if(kakao)26 highlighted the direction of transitioning from conversational AI to execution- and trust-centric agentic AI.
Harness size was halved due to improved model performance, but verification mechanisms must be maintained.
Removing duplicate Alloy collection and optimizing labels reduced Loki CPU usage by 98% on a sandbox basis.
This article outlines methods to maximize bulk data read and write performance using Querydsl-based cursor pagination and batch processing.
This guide introduces strategies to resolve the N+1 problem of @DBRef using $lookup and to boost bulk update performance by 24x using bulkOps in Spring Data MongoDB.
KakaoPay Insurance safely removed legacy modules in production and laid the groundwork for a Spring Boot upgrade using an AOP-based Shadow comparison strategy.
A KakaoPay developer summarized essential concepts for adopting AI Agents, including the difference between LLMs and Agents, the ReAct pattern, tokens, and context windows.
As Yarn Berry's PnP and Zero-installs became a new bottleneck, the team switched to pnpm and improved memory usage and deployment efficiency.
The real cause of DB Connection Timeout was not a lack of connections, but the absence of JVM warm-up.
Using OpenTelemetry and ClickHouse, log ingestion latency was cut to under 20 seconds and costs were reduced by 85.6%.
Reorganizing the repo structure with FSD made code location and dependencies clear.
They transformed manual, legacy AI operations through Kubeflow and GPU optimization.
Multiverse is an in-house platform that lets you spin up virtual devices from a single MacBook to help with app testing and log checking.
Kakaopay Securities has introduced the 'Pink Ward' project, which visualizes service status from the customer's perspective and automates the incident response process.
This explains the process by which an LLM receives a question and generates an answer, broken down into 6 steps.
A team of non-developers from Kakaopay used **AWS Gen AI** to build a facial recognition-based hyper-personalized kiosk, winning 3rd place at a hackathon.
Kakaopay has developed 'Pageny,' a financial AI concierge that uses a multi-agent architecture based on AWS Bedrock.
Kakao Pay's CreditClan team used AWS Bedrock to develop a personalized AI loan coach service for users.
KakaoPay adopted **MCP** to efficiently connect AI agents with its payment API, and developed a multi-framework **Agent Toolkit**.
Based on their experience refactoring an encryption module, they summarized the history of encryption and the differences between symmetric and asymmetric key methods.
The Kakao Pay team used AWS AI services to develop the 'Geum.Yong.Sa.' service, which explains difficult financial terms and recommends related products.
The Kakaopay development team built AIVA, an AI recruiting assistant that analyzes resumes and technical assignments, through a hackathon.
Shares hands-on experience analyzing and resolving CORS errors and Version Skew issues occurring in a Next.js SSR environment.
Kakao Pay shares the process of internalizing its credit core system by applying DDD to efficiently manage its complex credit business.
AI Briefing