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AI News2026-05-13·11 min read·1
KAIST AI Climate Research Commentary: Why climate policy must first reduce IAM calculation bottlenecks, which are slower than larger models
Based on the AI-based climate research framework and high-speed emulator released by KAIST in May 2026, we summarized from a practical perspective why the key to climate policy decision-making is resolving the Integrated Assessment Model (IAM) bottleneck, which is slower than the larger foundation model.
#KAIST#Climate AI#IAM#climate policy+1
AI How-to2026-05-13·10 min read·3
Gemini CLI Subagents: A Controlled Investigation Workflow for Coding Teams
Use Gemini CLI subagents to isolate bounded repository investigations, return checkable evidence, and keep write authority with the session that accepts the result.
#Gemini CLI subagents#AI coding workflow#context isolation#agent permissions+2
Development2026-05-12·9 min read·1
Google Agents CLI Commentary: Why you should start by fixing the deployment lifecycle CLI rather than creating an agent with ADK
We described Google Agents CLI as a lifecycle layer that bundles evaluation, deployment, and observability of ADK agents, rather than a simple generator. We also summarized when to introduce it compared to Raw ADK and AWS AgentCore.
#Google Agent CLI#ADK#Agent Platform#Gemini CLI+2
AI How-to2026-05-11·11 min read·1
Coder Agents Practical Introduction Guide: Why you should separate the control plane and template boundaries rather than installing more Claude Code
To safely run an in-house AI coding agent, you need to design the control plane, template description, and network perimeter before model selection. Based on the Coder Agents beta, we have summarized what makes it different from existing Tasks/installable agents, who it is suitable for, and how to start the pilot.
#Coder Agents#AI Coding Agent#platform engineering#security+3
AI News2026-05-11·10 min read·1
NVIDIA Star Elastic Commentary: Why you should fold deployment classes into a single checkpoint rather than training more models
Star Elastic, released by NVIDIA in May 2026, bundles 30B, 23B, and 12B inference models into one checkpoint. This article is not a simple introduction to the model, but explains in practical terms why the key to operating a model family is to design distribution class, memory, and latency control at once rather than additional learning.
#NVIDIA#Star Elastic#Nemotron#reasoning L.L.M.+3
Development2026-05-10·9 min read·1
Claude Black Market Proxy Commentary: The first thing to look at before a 90% discount on API is not the proxy, but the log, secret, and distillation boundaries.
The essence of the Claude 90% discount proxy issue reported by AI Times is not a cheap expedient. What the working team must first look at is the structure in which prompts, responses, and confidential information are converted into learning data and security incident surfaces through a third-party proxy.
#Claude API#API proxy#model distillation#AI Security+1
AI News2026-05-10·9 min read
Gemini API Webhooks Commentary: Why you should design completion event contracts before polling the longer your AI agent works
Google added event-driven Webhooks to the Gemini API on May 4, 2026. Rather than simply introducing features, this article explains in practical terms why completion event contracts, signature verification, and retry boundaries should be designed first rather than polling in asynchronous AI tasks such as long-term batching, deep research, and video creation.
#Gemini API#Webhooks#Batch API#Deep Research+2
Development2026-05-09·10 min read·1
GitHub Agentic Workflows Token Optimization Commentary: Why you should design CLI dictionary collection and non-inference gates first rather than adding a lot of MCP tools
Based on the Agentic Workflows optimization case released by GitHub in May 2026, we summarized on a practical basis why the key to reducing agent costs lies in MCP tool organization, gh CLI dictionary collection, and LLM skip gate design rather than smaller models.
#GitHub Agentic Workflows#GitHub MCP#gh CLI#Token Optimization+1
AI How-to2026-05-09·8 min read·1
OpenAI Codex Chrome Extension Practical Guide: Why browser agents should be designed from site approval/history boundaries rather than @Chrome calls
We explained the OpenAI Codex Chrome extension based on browser agent operation, not just feature news. We have summarized domain authorization, history access, and in-app browser fallback rules that must be established before automating login sessions.
#OpenAI#Codex#Chrome Extension#browser agent+2

KAIST AI Climate Research Commentary: Why climate policy must first reduce IAM calculation bottlenecks, which are slower than larger models

Gemini CLI Subagents: A Controlled Investigation Workflow for Coding Teams

Google Agents CLI Commentary: Why you should start by fixing the deployment lifecycle CLI rather than creating an agent with ADK

Coder Agents Practical Introduction Guide: Why you should separate the control plane and template boundaries rather than installing more Claude Code

NVIDIA Star Elastic Commentary: Why you should fold deployment classes into a single checkpoint rather than training more models

Claude Black Market Proxy Commentary: The first thing to look at before a 90% discount on API is not the proxy, but the log, secret, and distillation boundaries.

Gemini API Webhooks Commentary: Why you should design completion event contracts before polling the longer your AI agent works

GitHub Agentic Workflows Token Optimization Commentary: Why you should design CLI dictionary collection and non-inference gates first rather than adding a lot of MCP tools

OpenAI Codex Chrome Extension Practical Guide: Why browser agents should be designed from site approval/history boundaries rather than @Chrome calls