I use a single Knowledge Base as the starting point of each and every AI session.
That knowledge Base doubles down as an Obsidian vault in which I also have various databases, plugins, etc).
My knowledge base contains notes about my goals, projects, tasks, products, ideas, etc. It also contains my articles (dsebastien.net), my tools and infrastructure, my open source and private projects with their repositories, specific instructions for each, etc.
In addition, I have built tools to help understand the taxonomy/ontology of my knowledge base, know which types of notes exist, which properties, which template to use, where to store what, etc.
On the human side, everything is automated, which lets me capture efficiently. On the AI side, all the knowledge is easy to find and leverage.
The whole knowledge base is like composable context for AI.
I can start sessions with sentences like "let's work on the music practice plugin; add xxxx" and AI will understand what project it is, where to find the sources, where to clone or update the repo on my machine, etc. And from the context within the repository itself, it will then know how the code is organized etc. The nice thing is that this also enables me to work on multiple projects in parallel easily; either in different AI sessions, or in a single session for work that spans multiple projects/repositories. For instance I have a repo which a product, another with the product documentation. When I ask for something in the context of that project, AI works seamlessly on both.
My knowledge base contains notes about my goals, projects, tasks, products, ideas, etc. It also contains my articles (dsebastien.net), my tools and infrastructure, my open source and private projects with their repositories, specific instructions for each, etc.
In addition, I have built tools to help understand the taxonomy/ontology of my knowledge base, know which types of notes exist, which properties, which template to use, where to store what, etc.
On the human side, everything is automated, which lets me capture efficiently. On the AI side, all the knowledge is easy to find and leverage.
The whole knowledge base is like composable context for AI.
I can start sessions with sentences like "let's work on the music practice plugin; add xxxx" and AI will understand what project it is, where to find the sources, where to clone or update the repo on my machine, etc. And from the context within the repository itself, it will then know how the code is organized etc. The nice thing is that this also enables me to work on multiple projects in parallel easily; either in different AI sessions, or in a single session for work that spans multiple projects/repositories. For instance I have a repo which a product, another with the product documentation. When I ask for something in the context of that project, AI works seamlessly on both.
BTW this product of mine is a 1:1 copy of what I use for myself: https://store.dsebastien.net/osk