<?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>Open Weights on TechLLM</title><link>https://techllm.github.io/tags/open-weights/</link><description>Recent content in Open Weights on TechLLM</description><generator>Hugo</generator><language>ko</language><lastBuildDate>Wed, 22 Apr 2026 01:41:48 +0900</lastBuildDate><atom:link href="https://techllm.github.io/tags/open-weights/index.xml" rel="self" type="application/rss+xml"/><item><title>Kimi K2.6, 오픈 웨이트 진영의 새 선두주자일까</title><link>https://techllm.github.io/posts/kimi-k2-6-leading-open-weights-model/</link><pubDate>Wed, 22 Apr 2026 01:41:48 +0900</pubDate><guid>https://techllm.github.io/posts/kimi-k2-6-leading-open-weights-model/</guid><description>Artificial Analysis의 Kimi K2.6 분석 글을 바탕으로, 왜 이 모델이 오픈 웨이트 진영에서 주목받는지 한국어로 정리했습니다. 성능, 비용, 포지셔닝, 실전 의미를 함께 봅니다.</description></item></channel></rss>