Insights & Updates from Nexus-Catalyst
Articles, tutorials, and stories about building better AI workflows with persistent memory.
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What Should an AI Remember - and What Should It Forget?
A practical framework for deciding which preferences, decisions, events, and project details belong in persistent AI memory—and which should expire or be deleted.
How to Move AI Context Between ChatGPT, Claude, Cursor, and Codex
Compare copy-paste, project files, export/import, and shared memory for carrying decisions and project context between AI tools.
How AI Memory Retrieval Fails: Similarity, Recency, and Contradictions
AI memory can store the right information and still retrieve the wrong context. Learn the most common failure modes and the signals that improve recall quality.
ChatGPT Memory vs. Claude Memory vs. a Shared MCP Memory Layer
Compare native ChatGPT and Claude memory with a shared MCP memory layer—and learn when personalization, project continuity, or cross-tool portability matters most.
Shared AI Memory for Software Teams: Onboarding, Decisions, and Handoffs
See how shared AI memory can preserve engineering decisions, improve handoffs, and help new team members understand why a system works the way it does.
How to Give Cursor Persistent Memory Across Projects
Connect Cursor to a shared MCP memory layer so decisions, fixes, and preferences remain available across sessions and repositories.
How to Evaluate an AI Memory System: 10 Questions to Ask
A practical buyer and builder checklist for evaluating AI memory quality, portability, privacy, deletion, integrations, team controls, and failure handling.
RAG vs. AI Memory: What's the Difference?
RAG retrieves external knowledge for a question. AI memory preserves context learned through ongoing work. Learn where they overlap and why agents often need both.
How to Add Persistent Memory to Claude Code with MCP
Connect Claude Code to a shared MCP memory server so project decisions, fixes, and preferences remain available across coding sessions.
Local vs. Cloud AI Memory: Privacy, Portability, and Tradeoffs
Compare local and cloud AI memory across privacy, setup, availability, collaboration, portability, security, and operational responsibility.
Semantic vs. Episodic vs. Procedural Memory for AI Agents
Learn how semantic, episodic, procedural, and working memory serve different roles in an AI agent—and why useful systems need more than one kind.
Vector Search vs. Knowledge Graph for AI Memory
Two very different ways to find a memory. One is good at 'what does this relate to,' the other at 'give me the exact fact.' Here's where each breaks down.
How to Give ChatGPT and Claude the Same Memory
You use more than one AI. Here's how to make them draw from a single shared memory instead of each keeping its own isolated notes.
Choosing a Vector Database for AI Memory: What We Learned Evaluating Qdrant
We ran a head-to-head evaluation of a self-hosted vector database against a managed provider's vector store before committing to either. Here's what the numbers actually showed.
How MCP-Based Persistent Memory Works
A plain-English breakdown of what a memory server actually does — recall, response, and storage — and why hybrid vector + knowledge graph retrieval matters.
Building a Per-User Vector Store System
Semantic search over AI memory only works if isolation is airtight. Here's how we designed per-user vector storage that scales without leaking a single embedding across tenants.
Shared Context for AI Coding Teams
When a whole team codes with AI, each person's assistant knows only their slice of the project. Shared context turns those isolated views into one.
Vendor Lock-In in AI Platforms: The Hidden Cost
Lock-in on AI platforms rarely shows up as a line item. It shows up as a migration you can't afford to make. Here's what it actually costs, and what to check for before you commit.
Cross-Platform AI Memory: Architecture Decisions
Making memory work identically across Claude, ChatGPT, and Cursor means designing for the lowest common denominator without losing the features that make it useful. Here's how we approached it.
Switching Between Cursor, Claude Code, and ChatGPT: What Happens to Your Context?
The real cost of moving between AI coding tools isn't a missing feature in any one of them — it's that none of them talk to each other.
Why Your AI Memories Should Be Portable (The GitHub Model for AI)
AI memory locked inside one chat client disappears the moment you switch tools. Here's why portability, not any single vendor's ecosystem, is the right foundation.
What Is a Memory Layer for AI Agents?
A definition of the emerging category — what a memory layer is, what it isn't, and why AI agents need one to be useful beyond a single session.