<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Memory on Noureddine RAMDI</title><link>https://ramdi.fr/tags/ai-memory/</link><description>Recent content in Ai-Memory on Noureddine RAMDI</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 23 May 2026 20:41:27 +0000</lastBuildDate><atom:link href="https://ramdi.fr/tags/ai-memory/index.xml" rel="self" type="application/rss+xml"/><item><title>Cavemem: deterministic compression and local memory for AI coding assistants</title><link>https://ramdi.fr/github-stars/cavemem-deterministic-compression-and-local-memory-for-ai-coding-assistants/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/cavemem-deterministic-compression-and-local-memory-for-ai-coding-assistants/</guid><description>Cavemem offers a local-first persistent memory layer for AI coding assistants, compressing session data with a deterministic grammar to reduce token count by 75%. It stores observations in SQLite with hybrid search and runs without a daemon.</description></item><item><title>Supermemory: a unified memory and context engine for AI applications</title><link>https://ramdi.fr/github-stars/supermemory-a-unified-memory-and-context-engine-for-ai-applications/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/supermemory-a-unified-memory-and-context-engine-for-ai-applications/</guid><description>Supermemory replaces traditional RAG stacks with a unified AI memory layer, offering fast, hybrid memory retrieval and automatic fact extraction in ~50ms. Here&amp;rsquo;s how it works and how to try it.</description></item><item><title>claude os: speeding up persistent ai memory for code with hybrid tree-sitter indexing</title><link>https://ramdi.fr/github-stars/claude-os-speeding-up-persistent-ai-memory-for-code-with-hybrid-tree-sitter-indexing/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/claude-os-speeding-up-persistent-ai-memory-for-code-with-hybrid-tree-sitter-indexing/</guid><description>Claude OS cuts codebase indexing from hours to seconds using hybrid tree-sitter parsing, enabling fast persistent AI memory for Claude Code projects with local-first data storage.</description></item><item><title>Memcord: a privacy-first self-hosted MCP server for AI memory management</title><link>https://ramdi.fr/github-stars/memcord-a-privacy-first-self-hosted-mcp-server-for-ai-memory-management/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/memcord-a-privacy-first-self-hosted-mcp-server-for-ai-memory-management/</guid><description>Memcord is a self-hosted MCP server enabling local-first AI memory with slot-based context isolation and multiple summarization backends, designed for privacy and developer ergonomics.</description></item><item><title>MemPalace: local-first AI memory with strong semantic retrieval and no cloud dependency</title><link>https://ramdi.fr/github-stars/mempalace-local-first-ai-memory-with-strong-semantic-retrieval-and-no-cloud-dependency/</link><pubDate>Sun, 26 Apr 2026 17:51:11 +0000</pubDate><guid>https://ramdi.fr/github-stars/mempalace-local-first-ai-memory-with-strong-semantic-retrieval-and-no-cloud-dependency/</guid><description>MemPalace offers a local-first AI memory system with 96.6% recall on conversation history retrieval without any cloud or LLM calls, emphasizing privacy and efficient semantic search.</description></item></channel></rss>