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Code Quality Improvement Techniques Part 30 (opens in new tab)

Code quality often suffers when functions share implicit dependencies, where the correct behavior of one relies on the state or validation provided by another. This "invisible" connection creates fragile code that is prone to runtime errors and logic mismatches during refactoring or feature expansion. To solve this, developers should consolidate related logic or make dependencies explicit to ensure consistency and safety. ## Problems with Implicit Function Dependencies When logic is split across separate functions—such as one for validation (`isContentValid`) and another for processing (`getMessageText`)—developers often rely on undocumented preconditions. * **Fragile Runtime Safety:** In the provided example, `getMessageText` throws a runtime error if called on invalid data, assuming the caller has already checked `isContentValid`. * **Maintenance Burden:** When new data types (e.g., a new message type) are added, developers must remember to update both functions to keep them in sync, increasing the risk of "forgotten" updates. * **Hidden Logic Flow:** Callers might not realize the two functions are linked, leading to improper usage where the transformation function is called without the necessary prior validation. ## Consolidating Logic for Single-Source Truth The most effective way to eliminate implicit dependencies is to merge filtering and transformation into a single function. This ensures that the code cannot reach a processing state without passing through the necessary logic. * **Nullable Returns:** By changing the transformation function to return a nullable type (`String?`), the function can signal that a piece of data is "invalid" or "empty" directly through its return value. * **Simplified Caller Logic:** The UI layer no longer needs to call two separate functions; it simply checks if the result of the transformation is null to determine visibility. * **Elimination of Redundant Branches:** This approach reduces the number of `when` or `if-else` blocks that need to be maintained across the codebase. ## Establishing Explicit Consistency In scenarios where separate functions for validation and transformation are required for clarity or architectural reasons, the validation logic should be defined in terms of the transformation. * **Dependent Validation:** Instead of writing a separate `when` block for `isContentValid`, the function should simply check if `getMessageText` returns a non-null value. * **Guaranteed Synchronization:** This structure makes the relationship between the two functions explicit and guarantees that if a message is deemed "valid," it will always produce a valid text output. * **Improved Documentation:** Defining functions this way serves as self-documenting code, showing future developers exactly how the two operations are linked. When functions share a "red thread" of logic, they should either be merged or structured so that one acts as the source of truth for the other. By removing the need for callers to remember implicit preconditions, you reduce the surface area for bugs and make the codebase significantly easier to extend.

Code Quality Improvement Techniques Part 29 (opens in new tab)

Complexity in software often arises from "Gordian Variables," where tangled data dependencies make the logic flow difficult to trace and maintain. By identifying and designing an ideal intermediate data structure, developers can decouple these dependencies and simplify complex operations. This approach replaces convoluted conditional checks with a clean, structured data flow that highlights the core business logic. ## The Complexity of Tangled Dependencies Synchronizing remote data with local storage often leads to fragmented logic when the relationship between data IDs and objects is not properly managed. * Initial implementations frequently use set operations like `subtract` on ID lists to determine which items to create, update, or delete. * This approach forces the program to re-access original data sets multiple times, creating a disconnected flow between identifying a change and executing it. * Dependency entanglements often necessitate "impossible" runtime error handling (e.g., `error("This must not happen")`) because the compiler cannot guarantee data presence within maps during the update phase. * Inconsistent processing patterns emerge, where "add" and "update" logic might follow one sequence while "delete" logic follows an entirely different one. ## Designing Around Intermediate Data Structures To untangle complex flows, developers should work backward from an ideal data representation that categorizes all possible states—additions, updates, and deletions. * The first step involves creating lookup maps for both remote and local entries to provide O(1) access to data objects. * A unified collection of all unique IDs from both sources serves as the foundation for a single, comprehensive transformation pass. * A specialized utility function, such as `partitionByNullity`, can transform a sequence of data pairs (`Pair<Remote?, Local?>`) into three distinct, non-nullable lists. * This transformation results in a `Triple` containing `createdEntries`, `updatedEntries` (as pairs), and `deletedEntries`, effectively separating data preparation from business execution. ## Improved Synchronization Flow Restructuring the function around categorized lists allows the primary synchronization logic to remain concise and readable. * The synchronization function becomes a sequence of two phases: data categorization followed by execution loops. * By using the `partitionByNullity` pattern, the code eliminates the need for manual null checks or "impossible" error branches during the update process. * The final implementation highlights the most important part of the code—the `forEach` blocks for adding, updating, and deleting—by removing the noise of ID-based lookups and set mathematics. When faced with complex data dependencies, prioritize the creation of a clean intermediate data structure over-optimizing individual logical branches. Designing a data flow that naturally represents the different states of your business logic will result in more robust, self-documenting, and maintainable code.

