You sit down to work, you ask a few questions,
and suddenly the limit is reached. Where did
the tokens go? To sex silence. Welcome to this
deep dive. Thank you. It is great to be here.
Today we are going on a bit of a mission together.
We are exploring a fascinating breakdown of the
12 hidden reasons why your clawed co -work usage
limit just vanishes. Yeah, and the most interesting
part is that it's almost never because you are
doing too much actual work. I think that is a
huge relief for a lot of you listening, because
I have to make a bit of a vulnerable admission
here. Right. Yeah. I still wrestle with prompt
drift myself, just letting context pile up without
really thinking about it. Right. It is so easy
to do. We just assume the machine can handle
everything effortlessly. Exactly. We assume it
only charges us for the exact question we just
asked. But the source text we are analyzing today
paints a very different picture. It really does.
Fixing this issue isn't about working less or
cutting back on your actual output. It is about
cleaning up a messy, bloated setup. So we are
basically going to travel from the simplest quick
fixes all the way to some really smart automation
habits. Exactly. But before we fix the limit,
we really have to understand where the credits
are actually leaking in the background. Right.
Because clearly it is not just the words I am
typing into the little prompt box. Not at all.
You need to think of every single session as
having two distinct bills you have to pay. Okay,
two bills. What is the first one? The first bill
is the actual work. This is the stuff you see
happening on the screen. Like the text that generates
for you. Yeah, the text generation, the web searching.
running Python code. That is the explicit work.
That makes total sense. I am asking for a service.
I pay for that service. Right. But the second
bill is where people get caught. That is the
automatic background loading. And this happens
completely out of sight. Entirely. It happens
before you even finish typing your first message.
Your setup files load up. Your active tools load.
And the source material points out that Cowork
specifically drains limits much faster than regular
chat, right? It does, yes. Because Cowork combines
file reading, searching, and coding all into
one unified environment. It is just a heavier
lift overall. Because every single one of those
actions requires the underlying model to process
tokens. Exactly. We should probably pause and
just define that AI jargon quickly for everyone
listening. What exactly is a token? Sure. Tokens
are just pieces of words the AI reads and writes.
So it is the basic currency of the whole system.
Right. It is the raw fuel. So if I have a bloated
setup, the AI is reading thousands of these tokens
before doing any real work. So leaving unused
files in your setup is like leaving your car
engine running while parked. That is a great
analogy. You are burning expensive fuel to go
absolutely nowhere. So background tasks can actually
cost more than the prompt. Exactly. Invisible
background loading. quietly eats your daily limit.
Wow. Okay, so now that we know how these credits
are being spent, let us look at the fastest fix
available. Yeah, this literally takes five seconds.
It is all about choosing the right engine for
the task. Model matchmaking. I am definitely
guilty of messing this up. I tend to just default
to Opus 4 .7 for absolutely everything. You and
almost everyone else. It is a safety blanket.
It totally is. I just want the best possible
answer, even if I am just drafting a basic email.
But based on this guide, I am kind of just throwing
credits away. You really are. Opus 4 .7 is an
incredible model, but it is built for complex,
multi -step reasoning. So using it for simple
stuff is overkill. Totally. It is like using
a sledgehammer for a thumbtack. Right. It gets
the job done, but it is exhausting and wasteful.
So what should we be using instead? You have
three main options. Haiku 4 .5 is the lightest.
You want to use that for quick formatting, simple
summaries, or basic emails. And then there is
Sonnet? Yeah, Sonnet 4 .6. That is your daily
driver. It is the best all -around model for
regular work and light research. You only bring
in Opus 4 .7 for the heavy logical lifting. Okay,
so we match the model to the task, but there
is also a massive difference between what we
send the AI and what it sends back, right? A
huge difference. Output tokens are significantly
more expensive than input tokens. Let us look
at the specific example from the source regarding
the Sonnet 4 API. Right. So the API pricing shows
that input tokens cost about $3 per million.
OK, $3. But the output tokens, the words the
AI generates for you, those cost $15 per million.
Whoa. That is a massive jump. It is five times
more expensive. Why do long, detailed answers
drain my credits so fast? Because output tokens
cost five times more than input tokens. So the
natural instinct of the AI to give these long,
beautifully structured essays is actually hurting
my daily limit. Exactly. It wants to be helpful,
but thoroughness is incredibly expensive. So
how do we fix that? It is brilliantly simple.
Just tell Claude to be brief. Literally just
add a constraint to the prompt. Yes. Just keep
it under five sentences. That single instruction
saves you a massive amount of output compute.
That makes perfect sense. We trim the outputs,
but earlier we talked about the permanent instructions
we forced the AI to carry. Write the background
files. The biggest offender here is the ClaudeDD
.md file. This is a file that sits in your project
and tells the AI how to behave, right? Exactly.
It holds your custom instructions. The problem
is that this file loads every single time you
send a new message. Even for a quick follow -up
question. Every single time. If your file is
2 ,000 words long, the AI has to read those 2
,000 words before it even looks at your new prompt.
That sounds incredibly wasteful. It is. The rule
from the breakdown is very clear. Keep that file
under 200 lines. Okay, under 200 lines. What
should actually go in there? Only the universal
stuff. Your core business identity, your general
tone of voice, maybe a few absolute non -negotiable
rules. But what if I have a massive checklist
for writing blog posts or a complex translation
workflow? You do not put those in the main file.
You move heavy, specific instructions into skills.
