kakao·Jun 8, 2026·3 min readCurated summary

Key Players in the Agentic AI Ecosystem: MCP Player 10 Wraps Up, and What’s Next!

Kakao’s first MCP Player 10 competition showcased how developers are using Model Context Protocol (MCP) to build practical agentic AI services. More than 150 teams participated, and ten finalists were selected for solutions addressing childcare, startup support, culture, gaming, legal research, and safety. Kakao plans to expand this ecosystem through the upcoming Agentic Player 10 competition and deeper integration with Kakao Tools. ## The MCP Player 10 Competition - The competition ran from December 19, 2025, to January 18, 2026, on Kakao’s PlayMCP open platform. - It emphasized: - Creativity - Everyday usefulness - Technical stability - The goal was to encourage developers to create MCP servers that solve real-world problems with AI. - Ten teams were selected after internal evaluation and received a share of 21 million won in support funding, along with opportunities to collaborate with Kakao. ## Award-Winning MCP Services ### 어린이ZIP: AI Assistant for Childcare Teachers - Automates administrative work for daycare and kindergarten teachers. - Analyzes uploaded activity photos to generate drafts of parent notices and childcare journals. - Remembers child-specific details such as allergies and pickup arrangements. - Produces personalized responses in a warm, professional tone. ### SeedUp: Startup Support-Program Research - Collects and analyzes fragmented government startup-support announcements. - Summarizes eligibility requirements and relevant opportunities. - Helps founders develop application strategies. - Supports natural-language requests such as finding weekly deadlines or analyzing an uploaded announcement. ### Other Selected Services - **공유 비밀의 방:** An anonymous platform for sharing and empathizing with personal stories and AI conversations. - **바우만 16 안티에이징솔루션:** Recommends skincare routines using the Baumann 16 skin-type classification, cosmetic ingredient data, and skin pH analysis. - **아라드도우미:** A Dungeon & Fighter assistant using RAG and Vision AI to analyze patch notes, item trends, and optimized character builds. - **키즈허브:** Aggregates public data such as emergency-room availability, childcare waiting lists, and child-development information. - **택배추적기:** Combines package tracking with AI-based detection of smishing URLs in delivery-related messages. - **ArtBridge:** Recommends performances and exhibitions from approximately 200,000 records across nine cultural categories, using location, budget, and preferences. - **KidSafe:** Detects harmful language and emotional-crisis signals in children’s chatbot conversations, escalating serious cases to guardians or professional resources. - **LexiLink_ko:** Searches and organizes statutes, court precedents, and administrative interpretations through natural-language queries. All ten MCP servers are now officially available through the PlayMCP platform. ## PlayMCP’s Future Direction - PlayMCP will remain a developer-focused environment for building and distributing MCP servers. - Kakao Tools, available through ChatGPT for Kakao, will focus on helping general users experience MCP-based services. - Kakao plans to connect the two platforms more closely. - Kakao is considering managed infrastructure, including: - Kakao Cloud-based server support - Automated deployment - Greater operational responsibility for MCP service stability - PlayMCP may also support richer in-app interfaces through JSON-based widgets, similar to those already available in ChatGPT for Kakao. ## The Next Competition: Agentic Player 10 Kakao announced a second competition, Agentic Player 10, designed to connect developer-created agents with Kakao Tools and expose them to a broader audience. The program is positioned as an opportunity for startups and aspiring founders to test their services with real users and potentially bring their agents into KakaoTalk. Developers interested in building practical AI agents are encouraged to use PlayMCP and participate in Agentic Player 10 as the next step in Kakao’s expanding agentic AI ecosystem.

