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Superorganizers
How GPT-3 will turn your notes into an *actual* second brain
by Dan Shipper
January 6, 2023
â„ 112
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I hate to be the bearer of bad news, but all of the time weâve spent organizing our notes was probably wasted.
Instead, in the immediate future, our notes will be organized for us by large language models (LLMs) like GPT-3. Letâs explore.
. . .
Note taking is building a relationship with a future version of yourself.
Notes record facts, quotes, ideas, events, and more so they can eventually be used to make better decisions, create more interesting writing, and find solutions to problems.
For a long time, the way weâve tried to make this relationship work is by creating organizational systems. The best way to make sure future versions of ourselves had the right notes at the right time was by constructing Rube Goldberg machines of tags, notebook hierarchies, and bi-directional links so that we could pull up our notes when we needed them. Or at the very least, we could easily find them through search if we knew what we were looking for.
But ultimately, the organizing solutions weâve built are brittle. We build and abandon new systems all the time, and rarely, if ever, go back to look at old notes. Tags get created and then abandoned. Links rarely get followed. And we feel guilty: thereâs a lot of value locked up in what weâve collected over the years, if we could just figure out how to use it. Paying for a new notes tool is like signing up for a gym membership on January 1. You know youâll abandon it, but the money you spend soothes your anxiety about not making the most of what you have.
AI changes this equation. 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. When you have intelligence at your disposal, you donât need to organize.
If we want to understand how AI fixes organizing, first we need to understand why organizing notes is so hard. Then we can talk about what might be different about it in the future.
Why organizing notes is so hard
The more precisely we know what to use a piece of information for, the more easily we can organize it.
The problem is, we put things into notes because we don't know what we'll use them for. You write down a quote from a book because you could eventually use it in 1,000 different ways. You could use it to help you make a decision, or write an essay, or lift a friendâs spirit when theyâre going through a tough time (and you might use it for all three). Same thing for writing down notes from a meeting, or thoughts about a new person you met.
As I argued in â The Notetaking Cold War ,â this makes finding a single organizing system for your notes quite challenging. Youâll continually reorganize your system, or feel a pull to put a note in many different places, or tag it to make sure it pops up again in different contexts.
This usually doesnât work so well, and even when you do bump into an old note at the right time, youâre faced with another problem:
Looking at old notes is a bit like looking at stale garbage.
A note thatâs been dashed off in a meeting or hurriedly taken down in the middle of the night when you get an idea is usually hard to understand, and takes a while to parse. As I wrote in â The Fall of Roam ,â when you read an old note you have to load its context back into your head about when you took it and why before you understand what itâs saying, and whether or not itâs relevant to the task at hand.
So you rarely go back to use your old notes. Itâs too cognitively expensive and not rewarding enough. For an old note to be helpful it needs to be presented to Future You in a way that clicks into what youâre working on instantlyâwith as little processing as possible.
This is where large language models come in.
How AI models solve the note organizing problem
AI models like GPT-3 can solve organizing in a few key ways.
First , they can automatically tag and link notes together with no manual work required. It doesnât even require an LLMâthere are less advanced, cheaper models that can do this out of the box today.
Second , they can enrich notes as youâre writing them and synthesize them into research reports, eliminating much of the need for tagging and linking in the first place.
Third , they can resurface key information from previous notes into a CoPilot-like experience for note-taking. This makes searching through old notes unnecessary and helps you bring to bear all of the information youâve ever written down every time you tap a key.
Letâs break down each of these.
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. Researchers like Linus Lee, who I interviewed previously , are building versions of this for themselves. His demo doesnât even use entity recognition, just word frequency tracking, to make the backlinks. These technologies will get more advanced over time.
Source: Linus Lee
Beyond tagging and linking, LLMs can help create an automated taxonomy of your notes that makes it easier for you to navigate through them. Think something like the Apple Photos experience, but for your notes:
Source: Me playing around in Figma.
These taxonomies can even be created on the fly for new projects, so as your needs change, your notes can reorganize themselves into new views that help you navigate them more effectively. A simple example might be something like an automatically updating list of every book youâve read this year. Iâve been banging on this drum for the last two years, and the time has finally come for these kinds of automated taxonomies to happen.
The real power of AI models for organizing goes beyond taxonomizing, though.
Automated research reports
LLMs can enrich and write your notes for you. They can synthesize and write a report based on everything youâve ever written about a topic, so you can load it into your brain without having to ever go back through your archive.
