For personal knowledge, most graph-based ideas were pioneered by Niklas Luhmann in his Zettelkasten system. This system is described with lots of detail in [1] and made him extraordinarily productive.
Essentially, Luhmann had one small card per semantic unit. Cards had alphanumeric IDs. Cards backlinked to other cards using said IDs. He also used a card branching mechanism implemented in IDs as e.g. 123 -> 123a -> 123a1 which he called Folgezettel.
Lastly, he also had cards whose role was mostly to connect topics by serving as a link hub.
It's a really simple system that you can implement using plain text, Org, Markdown or some note taking application like Apple Notes or OneNote plus a few conventions.
After trying many things, for personal use I think nothing beats plain text (or a plain text format). I don't need a server, I can easily sync things, and it's really future proof.
I have also scaled this kind of setup to larger organizations, albeit using a more classical wiki-like approach (read longer articles instead of small semantic unit cards). For example, GitLab has excellent continuous integration. You can use an Emacs or Pandoc inside a Docker to export Org or Markdown files into an HTML.
org-mode is immediately where my mind went when I saw this story on HN.
It's an interesting problem domain that I think benefits from having so many people approach it from different directions, like the person you mention, OP, and all the people that hack on GTD apps, or org-mode customizations, etc etc.
I don't really have any valuable insight in this, just a mishmash of documents with TODOs, notes, plans and knowledge. I wonder how disruptive it would be if we had a highly focused AI that really understood how to organize people?
Org is great because it's plain text, but at the same time offers outlining, timestamps, tables, links and lots of other features. Following Emacs tradition, it doesn't impose any workflow on you. It just gives you some primitives to build the system you want.
A great feature of Org are programmatic views of your data, possibly from many files at the same time. Some are already implemented, but still customizable, like Org Agenda. I use Org Agenda to have a Kanban-like view of my projects. I can simply see all WIP tasks, approaching deadlines and events in a little plain text window.
That's, I think, the minimum viable productivity system. You have a list of projects with tasks, some deadlines, some events and some inbox. You select a few tasks to do every day, like Ivy Lee suggested, and you do them. You want to see these plus deadlines and scheduled events which can be easily achieved with Org Agenda.
A GTD-like system would tag tasks with contexts, and offer some context-specific views, but I find that's too much work. Another pitfall of GTD, in my opinion, is that it doesn't encourage WIP limits which eventually makes it overwhelming.
I credit GTD with popularizing the need for an inbox, the task-centric view to time management, and the distinction between tasks, tasks with deadlines and events. But I think GTD leads to too much planning waste, projects broken into tasks that get constantly outdated.
I have been thinking about incorporating initial AI/machine learning support in Contextualise including auto-tagging and relationship (associations) recommendations.
Essentially, Luhmann had one small card per semantic unit. Cards had alphanumeric IDs. Cards backlinked to other cards using said IDs. He also used a card branching mechanism implemented in IDs as e.g. 123 -> 123a -> 123a1 which he called Folgezettel.
Lastly, he also had cards whose role was mostly to connect topics by serving as a link hub.
It's a really simple system that you can implement using plain text, Org, Markdown or some note taking application like Apple Notes or OneNote plus a few conventions.
After trying many things, for personal use I think nothing beats plain text (or a plain text format). I don't need a server, I can easily sync things, and it's really future proof.
I have also scaled this kind of setup to larger organizations, albeit using a more classical wiki-like approach (read longer articles instead of small semantic unit cards). For example, GitLab has excellent continuous integration. You can use an Emacs or Pandoc inside a Docker to export Org or Markdown files into an HTML.
[1] https://takesmartnotes.com/