Your AI agent probably doesn't have a

brain. And what I mean by that is it

doesn't have a system that allows it to

read and think through context that you

have developed over months and years and

reliably come back and be proactive

with. I published a whole guide on the

second brain last month. It was super

popular. A lot of people built it. A lot

of people improved on it. You can use

Zapier. You can use Notion. You can use

N8N. You can use an MCP server. You can

use Obsidian. I have all of those

pieces. But what I don't have is the

agent piece and that matters because in

the intervening period in the last few

weeks we are now at a point where agents

are becoming mainstream. Anthropic is

working on one. OpenAI hired Peter

Steinberger the inventor of Open Claw.

Open Claw itself passed 190,000 GitHub

stars and spawned over one and a half

million autonomous agents in just a

couple of weeks. We need a second brain

system that is agent readable. And so

what I'm going to lay out here today is

the architecture for what I am calling

an open brain. A databasebacked AI

accessible knowledge system that you own

outright with no SAS middlemen that can

break or repric or disappear. One brain

that every AI you use, Claude, Chat,

GPT, Cursor, whatever ships next month,

can plug into via MCP. You can type a

thought in Slack and five seconds later

it's embedded. It's classified. It's

searchable by meaning from any AI tool

you touch or any AI agent that wants to

touch it. The total cost and yes we've

benchmarked this. It's roughly 10 to 30

cents a month. I'm publishing a

companion guide on the Substack to

handle the step by step. This video is

about why the architecture of an agent

readable system matters much more than

the individual tools you choose and why

the memory problem we're talking about

here is secretly the bottleneck in

everything you're doing with AI today

and why people who solve it for agents

and themselves will have a compounding

advantage that whitens every single

week. So, first let's talk about the

memory problem that is hiding inside

your prompting. If you've been following

my videos for a while, you know I keep

coming back to one idea. The quality of

AI output depends entirely on the

quality of your ability to specify.

That's not a nice to have principle

anymore. That is the whole game. I laid

out the full framework I see for

prompting in 2026 in a video I did last

week. From prompt craft through context

engineering to intent engineering to

specification engineering, that

hierarchy is real. And the people who

are 10x more effective than their peers

have built context infrastructure that

does the heavy lifting on all of those

pieces, the context engineering, the

specification engineering before they

have to type a single prompt. And what I

want to talk about in this video is how

you take that abstract skill set and how

you turn it into a memory problem that

gives you a leg up on everybody else. In

other words, if you're going to do

context engineering, if you're going to

do specification engineering, seriously,

you need to invest in a memory system

that is yours, that is agent readable,

that makes calling and retrieving that

context, that makes specifying easier.

The best prompt in the world cannot

compensate for an AI that does not know

what you've been working on, what you've

already tried, what your constraints

are, who the key people in your life

are, or what you decided last Tuesday.

And by the way, that is also the

constraint working with agents. They

need that context, too. And right now,

that's exactly what most of us are

struggling with when it comes to AI.

Every single time we open a new chat, we

often start from zero. Every single time

we switch from claw to chat GPT to

cursor, we tend to lose things, which is

why we gravitate toward one of those

systems more than another. Think about

how much of your prompting is asking AI

to catch up on what you know already.

The background here is you're burning up

your best thinking on context transfer

instead of real work. A Harvard Business

Review study found that digital workers

toggle between applications nearly 1,200

times a day. I get tired saying that

sentence. Every switch seems really

small but collectively this is

devastating our attention. I have

watched this context switching issue

play out over and over and over again in

my own life in the lives of others and

what I keep coming back to is the

insight that our desire to specify to be

clear with AI is only getting higher and

it's demanding more of our memory

systems and our memory systems and

memory structures are not keeping up.

Memory architecture determines agent

capabilities much more than model

selection does. That's widely

misunderstood. And when you construct

memory incorrectly, you're stuck

reexplaining yourself forever or you're

stuck in a world where you know how to

access memory and the agent doesn't. I

believe we can make a stable memory

system that is reasonably futureproofed

that enables us to plug in new tools via

MCP server very efficiently. So we don't

have to keep updating our system. And

yes, I want to acknowledge something.

