For face or fingerprint unlock methods that log in but don't unlock the keyring
This works on Pop OS and probably any Ubuntu based distro
Uses https://codeberg.org/umglurf/gnome-keyring-unlock and https://github.com/tpm2-software/tpm2-tools
For face or fingerprint unlock methods that log in but don't unlock the keyring
This works on Pop OS and probably any Ubuntu based distro
Uses https://codeberg.org/umglurf/gnome-keyring-unlock and https://github.com/tpm2-software/tpm2-tools
The only way I've succeeded so far is to employ SSH.
Assuming you are new to this like me, first I'd like to share with you that your Mac has a SSH config file in a .ssh directory. The config file is where you draw relations of your SSH keys to each GitHub (or Bitbucket) account, and all your SSH keys generated are saved into .ssh directory by default. You can navigate to it by running cd ~/.ssh within your terminal, open the config file with any editor, and it should look something like this:
Host * AddKeysToAgent yes
> UseKeyChain yes
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.