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RAGAI AgentsModel Context ProtocolJson+3

kakao·Mar 12, 2026·4 min readCurated summary

From Student to Developer: Learning Rational Choices Over Right Answers—From DB and Security to AI

The onboarding of 40 new Kakao developers shifted their perspective from making features work to designing systems that survive real-world operations. Across databases, security, and AI, they learned that there is rarely one perfect answer; the best choice depends on scale, risk, maintainability, and business needs. The central lesson was to replace theoretical correctness with responsible, adaptable engineering judgment. ## Database: From Finding the Right Answer to Preparing for Change - Database design must be evaluated by whether it can withstand traffic, schema changes, and operational demands—not only by theoretical correctness. - Foreign keys are not automatically the best choice: - They can introduce locking, performance, and flexibility concerns. - Referential integrity can instead be managed at the application layer, provided testing and correction processes are strong. - Soft deletion, using fields such as `deleted_at`, supports auditability and recovery and is often an essential operational strategy. - Indexes should be selected according to the questions the database must answer: - B-tree, GIN, GiST, SP-GiST, and vector indexes serve different data and query patterns. - Execution plans reveal whether SQL uses indexes or performs full table scans, directly affecting I/O and response times. - Duplication is not always harmful: - Intentional denormalization can avoid expensive joins. - Snapshot data can simplify reads and preserve the information needed by a business workflow. - In MongoDB, embedding selected related data can make screen queries much simpler than relying exclusively on references. - Different database systems embody different trade-offs among performance, consistency, scalability, and operational cost. - The training covered MySQL high availability, PostgreSQL primary-key structures, cloud-native systems such as Neon, and the broader storage-to-analysis pipeline of Hadoop and Spark. - The resulting mindset favors designs that are safe to change and affordable to operate over designs that are theoretically perfect. ## Security and IT: From Someone Else’s Responsibility to a Personal Default - Security became a direct consequence of developers’ code rather than merely a compliance or infrastructure concern. - Everyday safeguards such as development/production separation, VPNs, and antivirus software demonstrate that safety often requires accepting some inconvenience. - DDoS defense is not only about blocking traffic: - It can be difficult to distinguish an attack from legitimate traffic spikes caused by a popular event. - Developers should apply basic controls such as rate limiting and escalate suspicious activity through established response channels. - Hands-on API exploitation made vulnerabilities concrete and encouraged developers to view security through an attacker’s perspective. - Security must be continuous: - AI is increasingly being used both to discover vulnerabilities and to strengthen attacks. - Social-engineering methods involving QR codes, app permissions, and human behavior require more than purely technical defenses. - Security checks should be integrated from the beginning of development, not performed only at the end. - Software quality also depends on people: - Code should remain understandable enough for another developer to take over quickly. - Strong engineering means choosing and communicating the most appropriate solution for the business context, not merely finding a technically possible one. ## AI: From Chatting with Models to Designing Systems - An AI agent is not simply a model; it is an architecture composed of tools, routing logic, error handling, and model calls. - Agent development applies familiar software-engineering practices to probabilistic models. - Because LLM outputs can vary, reliable systems need deliberate controls: - Prompt chaining breaks large tasks into smaller steps and limits context contamination. - Few-shot examples clarify required output formats. - Routing selects different prompts or workflows based on conditions. - Multi-agent systems divide responsibilities among specialized agents, echoing the modularity and scalability principles of microservices. - RAG reduces hallucinations structurally by: - Chunking documents. - Searching for semantically similar vectors. - Supplying retrieved information to the model as additional context. - MCP exposes internal systems and data as callable tools, effectively enabling remote function calling and connecting AI to enterprise capabilities. - Effective AI use shifted from criticizing poor answers to specifying clear objectives, formats, examples, context, and supporting data. - The goal is not merely to receive an intelligent response, but to design a system that consistently produces intelligent behavior. The training ultimately marked a transition from student-style problem solving to professional engineering. Developers should consider operational resilience, security, maintainability, and business value, then make and clearly explain the most reasonable choice for the circumstances.

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RAGDatabase DesignLarge Language ModelsAI Agents+4

kakao·Dec 11, 2025Original 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.

RAGLarge Language ModelsContrastive LearningMultimodal Embedding+4