Skills. Okay, how do those differ? Skills operate
differently. They do not load globally. They
only load strictly on demand. Oh, I see. So instead
of carrying your entire high school locker in
your backpack, using skills is like stacking
Lego blocks of data only when you need them.
That is a perfect way to visualize it. If you
ask it to translate a document, it reaches out,
grabs the translation skill block, and uses it
just for that task. So... The MD file is always
on, but skills are on demand. Right. Skills only
load when your specific task actually needs them.
That clears up so much unnecessary weight. Sponsor?
Okay, we are back. So we have fixed the permanent
setup files. But what about the actual workspace?
Right. How we structure our daily conversations
is the next big trap. And the source guide leans
heavily into using projects for this. Yes. Projects
are vital. You have to separate your context.
You need a space for content, a separate one
for client work, personal stuff, operations.
You don't want to mix YouTube scripts with grocery
lists. Exactly. But even if you use projects
perfectly, the individual chats themselves can
drain your account. Long chats are incredibly
expensive. Because we treat it like a texting
thread with a friend, we just keep replying in
the same window all day. And that is a huge mistake.
The AI does not remember the chat like a human
does. Right. Claude has to reread the entire
history of the chat with every single new message
you send. Wait, the entire history from the very
first... Hello! Yes. A 20 -message session costs
two to three times more than a 10 -message session.
And if you keep going... A 30 -message session
is four to five times more expensive. One developer
found that in long threads, most of your tokens
just go to rereading the past. Whoa! Imagine
scaling to a billion queries. The amount of wasted
compute just rereading history is staggering.
It is an architectural quirk of how the models
work right now. Do older messages in a thread
keep charging me tokens? Yes. The AI rereads
the entire chat history every single time. So
how do we stop the bleeding here? One task per
session. That is the rule. Once the specific
task is done, you close the chat. But what if
the task takes a long time and I need that previous
context to keep going? Then you ask Claude to
summarize the chat so far. You copy that short
summary, open a brand new clean session, and
paste the summary in. Oh, that is so smart. You
keep the core knowledge without dragging the
heavy transcript along with you. Exactly. It
resets your token cost back to zero while keeping
the momentum. OK, so we are clearing out the
active chat history. There is also hidden history
gathering dust elsewhere in the workspace, right?
Yes. Connectors and memory files. Let us start
with connectors. These are the plugins like Canva,
Gmail, Google Drive. Right. They are super useful,
but they add significant context weight to your
profile just by being plugged in. Even if I am
not actively using them in that session. Even
if you are not using them. The system has to
allocate memory just to keep them on standby.
It is like having ten browser tabs open from
last month that you were still paying rent on.
That is exactly what it is. The fix here is an
audit. Disconnect any integration you haven't
used in the last two weeks. Cut the dead weight.
And what about memory files? Memory files sit
inside a project. They might be past feedback
you gave or old formatting rules. And they load
automatically? Yes. They load at the start of
every conversation in that specific project.
If they're outdated, they're just expensive noise.
Are old memory files quietly draining my account
in the background? Yeah. You are paying for outdated
noise in every single session. So I just need
to go in and delete them? Yep. Review and clean
them out every two weeks. It takes five minutes.
OK. So we have leaned out the system. We trimmed
the MD file. Use skills. kept chats short, and
deleted old memory. Your system is now incredibly
lean, which means we can finally talk about maximizing
the limits you have left using automation. The
source text mentions scheduled tasks, like a
morning email briefing or a weekly report. Right.
Scheduled tasks are incredibly credit efficient.
Why is that? Because they start fresh. They have
absolutely zero chat history to read. They just
wake up, do the job, and shut down. But what
happens if an automated task hits my limit right
in the middle of a run? It crashes. Which is
bad if it is an important client report. But
there is a safety net you can set up. Extra usage,
right. Yeah. It is basically a pay -as -you -go
buffer. You put $5 or $10 into the account. So
if my main limit vanishes, it just dips into
that $5 to finish the job. Exactly. It ensures
your automations never abruptly stop. That is
a great fail safe. There is one more variable
the guide mentions, and I found this one really
surprising. Timing. Timing is huge. Peak hours
are weekdays from 5 a .m. to 11 a .m. Pacific
time. Wait, the actual time of day I run a prompt
changes how the limit feels. Right. And it changes
how the system handles your requests. During
peak hours, the system load is much higher. Millions
of people are logging on to work. So the available
compute shrinks for everyone. Exactly. The limits
will feel much tighter during those hours. So
the fix is just scheduling around the rush hour.
Yes. Take your heavy automated jobs like massive
data scraping or weekly reports and schedule
them to run late at night or on the weekends.
Why should I schedule my heavy automated tasks
at night? System load drops, which keeps your
automated workflows running smoothly. It is such
a simple adjustment, but it makes so much sense.
Beat. So stepping back from all these technical
fixes, what is the big idea here? The main takeaway
is a shift in perspective. Hitting limits isn't
a sign that you are working too hard or being
too productive. Right. It is usually a sign that
your system is carrying too much baggage. We
have to keep our inputs lean. We have to match
the models correctly. Haiku, Sonnet. Opus. Exactly.
And cutting all that dead weight gives you massive
runway to do the actual creative work you want
to do. It really makes you think. If AI context
windows are like human working memory, are we
treating our tools the way we treat ourselves?
Overloading them with irrelevant baggage and
anxieties from the past, instead of giving them
a clean slate to focus on the task at hand? Beat.
Take five minutes today, log in and look at your
cladu .md file. trim it down to just the essentials.
It really will change how you work. Thank you
for taking this deep dive with us today.