라인 / gen-ai

입사 일주일 만에 일본 출장을? LINE Plus Developer Relations 뉴비의 바쁜 적응기 (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.

사내 AI 리터러시를 향상하기 위한 AI Campus Day를 개최했습니다 (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.

한 달짜리 과제, 바이브 코딩으로 5일 만에!(ChatGPT·Cursor) (opens in new tab)

This blog post explores how LY Corporation reduced a month-long development task to just five days by leveraging "vibe coding" with Generative AI tools like ChatGPT and Cursor. By shifting from traditional, rigid documentation to an iterative, demo-first approach, developers can rapidly validate multiple UI/UX solutions for complex problems like restaurant menu registration. The author concludes that AI's ability to handle frequent re-work makes it more efficient to "build fast and iterate" than to aim for perfection through long-form specifications. ### Strategic Shift to Rapid Prototyping * Traditional development cycles (spec → design → dev → fix) are often too slow to keep up with market trends due to heavy documentation and impact analysis. * The "vibe coding" approach prioritizes creating "working demos" over perfect specifications to find "good enough" answers through rapid feedback loops. * AI reduces the psychological and logistical burden of "starting over," allowing developers to refine the context and quality of outputs through repeated interaction without the friction of manual re-documentation. ### Defining Requirements and Solution Ideation * Initial requirements are kept minimal, focusing only on the core mission, top priorities, and essential data structures (e.g., product name, image, description) to avoid limiting AI creativity. * ChatGPT is used to generate a wide range of solution candidates, which are then filtered into five distinct approaches: Stepper Wizards, Live Previews with Quick Add, Template/Cloning, Chat Input, and OCR-based photo scanning. * This stage emphasizes volume and variety, using AI-generated pros and cons to establish selection criteria and identify potential UX bottlenecks early in the process. ### Detailed Design and Multi-Solution Wireframing * Each of the five chosen solutions is expanded into detailed screen flows and UI elements, such as progress bars, bottom sheets, and validation logic. * Prompt engineering is used iteratively; if an AI-generated result lacks a specific feature like "temporary storage" or "mandatory field validation," the prompt is adjusted to regenerate the design instantly. * The focus remains on defining the "what" (UI elements) and "how" (user flow) through textual descriptions before moving to actual coding. ### Implementation with Cursor and Flutter * Cursor is utilized to generate functional code based on the refined wireframes, using Flutter as the framework to ensure rapid cross-platform development for both iOS and Android. * The development follows a "skeleton-first" approach: first creating a main navigation hub with five entry points, then populating each individual solution module one by one. * Technical architecture decisions, such as using Riverpod for state management or SQLite for data storage, are layered onto the demo post-hoc, reversing the traditional "stack-first" development order to prioritize functional validation. ### Recommendation To maximize efficiency, developers should treat AI as a partner for high-speed iteration rather than a one-shot tool. By focusing on creating functional demos quickly and refining them through direct feedback, teams can bypass the bottlenecks of traditional software requirements and deliver user-centric products in a fraction of the time.

PD1 AI 해커톤, 그 뜨거웠던 열기 속으로! (opens in new tab)

