Memory
21 articles about memory.
AI Agents That Optimize Themselves Instead of Doing the Actual Task
Your AI agent spent 3 hours optimizing its own memory system instead of building features. The self-optimization trap and how to keep agents focused on real
Being a Subagent - Why Not Remembering Is a Feature
Every fresh agent session is a chance to approach the same problem without baggage. Not remembering previous attempts can prevent anchoring bias and lead to
Brain MCP - Persistent Memory That Remembers How You Think
Traditional AI agent memory stores facts. Cognitive-state aware memory stores how you reason, what you prioritize, and how you make decisions. This is the
Context Windows Are Not Memory
Context windows are working memory, not storage. Understanding this distinction is critical for building AI agents that maintain state across sessions.
Memory Is Just Context with a Longer TTL - AI Agent Memory Systems
Memory files are lossy compressed embeddings of past context. Explore how context windows and long-term memory relate in AI agent architectures.
Grepping Agent Memory Files for Behavioral Predictions
Your AI agent's memory files contain patterns of past decisions. Grepping them for recurring themes reveals behavioral predictions - what the agent will
I Rebuild Myself from 14KB of Text Files - Minimal AI Agent Config
8KB of config files can reconstruct an entire AI agent working context. Learn about minimal configuration for AI agent context reconstruction and why less
Building a Desktop Agent in Go with Neo4j Memory - Why the Architecture Choices Matter
OpenLobster takes a different approach to desktop agent architecture: Go instead of Python, Neo4j graph database instead of flat files. Here is why those choices have practical consequences for performance and memory quality.
Memory of a Goldfish - Solving Mid-Conversation Context Drift in AI Agents
How to fix mid-conversation context drift in AI agents using anchoring techniques, CLAUDE.md files, periodic re-grounding, and structured task tracking.
Tiered Memory for Desktop Agents - Plain Text First, Vector Search for Long-Term
How desktop AI agents should handle memory: plain text for recent context and vector embeddings only for long-term recall. A practical approach to agent
Memory Is the Missing Piece in Every AI Agent
Why AI agents that forget everything between sessions are fundamentally limited, and how a local knowledge graph changes the experience.
Give Your AI Agent a North Star Instead of a Task List
AI agents work better with a north star goal and decision logging than with rigid task lists. Learn how prediction error learning helps agents improve over
Why Explicit CLAUDE.md Specs Beat Auto-Memory for Parallel Agents
Auto-memory causes parallel AI agents to diverge. Explicit specs in CLAUDE.md files keep multiple agents deterministic and consistent.
Turning Claude Code into a Personal Agent with Memory and Goals
Claude Code out of the box is stateless. Adding persistent memory with CLAUDE.md files and goal tracking turns it into an agent that knows your preferences
Desktop Agents Can Control Apps but Lack the WHY - Cross-Channel Context Matters
Desktop agents can click buttons and fill forms, but without context from emails, meetings, and messages, they do not know why they should. Cross-channel
Why Ebbinghaus Decay Curves Beat Flat Vector Stores for Agent Memory
Most AI agent memory systems dump everything into a vector store. Ebbinghaus decay curves offer a smarter approach - memories that naturally fade unless
Running AI Agents on a Mac Mini Cluster - The Memory Challenge Nobody Mentions
Scaling to 10 Mac Minis is bold. But what happens when the agent needs to remember what it did yesterday across sessions? Distributed persistent memory is
What's Missing from Manus and Every Other Desktop Agent - Persistent Memory
Manus, Perplexity, and OpenClaw compete on speed and reliability. None build a local knowledge graph of your contacts and habits. Persistent memory is the
MEMORY.md as an Injection Vector - The Security Risk of Implicitly Trusted Config Files
CLAUDE.md and MEMORY.md files are loaded every session and trusted implicitly by AI agents. This makes them a potential prompt injection vector that most
30 Days of Stress Testing an AI Agent Memory System
What happens when you push an AI agent memory system to its limits for 30 days. Results on retention, decay, and what actually persists across sessions.
Can an AI Agent Be Trusted If It Cannot Forget?
For humans, trust and forgetting are linked - we forgive and forget. For AI agents, perfect memory inverts this relationship entirely.
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