Claude has memory now. Chad GPT has

memory now. Grock has memory now. Google

has memory now. These features are

getting better all the time. But think

about what they give you and what they

don't.

Claude's memory doesn't know what you

told Chad GPT. Chad GPT's memory doesn't

follow you into cursor. Your phone app

doesn't share context with your coding

agent. Every platform has built a walled

garden of memory and none of them talk

to each other. There's a whole new

category of products emerging in early

2026 specifically because platforms

refuse to solve this products like

memcync

one context. The problem is real enough

to spawn an entire VC backed industry.

So what you've really got is multiple AI

tools getting upgraded all the time,

adding AI tools all the time to

experiment with them, and you have a

thin siloed layer of context that only

works inside each of those individual

tools. You know what? That's not really

memory. That is five separate piles of

sticky notes on five separate desks. And

now let's add autonomous agents into the

picture. The agent category has

absolutely detonated in the last few

weeks, but the use cases that are

shining, like the guy who got thousands

of dollars off a car purchase, they're

shining because the agent has the

ability to securely and safely access

relevant memories, relevant context from

the user. Whereas agents that just guess

contacts or have to fill in the dots

because you aren't able to provide them

secure access to all of your systems,

they're not going to be nearly as useful

for you. And whether we're talking about

agents or we're talking about tools, the

part that should bother you even more is

that these systems that corporations are

designing are all designed to create

lock in. Memory is supposed to be a lock

in on chat GPT, ditto on other systems.

So you've spent a long time building up

history with a tool and now if you want

to try the latest other model, let's say

you're on chat GPT and you want to try

Gemini or you want to try Claude or you

want to try another model, you lose all

of that context, not because the new

model is worse, but because your context

is trapped in the old one and oh by the

way, all of that memory in those

individual tools, that is not agent

readable. And so as we get to a world

where autonomous agents are becoming

more and more and more a thing, the big

corporations are betting that if they

can trap you with memory, you will only

use their agents and they will get to

keep you and your attention and your

dollars forever. But your knowledge

should not be a hostage to any single

platform. And for most of us right now,

frankly, it is. And that's shaping our

entire AI future. We don't necessarily

have a free choice between tools right

now because the product strategy of

these large businesses is to keep you to

keep you engaged to keep you

entertained. I've talked about how in

many cases you're pushing for engagement

with these models. One of the reasons

why chat GPT40 was so mourned and so

grieved was because it was an engagement

optimized model and people liked the

engagement. It works. Ditto with memory.

Memory is engaging. Feeling known is

engaging. It works. It's smart product

strategy. But you're smart, too, and you

don't have to go along with that product

strategy. And you might be thinking at

this point, Nate, you made a video on

second brain. I can just connect it to

my open claw and I'm fine. Absolutely,

you can try that. But you're going to

run into a structural mismatch that most

people haven't noticed. That explains

why the current generation of notetaking

tools needs a different more structural

memory layer underneath. The internet

right now is forking. I've talked about

that. There's the human web with fonts,

with layouts, with what you're reading.

And there's the agent web that's

emerging with APIs, with structured data

that's built for machine to- machine

readability. That fork is happening to

your memory architectures and your notes

as well. Your notion workspace, for

example, is built for human eyes. It's

built for pages, for databases, for

views, for toggles, for cover images.

It's beautiful for you. It's useless for

an AI agent that needs to search by

meaning, not by folder structure. Your

Apple notes are locked into an

ecosystem. Your Evernote has a decade of

accumulated clutter with no semantic

structure. Your bookmarks are a

graveyard of things you've meant to

read. These tools were built for the

human web back in the 2010s. They were

designed for you to browse, to organize,

to read. They were never designed

fundamentally with the expectation that

AI agents would query them. That got

bolted on later, much more recently. And

the apps adding AI features today are

mostly doing it as bolt-ons, like chat

with your notes. Great. You have one AI

that can kind of search one app. What

about the other five tools you use every

week? We're still in a world of separate

sticky notes on separate desks. You've

traded one silo for another. Every

second brain app has been reaching for

something that required a different

layer entirely. Infrastructure built for

the agent web, not the human web. And

that's what I want to focus on here.

