Empirical is a memory system for everything you do with AI. Code, notes, ideas, project context it follows you across tools and sessions. Most AI tools trap your ideas inside their app. Every time you switch, you lose context or have to start over. Empirical works across ChatGPT, Claude, Codex, CLIs and more, keeping your notes, code, and ideas connected. Give your claw agent cloud memory storage and recall. A personal memory layer. Not another AI. The memory your AI tools have been missing.
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Maker
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What inspired you to build this? What problem were you trying to solve? How did your approach evolve?
I started using a bunch of AI tools at the same time. Claude, Copilot, Codex, ChatGPT, even their CLI tools. Whatever worked best for what I was doing.
But pretty quickly, everything got messy.
My ideas were all over the place. Some in chats, some in terminals, some just gone. I kept having to repeat myself or lost stuff I already figured out.
That’s what made me start building this.
It wasn’t that the tools were bad. It was that my memory didn’t go with me. There wasn’t one place where my thinking actually lived and grew over time.
At first, I thought I just needed a better notes app. But that wasn’t it.
As I worked on it, I realized the real problem was continuity. My thinking kept resetting every time I switched tools.
So I shifted to building something that keeps track of that. A memory layer that holds onto context and lets me pick up where I left off, no matter what tool I’m using.
The goal is simple. Stop starting over. Start building on what I already know.
And over time, that doesn’t just mean text. It could be notes, images, whatever I capture, all connected and easy to find again.
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Maker
I’ve been using Empirical as my memory layer across AI tools.
ChatGPT memory helps. Local MD files help.
But neither travels cleanly across everything I use, and packing too much into MD files eats context and tokens.
With Empirical, I keep my AGENTS.md lean and let Codex pull context dynamically when it actually needs it.
I can open ChatGPT on my phone, connected to Empirical, and it pulls the same memory context and writing tone I use in Codex or any other connected AI tool. That means: * less repeated setup * cleaner, cheaper prompts * more consistent output across sessions
I’ve been using Empirical as my memory layer across AI tools.
ChatGPT memory helps.
Local MD files help.
But neither travels cleanly across everything I use, and packing too much into MD files eats context and tokens.
With Empirical, I keep my AGENTS.md lean and let Codex pull context dynamically when it actually needs it.
I can open ChatGPT on my phone, connected to Empirical, and it pulls the same memory context and writing tone I use in Codex or any other connected AI tool.
That means:
* less repeated setup
* cleaner, cheaper prompts
* more consistent output across sessions
This is just the tip of the iceberg.
I wrote up a Codex example here:
https://empirical.gauzza.com/blog/codex-session-tone-voice-how-i-used-codex-empirical-to-lock-in-my-writing-voice/