Think about starting a projectâmaybe youâre writing an article about a new topicâand having an LLM automatically write and present to you a report outlining key quotes and ideas from books youâve read that are relevant to the article youâre writing.
Source: This is one thing I imagine ChatGPT will do in the future.
If you were confident that this would be done for you in a high-quality way, youâd never worry about how to tag or link a quote from a book or an article again. Youâd just file it into your notes archive and feel confident that the softwareâacting as a research assistantâwould find and present it later for you.
There are deeper implications, though. Our notes are a reflection of our lives. Think about using an LLM to summarize a key relationship or pattern in your thinking over time. It could produce a history of your mind on a particular topic, including a summary and a timeline of key events that could help you understand yourself, and your world, better.
This is possible todayâsomeone just needs to build it.
CoPilot for notes
Research reports are valuable, but what you really want is to mentally download your entire note archive every time you touch your keyboard. Imagine an autocomplete experienceâlike GitHub CoPilotâthat uses your note archive to try to fill in whatever youâre writing.
Here are some examples:
When you make a point in an article youâre writing, it could suggest a quote to illustrate it.
When youâre writing about a decision, it could suggest supporting (or disconfirming) evidence from the past.
When youâre writing an email, it could pull previous meeting notes to help you make your point.
An experience like this turns your note archive into an intimate thought partner that uses everything youâve ever written to make you smarter as you type.
Again, all of this is possible today. Itâs just a matter of building it.
The future of note-taking
Organizing is going to become unnecessary because no one actually wants to go back and look at their old notes.
What you really want is the information in your notes, synthesized and presented to you at the right place and at the right time.
The way this is done should be personal to you. It should be lively and surprising. It should help you see new patterns, look at what youâve collected in new ways, and bring back facts, people, and events that youâd long forgotten about. It should help you learn from and utilize everything youâve written down previously to the task at hand.
LLMs can truly turn your notes into a second brain. They can enrich notes as youâre writing them to create more context, automatically taxonomize and synthesize them, and present them back to you in a way that clicks later onâso you can actually use them.
In the future, notes wonât be organized by usâtheyâll be organized for us. The ultimate tool for thought is tools that think.
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@miguelmarcos
2 days ago
Iâm not pompous so I will easily admit machine learning-based organization is great. It can be tackled really well with enough horsepower. However, I donât find it exciting and Iâm not unhappy with my current system.
I believe whatâs missing is intelligent linking between notes in such a way where novel relationships appear. Relationships that make sense, that are not patently absurd or nonsensical, the result of combinatorial . I will be truly impressed when machine learning tools produce Aha! moments.
⥠0
Reply
Dan Shipper
2 days ago
@miguelmarcos agreed I definitely think intelligent linking and synthesis is hugely important. tbh I've had this feeling alreadyâfor example, having GPT-3 summarize transcripts of therapy sessions or journal entries and pull out patterns has been eye-opening. but there's a lot more work to be done
⥠0
Reply
Tintin
2 days ago
Great idea to use AI to help you with notes. This appeals to me since I've not stuck with any tool over the years â notepad++, evernote, roam, obsidian, logseq, none of them. However, note (no pun) that when you truly understand something when you write it with your own words, not the AI's.
⥠0
Reply
Dan Shipper
2 days ago
@Tintin thanks!! yes that's definitely true. I don't think of it as a replacement for understandingâbut it can be a good prompt to remember something you already understood
⥠0
Reply
@shikatachi-reads
2 days ago
I fed one page of my RoadResearch notes to ChatGPT and was able to draw some pretty cool insights from it.
⥠0
Reply
Alex Jin
about 4 hours ago
Co-pilot for notes sounds like mem.ai (Iâve been an early user of it since 2020). Still not quite frictionless but itâs getting there pretty quick. Highly recommend checking it out for anyone whoâs interested.
