You spend 20 minutes perfectly explaining a task.
You get the details just right. The formatting
is flawless. Then you wake up the next morning,
you open a new chat window, and it has completely
forgotten who you are. Yeah, it's the classic
amnesiac chat bot problem. I mean, we've all
been there. It's incredibly frustrating, honestly.
You're basically training a new employee from
scratch every single day. Well, welcome to today's
deep dive. We're shifting away from those temporary
AI chats. The goal today is building a permanent
living environment. We're calling it a co -work
operating system, and we're doing it using Claude
projects. So we have a really fascinating journey
mapped out for you today. First, we'll clearly
define the difference between standard Claude
chat and this new co -work concept. Then we'll
unpack the underlying architecture. It relies
on three surprisingly simple text files that
give the AI persistent memory. After that, we're
mapping out structured workstations. And finally,
we'll look at the advanced workflows to keep
this whole system incredibly fast and cheap.
Before we jump into the mechanics, I have to
confess something. I still catch myself pasting
the exact same five paragraph instruction prompt
every morning. I'm just so conditioned to the
old way of working. I still wrestle with prompt
drift myself. Listen, it makes complete sense
that you do. We've all been trained over the
last few years to treat AI like a disposable
tool. You use it, you close the tab, and it vanishes.
But to build a true AI operating system, you
have to completely change the environment. We
need to start by looking at the three distinct
flavors of Claude that currently exist. Right.
Let's break down that baseline. We need to understand
the environment before we can build the system.
Exactly. So first, you have Cloud Chat. This
is the web browser version. Everybody knows.
It's great for doing some quick market research.
It's helpful if you just need to draft a quick
apology email to a client. But it starts fresh
every single session. It has absolutely zero
memory of what you did yesterday. It's essentially
a blank slate every time you hit Enter. Then
you have Cloud Code. Now, this is a very different
beast. It's a terminal -based tool built specifically
for developers. Meaning a text -only screen where
programmers type direct system commands. Precisely.
It connects directly to a software code base.
It can actually autonomously edit files, run
tests, and execute commands. But it's highly
specialized for software engineering. It's definitely
not built for your general day -to -day workflow.
So if chat is entirely temporary and code is
strictly for programmers, there has to be a middle
ground, something built for persistent, everyday
knowledge work. And that brings us to the third
flavor, Claude Cowork. This is the actual desktop
application. It's engineered specifically for
long -term compounding workflows. The true magic
here isn't just the AI itself. It's the ability
to natively mount local folders from your computer.
It maintains a persistent, evolving memory. And
it uses external connectors to link directly
to your gisting tools, things like Gmail, your
Google Calendar, or your Notion databases. OK,
let me try to contextualize this. Standard Cloud
Chat is basically a temp worker. They show up,
do a decent job. but they get a complete memory
wipe every single night. It's literally like
that movie, 50 First Dates. Beat. But co -work
is fundamentally different. It's like hiring
a full -time partner. You're giving them a dedicated
desk. You're giving them a permanent filing cabinet.
That desk analogy is perfect. You're giving the
AI a physical home on your machine. It doesn't
exist in a vacuum anymore. It has a spatial awareness
of your digital life. But that raises a pretty
massive privacy concern for me. Does mounting
a local folder mean the AI is just freely reading
everything on my computer? Is it scanning my
personal photos while I sleep? No, absolutely
not. The permissions are strictly ring -fenced.
It only reads the specific folder you explicitly
mount and allow inside the application. It has
zero access to the rest of your hard drive. So
it's a fenced -in playground, not a master key
to my hard drive. Right. You dictate the boundaries.
It only sees what you place inside the fence.
Now that we understand Co -Work acts like a dedicated
filing cabinet, we need to know what actually
goes inside those folders. How does it physically
remember us? This is where it gets genuinely
fascinating. You would think an advanced AI operating
system requires massive, complex databases. But
the whole system runs on a surprisingly simple
architecture. You literally just need three plain
text files sitting inside a root folder. We'll
call that master folder your Co -Work OS. Just
three files. I mean, that feels almost too simple.
