<?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>MemAlign on TechLLM</title><link>https://techllm.github.io/tags/memalign/</link><description>Recent content in MemAlign on TechLLM</description><generator>Hugo</generator><language>ko</language><lastBuildDate>Thu, 30 Apr 2026 19:15:00 +0900</lastBuildDate><atom:link href="https://techllm.github.io/tags/memalign/index.xml" rel="self" type="application/rss+xml"/><item><title>AI 에이전트의 다음 확장축, 메모리 스케일링</title><link>https://techllm.github.io/posts/databricks-memory-scaling-ai-agents/</link><pubDate>Thu, 30 Apr 2026 19:15:00 +0900</pubDate><guid>https://techllm.github.io/posts/databricks-memory-scaling-ai-agents/</guid><description>Databricks의 Memory Scaling for AI Agents를 바탕으로 에이전트 성능을 높이는 새로운 축으로서 메모리 스케일링, MemAlign 실험, 조직 지식 저장소와 거버넌스 문제를 정리한다.</description></item></channel></rss>