Carrot launched Korea's first advertising MCP as a public beta on September 16.
Danggeun reduced costs to one-fifth by adding explicit breakpoints after costs increased 3.2x due to automatic cache rule changes following the transition to GPT-5.6.
A flaw has been identified in Aurora MySQL versions 3.08.0 to below 3.14 where index DDL operations clear the Reader buffer pool.
The platform introduces a structure where AI generates test flows, executes them on real devices, and performs self-recovery upon failure.
Handled 15 ordering change requests over 4.5 months, validated by 29 CI test cases.
Daangn outlines the evolution of its authentication system from SMS verification and identity confirmation to SSO and global adaptation.
Karrot adopted Lynx to eliminate WebView's initial loading overhead.
Danggeun adopted Lynx as a screen-level rendering layer to reduce the initial loading limitations of WebView.
Daangn's Data Value Team introduces an in-house experiment platform that supports everything from A/B test design to analysis.
Karrot Market shared its experience of integrating frontend and backend developers into a single team and the resulting outcomes.
This introduces the system architecture and technical choices behind the community service that Karrot built to handle 10 million MAU.
This piece covers what Danggeun's design system team thinks about beyond simply making components, in order to improve both the product's efficiency and quality.
Karrot built DT Platform, a dedicated ELT platform, to efficiently transfer data from more than 200 global service DBs to BigQuery.
Karrot accumulated deployment records in Notion and used an LLM to automatically generate release notes.
Danggeun blocked a Supply Chain Attack by applying a cooldown to its internal PyPI Proxy.
Danggeun introduced a proxy cooldown to block Python package supply chain attacks.
Instead of focusing on screen generation, Daangn evolved Kraft into an admin, CLI, and agent form aimed at automating SEED decision-making.
Karrot's Data Value Team sees AI not as a tool but as the core driver of transformation in data work, platforms, and culture.
Using PodAffinity and do-not-disrupt, we auto-scaled EKS node groups for Job workloads.
A case study on introducing EKS Node Group autoscaling tailored to Job workloads.
Danggeun built an LLM-based Taxonomy Management System to automatically classify categories and attributes at scale.
User embeddings trained on long-term logs, combined with RCBS, significantly boosted recommendation performance.
Karrot's short-form team split off **40MB** of editing features into an **On-demand DFM**, reducing the burden on global users.
We worked around the keyboard-push issue in iOS Webview using an opacity trick.
AI Briefing