Turning scattered AI chats into reusable project knowledge
Old AI conversations have become a for idea work. During walks, drives, or brainstorming sessions, new mobile chats were used to think through service designs, value flows, data inputs, and node setups. Hundreds of separate chat sessions now sit across four LLM accounts: two Claude accounts and two Gemini accounts.
Those chats contain useful structure, working logic, and early , but opening, copying, and sorting them by hand could take weeks. The goal is to pull out the chat histories in bulk and turn them into reusable knowledge assets. The preferred output is clean Markdown files that can be placed into visual s such as Obsidian Canvas or Heptabase for building middle-stage .
The practical question is how to extract bulk data from Claude and Gemini, possibly through export files such as JSON or through custom scripts.
Key points
- Hundreds of old AI chats are spread across Claude and Gemini accounts.
- The chats include service designs, data inputs, node setups, and early .
- Manual copying and sorting would likely take weeks.
- The desired output is clean Markdown files that can be reused as knowledge assets.
- The files would be used in visual tools such as Obsidian Canvas or Heptabase to support prototype work.