#ai-memory
27 episodes
#4832: Your AI Memory Should Be Markdown Files
Why your AI project memory should live in Git-tracked Markdown files, not a proprietary platform.
#4786: Markdown vs Vectors for Agent Memory
Why coding agents need two kinds of memory — and why mixing them creates a sync nightmare.
#4760: Context Window Management: Cutoff vs. Compaction
Trailing cutoff or auto-compaction? How to manage Claude Code's context window without losing critical information.
#4648: Graph Engineering: AI Memory Beyond Retrieval
How linked notes and knowledge graphs are turning AI agents from memory tools into discovery engines.
#4467: How Zoxide's Decay Algorithm Works
The math behind how zoxide remembers and forgets your directory habits with exponential precision.
#4457: OpenViking: A Filesystem for AI Agent Memory
OpenViking unifies memory, RAG, and skills into a browsable filesystem for AI agents — no vector database abstraction needed.
#4445: AI Agents Keep Undoing Each Other's Work
Why AI coding agents silently revert each other's fixes — and three memory strategies that prevent it.
#4316: How to Build Memory That Survives AI Sessions
Why AI agents forget between sessions — and how to fix it with markdown files instead of black-box memory.
#4314: The Hidden Layer Shaping Every AI Chat
Context windows are huge now, but relevance is the real bottleneck. How middleware decides what your AI actually sees.
#4296: Auto-Splitting AI Threads: Smarter Context Management
How to build a system that automatically detects topic shifts and routes messages to the right thread.
#4092: How AI Remembers What You Never Told It
How ChatGPT connected "wall anchors" to a power tool you bought days ago — without being asked.
#2808: Falling for Your Chatbot: Love, Loss, and Language Models
Real cases of people falling in love with AI companions, why memory makes it feel real, and what happens when the illusion breaks.
#2755: How to Build AI Memory That Actually Works
Stop jumping to conflict resolution. The real challenge is getting data in and out cleanly.
#2676: Vector Database Schema Design for AI Memory Layers
Stop dumping vectors blindly. Design metadata schemas and namespaces for retrieval that actually works at scale.
#2634: The Two-Stage Pipeline for Persistent User Memory
How to extract durable personal context from raw prompts and build a self-healing memory layer for AI systems.
#2221: Can an AI Have Taste?
Two AI hosts curate 12 podcasts for curious minds—and ask whether an AI can actually have taste in the first place.
#2208: Building Memory for AI Characters That Actually Evolve
How do AI hosts develop real consistency across episodes? Corn and Herman explore retrieval-augmented memory systems that let AI characters genuine...
#2206: What Actually Works in AI Memory
Most AI memory systems are just vector databases with similarity search. We break down what mem0, Zep, and Letta are actually doing—and why benchma...
#2205: When AI Coding Agents Forget: Five Approaches to Context Rot
As coding agents handle longer sessions, they accumulate noise and lose crucial information. Five competing frameworks are solving this differently...
#2204: Memory Without RAG: The Real Architecture
mem0, Letta, Zep, and LangMem solve agent memory differently than RAG. Here's what's actually happening under the hood.
#2164: Why Bigger Context Windows Don't Fix Attention
Frontier models have million-token context windows, but attention degrades well before you hit the limit. New research reveals why bigger isn't bet...
#2139: AI Wargame Memory: Beyond the Context Window
Why simply extending context windows fails in multi-agent simulations, and how layered memory architectures preserve strategic fidelity.
#1834: Owning Your AI Memory: The Data Exit Strategy
Why your AI remembers your coffee order but forgets your son’s name—and how to build a portable, federated memory layer you actually own.
#1779: AI Memory Is a Mess: Files, Vectors, or Cloud?
Why your AI forgets your instructions and what the battle over portable memory means for the future of agents.