I built a token burn dashboard and in
this video I'm going to show you how I
did it. But the point is not to burn
tokens. The point is not to brag about
how many tokens you burned. Yes, I
burned I think it's 800 million tokens
uh last Thursday. That's fine. The the
point is what I did with it. And the
heart of this video is to share with you
that you need to imagine your entire
computing experience differently. And
that one of the greatest tools to do
that is to see how you use your computer
with AI. And that's what this token
dashboard does. It actually shows you
what are my habits with AI. How do I use
it? Do I use it well? Could I be using
it more? I am really, really into tools
that expand the imagination. I'm not
into tools that just show you what you
did and say, "So far so good." And so I
want to walk you through how I used AI
to make this dashboard and how I'm using
it every day to stretch my own
imagination and expand what is possible.
So first, how do you think about a
dashboard for AI? What does that mean
and what does it involve? I got to be
honest with you, the easiest tool that
you can use for this is codeex. And part
of why is because codeex makes it easy
to measure the tokens codeex uses. You
know how we always used to say, well, AI
doesn't know what version it is. It's
kind of the same thing with tokens now.
If you're using Claude, which I love,
you can't tell how many tokens Claude is
using unless you're in the API. You
cannot easily tell if you are in Claude
co-work, if you're in Claude chat, how
many tokens you have used in your
session, even if you've done very heavy
work. But in codeex, you know it down to
the token. And so the first thing to be
honest with is I had to do some fancy
math to approximate my claude usage as a
part of this dashboard work. And yes,
we'll we'll talk about that. And why do
we care? Why do we care about
approximating usage, getting the usage
right? Well, first and foremost, if you
don't know what the ratio of your actual
behavioral usage is of tools, it's hard
for you to learn and self-improve over
time and how you use AI. It it is really
tough. One of the things I noticed that
just opened my eyes when I built my
dashboard is that I could see in the
chart the way my behavior actually
shifted when I started using codeex. I
could see the number of tokens going up.
It showed me that that particular tool
was unlocking my imagination in a way
that previous tools had not. I don't
think I would have had that insight in
the same way. Certainly not at that same
level if I hadn't had a chart. Now, if
you're wondering, what does this chart
look like, Nate? How did you get this
chart to look this good? I've got a
simple answer for you. I found an open-
source skill which I'll be sharing
called a Tufty skill. Tufty was a famous
data visualizer. And all I did to make
this chart was I actually worked with
Codeex and I told Codeex what I wanted
in terms of features very clearly in
plain English. It wasn't a fancy prompt.
I said I wanted to see my token burn. I
wanted to see it in a GitHub dotstyle
chart. I wanted to see more particularly
what I was doing on given days. I wanted
to see same day usage so I could
understand in a lived experience kind of
way what activities were burning tokens
versus what weren't. Uh and I wanted to
understand the model distribution and so
I had to do work to actually reason from
artifacts to reason from logs to
approximate model usage for claude so
that I could actually map that in and
understand the relationship uh in my
activities. Your particular chart may
look different. One of the things that's
cool is that I actually put together
multiple versions of this for you over
on the Substack. So, you can have one
that is more claiming. You can have one
that is more chat GPT leaning. You can
have one with multiple lines that show
your cloud uh usage versus your chat GPT
uses versus your codeex usage. All of
that's great, but remember the point is
not to show usage. The point is to show
where you go next. And so I crafted mine
specifically to help me understand what
am I doing today on AI and how is what
I'm doing today on AI different from
previous days when I was a heavy AI
user. So I can see that same day. Now
let me give you a specific example
because I think that that's that that's
going to be useful. One of the things
that dropped this week was
slashworkflows which is not from Codeex.
It's actually from the Opus 4.8 8
release day and slightly compose a
workflow with sub aents using claude
code. Yes, claude code. And when you do
that, what claude code does is it
dynamically spins up a plan, an
orchestration plan to get your work done
and then spins up sub aents and actually
gets it done, which sounds great. It
makes multi-agent orchestration for
personal productivity really easy. Well,
of course, the internet jumped on that.
They immediately created an open- source
skill. I grabbed that open source skill.