Because if you can build infrastructure

for the agent web, you are suddenly in a

position to make a lot more

human-friendly decisions with how you

plug into that infrastructure. The

infrastructure is yours. It's something

your agent can plug into. It's something

your chat bots can plug into, but you

control and manage it. This frees you

from having memory that only lives with

one of these corporations and their

clouds AI systems. You don't have to

depend on chat GPT memory anymore. It

also frees you from having to depend on

an individual SAS company not changing a

setting in order to keep your own second

brain working. And ultimately, as agents

get better, it frees you from having to

do as much manual work to retrain a

second brain. And so, this is me

essentially giving you a sense of how

agents unlocking are changing our

perspective on memory and changing our

perspective on prompting and changing

what we need to be digital citizens.

Just as we needed a personal computer to

be digital citizens over the 2010s, over

the 1990s, over the 2000s, we need our

own memory architectures to be

responsible AI citizens now. But we

haven't really had a way to do that. And

until very recently, until the last few

weeks, we haven't had AI agents that

would make that really practical. Now we

do, and now the world has moved, and now

it's time to talk about it. So, let's

get specific. What am I proposing here?

Instead of storing your thoughts in an

app designed for humans, you should

store them in infrastructure designed

for anything. A real database, vector

embeddings that capture meaning, not

just keywords, a standard protocol that

any AI can speak. I'm calling it open

brain because the architecture is what

matters and you should not be forced to

choose any given model. This is all

possible because of MCP, the protocol

shift that I talked about briefly above.

It started as Anthropic's open- source

experiment in November of 2024, but it's

since become the HTTP infrastructure of

the AI age. It's the USBC of AI. It's

one protocol. Every AI, your data is

yours. It stays in one place, but every

tool that speaks MCP can read it. So, at

a high level, I don't want to make you

go and click somewhere. Let me show you

what this actually looks like.

Your thoughts live in a Postgress

database you control, not somebody

else's proprietary format. This is the

most boring battle tested technology you

can imagine. Postgress is not exciting.

It's not deprecating. Postgress isn't

chasing a growth metric. Postgress isn't

VC backed and needing to hit a billion

dollar unicorn valuation. It's just a

standard way of storing data. And you

want that boringness because everything

else needs to plug into it. The nice

thing about the database is that if you

construct it properly, if you vectorize

it, every thought you capture gets

converted into a vector embedding, which

means it's a mathematical representation

of what it means that is immediately

natively AI readable. So when you ask

what was I thinking about career changes

last month, it can find your note about

how you were considering moving into

consulting or how you were considering

moving into product even if you never

used the word career in the original

thought. is called semantic search and

it's a whole different universe from F.

So what this looks like when you have

Postgress hooked up with an MCP server

is you can type into a Slack channel,

hey I was talking with Sarah. She

mentioned she's thinking about leaving

her job to start a consulting business.

She's been really unhappy since the

reorg. 5 seconds later, the system has

stored the raw text, generated a vector

embedding of the meaning, extracted the

metadata, the people, the topics, the

type, the action items, and filed all of

it in a real database. Now, any AI that

you're working with can go see that. If

you're in Claude working on a coaching

framework, hey, search my brain for

notes about people considering career

transition. Found it. If I'm in chat GPT

drafting an email, same search, same

result. If I'm in cursor building a tool

and I need to remember a decision I made

last week, hit the MCP server, it's

right there. One brain, every AI

persistent memory that never starts from

zero. Even if you start a new tool

tomorrow and you've never touched it

before. So this has two basic parts,

right? Capture runs through any tool you

have open. You type a thought, it hits a

superbase edge function that generates

an embedding and it extracts the

metadata in parallel and stores both in

a Postgress database with PG vector and

it just replies in thread with a

confirmation showing what it captured.

