Modern AI tools are powerful, but only if you understand their blind spots. One of the biggest issues I’ve hit recently? Memory.
Not my memory. The model’s.
I’ve been building an internal system to automate my weekly blog posts, using Claude for real-time web search and analysis and ChatGPT for formatting, workflow orchestration, and integrations with tools like Zapier. But even with two advanced agents working side by side, I ran into the same wall every time:
AI tools don’t remember what you need them to—unless you make them.
The Core Issue: AI Has No Native Shared Memory
Let’s get specific.
- Claude’s API, as of now, does not retain any persistent memory between sessions. It can search the web and return excellent results, but it has no way to track progress across multiple tasks unless you build external memory handling around it.
- ChatGPT, on the other hand, does have memory…but only within the ChatGPT web interface. The OpenAI API (used in automations or apps) has no memory unless you explicitly simulate it using a database or token-passing structure.
This creates a strange problem for anyone trying to build a recurring, high-context workflow: your AI co-pilots forget what they did last time.
Real Example: Automating My Weekly Blog Post
Here’s what I wanted to do:
- Use Claude to pull real-time info and summarize trends related to the projects I’m working on.
- Pipe that into ChatGPT to format a blog post based on its memory of my projects and trigger workflows (e.g., add it to Notion or create a Google Doc).
- Do this weekly, without repeating past content or manually updating context every time.
Seems simple, right? It’s not.
Because neither AI retains memory across systems, you have to manually:
- Feed each update from week to week.
- Store and track published blog history.
- Format prompts carefully to avoid duplication.
- Simulate continuity through external logic (Zapier, Notion, or custom code).