How GPT-3 will turn your notes into an actual second brain

How GPT-3 will turn your notes into an actual second brain
How GPT-3 will turn your notes into an actual second brain

All of the time we’ve spent organizing our notes was probably wasted.

A better way to unlock the value in your old notes is to use intelligence to surface the right note, at the right time, and in the right format for you to use it most effectively

Why organizing notes is so hard

  • We put things into notes because we don’t know what we’ll use them for
  • This makes finding a single organizing system for your notes quite challenging
  • For an old note to be helpful it needs to be presented to Future You in a way that clicks into what you’re working on instantly

Automated research reports

LLMs can enrich and write your notes for you.

They can synthesize and write a report based on everything you’ve ever written about a topic, so you can load it into your brain without having to ever go back through your archive.

CoPilot for notes

Imagine an autocomplete experience that uses your note archive to try to fill in whatever you’re writing. For example, when you make a point in an article, it could suggest a quote to illustrate it.

When you write an email, it might pull previous meeting notes to help you make your point.

How AI models solve the note organizing problem

GPT-3s can automatically tag and link notes together with no manual work required

  • They can enrich notes as you’re writing them and synthesize them into research reports, eliminating much of the need for tagging and linking in the first place.
  • Recycled key information from previous notes into a CoPilot-like experience for note-taking.

The future of note-taking

In the future, notes won’t be organized by us-they’ll be organized for us.

LLMs can enrich notes as you’re writing them to create more context, automatically taxonomize and synthesize them, and present them back to you in a way that clicks later on

Automatic tagging, linking, and taxonomies

At the most basic level, the tagging and linking required by current note-taking systems can be done by an LLM (or another, simpler machine learning model).

Entity recognition is cheap and reliable enough for a model to find people, places, companies, books, and other things that repeatedly pop up in your notes. e.g. A simple example might be an automatically updating list of every book you’ve read this year.

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