The PD1 AI Hackathon 2025 served as a strategic initiative by LY Corporation to embed innovative artificial intelligence directly into the LINE messaging ecosystem. Over 60 developers collaborated during an intensive 48-hour session to transition AI from a theoretical concept into practical features for messaging, content, and internal development workflows. The event successfully produced several high-utility prototypes that demonstrate how AI can enhance user safety, creative expression, and technical productivity. ## Transforming Voice Communication through NextVoIP * The "NextVoIP" project utilized Speech-to-Text (STT) technology to convert 1:1 and group call audio into real-time data for AI analysis. * The system was designed to provide life security features by detecting potential emergency situations or accidents through conversation monitoring. * AI acted as a communication assistant by suggesting relevant content and conversation topics to help maintain a seamless flow during calls. * Features were implemented to allow callers to enjoy shared digital content together, enriched by AI-driven recommendations. ## Creative Expression with MELODY LINE * This project focused on the intersection of technology and art by converting chat conversations into unique musical compositions. * The system analyzed the context and emotional sentiment of messages to automatically generate melodies that matched the atmosphere of the chat. * The implementation showcased the potential for generative AI to provide a multi-sensory experience within a standard messaging interface. ## AI-Driven QA and Test Automation * The grand prize-winning project, "IPD," addressed the bottleneck of repetitive manual testing by automating the entire Quality Assurance lifecycle. * AI was utilized to automatically generate and manage complex test cases, significantly reducing the manual effort required for mobile app validation. * The system included automated test execution and a diagnostic feature that identifies the root cause of failures when a test results in an error. * The project was specifically lauded for its immediate "production-ready" status, offering a direct path to improving development speed and software reliability. The results of this hackathon suggest that the most immediate value for AI in large-scale messaging platforms lies in two areas: enhancing user experience through contextual awareness and streamlining internal engineering via automated QA. Organizations should look toward integrating AI-driven testing tools to reduce technical debt while exploring real-time audio and text analysis to provide proactive security and engagement features for users.

자네, 해커가 되지 않겠나? Hack Day 2025에 다녀왔습니다! (opens in new tab)

Hack Day 2025 serves as a cornerstone of LY Corporation’s engineering culture, bringing together diverse global teams to innovate beyond their daily operational scopes. By fostering a high-intensity environment focused on creative freedom, the event facilitates technical growth and strengthens interpersonal bonds across international branches. This 19th edition demonstrated how rapid prototyping and cross-functional collaboration can transform abstract ideas into functional AI-driven prototypes within a strict 24-hour window. ### Structure and Participation Dynamics * The hackathon follows a "9 to 9" format, providing exactly 24 hours of development time followed by a day for presentations and awards. * Participation is inclusive of all roles, including developers, designers, planners, and HR staff, allowing for holistic product development. * Teams can be "General Teams" from the same legal entity or "Global Mixed Teams" comprising members from different regions like Korea, Japan, Taiwan, and Vietnam. * The Developer Relations (DevRel) team facilitates team building for remote employees using digital collaboration tools like Zoom and Miro. ### AI-Powered Personality Analysis Project * The author's team developed a "Scouter" program inspired by Dragon Ball, designed to measure professional "combat power" based on communication history. * The system utilizes Slack bots and AI models to analyze message logs and map them to the Big 5 Personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). * Professional metrics are visualized as game-like character statistics to make personality insights engaging and less intimidating. * While the original plan involved using AI to generate and print physical character cards, hardware failures with photo printers forced a technical pivot to digital file downloads. ### High-Pressure Presentation and Networking * Every team is allotted a strict 90-second window to pitch their product and demonstrate a live demo. * The "90-second rule" includes a mandatory microphone cutoff to maintain momentum and keep the large-scale event engaging for all attendees. * Dedicated booth sessions follow the presentations, allowing participants to provide hands-on experiences to colleagues and judges. * The event emphasizes "Perfect the Details," a core company value, by encouraging teams to utilize all available resources—from whiteboards to AI image generators—within the time limit. ### Environmental Support and Culture * The event occupies an entire office floor, providing a high-density yet comfortable environment designed to minimize distractions during the "Hack Time." * Cultural exchange is encouraged through "humanity snacks," where participants from different global offices share local treats in dedicated rest areas. * Strategic scheduling, such as "Travel Days" for international participants, ensures that teams can focus entirely on technical execution once the event begins. Participating in internal hackathons provides a vital platform for testing new technologies—like LLMs and personality modeling—that may not fit into immediate product roadmaps. For organizations with hybrid work models, these intensive in-person events are highly recommended to bridge the communication gap and build lasting trust between global teammates.