Building an Enterprise LLM Service 1 (opens in new tab)

LY Corporation’s engineering team developed an AI assistant for their private cloud platform, Flava, by prioritizing "context engineering" over traditional prompt engineering. To manage a complex environment of 260 APIs and hundreds of technical documents, they implemented a strategy of progressive disclosure to ensure the LLM receives only the most relevant information for any given query. This approach allows the assistant to move beyond simple RAG-based document summarization to perform active diagnostics and resource management based on real-time API data. ### Performance Limitations of Long Contexts * Research indicates that LLM performance can drop by 13.9% to 85% as context length increases, even if the model technically supports a large token window. * The phenomenon of "context rot" occurs when low-quality or irrelevant information is mixed into the input, causing the model to generate confident but incorrect answers. * Because LLMs are stateless, maintaining conversation history and processing dense JSON responses from multiple APIs quickly exhausts context windows and degrades reasoning quality. ### Progressive Disclosure and Tool Selection * The system avoids loading all 260+ API definitions at once; instead, it analyzes the user's intent to select only the necessary tools, such as loading only Redis-related APIs when a user asks about a cluster. * Specific product usage hints, such as the distinction between private and CDN settings for Object Storage, are injected only when those specific services are invoked. * This phased approach significantly reduces token consumption and prevents the model from being overwhelmed by irrelevant technical specifications. ### Response Guidelines and the "Mock Tool Message" Strategy * The team distinguished between "System Prompts" (global rules) and "Response Guidelines" (situational instructions), such as directing users to a console UI before suggesting CLI commands. * Injecting specific guidelines into the system prompt often caused "instruction conflict," where the LLM might hallucinate information to satisfy a guideline while ignoring core requirements like using search tools. * To resolve these conflicts, the team utilized "ToolMessages" to inject guidelines; by formatting instructions as if they were results from a tool execution, the LLM treats the information as factual context rather than a command that might override the system prompt. To build a robust enterprise LLM service, developers should focus on dynamic context management rather than static prompt optimization. Treating operational guidelines as external data via mock tool messages, rather than system instructions, provides a scalable way to reduce hallucinations and maintain high performance across hundreds of integrated services.

Code Quality Improvement Techniques Part (opens in new tab)

LY Corporation’s technical review highlights that making a class open for inheritance imposes a "tax" on its internal constraints, particularly immutability. While developers often use inheritance to create specialized versions of a class, doing so with immutable types can allow subclasses to inadvertently or intentionally break the parent class's guarantees. To ensure strict data integrity, the post concludes that classes intended to be immutable should be made final or designed around read-only interfaces rather than open for extension. ### The Risks of Open Immutable Classes * Kotlin developers often wrap `IntArray` in an `ImmutableIntList` to avoid the overhead of boxed types while ensuring the collection remains unchangeable. * If `ImmutableIntList` is marked as `open`, a developer might create a `MutableIntList` subclass that adds a `set` method to modify the internal `protected valueArray`, violating the "Immutable" contract of the parent type. * Even if the internal state is `private`, a subclass can override the `get` method to return dynamic or state-dependent values, effectively breaking the expectation that the data remains constant. * These issues demonstrate that any class with a "fundamental" name should be carefully guarded against unexpected inheritance in different modules or packages. ### Establishing Safe Inheritance Hierarchies * Mutable objects should not inherit from immutable objects, as this inherently violates the immutability constraints established by the parent. * Conversely, immutable objects should not inherit from mutable ones; this often leads to runtime errors (such as `UnsupportedOperationException`) when a user attempts to call modification methods like `add` or `set` on an immutable instance. * The most effective design pattern is to use a "read-only" (unmodifiable) interface as a common parent, similar to how Kotlin distinguishes between `List` and `MutableList`. * In this structure, mutable classes can inherit from the read-only parent without issue (adding new methods), and immutable classes can inherit from the read-only parent while adding stricter internal constraints. To maintain high code quality and prevent logic errors, developers should default to making classes final when immutability is a core requirement. If shared functionality is needed across different types of lists, utilize composition or a shared read-only interface to ensure that the "immutable" label remains a truthful guarantee.