⥠0
Reply
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Subscribe. About. Foundersâ Letter. Publications. Collections. Contact Us. Become a Sponsor. Login. Superorganizers. How GPT-3 will turn your notes into an *actual* second brain. by Dan Shipper. January 6, 2023. â„ 112. Listen. Sponsor Every. Do you run a software company looking to reach an audience of early-adopters? Consider sponsoring our smart long-form essays on tech, AI, and productivity: Sponsor Every. Want to hide ads? Become a subscriber. I hate to be the bearer of bad news, but all of the time weâve spent organizing our notes was probably wasted. Instead, in the immediate future, our notes will be organized for us by large language models (LLMs) like GPT-3. Letâs explore. . . . Note taking is building a relationship with a future version of yourself. Notes record facts, quotes, ideas, events, and more so they can eventually be used to make better decisions, create more interesting writing, and find solutions to problems. For a long time, the way weâve tried to make this relationship work is by creating organizational systems. The best way to make sure future versions of ourselves had the right notes at the right time was by constructing Rube Goldberg machines of tags, notebook hierarchies, and bi-directional links so that we could pull up our notes when we needed them. Or at the very least, we could easily find them through search if we knew what we were looking for. But ultimately, the organizing solutions weâve built are brittle. We build and abandon new systems all the time, and rarely, if ever, go back to look at old notes. Tags get created and then abandoned. Links rarely get followed. And we feel guilty: thereâs a lot of value locked up in what weâve collected over the years, if we could just figure out how to use it. Paying for a new notes tool is like signing up for a gym membership on January 1. You know youâll abandon it, but the money you spend soothes your anxiety about not making the most of what you have. AI changes this equation. 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. When you have intelligence at your disposal, you donât need to organize. If we want to understand how AI fixes organizing, first we need to understand why organizing notes is so hard. Then we can talk about what might be different about it in the future. Why organizing notes is so hard. The more precisely we know what to use a piece of information for, the more easily we can organize it. The problem is, we put things into notes because we don't know what we'll use them for. You write down a quote from a book because you could eventually use it in 1,000 different ways. You could use it to help you make a decision, or write an essay, or lift a friendâs spirit when theyâre going through a tough time (and you might use it for all three). Same thing for writing down notes from a meeting, or thoughts about a new person you met. As I argued in â The Notetaking Cold War ,â this makes finding a single organizing system for your notes quite challenging. Youâll continually reorganize your system, or feel a pull to put a note in many different places, or tag it to make sure it pops up again in different contexts. This usually doesnât work so well, and even when you do bump into an old note at the right time, youâre faced with another problem: Looking at old notes is a bit like looking at stale garbage. A note thatâs been dashed off in a meeting or hurriedly taken down in the middle of the night when you get an idea is usually hard to understand, and takes a while to parse. As I wrote in â The Fall of Roam ,â when you read an old note you have to load its context back into your head about when you took it and why before you understand what itâs saying, and whether or not itâs relevant to the task at hand. So you rarely go back to use your old notes. Itâs too cognitively expensive and not rewarding enough. For an old note to be helpful it needs to be presented to Future You in a way that clicks into what youâre working on instantlyâwith as little processing as possible. This is where large language models come in. How AI models solve the note organizing problem. AI models like GPT-3 can solve organizing in a few key ways. First , they can automatically tag and link notes together with no manual work required. It doesnât even require an LLMâthere are less advanced, cheaper models that can do this out of the box today. Second , they can enrich notes as youâre writing them and synthesize them into research reports, eliminating much of the need for tagging and linking in the first place. Third , they can resurface key information from previous notes into a CoPilot-like experience for note-taking. This makes searching through old notes unnecessary and helps you bring to bear all of the information youâve ever written down every time you tap a key. Letâs break down each of these. 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. Researchers like Linus Lee, who I interviewed previously , are building versions of this for themselves. His demo doesnât even use entity recognition, just word frequency tracking, to make the backlinks. These technologies will get more advanced over time. Source: Linus Lee. Beyond tagging and linking, LLMs can help create an automated taxonomy of your notes that makes it easier for you to navigate through them. Think something like the Apple Photos experience, but for your notes: Source: Me playing around in Figma. These taxonomies can even be created on the fly for new projects, so as your needs change, your notes can reorganize themselves into new views that help you navigate them more effectively. A simple example might be something like an automatically updating list of every book youâve read this year. Iâve been banging on this drum for the last two years, and the time has finally come for these kinds of automated taxonomies to happen. The real power of AI models for organizing goes beyond taxonomizing, though. Automated research reports. LLMs