Sharing the journey of LINE DEV
AI adoption at LY Corporation has moved beyond experimentation toward learning how to use these tools effectively in real work. The LINE DEV AI Reporters program connects scattered individual and team experiences through internal sharing sessions, helping practical lessons spread across the organization. Its central conclusion is that AI productivity depends not only on tools, but also on clear specifications, sound engineering practices, and a culture of continuous sharing. ## Turning Individual Experiments into Organizational Knowledge - AI enthusiasts across LY Corporation were independently experimenting with tools such as ChatGPT and Claude Code. - These experiences often remained limited to individuals or small teams. - AI Reporters brought together members of different roles and seniority who had experience sharing AI-related work. - Their goal was to turn personal trial and error into reusable organizational knowledge. ## Starting with Informal Personal Experiments - Early AI sharing sessions emphasized accessibility rather than polished success stories. - Multimedia Platform Dev’s Choi Jeong-min shared a “one service a day” vibe-coding experiment using Claude Code and Antigravity. - The experiment demonstrated that rapid implementation increases the importance of clearly defining what to build. - Sharing failures and unfinished experiments reduced the pressure to perform and encouraged more employees to try AI themselves. ## Applying AI to Real Development Work - As interest grew, discussions shifted from fun experiments to practical workplace applications. - Data Dev4’s Lee Yun-seong shared more than a month of project experience using Claude Code, project templates, and Vibe Kanban. - Developers spent more time on planning, design, review, and coordination while agents handled implementation. - Because the current codebase becomes the context for future agent work, poor architecture and coding styles can quickly be reproduced and amplified. - Continuous testing, refactoring, documentation, interface management, and architectural cleanup are therefore essential. - Skipping automated tests before commits led to increasing numbers of broken changes during later merges. - Humans remain responsible for ensuring that AI-generated code actually contributes to the project. - The most valuable skills increasingly involve task design, project management, system context, and meta-programming rather than implementation alone. - Developers can work in parallel with agents by planning the next task, researching requirements, and reviewing completed code while agents execute current work. ## Expanding from Teams to Organization-Wide Programs - Fintech Engineering organized a hands-on workshop covering the full path from idea to deployment. - Participants connected ChatGPT, Claude Code, and Stitch AI to plan, design, build, and complete a working service. - The integrated workflow helped participants understand how AI tools can support an entire product-development process, not just prototyping. - The GAI Study Group in the advertising organization broadened discussions to AI strategy, trends, agent behavior, developer workflows, and business applications. - Topics included: - AI agent reliability - Implementing interactions between PyTorch-based LLMs and MCP servers - Senior and junior developers’ vibe-coding workflows - NotebookLM-based RAG using wiki pages and Slack conversations - One session examined MCP internals by implementing JSON-RPC messaging and session-state management directly, revealing complexities hidden by libraries such as FastMCP. - Sessions were opened to participants and presenters from other teams, with some content published online for wider access. ## Building a Culture of Continuous Sharing - The most useful AI knowledge came from real workplace attempts, failures, and revisions—not only from polished documentation or external trends. - AI Reporters made existing but scattered experiences visible and connected them through presentations, Slack discussions, and monthly meetings. - Informal conversations such as “I tried this—how did it work for you?” helped normalize experimentation and learning from mistakes. - AI adoption is treated as an ongoing practice because tools and workflows continue to change. LY Corporation’s experience suggests that organizations should create lightweight, recurring forums where employees can share practical AI experiments. The combination of rapid experimentation, disciplined engineering, and open knowledge exchange allows individual discoveries to become lasting organizational capability.
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