A Business Trip to Japan After Only (opens in new tab)

Joining the Developer Relations (DevRel) team at LINE Plus, a new employee was immediately thrust into a high-stakes business trip to Japan just one week after onboarding to support major global tech events. This immersive experience allowed the recruit to rapidly grasp the company’s engineering culture by facilitating cross-border collaboration and managing large-scale technical conferences. Ultimately, the journey highlights how a proactive onboarding strategy and a culture of creative freedom enable DevRel professionals to bridge the gap between complex engineering feats and community engagement. ### Global Collaboration at Tech Week * The trip centered on participating in **Tech-Verse**, a global conference featuring simultaneous interpretation in Korean, English, and Japanese, where the focus was on maintaining operational detail across diverse technical sessions. * Operational support was provided for **Hack Day**, an in-house hackathon that brought together engineers from various countries to collaborate on rapid prototyping and technical problem-solving. * The experience facilitated direct coordination with DevRel teams from Japan, Thailand, Taiwan, and Vietnam, establishing a unified approach to technical branding and regional community support. * Post-event responsibilities included translating live experiences into digital assets, such as "Shorts" video content and technical blog recaps, to maintain engagement after the physical event concluded. ### Modernizing Internal Technical Sharing * The **Tech Talk** series, a long-standing tradition with over 78 sessions, was used as a platform to experiment with "B-grade" humorous marketing—including quirky posters and cup holders—to drive offline participation in a remote-friendly work environment. * To address engineer feedback, the format shifted from passive lectures to **hands-on practical sessions** focusing on AI implementation. * Specific technical workshops demonstrated how to use tools like **Claude Code** and **ChatGPT** to automate workflows, such as generating weekly reports by integrating **Jira tickets with internal Wikis**. * Preparation for these sessions involved creating detailed environment setup guides and troubleshooting protocols to ensure a seamless experience for participating developers. ### Scaling AI Literacy via AI Campus Day * The **AI Campus Day** was a large-scale event designed for over 3,000 participants, aimed at lowering the barrier to entry for AI adoption across all departments. * The "Event & Operation" role involved creating interactive AI photo zones using **Gemini** to familiarize employees with new internal AI tools in a low-pressure setting. * Event production utilized AI-driven assets, including AI-generated voices and icons, to demonstrate the practical utility of these tools within standard business communication and video guides. * The success of the event relied on "participation design," ensuring that even non-technical staff could engage with AI concepts through hands-on play and peer mentoring. For organizations looking to strengthen their technical culture, this experience suggests that integrating new hires into high-impact global projects immediately can be a powerful onboarding tool. Providing DevRel teams the psychological safety to experiment with unconventional marketing and hands-on technical workshops is essential for maintaining developer engagement in a hybrid work era.

Code Quality Improvement Techniques Part 27 (opens in new tab)

Over-engineering through excessive Dependency Injection (DI) can introduce unnecessary complexity and obscure a system's logic. While DI is a powerful tool for modularity, applying it to simple utility functions or data models often creates a maintenance burden without providing tangible benefits. Developers should aim to balance flexibility with simplicity by only injecting dependencies that serve a specific architectural purpose. ### The Risks of Excessive Dependency Injection Injecting every component, including simple formatters and model factories, can lead to several technical issues that degrade code maintainability: * **Obscured Logic Flow:** When utilities are hidden behind interfaces and injected via constructors, tracing the actual execution path requires navigating through multiple callers and implementation files, making the code harder to read. * **Increased Caller Responsibility:** Requiring dependencies for every small component forces the calling class to manage a "bloated" set of objects, often leading to a chain reaction where high-level classes must resolve dozens of unrelated dependencies. * **Data Inconsistency:** Injecting multiple utilities that rely on a shared state (like a `Locale`) creates a risk where a caller might accidentally pass mismatched configurations to different components, breaking the expected association between values. ### Valid Use Cases for Dependency Injection DI should be reserved for scenarios where the benefits of abstraction outweigh the cost of complexity. Proper use cases include: * **Lifecycle and Scope Management:** Sharing objects with specific lifecycles, such as those managing global state or cross-cutting concerns. * **Dependency Inversion:** Breaking circular dependencies between modules or ensuring the code adheres to specific architectural boundaries (e.g., Clean Architecture). * **Implementation Switching:** Enabling the replacement of components for different environments, such as swapping a real network repository for a mock implementation during unit testing or debugging. * **Decoupling for Build Performance:** Separating implementations into different modules to improve incremental build speeds or to isolate proprietary third-party libraries. ### Strategies for Refactoring and Simplification To improve code quality, developers should identify "transparent" dependencies that can be internalized or simplified: * **Direct Instantiation:** For simple data models like `NewsSnippet`, replace factory functions with direct constructor calls to clarify the intent and reduce boilerplate. * **Internalize Simple Utilities:** Classes like `TimeTextFormatter` or `StringTruncator` that perform basic logic can be maintained as private properties within the class or as stateless `object` singletons rather than being injected. * **Selective Injection:** Reserve constructor parameters for complex objects (e.g., repositories that handle network or database access) and environment-dependent values (e.g., a user's `Locale`). The core principle for maintaining a clean codebase is to ensure every injected dependency has a clear, documented purpose. By avoiding the trap of "injecting everything by default," developers can create systems that are easier to trace, test, and maintain.

