Watching Codex write a parser while Claude reviews the same change in the next pane is no longer a party trick. Anthropic Code Review, VS Code multi-agent workflows, builder–validator chains, and cross-provider hubs like CCB (Claude Codex Bridge) all point the same way: trust comes less from one model's intelligence than from agents with different failure modes inspecting each other. Once the copy-paste tax disappears, the real design work is circuit breakers, role separation, and knowing when two agents are enough.

In July, Musinsa's tech blog posted numbers you rarely see from a Korean company: 64% LLM cost reduction, 98% cache hit rate. It's effectively the first Korean tech blog post to publish measured prompt-caching results. But what lingers isn't the savings — it's the nature of the number itself. Cache hit rate isn't a discount line on your invoice; it's a graded score on one design question: did you put what never changes in front and what always changes at the end? This post lines up the other answer sheets to the same exam — a security agent that jumped from 7% to 84%, Anthropic declaring incidents when hit rate drops, Manus calling KV-cache hit rate 'the single most important metric for a production agent' — and walks from how one token breaks the hash chain, through a four-provider caching policy comparison, to the order in which to raise your own score.

In RAG tutorials, chunking is the one-line preprocessing chore. A library that does only that step now pulls 1.19 million monthly downloads, and its two creators went through Y Combinator. The interesting part comes after: the '33x faster' claim was against the slowest competitor (1.06x against the leader), '100GB/s' is what you get when you stop counting tokens entirely, and the founder himself admitted on HN that 'perfect splits don't move the needle much' for retrieval quality. Downloads kept climbing anyway. Against a research landscape that keeps confirming semantic chunking can't beat plain recursive splitting, this post dissects what people were actually buying, why long context didn't kill chunking, which of the 11 chunkers to pick, and how the company pivoted away while the library stayed.

How do you engineer an LLM agent harness for a domain where wrong answers are expensive? Using Korea's ISMS-P (102 controls) as the vehicle, Claude Code reviews and audits through a domain-experts → interviewer → auditor pipeline. 10 reusable harness patterns + honest limits.

Public domain novel characters as AI agents. RAG + cognitive engine over the original text -- talk to Sherlock Holmes.

A native macOS meeting notes app. Real-time recording, automatic transcription, and AI-powered summaries -- all processed locally with on-device STT and LLM.