AI tools will come and go. Your thinking shouldn't disappear with them.
Why I built ThreadRecall, a local memory layer for the work people now do inside AI conversations.
I'm not an engineer.
I'm a creative director who started using AI tools the way a lot of people now use them: constantly, across different apps, for thinking as much as doing.
Claude for deeper reasoning. ChatGPT for quick drafts. Gemini or Perplexity for another angle. Codex when I am working through software. Different tools, different strengths, often in the same day.
The fragile part
At first, that felt powerful. Then it started to feel strangely fragile.
Every good conversation lived in its own place. A product decision in Claude. A naming discussion in ChatGPT. A technical path in Codex. A research thread somewhere else. Each one made sense while I was inside it, but the moment I switched tools, the thinking stopped traveling with me.
Not just the answer. The path to the answer.
That is the part that bothered me.
Not just the final answer. The path to the answer. The messy middle. The moment where a phrase finally clicked. The tradeoff I almost forgot. The reason I chose one direction over another. The small bit of context that made the next conversation better.
AI tools are becoming places where real work happens. Not just prompts and outputs, but judgment, taste, strategy, debugging, research, planning, writing, and decision-making. Yet most of that work is trapped inside app histories that do not talk to each other.
Even inside one tool, memory is not really memory
Long conversations get compressed. Context gets summarized. Details fall away. The interface makes it feel like the thread is still there, but the exact thinking can be gone, softened, or buried.
That may make sense for the AI companies. Perfect memory is expensive. Cross-tool memory is not in any one company's interest. Claude has no reason to remember what I did in ChatGPT. ChatGPT has no reason to preserve what I worked out in Claude. Each tool wants to be the place where the work happens.
But I do not think my thinking should belong to the tool I happened to be using that day.
Why ThreadRecall is local and narrow
That was the starting point for ThreadRecall.
I wanted something outside the AI apps. Something quiet. Something local. Something that could remember the conversations that mattered without recording my whole screen, uploading my archive, or making me manage yet another knowledge system.
ThreadRecall is built around a simple idea: your AI conversations should become your memory layer.
Not everything on your computer. Not every meeting. Not every tab. Just the high-signal conversations where you were actively thinking with AI.
It captures supported AI conversations on your Mac, keeps them local, makes them searchable, and lets you bring the right context back when you need it. A ChatGPT thread can become useful inside Claude. A Codex debugging session can be found later. A decision you made weeks ago does not have to be reconstructed from scratch.
The tool is not the durable thing
AI tools will keep changing.
Some will get acquired. Some will pivot. Some will disappear. Some will introduce memory features that only work inside their own product. Some will ask you to trust a cloud archive. Some will be great for a while and then stop fitting your workflow.
The tool is not the durable thing.
The thinking is.
That is why ThreadRecall is local-first. It is why it is narrow by design. It is why I care more about preserving the thread of your work than recording every possible thing on your Mac.
I built it because I needed it. I suspect a lot of people who work seriously with AI need some version of it too.
Not because we want more software. Because we are tired of starting from zero.
ThreadRecall captures supported AI conversations locally on your Mac, then lets you search and recall them when you need the context again.