Yeah, but they're just markdown files. Meaning
basic text files with simple formatting headers
and bullet points. Right. There's no complex
programming logic. No Python scripts. The first
file is called Claude .md. Think of this file
as the master operating manual or the global
routing map. When you open a new session, this
file tells Claude what to load and where to look.
It establishes the fundamental ground rules for
how it should behave. Yes, and it has three core
components. First is the memory system itself.
It instructs Claude to always check your pass
context before answering. Second is the routing
map. This prevents the AI from guessing. If you
ask a question about your taxes, the map routes
it straight to your financial folder. Third,
you have references. These are pointers that
tell Claude where to find extra context, but
only when it actually needs it. Two secs silence.
I really have to push back here. It feels wild
to me. The absolute cutting edge of artificial
intelligence relies on basic text files from
the 1990s to function. And the sources are very
strict about this. They say we absolutely must
cap this Claude .md file at 300 lines. Why cap
it? If this is my master operating manual, why
not dump every single rule, preference, and workflow
I have in there? It's a really common trap people
f— fall into to understand the cap, you have
to understand how token limits actually work
under the hood. Meaning the maximum amount of
text the AI can process simultaneously. Think
of it like the AI's cognitive battery power for
a single conversation. Your large root files
load every single time you start a new session.
Every time you say hello, Claude has to silently
read that entire Claude .md file first. If your
manual is 20 pages long, it slowly drains your
token limits before you even start working. It
costs more money. It severely slows down the
response time. Leaving barely any processing
power to actually complete the task I asked for.
Exactly. You're giving it cognitive overload.
That's why you keep that main routing file under
300 lines. It should be a traffic cop, not encyclopedia.
Well, that makes a lot of sense. So if Claude
.md is the traffic cop, what's the second file
handling? The second file is memory .md. This
functions as your running ledger. It holds your
active, ongoing projects and your saved personal
preferences. This specific file is the magic
ingredient that stops your conversations from
starting at zero every day. It's the actual continuity
mechanism. Yeah, it tracks the reality of your
current work. We usually split it into two sections.
One section tracks active projects so it knows
your upcoming newsletter is currently in the
rough draft stage and you're struggling with
the conclusion. The second section tracks granular
preferences. Like knowing a specific client absolutely
hastes bullet points and prefers numbered lists.
Okay, and the third file in this architecture?
That one is called VoicePrinciples .md. We usually
drop this into a subfolder called Resources.
This file's entire job is teaching the AI exactly
how you write and communicate. We'll definitely
dig into that voice file in a minute because
sounding human is huge. But first, just managing
these individual markdown files sounds potentially
messy. If I'm manually editing text files all
day, I feel like I'm stepping backward in productivity.
That's a totally valid concern, and it's why
a lot of power users rely on an app called Obsidian.
It's a completely free markdown editor. You don't
have to migrate anything. You just open your
existing Cowork OS folder as a vault inside Obsidian.
It instantly gives you a beautiful visual interface.
You can easily view, link, and manage all these
text files in one clean dashboard. But practically
speaking, do I have to manually open that dashboard
and type into memory .md every single time I
finish a task? That feels like a lot of friction.
No, not at all. That's the beauty of the system.
You just tell Claude in your normal chat window
to remember this. The AI takes your natural language
instruction. reformats it, and silently writes
it to the Markdown file automatically. You just
talk to it and it updates its own memory notes.
It completely removes the administrative burden.
You just do your work and the system documents
itself. Okay, so the AI can take its own notes.
But let's circle back to that third file. How
do we actually stop it from sounding like a generic,
overly enthusiastic robot? And as we scale this
up, how do we keep it from mixing up completely
different parts of your life? Those are two separate
but incredibly important challenges. That's where
the voice system and the concept of workstations
come into play. Let's tackle the voice system
first. How do we actually build that voice principles
file without it taking weeks of training? Well,
you have to feed the AI real historical examples
of your authentic writing. If your Gmail is already
connected to your cowork app, you can literally
just have it analyze your last 50 cent emails.
If you don't want to connect your email, just
copy and paste five to 10 past articles or reports
you've written right into the chat. You ask it
to reverse engineer your tone. Yes. But here's
the critical failure point for most people. Vague
rules fail miserably. If you tell Claude to be
warm and professional, you're going to get terrible
generic AI output. It'll sound like every corporate
memo ever written. You have to extract highly
specific mechanical rules. You tell it, keep
paragraphs under three sentences. Or you instruct
it, never use passive voice and avoid exclamation
points. Because specificity is what creates repeatable
behavior. A machine doesn't understand warmth,
but it perfectly understands a maximum word count.