can enrich and write your notes for you. They can synthesize and write a report based on everything youâve ever written about a topic, so you can load it into your brain without having to ever go back through your archive. Think about starting a projectâmaybe youâre writing an article about a new topicâand having an LLM automatically write and present to you a report outlining key quotes and ideas from books youâve read that are relevant to the article youâre writing. Source: This is one thing I imagine ChatGPT will do in the future. If you were confident that this would be done for you in a high-quality way, youâd never worry about how to tag or link a quote from a book or an article again. Youâd just file it into your notes archive and feel confident that the softwareâacting as a research assistantâwould find and present it later for you. There are deeper implications, though. Our notes are a reflection of our lives. Think about using an LLM to summarize a key relationship or pattern in your thinking over time. It could produce a history of your mind on a particular topic, including a summary and a timeline of key events that could help you understand yourself, and your world, better. This is possible todayâsomeone just needs to build it. CoPilot for notes. Research reports are valuable, but what you really want is to mentally download your entire note archive every time you touch your keyboard. Imagine an autocomplete experienceâlike GitHub CoPilotâthat uses your note archive to try to fill in whatever youâre writing. Here are some examples: When you make a point in an article youâre writing, it could suggest a quote to illustrate it. When youâre writing about a decision, it could suggest supporting (or disconfirming) evidence from the past. When youâre writing an email, it could pull previous meeting notes to help you make your point. An experience like this turns your note archive into an intimate thought partner that uses everything youâve ever written to make you smarter as you type. Again, all of this is possible today. Itâs just a matter of building it. The future of note-taking. Organizing is going to become unnecessary because no one actually wants to go back and look at their old notes. What you really want is the information in your notes, synthesized and presented to you at the right place and at the right time. The way this is done should be personal to you. It should be lively and surprising. It should help you see new patterns, look at what youâve collected in new ways, and bring back facts, people, and events that youâd long forgotten about. It should help you learn from and utilize everything youâve written down previously to the task at hand. LLMs can truly turn your notes into a second brain. They can enrich notes as youâre writing them to create more context, automatically taxonomize and synthesize them, and present them back to you in a way that clicks later onâso you can actually use them. In the future, notes wonât be organized by usâtheyâll be organized for us. The ultimate tool for thought is tools that think. Huberman Lab. What did you think of this post? Amazing. Good. Meh. Bad. login. sign up. Like this? Become a subscriber. Subscribe â. Or, learn more . Read this next: Superorganizers. Managing Your Manager How Helping Your Manager Succeed Will Help You Succeed. â„ 173 Mar 9, 2022 by Brie Wolfson. Superorganizers. The CEO of No How entrepreneur Andrew Wilkinson turns emails into opportunities. â„ 441 Sep 11, 2020 by Dan Shipper. Superorganizers. How to Make Yourself Into a Learning Machine Shopifyâs Director of Production Engineering explains how reading broadly helps him get to the bottom of things â„ 165 Mar 3, 2020. Napkin Math. It Is Always Time to B uild Interest rates do not equal innovation rates. â„ 60 Jan 5, 2023 by Evan Armstrong. How to Live By Your V alues This Year A primer on staying connected to what matters. â„ 74 Jan 2, 2023 by Casey Rosengren. Comments. login. Sign up! @miguelmarcos. 2 days ago. Iâm not pompous so I will easily admit machine learning-based organization is great. It can be tackled really well with enough horsepower. However, I donât find it exciting and Iâm not unhappy with my current system. I believe whatâs missing is intelligent linking between notes in such a way where novel relationships appear. Relationships that make sense, that are not patently absurd or nonsensical, the result of combinatorial . I will be truly impressed when machine learning tools produce Aha! moments. ⥠0. Reply. Dan Shipper. 2 days ago. @miguelmarcos agreed I definitely think intelligent linking and synthesis is hugely important. tbh I've had this feeling alreadyâfor example, having GPT-3 summarize transcripts of therapy sessions or journal entries and pull out patterns has been eye-opening. but there's a lot more work to be done. ⥠0. Reply. Tintin. 2 days ago. Great idea to use AI to help you with notes. This appeals to me since I've not stuck with any tool over the years â notepad++, evernote, roam, obsidian, logseq, none of them. However, note (no pun) that when you truly understand something when you write it with your own words, not the AI's. ⥠0. Reply. Dan Shipper. 2 days ago. @Tintin thanks!! yes that's definitely true. I don't think of it as a replacement for understandingâbut it can be a good prompt to remember something you already understood. ⥠0. Reply. @shikatachi-reads. 2 days ago. I fed one page of my RoadResearch notes to ChatGPT and was able to draw some pretty cool insights from it. ⥠0. Reply. Alex Jin. about 4 hours ago. Co-pilot for notes sounds like mem.ai (Iâve been an early user of it since 2020). Still not quite frictionless but itâs getting there pretty quick. Highly recommend checking it out for anyone whoâs interested. ⥠0. Reply. Thanks for reading Every! Sign up for our daily email featuring the most interesting thinking (and thinkers) in tech. Subscribe. Already a subscriber? Login. Contact Us · Become a Sponsor · Search · Terms. ©2023 Every Media, Inc.