We held AI Campus Day to (opens in new tab)

LY Corporation recently hosted "AI Campus Day," a large-scale internal event designed to bridge the gap between AI theory and practical workplace application for over 3,000 employees. By transforming their office into a learning campus, the company successfully fostered a culture of "AI Transformation" through peer-led mentorship and task-specific experimentation. The event demonstrated that internal context and hands-on participation are far more effective than traditional external lectures for driving meaningful AI literacy and productivity gains. ## Hands-on Experience and Technical Support * The curriculum featured 10 specialized sessions across three tracks—Common, Creative, and Engineering—to ensure relevance for every job function. * Sessions ranged from foundational prompt engineering for non-developers to advanced technical topics like building Model Context Protocol (MCP) servers for engineers. * To ensure smooth execution, the organizers provided comprehensive "Session Guides" containing pre-configured account settings and specific prompt templates. * The event utilized a high support ratio, with 26 teaching assistants (TAs) available to troubleshoot technical hurdles in real-time and dedicated Slack channels for sharing live AI outputs. ## Peer-Led Mentorship and Internal Context * Instead of hiring external consultants, the program featured 10 internal "AI Mentors" who shared how they integrated AI into their actual daily workflows at LY Corporation. * Training focused exclusively on company-approved tools, including ChatGPT Enterprise, Gemini, and Claude Code, ensuring all demonstrations complied with internal security protocols. * Internal mentors were able to provide specific "company context" that external lecturers lack, such as integrating AI with existing proprietary systems and data. * A rigorous three-stage quality control process—initial flow review, final end-to-end dry run, and technical rehearsal—was implemented to ensure the educational quality of mentor-led sessions. ## Gamification and Cultural Engagement * The event was framed as a "festival" rather than a mandatory training, using campus-themed motifs like "enrollment" and "school attendance" to reduce psychological barriers. * A "Stamp Rally" system encouraged participation by offering tiered rewards, including welcome kits, refreshments, and subscriptions to premium AI tools. * Interactive exhibition booths allowed employees to experience AI utility firsthand, such as an AI photo zone using Gemini to generate "campus-style" portraits and an AI Agent Contest booth. * Strong executive support played a crucial role, with leadership encouraging staff to pause routine tasks for the day to focus entirely on AI experimentation and "playing" with new technologies. To effectively scale AI literacy within a large organization, it is recommended to move away from passive, one-size-fits-all lectures. Success lies in leveraging internal experts who understand the specific security and operational constraints of the business, and creating a low-pressure environment where employees can experiment with hands-on tasks relevant to their specific roles.

Safety is a Given, Cost Reduction (opens in new tab)

