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AI How-to2026-06-14·13 min read
Google Open Knowledge Format Commentary: Why AI agent data sharing should design context file contracts before vector DB
Based on the Open Knowledge Format v0.1 released by Google Cloud, we explain the context file contract that allows AI agents to read data catalogs, wikis, schemas, and runbooks in the same way. We organized Markdown, YAML frontmatter, link graph, and git operation standards into an actual introduction checklist.
#Open Knowledge Format#OKF#Google Cloud#AI agent+3
AI How-to2026-05-29·9 min read·1
GPT-5.3-Codex Practical Introduction Guide: Why long-term coding agents should fix task decomposition, breakpoints, and verification runbooks before model replacement
Instead of simply replacing GPT-5.3-Codex with the latest coding model, we organized task cards, permission profiles, verification commands, and breakpoint criteria into a practical runbook to safely entrust long-term tasks.
#GPT-5.3-Codex#Codex#AI Coding Agent#Refactoring+3
AI How-to2026-05-27·11 min read·1
SK Hynix 1 Trillion Dollar Club Commentary: Why AI service costs should be looked at from the HBM capacity, power, and supply bottlenecks rather than the model
We explain AI Times' report on SK Hynix's $1 trillion club from the perspective of AI service operation. We have summarized the reasons why HBM capacity, power, supply bottlenecks, and peak costs should be measured before model unit prices.
#SK Hynix#HBM#AI memory#AI infrastructure+3
AI How-to2026-05-25·12 min read·1
Kimi K2.6 + Cerebras Commentary: Why agent coding should design inference speed budget and routing criteria before model scores
The news that Cerebras ran Kimi K2.6 at a speed of 981 tokens per second is not a simple speed competition, but a signal to redesign the model routing standards for agent coding workloads. This article organizes by implementation criteria when to use GPU API, wafer-scale inference, and self-deployment.
#Kimi K2.6#Cerebras#AI inference#agent coding+2
AI How-to2026-05-23·12 min read·1
OpenAI Agent Improvement Loop Practical Guide: Why agents need to be continuously modified through trace·eval·Codex handoff after deployment
Based on the Agent Improvement Loop example in the OpenAI Cookbook, we organize a practical structure to continuously improve the agent during operation by connecting trace, feedback, eval, and Codex handoff.
#OpenAI Agents SDK#Agent Improvement Loop#AI agent operation#Trace+2
AI How-to2026-05-21·11 min·9
AI Image Provenance Workflow: C2PA, Watermarks, and Human Review
Build an evidence-first image-provenance workflow with original-file retention, C2PA validation, watermark signals, public labels, and a human review path. Use it when an absent signal must remain unknown rather than become a verdict.
#AI image provenance workflow#C2PA validation#Content Credentials#watermark verification+2
AI How-to2026-05-20·11 min read·1
Cursor 3 Agents Window Practical Introduction Guide: Why you should first fix the workbench, work tree, and review flow rather than launching many parallel agents.
Based on Cursor 3's Agents Window and Cloud Agents documents, we outlined the order in which you should fix interface selection, worktree isolation, environment setup, and hook-based approval gates when introducing a parallel coding agent to your team.
#Cursor 3#Agents Window#Cloud Agents#worktree+1
AI How-to2026-05-18·10 min read·1
arXiv AI False Reference Sanction Explanation: Why papers and technical documents written with AI must be fixed at the source verification gate before submission
As arXiv announced strong sanctions against false references and unverified phrases created by AI, the key to AI writing has become the pre-submission verification process, not the speed of creation. This article summarizes the source verification workflow and completion criteria that can be applied immediately by authors of papers, technical documents, and blogs.
#arXiv#AI Writing#false references#LLM Verification+2
AI How-to2026-05-16·10 min read·1
GitHub Copilot Cloud Agent Practical Introduction Guide: Why you should first fix Agents Secret, REST Task, and Auto model policies rather than running many agents
Based on the Copilot cloud agent update announced by GitHub in May 2026, we have summarized the order in which organization secret scope, task API permission, and Auto model cost policy should be tied to operation as a practical standard.
#GitHub Copilot#Copilot cloud agent#Agent tasks API#MCP+1

Google Open Knowledge Format Commentary: Why AI agent data sharing should design context file contracts before vector DB

GPT-5.3-Codex Practical Introduction Guide: Why long-term coding agents should fix task decomposition, breakpoints, and verification runbooks before model replacement

SK Hynix 1 Trillion Dollar Club Commentary: Why AI service costs should be looked at from the HBM capacity, power, and supply bottlenecks rather than the model

Kimi K2.6 + Cerebras Commentary: Why agent coding should design inference speed budget and routing criteria before model scores

OpenAI Agent Improvement Loop Practical Guide: Why agents need to be continuously modified through trace·eval·Codex handoff after deployment

AI Image Provenance Workflow: C2PA, Watermarks, and Human Review

Cursor 3 Agents Window Practical Introduction Guide: Why you should first fix the workbench, work tree, and review flow rather than launching many parallel agents.

arXiv AI False Reference Sanction Explanation: Why papers and technical documents written with AI must be fixed at the source verification gate before submission

GitHub Copilot Cloud Agent Practical Introduction Guide: Why you should first fix Agents Secret, REST Task, and Auto model policies rather than running many agents