LY의 테크 컨퍼런스, 'Tech-Verse 2025' 후기 (opens in new tab)

LY Corporation’s Tech-Verse 2025 conference highlighted the company's strategic pivot toward becoming an AI-centric organization through the "Catalyst One Platform" initiative. By integrating the disparate infrastructures of LINE and Yahoo! JAPAN into a unified private cloud, the company aims to achieve massive cost efficiencies while accelerating the deployment of AI agents across its entire service ecosystem. This transformation focuses on empowering engineers with AI-driven development tools to foster rapid innovation and deliver a seamless, "WOW" experience for global users. ### Infrastructure Integration and the Catalyst One Platform To address the redundancies following the merger of LINE and Yahoo! JAPAN, LY Corporation is consolidating its technical foundations into a single internal ecosystem known as the Catalyst One Platform. * **Private Cloud Advantage:** The company maintains its own private cloud to achieve a four-fold cost reduction compared to public cloud alternatives, managed by a lean team of 700 people supporting 500,000 servers. * **Unified Architecture:** The integration spans several layers, including Infrastructure (Project "DC-Hub"), Cloud (Project "Flava"), and specialized Data and AI platforms. * **Next-Generation Cloud "Flava":** This platform integrates existing services to enhance VM specifications, VPC networking, and high-performance object storage (Ceph and Dragon). * **Information Security:** A dedicated "SafeOps" framework is being implemented to provide governance and security across all integrated services, ensuring a safer environment for user data. ### AI Strategy and Service Agentization A core pillar of LY’s strategy is the "AI Agentization" of all its services, moving beyond simple features to proactive, personalized assistance. * **Scaling GenAI:** Generative AI has already been integrated into 44 different services within the group. * **Personalized Agents:** The company is developing the capacity to generate millions of specialized agents that can be linked together to support the unique needs of individual users. * **Agent Ecosystem:** The goal is to move from a standard platform model to one where every user interaction is mediated by an intelligent agent. ### AI-Driven Development Transformation Beyond user-facing services, LY is fundamentally changing how its engineers work by deploying internal AI development solutions to all staff starting in July. * **Code and Test Automation:** Proof of Concept (PoC) results showed a 96% accuracy rate for "Code Assist" and a 97% reduction in time for "Auto Test" procedures. * **RAG Integration:** The system utilizes Retrieval-Augmented Generation (RAG) to leverage internal company knowledge and guidelines, ensuring high-quality, context-aware development support. * **Efficiency Gains:** By automating repetitive tasks, the company intends for engineers to shift their focus from maintenance to creative service improvement and innovation. The successful integration of these platforms and the aggressive adoption of AI-driven development tools suggest that LY Corporation is positioning itself to be a leader in the "AI-agent" era. For technical organizations, LY's model serves as a case study in how large-scale mergers can leverage private cloud infrastructure to fund and accelerate a company-wide AI transition.

테크 컨퍼런스 Tech-Verse 2025를 개최합니다 (opens in new tab)