AI developers often rely on system prompts to enforce safety rules, but this integrated approach frequently leads to "over-refusal" and unpredictable shifts in model performance. To ensure both security and operational efficiency, it is increasingly necessary to decouple safety mechanisms into separate guardrail systems that operate independently of the primary model's logic. ## Negative Impact on Model Utility * Integrating safety instructions directly into system prompts often leads to a high False Positive Rate (FPR), where the model rejects harmless requests alongside harmful ones. * Technical analysis using Principal Component Analysis (PCA) reveals that guardrail prompts shift the model's embedding results in a consistent direction toward refusal, regardless of the input's actual intent. * Studies show that aggressive safety prompting can cause models to refuse benign technical queries—such as "how to kill a Python process"—because the model adopts an overly conservative decision boundary. ## Positional Bias and Context Neglect * Research on the "Lost in the Middle" phenomenon indicates that LLMs are most sensitive to information at the beginning and end of a prompt, while accuracy drops significantly for information placed in the center. * The "Constraint Difficulty Distribution Index" (CDDI) demonstrates that the order of instructions matters; models generally follow instructions better when difficult constraints are placed at the beginning of the prompt. * In complex system prompts where safety rules are buried in the middle, the model may fail to prioritize these guardrails, leading to inconsistent safety enforcement depending on the prompt's structure. ## The Butterfly Effect of Prompt Alterations * Small, seemingly insignificant changes to a system prompt—such as adding a single whitespace, a "Thank you" note, or changing the output format to JSON—can alter more than 10% of a model's predictions. * Modifying safety-related lines within a unified system prompt can cause "catastrophic performance collapse," where the model's internal reasoning path is diverted, affecting unrelated tasks. * Because LLMs treat every part of the prompt as a signal that moves their decision boundaries, managing safety and task logic in a single string makes the system brittle and difficult to iterate upon. To build robust and high-performing AI applications, developers should move away from bloated system prompts and instead implement external guardrails. This modular approach allows for precise security filtering without compromising the model's creative or logical capabilities.

Code Quality Improvement Techniques Part 26 (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.

Why did Athenz engineers take on the (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.

Introducing a New A/B (opens in new tab)

LY Corporation has developed an advanced A/B testing system that moves beyond simple random assignment to support dynamic user segmentation. By integrating a dedicated targeting system with a high-performance experiment assigner, the platform allows for precise experiments tailored to specific user characteristics and behaviors. This architecture enables data-driven decisions that are more relevant to localized or specialized user groups rather than relying on broad averages. ## Limitations of Traditional A/B Testing * General A/B test systems typically rely on random assignment, such as applying a hash function to a user ID (`hash(id) % 2`), which is simple and cost-effective. * While random assignment reduces selection bias, it is insufficient for hypotheses that only apply to specific cohorts, such as "iOS users living in Osaka." * Advanced systems solve this by shifting from general testing across an entire user base to personalized testing for specific segments. ## Architecture of the Targeting System * The system processes massive datasets including user information, mobile device data, and application activity stored in HDFS. * Apache Spark is used to execute complex conditional operations—such as unions, intersections, and subtractions—to refine user segments. * Segment data is written to Object Storage and then cached in Redis using a `{user_id}-{segment_id}` key format to ensure low-latency lookups during live requests. ## A/B Test Management and Assignment * The system utilizes "Central Dogma" as a configuration repository where operators and administrators define experiment parameters. * A Test Group Assigner orchestrates the process: when a client makes a request, the assigner retrieves experiment info and checks the user's segment membership in Redis. * Once a user is assigned to a specific group (e.g., Test Group 1), the system serves the corresponding content and logs the event to a data store for dashboard visualization and analysis. ## Strategic Use Cases and Future Plans * **Content Recommendation:** Testing different Machine Learning models to see which performs better for a specific user demographic. * **Targeted Incentives:** Limiting shopping discount experiments to "light users," as coupons may not significantly change the behavior of "heavy users." * **Onboarding Optimization:** Restricting UI tests to new users only, ensuring that existing users' experiences remain uninterrupted. * **Platform Expansion:** Future goals include building a unified admin interface for the entire lifecycle of an experiment and expanding the system to cover all services within LY Corporation. For organizations looking to optimize user experience, transitioning from random assignment to dynamic segmentation is essential for high-precision product development. Ensuring that segment data is cached in a high-performance store like Redis is critical to maintaining low latency when serving experimental variations in real-time.

Code Quality Improvement Techniques Part (opens in new tab)

