can unlock significant time and clarity for dozens of participants and observers. ### Incremental Implementation and Risk Mitigation * Avoid the "all-or-nothing" automation trap by deploying partial solutions that address solvable segments of a process immediately. * Utilize designated test periods for process changes to monitor for risks, such as team members missing interviews due to altered notification schedules. * Gather continuous feedback from stakeholders during small-scale experiments, allowing for iterative adjustments or quick reversals before a full rollout. To scale operations effectively, start by breaking your current workload into its smallest possible components and identifying the most frequent manual repetitions. True efficiency often comes from these small, validated adjustments and consistent feedback loops rather than waiting to build a perfect, fully automated end-to-end system.