I ported it over to codeex and I was
able to the same day use slashworkflows
inside codeex to help me tackle tough
tasks. I had a task where I had to
research a bunch of different schools
with my kids and I was able to use
slashworkflows to put together a
gigantic report around schools and like
what school would be best without any
significant additional effort. and it
used three or four agents and it was
much more uh useful as a result because
you're using more agents, right? So more
agents means more tokens burned, which
is again gets back to the token chart,
but it also means a higher probability
that you solve the problem correctly
because you're tackling it from multiple
different angles. And so if you're
wondering how this all relates to the
chart, it's very simple. As soon as you
look at the chart, you can see that my
behavior shifted the token usage. I can
see intuitively that my behavior led to
a higher quality result, which is
interesting. But that was because I
could not invoke slashworkflows in the
same way from the chat and from co-work
as I could in cloud code. And it was
difficult to get opus 4.8 to go to the
level of depth even when I did invoke
the SLworkflows command. And so
effectively looking at the chart enabled
me to see how a change in my behavior
was changing token burn and resulting in
new work. Because here's the secret,
guys. You are not going to understand
how to use AI unless you have a feedback
loop that allows you to take the AI work
you're doing, see how it's affecting
your token burn, and then see how it
results in higher quality work for you.
And the reason token burn in particular
matters is because it's easy to measure
and it's correlated with intelligence
and successful solutions. So, I'm not
measuring tokens just for the heck of
it. I'm measuring tokens because I'm
measuring my ability to deploy delegated
intelligence to solve problems. And we
have seen in study after study after
study done by the major labs that when
you spend more tokens, you get better
results. It's one of the most
predictable results in AI. And so if I'm
interested in understanding whether I'm
stretching my imagination on AI, I have
to kind of like look at my token usage
and find out and I have to see does it
work, does it not work, am I actually
spending more delegated intelligence
here or not? Now I'm not saying you want
to spend more tokens just to be
wasteful. I actually have skills in
place that help me to pause automations
I don't need, that help me to slim down
context windows where I don't need to
use them. And so it's not that we want
to waste tokens. We want to use them
effectively, but we have to know what
we're measuring to get anywhere at all.
And so if you want to dig in, here's the
secret. I told Codeex, use this tufty
skill that I talked about, right? Use
this design skill. Please, please,
please show me my top 10 days for AI.
What are the top 10 AI usage days I
have? What's in those top 10 days? What
activities did I do? And by the way, I
had to scrub some of this in the chart
I'm showing you because some of it is
quite confidential. Originally, it was
extremely specific and I recommend yours
be extremely specific as well if it's a
private dashboard because it helps you
to learn what you're actually using AI
for. And then I told Codeex to please
log how my how my token usage is
changing over time. And you can do it
any way you want. You can use an XYaxis.
I used a logarithmic axis, which is a
fancy way of saying I wanted to see big
changes over time because I was seeing
this huge scale problem where I would
have some days that were earlier in the
AI revolution where I would have only a
few million tokens and and days now
where it's almost a billion. How do you
show that in a scale without breaking
it? And so I worked with it on the
scale. I worked with it on the color
contrast. I worked with it on how clean
and easy it is to read. I worked with it
on showing multiple models. And I
actually used codeex to build an
approximation of my usage. Codeex gave
me a quiz when I asked for it and and it
said, "Help me understand your claim."
And then it reasoned from artifacts and
came up with a tight range for the
tokens that Claude used. Yes, it is
ironic that Codeex is helping me infer
my claic.
You got to fix this. Please make it
easier to measure our tokens on Claude.
I would love that so much. But once you
get to that point, once you actually
understand what you want to do, there is
not a special prompt that you have to
get give codeex to make this happen that
this is a wonderful example of 2026
building. It is about the clarity of
your intent. I had to see in my head
that I wanted the GitHub chart at the
top that I wanted a logarithmic scale
that showed a gain in tokens over time.
I had to see how I wanted the top 10
days and then I just had to keep asking
for it until it appeared. Yes, I also
could have specified it exactly in a
really fancy set of requirements up
front, but this is a home build and I'm
a little bit of a lazy builder sometimes
and I just had that picture in my head
and I kept asking for it till I got it
and it worked perfectly fine. And and
that should encourage you, right?
Building is not that hard. And if you're
wondering, did I build it all the way?
Yes, Codeex absolutely deployed it. was
able to handle the DNS change to change
it to the domain. Uh it's token
burn.mmarkdown
and it took care of it, right? Like it
took care of the entire thing for me.
And that is an example of using your
imagination for what AI can do. I
realize that's circulating. Another
example that's been really key for me is
using AI to keep my files better
organized. My files are not mine really
anymore. I use them as fodder for AI and
I let AI organize them. I actually had
Codeex go through and label and organize
all of my annoying little screenshots
that I take. When I take screenshots on
X, when I take screenshots other places,
I had to have them all organized. I had
to have them all labeled. It broke open.