Effective code review communication relies on a "conclusion-first" approach to minimize cognitive load and ensure clarity for the developer. By stating proposed changes or specific requests before providing the underlying rationale, reviewers help authors understand the primary goal of the feedback immediately. This practice improves development productivity by making review comments easier to parse and act upon without repeated reading. ### Optimizing Review Comment Structure * Place the core suggestion or requested code change at the very beginning of the comment to establish immediate context. * Follow the initial request with a structured explanation, utilizing headers or numbered lists to organize multiple supporting arguments. * Clearly distinguish between the "what" (the requested change) and the "why" (the technical justification) to prevent the intended action from being buried in a long technical discussion. * Use visual formatting to help the developer quickly validate the logic behind the suggestion once they understand the proposed change. ### Immutability and Data Class Design * Prefer the use of `val` over `var` in Kotlin `data class` structures to ensure object immutability. * Using immutable properties prevents bugs associated with unintended side effects that occur when mutable objects are shared across different parts of an application. * Instead of reassigning values to a mutable property, utilize the `copy()` function to create a new instance with updated state, which results in more robust and predictable code. * Avoid mixing `var` properties with `data class` features, as this can lead to confusion regarding whether to modify the existing instance or create a copy. ### Property Separation by Lifecycle * Analyze the update frequency of different properties within a class to identify those with different lifecycles. * Decouple frequently updated status fields (such as `onlineStatus` or `statusMessage`) from more stable attributes (such as `userId` or `accountName`) by moving them into separate classes. * Grouping properties by their lifecycle prevents unnecessary updates to stable data and makes the data model easier to maintain as the application scales. To maintain high development velocity, reviewers should prioritize brevity and structure in their feedback. Leading with a clear recommendation and supporting it with organized technical reasoning ensures that code reviews remain a tool for progress rather than a source of confusion.

Practical Security Knowledge Growing with (opens in new tab)

LINE CTF 2025 serves as a collaborative platform for global security experts to exchange technical knowledge and tackle real-world cybersecurity challenges through a competitive framework. Under the newly integrated LY Corporation, the event evolved to prioritize anti-AI problem design and enhanced privacy protections, reinforcing its position as a top-tier competition in the Asian security community. The event successfully demonstrated that high-quality problem engineering and community-focused operations can drive both individual growth and organizational security excellence. ## Strategic Shift and AI-Resilient Design * **Multisite Collaboration:** While previous years were led primarily by the Japanese team, 2025 saw a shift where the Korean security team led preparations and the Vietnamese team contributed the highest volume of technical challenges. * **Counter-AI Engineering:** To maintain fairness in an era of LLMs, problems were specifically designed to mislead automated AI analysis, requiring human logic and deep conceptual understanding to arrive at the correct "flag." * **Systemic Integration:** This was the first year applying the unified LY Corporation administrative and approval processes, resulting in a more refined timeline for problem verification and quality control. ## Competition Format and Problem Engineering * **Jeopardy-Style Challenges:** The event featured 13 independent challenges—6 Web, 4 Pwnable, and 3 Reverse Engineering—where teams earned points based on difficulty. * **Three-Stage Validation:** Every problem underwent a rigorous cycle of idea conception, technical environment isolation/testing, and internal peer review to eliminate unintended "cheese" solutions or bugs. * **Technical Philosophy:** Problems were modeled after real-world service vulnerabilities and latest security trends, targeting a difficulty level that requires several hours of dedicated analysis by a skilled researcher. ## Platform Evolution and Performance * **Privacy-First Infrastructure:** The team customized the open-source CTFd framework to remove email-based registration, instead using a recovery-code system to ensure participant anonymity and data security. * **Growing Technical Prestige:** The competition’s rating on CTFtime (a global community platform) has climbed steadily over three years, reaching a weight of 66.5 in 2025, reflecting its high quality and difficulty. * **Competitive Results:** The Korean team "The Duck" maintained dominance with a third consecutive win, while the battle for second place was decided by a dramatic last-minute solve by the Japanese team "GMO Ierae." Participating in CTFs like LINE CTF offers an invaluable practical learning environment for security engineers to master vulnerability analysis and exploit development. Aspiring and professional researchers are encouraged to engage with these challenges to sharpen their analytical skills and contribute to a more robust, collaborative global security culture.

Code Quality Improvement Techniques Part 24: The Value of Legacy (opens in new tab)