The whole round trip takes under 10

seconds. Retrieval runs through an MCP

server that connects to any compatible

AI client. You have three tools.

Semantic search, which is finding your

thoughts by meaning, listing recent,

which is browsing what you captured this

week. and stats. See your patterns,

right? You can hit this from Claude,

from Claude Code, from Chad GPT, from

cursor, from VS Code, from anywhere you

can query your brain through an MCP

server. If all of this sounds like Greek

to you, the companion guide walks you

through a complete setup. Copy paste, no

coding, about 45 minutes to set up. And

you know how I tested this? I asked

someone in my life to follow this guide

before I showed it to you. And she has

no coding experience whatsoever. And I

said, "Can you get to a point where you

can set this up?" And she could. And it

took her about 45 minutes. And I'm not

kidding about the cost because the total

running cost on the free tiers of say

Slack and Superbase, which is what I'm

talking about here, it's roughly a dime

to 30 cents a month and API calls for

about 20 thoughts a day. So you're going

to spend more on coffee this morning

than you're going to spend on the system

this month. Here's why getting memory at

the fundamental architectural level

matters beyond the nice feeling we get

from building a cool tool. I love to

build. You can probably tell people who

love to build will love to build anyway,

but it matters for everybody. It doesn't

just matter for those of us that like to

experiment. We are in the middle of a

massive shift in how AI integrates into

our daily work. The models keep getting

better at a terrifyingly fast pace and

you don't want to fall behind. Opus 4.6

6 shipped just a couple of weeks back.

The agent market is growing probably in

triple figures this year. Threeperson

engineering teams are routinely

outproducing teams 10 times their size.

And we're finally seeing this explosion

in AI productivity show up even in

economywide metrics. Eric Bjornson wrote

in the Financial Times last month that

US productivity grew roughly 2.7% in

2025, which is double the decade

average. And frankly, Eric attributed a

fair bit of that to AI agents and AI.

But the key is, as I've called out

before, AI adoption is not the same

everywhere. If you're just talking with

a single chatbot, I've said it over and

over, you're not really adopting and

working your workflows around AI in the

way you need to. And the people getting

those outsized results are not depending

on better models to get there. They're

actually restructuring how they work

with AI as a primary collaborator. But

you cannot collaborate with something

that has no memory of you. Think about

the difference between these two

workflows. Person A opens up Claude,

spends four minutes explaining their

role, their project, their constraints,

and the decision they're trying to make,

and they get a good answer. Person B

opens up Claude. It already knows her

role, her active projects, her

constraints, her team members, and the

decisions she made last week because all

of that lives via MCP server in Open

Brain.

All of it is loaded up before she types

a word. She asks for a question, she

gets an answer informed by six months of

accumulated context. If she wants to

switch to Chad GPT for a different

perspective, she'll get a different

model, but she'll get the same brain,

the same context, and the same answer

quality. Every single tool will have the

full picture for her. And the key is

that advantage will keep compounding.

Every thought person B captures makes

the next iteration better. Every

decision logged, every person noted,

every insight saved as another node to

what's a growing knowledge graph that

every AI in the system can access. So

person A is going to start from zero

every single time. The gap between I use

AI sometimes and AI is embedded in how I

think and work is the career gap of this

decade. And it comes down to memory and

context infrastructure. And the gap is

going to get wider as person B continues

to accumulate knowledge every week. The

people who build persistent, searchable,

AI accessible knowledge systems will

have AI that gets better at helping them

over time because it has more context to

work with. Every thought you capture

makes the next search smarter, the next

connection more likely to surface. And

that is a compounding advantage that you

own, that the big companies don't own.

Whereas the people who keep reexplaining

themselves in every chat window are

going to wonder why AI still feels like

a party trick. It's the same tech. It's

just wildly different outcomes. And the

variable here is your infrastructure.

