<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Systems Thinking on Haxlys's Blog</title><link>https://hugo-blog-static-site.haxlys.workers.dev/mental_models/systems-thinking/</link><description>Recent content in Systems Thinking on Haxlys's Blog</description><generator>Hugo -- 0.152.2</generator><language>kr-ko</language><lastBuildDate>Mon, 04 May 2026 22:21:45 +0900</lastBuildDate><atom:link href="https://hugo-blog-static-site.haxlys.workers.dev/mental_models/systems-thinking/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM 추론 파이프라인 완전 해부: Prefill, Decode, KV Cache, Quantization</title><link>https://hugo-blog-static-site.haxlys.workers.dev/posts/llm-inference-prefill-decode-kv-cache/</link><pubDate>Mon, 04 May 2026 22:21:45 +0900</pubDate><guid>https://hugo-blog-static-site.haxlys.workers.dev/posts/llm-inference-prefill-decode-kv-cache/</guid><description>프롬프트 입력부터 토큰 스트리밍까지, LLM 추론이 실제로 어떻게 돌아가는지 Prefill/Decode 분리와 KV 캐시 중심으로 정리합니다.</description></item><item><title>컴파운드 엔지니어링: AI 에이전트 개발의 복리 루프</title><link>https://hugo-blog-static-site.haxlys.workers.dev/software/compound-engineering/</link><pubDate>Thu, 19 Feb 2026 17:40:07 +0900</pubDate><guid>https://hugo-blog-static-site.haxlys.workers.dev/software/compound-engineering/</guid><description>Every의 Compound Engineering 가이드를 바탕으로, AI 에이전트 시대의 복리형 개발 루프와 핵심 원칙을 정리합니다.</description></item></channel></rss>