It looked at all of them. It labeled
them. It put them into files. And now
it's easy for it to find them and work
with them. I have no idea what the
folder structure is. I don't have to
care. That's an example of the kind of
computing that you can do once you
stretch your imagination. And so really
my invitation to you is not to build a
token dashboard. I know that's people
bragging about their AI usage. I know
that 800 million tokens in a day is not
the highest total out there. I don't
care. I'm using the AI the way I can
with delegated intelligence to solve
problems that matter to me. And it's
it's a loop that's helping me refresh my
thinking because I can look through my
top 10 days and I can say, "Oh, you
know, on my higher AI days, I'm actually
giving codec strong database work to do.
I should be doing that more because that
seems to be working better." Or, "Wow,
in the last hour, I burned 100 million
tokens and I could feel all eight
threads that I was running come to a
successful conclusion. I should be
parallel computing more." These are
insights that you don't get without
keeping a little bit of a closer run on
intelligence. Like if you had
intelligence, wouldn't you want to
actually meter it to understand how it
works? That's what we're talking about
here. We're talking about building a
compass and a speedometer for
intelligence.
And if you don't have that, it's hard to
know where you're going. And so the
visuals you're seeing in this video are
actual visuals of the chart that I made
for me. And if you want a complete
version of it, it's on Substack. You
don't have to copy mine. I'm actually
giving you multiple versions. Uh I love
diving deep on Substack. And you're
going to get multiple versions of how to
build this. You can build it for
yourself. You can share it. We're going
to make it easy to share on Talent
Board. Uh because I believe that in the
future, this is going to be as important
as GitHub. You're going to have to be
able to show people who are prospective
employers that yes, you do actually burn
tokens on AI. And if they walk in and
they say, "Wow, you you've been burning
three million tokens a day." I don't
know. That's going to become a factor.
Like if you have two people and both of
them show their charts and one's chart
is like a hundredth of the other. Yeah.
People are going to wonder about your AI
usage. And so I also think there's a
degree of public accountability that
helps us to stretch and think and grow
and and think more about how we can use
AI. We are so early.6%
of chat GPT users are using codeex right
now.6 not even 1%. And I don't say that
because codeex is the only way to do
this. I built this in codeex. Opus 4.8
is going to be great for this if you're
a cloud user. You can absolutely build
it in opus 4.8 as well. It's going to be
a little bit harder to measure your
cloud usage unless you are using the API
a fair bit and then it's very very easy.
But you can still build a model and
infer it and I provide instructions for
that as well. And that's certainly much
better than nothing. And again,
Anthropic, if you're listening, please,
please, please meter those Claude
tokens. It makes everything easier. It's
just, it's just a piece of transparency
we need. But there's a community aspect
to this that is really, really
important. So, I'm not just talking
about doing this for you. I am
challenging you to start doing this and
sharing your work. Because when we share
what we've done, when we're able to talk
about how we're using tokens, the the
effect of being in a community and
sharing the kinds of uses we've had for
intelligence is incredibly powerful.
That's one of the things I learned
actually talking with Emma at OpenAI
recently. She talks about this culture
they have that's called the you're cool
culture, right? Where like if you come
up with a new usage for AI that people
haven't thought of, you're the cool
person for the day, right? Because
people want to learn from that. We need
more of that, not just at the
hyperscalers. We all would benefit from
that. I would benefit from learning how
you're using AI and I do. In fact, the
Substack chat, the executive uh chat
that I have for folks on WhatsApp, those
are all places where I learn what my
folks who are subscribing actually use
AI for and I learn from them and I hope
they learn from me a little bit. It's
super fun. We need to learn from each
other. We need to learn from each other.
Uh, and if we don't learn from each
other, we're going to think only in
terms of our little world and what we
can accomplish and we're going to miss
the emergent possibilities of these
models. And that's something that I try
and emphasize over and over again. Part
of why things like this chart are super
important is because models are grown,
not made. They are not defined as a
series of parameters that everybody
understands. These models are actually
at 10 trillion weights or 10 trillion
parameters right now. Nobody knows all
of them. And they were evolved and grown
over the course of a reinforcement
learning training regime. They were not
designed by a scientist who understood
every aspect of them. And when people
portray them as traditional software,
it's deceptive and it's wrong. And by
the way, I see journalists doing this
all the time. You guys got to stop it.
AI is a fundamentally different kind of
architecture than we've ever had in
computing before. It's called
transformer architecture. Yes, I have
whole videos on them. You can find them.
I'm not going to do them here. We need
to realize that when you have a model
that has grown, not made, you do not
fully understand what it is capable of.
