But synthesizing your notes into tags and remember the index of tags is learning!

Isn’t that the goal — to process your notes, so as to come to new insights and connections?

some people have huge notebooks in real world, appended tags by sticky note or colour.

What do you mean by remembering which notes can be linked

> Are there faster more productive ways though?

I mean, given that you pose this question and leave it entirely unanswered, the rest of your response reads pretty condescendingly to me. A knowledge graph in markdown is not propped up by Big Notetaking, what an asinine stance.

> Are they faster more productive ways though?

I have ADHD, and I couldn't function without my notes.

I think this is an avenue LLMs will shine. Contextual search. "I was writing about one idea for an app I had a few weeks back. I think it had something to do with a calendar? Can you help?" And that should be enough for GPT4 to actually act on I'd say.

If you're okay with losing the privacy of your notes. A locally hosted model seems more reasonable, though I don't know when it'll get good enough to have the context it needs to do proper search in a thicc stack of notes. Something something vector databases, I guess.

Sure it will. I've got similar setups working effectively with both OpenAI Api and Falcon. Though there is room for improvement, it's moving at a rapid enough pace and it's already better than old school search.

There are (probably many) solutions being developed for this. Open source, self-hosted LLMs that can be used to query private data sets. You can query private data sets with an LLM hosted on your local machine. There are some projects online doing these kinds of projects with LangChain and using the leaked Meta LLM. It also ties into the "second brain" concept, being able to query your own data exclusively (there's a book on this, but you can get the gist of the concept on youtube).

I myself have a huge OneNote notebook, with all kinds of interesting projects or snippets that I've collected from HN or Reddit over the years. Being able to query that like a super-smart friend ("what was the error I ran into when I tried to implemt a self-hosted Synapse instance? How I can I fix that?") would be insane for personal productivity.

I think Apple is working on on-device LLM training. Which can fix this for privacy if they are actually handling it on-device.

> Are they faster more productive ways though? You're still operating off keywords

It's explore vs exploit (or hash table vs graph lookup); keywords are faster if you know what you're looking for. But reviewing linked notes leads to more exploration about related topics, and walking the graph can still be a reasonably efficient way to find what you're looking for. If you adopt networked notes, you get both at the cost of some notetaking overhead, but it's typically pretty small.

The one thing I hate about some of the notetaking apps is (and I understand folks need to make money) notes are really personal and private and need to stand the test of time. I can't have my notes locked away in a proprietary format or held hostage by some company charging $/mo so Notion is a no-go for me. I also don't really want to get locked into a workflow only to find out that the app is discontinued and then have to reinvent my workflow.

But yeah, networked thought is still cool and totally unrelated to why I don't use typical networked thought tools. Logseq came closest to converting me.