And it's an iterative process. Over the first
few weeks, you'll catch it making mistakes. You
correct the output. You tell it to remember that
specific correction. Over time, that voice principles
file evolves into a living, incredibly accurate
record of your actual voice. So that solves the
robot tone. What about keeping my life organized?
That requires building out workstations. Think
of workstations as dedicated subfolders for specific
domains of your life. I really like comparing
workstations to the rooms in a physical house.
You wouldn't keep your kitchen blender sitting
on your bed. It makes no sense. Similarly, your
highly confidential mortgage refinancing notes
absolutely shouldn't bleed into your weekly creative
writing drafts. That's a brilliant way to conceptualize
it. You need structural boundaries. And we generally
see two distinct types of workstations you can
build within this system. So if one type handles
broad categories, what does a universal workstation
look like? Universal workstations cover broad
functional areas that touch multiple parts of
your life. An email HQ is the perfect example.
You send emails for your day job, for your personal
life, maybe for side hustle clients. The core
rules of what makes a good email for you, like
your sign off and formatting, apply everywhere.
So that lives in a universal folder. And the
second type, I imagine it's something highly
siloed. Right. Dedicated workstations. These
cover one highly specific contained domain. a
folder strictly for personal finances, a folder
dedicated to a complex upcoming international
trip, or a folder locked down for one specific
client launch. And each one of these individual
workstations gets its own isolated setup. Yes.
Each dedicated workstation gets its own local
CLAW .md file and its own localized memory .md
file. The structure perfectly mirrors your main
root folder, but the rules inside only apply
when you're actively working inside that specific
space. Let me test the logic here. If I create
a really fantastic rule for formatting emails
inside my finance workstation, should I copy
paste that rule up into my universal email HQ?
No, never duplicate. You should never repeat
global rules in multiple places across the system.
It's a core design principle. Duplicate rules
waste your token limits, and worse, they cause
inconsistent AI behavior when the system tries
to reconcile two slightly different versions
of the same rule. Write the rule once at the
highest level. Never duplicate it. Exactly. It
keeps the entire architecture clean, efficient,
and predictable. Sponsor's hearing. OK, so we've
built the house. We furnished the individual
rooms with workstations. What does actually living
and working inside this system look like on a
random Tuesday afternoon? And crucially, how
do we pay the electric bill? Meaning, how do
we manage the token costs so we don't go broke
running this thing? Let's look at some advanced
daily workflows. When you integrate those external
connectors, the capability styrockets. The first
amazing use case is automatic meeting follow
-ups. Walk me through the actual mechanics of
that. How does it happen? Imagine your Google
Calendar is securely connected to Cowork. You
also use a standard tool that records and transcribes
your Zoom meetings. You simply open your chat
and ask Claude to handle the follow -up for your
2 p .m. meeting. Just one simple natural language
request. That's it. Claude pings the calendar
API to see who is in the 2 p .m. slot. It pulls
the WAW transcript from your local folder. It
reads the entire conversation. identifies the
agreed -upon next steps, and figures out who
is responsible for what. It then dynamically
routes to your email HQ workstation to grab your
communication rules. Finally, it drafts a perfectly
formatted follow -up email using your exact tone,
all from one single prompt. That is incredible
leverage. What about applying this to project
management? The sources mention Notion. Notion
auto projects are a huge unlock. Let's say you
tell Claude you're traveling to Boston for a
tech conference in June. Claude automatically
connects to your Notion workspace. Because it
checks your memory file, it already knows exactly
how you like your travel properties formatted.
It knows you like a column for confirmation numbers
and a separate view for daily itineraries. It
builds the complete customized travel board autonomously.
It's not just generating text anymore. It's generating
customized software environments based on my
historical preferences. Precisely. But if you
want to see the true power of persistent memory,
the finance tracking is where it gets really
wild. Tell me about the finance workstation.
