Bastra Recall
p/bastra-recall
Your AI's working memory. Local, open, in Markdown.
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17h ago

What does your AI assistant keep forgetting, even after you have explained it three times?

I am building a memory for AI assistants and I would rather collect real cases than assumptions before I go further.

For me it is three kinds that keep coming back. Decisions first: why we took a detour two months ago that every newcomer immediately wants to straighten out again. Then preferences: how I want things written, explained from scratch every time. And the expensive ones, hard-won fixes where I know I have had this problem before but no longer how I solved it.

What I am actually curious about: have you built a habit against it? I mostly see three approaches. Some keep a rules file in the project and stop maintaining it after a while. Some paste a block of context at the start of every session. Some have made their peace with it and just explain again.

And the question behind it: where would you want that memory to live? On your machine, in files you can read, or with a service that takes the work off your hands and keeps the data? I went with the first, but I am genuinely curious how many of you see it differently.

Bastra Recall - Your AI's working memory. Local, open, in Markdown.

Every new AI session starts from nothing. Bastra Recall gives your AI tools a memory that stays: preferences, decisions and hard-won fixes live as Markdown files on your own machine, ready again next session. Works with Claude Code, Claude Desktop, Codex/ChatGPT Desktop and Cursor. The files are yours: open them in Obsidian or any editor. Nothing leaves your machine, no account, no cloud. Open source under MIT. macOS fully supported, Linux runs daemon, CLI, MCP and hooks. Windows in progress.