And one thing I want to call out here,

I've given you a simple example where

you can retrieve a clear answer in text

in any AI tool you want with an MCP

server. But MCP servers are not just for

retrieval. And if you construct an open

brain, your MCP server can work in a lot

of different directions to give you

advantages you might not think of if you

are just used to using memory in a

single tool. MCP means you can write

directly into the brain from anywhere. I

really meant that. You can write into

Claude on the phone. You can use Chad

CPT on the desktop. You can use Claude

code in the terminal. You can rig it up

uh to talk to a messaging app. any MCP

compatible client becomes both a capture

point and a search tool. You're not

locked into Slack or any other system.

That's what open means. And then think

about what you can build over the top.

It's easy to use MCP to build a

dashboard that visualizes your thinking

patterns over time, a daily digest that

surfaces forgotten ideas based on what

you're working on. And do you know that

you don't need to use code to do that

because you can just ask the AI tool of

your choice to retrieve from the MCP

server the relevant slice of context and

build something because the data is

stored in a way that is easy to plug in

and easy to store and easy to access

from any tool out there. The ceiling is

wherever you decide to stop building.

Now I want to be honest the metadata

extraction isn't always perfect. The LLM

makes its best guess to classify with

limited context and it will sometimes

mclassify a thought or miss a name. It

doesn't matter as much with semantic

embedding because the embeddings handle

so much of the heavy lifting with

retrieval. Semantic search works even

when the metadata is off. The one real

requirement for this to work is that you

actually use it because the system

compounds. Every thought you capture

makes the next search smarter and the

next connection more likely to surface.

But it needs input. You need to build

the habit. You need to be dumping your

thinking into the system and let it do

the rest. Now, if you're a subscriber on

the Substack, I've put together four

prompts that cover the full life cycle.

And I actually want to describe them in

the video because even if you're not a

subscriber, you should understand how we

can use prompts in the architecture of

this system to think more deliberately

and make the memory architecture fit our

needs. The memory migration is the first

thing I'm going to suggest. You want to

run this right after setup. It extracts

everything your AI knows about you

already from Claude's memory, from Chad

GPT's memory, from wherever you've

accumulated context, and it saves it

into your open brain. Every other AI you

connect then starts with that foundation

instead of zero. So you want to run it

once and let it pull that stuff down.

I'm also building what I call the open

brain spark because I sometimes get

writer's block. So you want to have an

interview prompt that discovers how the

system fits your specific works. It asks

about your tools, your decisions, your

reexlanation patterns, your key people,

and then generates a personalized list

organized by category that suggests what

you should be putting into Open Brain

regularly. Use it when you're staring at

the Slack channel or you're staring at

your messaging app or you're staring at

Shed GBT and you're wondering what do I

type that I want to put into OpenBrain

today. I also put together quick capture

templates. So these are five sentence

long starters optimized for really clean

metadata extraction. So a decision

capture prompt, a person note, an

insight capture, uh a meeting debrief,

each one is designed to trigger the

right classification in your processing

pipeline. And after a week of capturing,

you'll find you don't need them as much

because you're going to develop your own

patterns. but they're really useful for

building that habit early without having

to think about how to sort of send the

system a coherent message where it's

likely to classify correctly.

The weekly review is another one I put

together. End of week synthesis across

everything you captured. It clusters by

topic. It scans for unresolved action

items. It detects patterns across days.

It finds connections you missed. And it

identifies gaps in what you're tracking.

So about 5 minutes on a Friday afternoon

becomes more valuable every week because

your open brain continues to grow.

If we zoom back out, when this thing

works, when you get the Postgress

database set up, you're starting to use

it in whatever messaging app you want,

you're starting to see the memory become

consistent across all your AI tools, and

you're starting to realize you do not

depend on proprietary paid for memory by

big AI companies.

something happens that's a little bit

hard to describe until you experience

it. Your AI in every single part of the

system, whether you're using Claude or

Chad GPT or both or Cursor or Grock,

whatever it is, it starts to know you.