LY Corporation is hosting its global technology conference, Tech-Verse 2025, on June 30 and July 1 to showcase the engineering expertise of its international teams. The event features 127 sessions centered on core themes of AI and security, offering a deep dive into how the group's developers, designers, and product managers solve large-scale technical challenges. Interested participants can register for free on the official website to access the online live-streamed sessions, which include real-time interpretation in English, Korean, and Japanese. ### Conference Overview and Access * The event runs for two days, from 10:00 AM to 6:00 PM (KST), and is primarily delivered via online streaming. * Registration is open to the public at no cost through the Tech-Verse 2025 official website. * The conference brings together technical talent from across the LY Corporation Group, including LINE Plus, LINE Taiwan, and LINE Vietnam. ### Multi-Disciplinary Technical Tracks * The agenda is divided into 12 distinct categories to cover the full spectrum of software development and product lifecycle. * Day 1 focuses on foundational technologies: AI, Security, Server-side development, Private Cloud, Infrastructure, and Data Platforms. * Day 2 explores application and management layers: AI Use Cases, Frontend, Mobile Applications, Design, Product Management, and Engineering Management. ### Key Engineering Case Studies and Sessions * **AI and Data Automation:** Sessions explore the evolution of development processes using AI, the shift from "Vibe Coding" to professional AI-assisted engineering, and the use of Generative AI to automate data pipelines. * **Infrastructure and Scaling:** Presentations include how the "Central Dogma Control Plane" connects thousands of services within LY Corporation and methods for improving video playback quality for LINE Call. * **Framework Migration:** A featured case study details the strategic transition of the "Demae-can" service from React Native to Flutter. * **Product Insights:** Deep dives into user experience design and data-driven insights gathered from LINE Talk's global user base. Tech-Verse 2025 provides a valuable opportunity for developers to learn from real-world deployments of AI and large-scale infrastructure. Given the breadth of the 127 sessions and the availability of real-time translation, tech professionals should review the timetable in advance to prioritize tracks relevant to their specific engineering interests.

AI와 글쟁이의 동행: 코드 주면 API 레퍼런스 써드려요 (opens in new tab)

LY Corporation is addressing the chronic shortage of high-quality technical documentation by treating the problem as an engineering challenge rather than a training issue. By utilizing Generative AI to automate the creation of API references, the Document Engineering team has transitioned from a "manual craftsmanship" approach to an "industrialized production" model. While the system significantly improves efficiency and maintains internal context better than generic tools, the team concludes that human verification remains essential due to the high stakes of API accuracy. ### Contextual Challenges with Generic AI Standard coding assistants like GitHub Copilot often fail to meet the specific documentation needs of a large organization. * Generic tools do not adhere to internal company style guides or maintain consistent terminology across projects. * Standard AI lacks awareness of internal technical contexts; for example, generic AI might mistake a company-specific identifier like "MID" for "Member ID," whereas the internal tool understands its specific function within the LY ecosystem. * Fragmented deployment processes across different teams make it difficult for developers to find a single source of truth for API documentation. ### Multi-Stage Prompt Engineering To ensure high-quality output without overwhelming the LLM's "memory," the team refined a complex set of instructions into a streamlined three-stage workflow. * **Language Recognition:** The system first identifies the programming language and specific framework being used. * **Contextual Analysis:** It analyzes the API's logic to generate relevant usage examples and supplemental technical information. * **Detail Generation:** Finally, it writes the core API descriptions, parameter definitions, and response value explanations based on the internal style guide. ### Transitioning to Model Context Protocol (MCP) While the prototype began as a VS Code extension, the team shifted to using the Model Context Protocol (MCP) to ensure the tool was accessible across various development environments. * Moving to MCP allows the tool to support multiple IDEs, including IntelliJ, which was a high-priority request from the developer community. * The MCP architecture decouples the user interface from the core logic, allowing the "host" (like the IDE) to handle UI interactions and parameter inputs. * This transition reduced the maintenance burden on the Document Engineering team by removing the need to build and update custom UI components for every IDE. ### Performance and the Accuracy Gap Evaluation of the AI-generated documentation showed strong results, though it highlighted the unique risks of documenting APIs compared to other forms of writing. * Approximately 88% of the AI-generated comments met the team's internal evaluation criteria. * The specialized generator outperformed GitHub Copilot in 78% of cases regarding style and contextual relevance. * The team noted that while a 99% accuracy rate is excellent for a blog post, a single error in a short API reference can render the entire document useless for a developer. To successfully implement AI-driven documentation, organizations should focus on building tools that understand internal business logic while maintaining a strict "human-in-the-loop" workflow. Developers should use these tools to generate the bulk of the content but must perform a final technical audit to ensure the precision that only a human author can currently guarantee.

AI로 생성한 이미지는 어떻게 평가할까요? (인페인팅 적용편) (opens in new tab)

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