How is that different from just using a spreadsheet
macro? Whoa. Beat. Imagine it just silently watching
a messy folder of bank statements. You literally
just drop raw PDF statements into the mounted
folder. It instantly reads them. It categorizes
an entire year of transactions. It groups your
software subscriptions, your travel expenses,
your dining out. Then it generates a clean Excel
summary. It's doing hours of tedious manual data
entry in seconds. But AI hallucinates. What happens
when it inevitably makes a mistake? What if it
categorizes a business lunch as a personal grocery
expense? You just correct it once in the chat.
You say, hey, anything from this specific restaurant
is a business expense. Remember this category
rule. It instantly writes that logic to the local
memory .md file inside the finance workstation.
It will never make that specific mistake again.
The system actually learns. Two -sex silence.
That is genuine compounding leverage. But processing
dozens of PDFs, reading calendar APIs, managing
Notion databases, doing all of this continuously
must cost an absolute fortune in API credits.
It definitely can if you aren't careful with
your architecture. You need strict cost control
measures in place. And the biggest secret to
keeping the bill low is intelligent model selection.
Explain how we manage that. Are we restricting
what it can do? Not restricting, just optimizing.
You should use Claude 3 .5 Sonnet for 80 % of
your routine tasks. Sonnet is incredibly capable.
It's very fast. and it is significantly cheaper
to run. You save the heavier model, Claude Erpus,
exclusively for highly complex multi -step reasoning
tasks. So, Sonnet is for the daily grind. Sorting
the emails, summarizing the transcripts, Obis
is for the heavy lifting. like analyzing a complex
legal contract. Exactly. You don't need a supercomputer
to draft a calendar invite. And the other cost
-saving measure brings us back to those text
files. Keep your route instructions ruthlessly
short. Let the routing map pull detailed contests
from deeper subfolders only when it is actually
needed for the task at hand. Before we wrap up,
the sources mentioned a specific habit to build.
What exactly is a session audit before closing
a chat window? It's a really powerful, simple
prompt to use at the end of your day. Before
you hit the close button and wipe that temporary
context window, you just ask Claude to scan our
current conversation. You ask it to find anything
worth saving to the memory files and update them.
It's a quick debrief to ensure nothing valuable
gets forgotten. Exactly. It ensures the system
keeps learning and compounding without you having
to sit there doing manual data entry or trying
to remember what you discussed three hours ago.
This whole conversation really highlights a massive
shift in how we interact with machines. We're
looking at a profound paradigm shift. For the
last two years, everyone has been obsessed with
finding the perfect combination of words. The
age of obsessing over individual prompt engineering
is ending. It really is. The focus on the magic
prompt was a symptom of temporary AI. We're entering
a completely new era. The new era is context
engineering. The ultimate utility of artificial
intelligence no longer depends on perfectly wording
a single prompt in an empty chat box. It depends
entirely on building an organized, logical folder
infrastructure around the AI. It's about building
a robust foundation. If the foundation is solid,
the AI does the heavy lifting effortlessly. Your
Co -Work OS is essentially a living database
of your professional life, and it compounds in
value over time. every single session, every
single correction, teaches the system a little
more about how you operate and how you think.
And that's the most beautiful part of this entire
framework. You don't need a perfect, massive
system on day one. In fact, you shouldn't try
to build one. You just need a working system.
So here is your call to action for today. Start
incredibly small. Build just your master root
folder. Set up your claw .md file. Maybe add
two simple workstations, perhaps an email HQ
and a project folder. Get comfortable with how
the active memory talks to the routing file.
Yeah, let your real actual daily needs dictate
the structure. Don't build elaborate workstations
you don't actually use just because they look
nice in a folder tree. It all comes back to that
initial frustration we talked about at the start,
the amnesiac chatbot. You don't have to live
with a tool that forgets you every single morning
anymore. You can build a system that actually
remembers. Right. You're building a permanent
partner. But stepping back, it leaves us with
something much deeper to consider. If this persistent
compounding system perfectly mimics your writing
style over time, if it learns your organizational
habits and begins anticipating your decision
-making processes, will there come a day where
you actually forget where your natural workflow
ends and the AI's structural influence begins?
Beat. What happens when your artificial coworker
knows your own habits and preferences better
than you do?