Even if you were the one who made it,
right? Even if OpenAI released it, they
don't know all the capabilities. We have
to discover it. And so tools like this
are essentially a way for us to say,
"Hey, one, we're using this
intelligence, which is a good thing if
you're trying to grow your career in
2026. Two, this is what we're using it
for so we can all learn from each other.
Three,
this is an idea of where to go next
based on what we're learning and
feeling." Because the other part of
this, right, like that's not on the
chart is how it feels to you to use AI
when you have a 100 million burn hour.
uh or how it feels to you to use AI when
you get a big project accomplished and
you can see how that hit the token burn
and that gives you an idea for what
works and how delegated intelligence
works that you can't substitute for.
Now, you're going to wonder how am I
using AI? What are the secrets of Nate?
Where is Nate using AI? Well, I've
already shown you a few pieces, right? I
talked about the school uh the school
choice. That's obviously a personal
thing, a family thing. I'm also using
codeex very very heavily to optimize my
computing right now. And so, I'm using
it to check email. I'm using it to check
Slack. I'm using it to And by the way,
people say, "Well, why are you why are
you doing that? That's that's overkill.
You don't need to do that." I got news
for you. Everybody's time would be
better spent not in email. Everybody's
time would be better spent not in Slack.
If I can have a tool that helps me
handle that and helps me focus better,
that is actually a very valuable use of
intelligence. I also use it, as I've
been saying, for handling files on my
computer. So I have a whole process uh
where I have work packs that I have set
up that are focused on particular
projects. I have a chief of staff that I
use that's a thread inside codeex that
spins up sub agents and that all burns
tokens because you're now you're
managing multiple agents and that works
for me because then I can have one
thread that has all the context of what
I'm working on and I keep the context
window clean for detail work with lots
of child threads. These are all examples
of how I'm actually using those tokens
and why I think that they make sense for
me. I'm not saying you have to do
exactly what I do, but I would invite
you to challenge and open yourself up to
using AI more than you might think you
can because these models are almost
never at the frontier at capacity. I
don't care that I burned 800 million
tokens or whatever in a day last week. I
am not nearly at capacity on AI. There
is so much more that I can be doing. And
I and when I think that way, I come up
with creative uses for AI that are
really really important. I the way I've
started to handle automations has been
very helpful for me for feeling like I'm
up to speed. That's another great
example. Internal dashboards
automatically with running automations.
That's another great example of how you
can use delegated intelligence. I do
that as well. The sky is the limit on
what you can use with AI. But we need
desperately some degree of
accountability and some degree of a
learning loop to help us to learn this
effectively the difference between
someone who's using a couple of million
tokens a day light user of AI and
someone who's using almost a billion
tokens a day. It is literally in tokens
a 99% difference. It is also in terms of
fluency in what you can get done at
least a 99% difference but it feels like
more because of the multiplicity of the
impact you can have with large multi-
aent runs. So the reason I'm emphasizing
this now is that we are living in two or
three different futures at once. There
are a few people who are doing the 1
billion token a day lifestyle where
they're actually using these agents to
their full capacity. I see this in the
comments like ah AI does what it does.
there's been no improvement in six
months. No, there has been an
improvement in six months, but your
imagination hasn't caught up with it.
And so, you're not realizing it can go
through and organize all your
screenshots and sort out your entire
files, sort out your downloads folder,
take care of cleaning up your hard
drive, take care of troubleshooting your
internet connection. All of those are
real things I've done with AI, by the
way, in the last week. It can do all of
that, but you got to ask it. You got to
ask it, and you got to have examples
around you that show that. And so that's
why I'm encouraging folks, please,
please, please build and show your token
burn chart. Show what you've been using
AI for so we can all learn from you. I'd
like to learn from you. I'm sure there's
a lot I can learn that I haven't thought
of. I want you to be the cool kid. You
tell me how you've been using AI. You
tell me something creative you've done
with AI. Is there something with books?
I'd love to know that. You know, I love
to read. And let's all grow our ability
to use these tokens intelligently
together. That's why I created the token
burn chart. That's why I think it's
important. That's why I made several
versions of it over on the Substack so
you can make it, too. Uh, and I think
it's really important. I I think it's
something that that that if we find ways
to share what we are doing with
intelligence, we're going to get smarter
with AI, and that matters a lot. All
right, I will see you next time. This
has been so much fun. I had fun making
this dashboard. I hope you can tell. You
should have fun making it, too. It
really isn't a pain to prompt and make.
I think I made this one in about uh an
hour. I went back and forth. I was a
very lazy prompter and it still took an
hour and it was just fantastic and fun
all along the way. So have fun with it
and uh I'll see you next time.