Not in the creepy corporate surveillance

way, in the hey, we were thinking about

this last week and it's relevant to what

you're asking me now kind of way. The

way a great colleague remembers what

matters. So every AI you use gets

better. You're less afraid of trying a

new AI because you can just plug it into

MCP and it finally has the context.

This is what an agent readable world

makes possible. And I want to call out

something really special here. When I

suggested the original second brain

guide, I built it before the agent

revolution went mainstream, which again

was only about a month and a half ago, a

month ago.

And it was useful for humans and it was

designed to solve a fundamental

cognitive problem that we've had which

is that we have trouble holding stuff in

our head and we need to see patterns

over time. LLMs can help us assess

patterns. That's all still true and you

can use this open brain in that way. But

when the agent revolution came through

in the last few weeks because again AI

is moving that fast. What we need to

move to is a second brain system that is

more foundational. Something that

enables both us and our agents to

reliably read from a system that isn't

SAS controlled, that isn't proprietary

company controlled, that is frankly

open- source LLM friendly. And when we

have that, we get two benefits. Yes, the

agent can read it. And that is in line

with where we're going with agents and

how quickly agents are going mainstream.

And that's the reason I'm making this

video. But second, look at how much

cleaner and clearer the human readable

part of this gets. We get downstream

benefits that we did not get when we

think about the system from only a human

readable perspective. Because if you

think about the system from a human

readable perspective, you get something

like what I described. You focus on

SASfriendly solutions with graphical

user interfaces that humans can easily

read because you want to make it easy

and accessible to build the system. And

that's what I did originally. But if

you're willing to get slightly technical

and follow a clean step-by-step tutorial

to get to something that is a true

database, what you get is a

futureproofed system that unlocks the

human benefit of touching any AI system

in the future that you may want to try

without doing any additional effort. And

so we humans reap a tremendous amount of

value from the clarity that comes from a

truly foundational architected memory

system. This reminds me of one of the

larger lessons I've been meditating on

in the AI revolution which is that AI is

forcing a clarity of thought in our work

in our lives that has a tremendous

amount of human benefit. Toby look has

said that he thinks a lot of corporate

politics amount to bad human context

engineering which is a very provocative

take and I think that that is something

that pops out here because we need

extraordinary clarity to work with AI

agents and when we develop that

extraordinary clarity through memory

architectures that are foundational

through good databases through a clean

MCP server We get the benefit of cleanly

and clearly being able to plug in and

work with that memory system anywhere.

We do good context engineering for our

human brains when we build the right

context engineering for AI, which is

kind of Toby's point about politics.

When we do good context engineering for

agents, we happen to do good context

engineering for people. And that makes

people less likely to play politics. So

the second brain you built, if you were

one of the thousands of people that

built it when I talked about it, was

always reaching for this. It was

reaching for a place where your thinking

lives, where it's searchable by meaning,

where it's accessible to any tool you

use. And those tools solve the capture

problem. They solve the organization

problem. But what they didn't realize

they needed to solve because it wasn't

really there yet was the agent readable

problem.

Open brain adds that foundational layer

not by replacing what you built but by

giving it an infrastructure underneath a

database, a protocol, your thoughts,

every AI you'll ever use. So you can

build it in a morning over coffee this

weekend. Yes, really you. And your

future AI, your future self as a human

will thank you for every thought you

start to capture. Now, if you have

already built a second brain, I'm also

including a special migration guide so

that you can figure out how to not lose

the thoughts you've been capturing and

make sure you get them into a system

that is more agent readable going

forward. Best of luck. Don't be afraid

of how this is slightly technical. There

have been lots of visuals all the way

through this YouTube helping you to see

what I mean. And you'll see more guides

in the substack if you're interested.

And honestly, I put enough visuals into

this video that if you are not ready to

hop into the Substack, totally fine. You

should still be able to get there. You

should be able to show this video to an

AI and say, "Help me build this." And it

should be able to do it.

Cheers.