<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Memory on Blackwell Systems</title><link>https://blog.blackwell-systems.com/tags/memory/</link><description>Recent content in Memory on Blackwell Systems</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 20 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.blackwell-systems.com/tags/memory/index.xml" rel="self" type="application/rss+xml"/><item><title>The Code Intelligence Landscape: Context, Memory, and Proofs</title><link>https://blog.blackwell-systems.com/posts/code-intelligence-memory-landscape/</link><pubDate>Wed, 20 May 2026 00:00:00 +0000</pubDate><guid>https://blog.blackwell-systems.com/posts/code-intelligence-memory-landscape/</guid><description>AI coding agents have a context problem. The tools solving it fall into four categories: context packers, code graphs, memory systems, and runtime observability. Each solves one piece. None versions the intelligence. None proves anything. None learns without poisoning itself over time. This article explores the landscape and argues that content-addressed code graphs with cryptographic proofs are the missing foundation.</description></item></channel></rss>