The LY Corporation Review Committee advocates for simplifying code by avoiding unnecessary inheritance when differences between classes are limited to static data rather than dynamic logic. By replacing complex interfaces and subclasses with simple data models and specific instances, developers can reduce architectural overhead and improve code readability. This approach ensures that configurations, such as UI themes, remain predictable and easier to maintain without the baggage of a type hierarchy. ### Limitations of Inheritance-Based Configuration * The initial implementation used a `FooScreenThemeStrategy` interface to define UI elements like background colors, text colors, and icons. * Specific themes (Light and Dark) were implemented as separate classes that overridden the interface properties. * This pattern creates an unnecessary proliferation of types when the only difference between the themes is the specific value of the constants being returned. * Using inheritance for simple value changes makes the code harder to follow and can lead to over-engineering. ### Valid Scenarios for Inheritance * **Dynamic Logic:** When behavior needs to change dynamically at runtime via dynamic dispatch. * **Sum Types:** Implementing restricted class hierarchies, such as Kotlin `sealed` classes or Java's equivalent. * **Decoupling:** Separating interface from implementation to satisfy DI container requirements or to improve build speeds. * **Dependency Inversion:** Applying architectural patterns to resolve circular dependencies or to enforce one-way dependency flows. ### Transitioning to Data Models and Instantiation * Instead of an interface, a single "final" class or data class (e.g., `FooScreenThemeModel`) should be defined to hold the required properties. * Individual themes are created as simple instances of this model rather than unique subclasses. * In Kotlin, defining a class without the `open` keyword ensures that the properties are not dynamically altered and that no hidden, instance-specific logic is introduced. * This "instantiation over inheritance" strategy guarantees that properties remain static and the code remains concise. To maintain a clean codebase, prioritize data-driven instantiation over class-based inheritance whenever logic remains constant. This practice reduces the complexity of the type system and makes the code more resilient to unintended side effects.

Connecting Thousands of LY Corporation Services (opens in new tab)

LY Corporation developed a centralized control plane using Central Dogma to manage service-to-service communication across its vast, heterogeneous infrastructure of physical machines, virtual machines, and Kubernetes clusters. By adopting the industry-standard xDS protocol, the new system resolves the interoperability and scaling limitations of their legacy platform while providing a robust GitOps-based workflow. This architecture enables the company to connect thousands of services with high reliability and sophisticated traffic control capabilities. ## Limitations of the Legacy System The previous control plane environment faced several architectural bottlenecks that hindered developer productivity and system flexibility: * **Tight Coupling:** The system was heavily dependent on a specific internal project management tool (PMC), making it difficult to support modern containerized environments like Kubernetes. * **Proprietary Schemas:** Communication relied on custom message schemas, which created interoperability issues between different clients and versions. * **Lack of Dynamic Registration:** The legacy setup could not handle dynamic endpoint registration effectively, functioning more as a static registry than a functional service mesh control plane. * **Limited Traffic Control:** It lacked the ability to perform complex routing tasks, such as canary releases or advanced client-side load balancing, across diverse infrastructures. ## Central Dogma as a Control Plane To solve these issues, the team leveraged Central Dogma, a Git-based repository service for textual configuration, to act as the foundation for a new control plane: * **xDS Protocol Integration:** The new control plane implements the industry-standard xDS protocol, ensuring seamless compatibility with Envoy and other modern data plane proxies. * **GitOps Workflow:** By utilizing Central Dogma’s mirroring features, developers can manage service configurations and traffic policies safely through Pull Requests in external Git repositories. * **High Reliability:** The system inherits Central Dogma’s native strengths, including multi-datacenter replication, high availability, and a robust authorization system. * **Schema Evolution:** The control plane automatically transforms legacy metadata into standard xDS resources, allowing for a smooth transition from old infrastructure to the new service mesh. ## Dynamic Service Discovery and Registration The architecture provides automated ways to manage service endpoints across different environments: * **Kubernetes Endpoint Plugin:** A dedicated plugin watches for changes in Kubernetes services and automatically updates the xDS resource tree in Central Dogma. * **Automated API Registration:** The system provides gRPC and HTTP APIs (e.g., `RegisterLocalityLbEndpoint`) that allow services to register themselves dynamically during the startup process. * **Advanced Traffic Features:** The new control plane supports sophisticated features like zone-aware routing, circuit breakers, automatic retries, and "slow start" mechanisms for new endpoints. ## Evolution Toward Sidecar-less Service Mesh A major focus of the project is improving the developer experience by reducing the operational overhead of the data plane: * **Sidecar-less Options:** The team is working toward providing service mesh benefits without requiring a sidecar proxy for every pod, which reduces resource consumption and simplifies debugging. * **Unified Control:** Central Dogma acts as a single source of truth for both proxy-based and proxyless service mesh configurations, ensuring consistent policy enforcement across the entire organization. For organizations managing large-scale, heterogeneous infrastructure, transitioning to an xDS-compliant control plane backed by a reliable Git-based configuration store is highly recommended. This approach balances the need for high-speed dynamic updates with the safety and auditability of GitOps, ultimately allowing for a more scalable and developer-friendly service mesh.