You know, we live in this strange era of digital
abundance. There are tools for absolutely everything.
And when you look at the software landscape,
especially internal tools, you notice something
peculiar. A lot of it looks sleek. You know,
it has the right color palettes, the rounded
corners. It looks like it belongs in a design
portfolio. But then you try to actually use the
thing, and you realize it's just decoration.
It's the eye candy problem. That's exactly it.
The difference between a dashboard that gets
sold for a premium and one that just sits there
looking pretty and collecting digital dust. Most
dashboards fail, not because the code is broken.
It's because they don't actually solve a problem
for the human being staring at the screen. It
makes you think about that tension between utility
and aesthetics. We often conflate the two. We
assume if it looks good, it must be good. But
today, we're going to dismantle that idea. We're
looking at a very specific systematic approach
to building professional AI dashboards. It's
called the BU -ILD framework. And what's interesting
here is that we aren't just talking about writing
code in the traditional sense. We're talking
about using tools like Gemini 3 and Google Antigravity
to essentially orchestrate a system. Right. It's
less about being a bricklayer, laying every single
line of code by hand, and more about being the
architect. The source material we're diving into
today lays out this acronym, BUILD. It stands
for business purpose, underlying data, information
hierarchy, layout, and dynamics. It's a roadmap
for taking a vague idea and turning it into a
really high value product, even if you've never
written the line of syntax in your life. Let's
start at the beginning then. The letter B, business
purpose. It sounds a little corporate, almost
too obvious, have a purpose. But I suspect there's
more to it. Oh, there is so much more. The source
emphasizes that the why has to be concrete before
you touch a single tool. If you build without
a goal, you're just burning compute credits.
The core concept here is signal versus noise.
Signiverse is noise. We hear that a lot. But
what does it mean for a dashboard specifically?
In this context, it means every single chart,
every number on that screen must lead to a specific
action. If a number drops, the user needs to
know exactly what to do next. If the dashboard
doesn't tell them that, it's just noise. It's
vanity metrics. Okay, but finding that signal
is hard. People often don't know what they really
want, so how do you find out what the user actually
needs before you start building? You have to
become a detective. Yeah. You have to find their
pain points, but not by guessing. The source
suggests some really tactical ways to do this.
You're looking for frustration. When people complain,
they are basically asking for a solution they'd
pay for. Give me an example. Where are we looking
for these complaints? The guide mentions using
something called Gummy Search to scan Reddit.
Reddit? Really? Isn't that mostly just people
venting and arguing? It feels like a lot of noise
to sift through. It is venting, but that's the
gold. You look for communities where your potential
users hang out and you search for phrases like,
I'm tired of checking. Or, how can I track? You're
literally looking for emotional friction. So
the complaint is a product request in disguise.
Precisely. And you can go further. You can use
Answer the Public to see what people are typing
into Google. Or check Product Hunt to see what's
standard. But here's the key part. Once you have
all this raw data, you don't start coding. You
use AI to synthesize it. This is where Claude
comes in, I assume. But why bring in an LLM so
early? Because you need a plan. A concrete plan.
The strategy is to use Claude to generate a standard
operating procedure, an SOP. So let's say you
want to build a dashboard for a coffee shop chain.
You don't just say, make a coffee dashboard.
You tell Claude, I need to help owners decide
when to restock oat versus almond milk based
on waste. And you ask it to write out a detailed
SOP with the key metrics and the decisions the
owner has to make. Pause there for a second.
That feels... counterintuitive. Usually, developers
want to get their hands dirty immediately. They
want to see an interface. You're saying the discipline
here is to not code. The discipline is to write
a constitution for the software first. If you
ask an AI to just write code, it's going to guess
at the business logic. It'll hallucinate features.
But if you feed it an SOP, a text document explaining
the logic in plain English, you're telling it
what to think before you tell it how to code.
That's a huge shift in approach. So, probing
question for you then. Why write a text SOP before
a single line of code? Because ambiguity in the
plan creates chaos in the code. That makes a
lot of sense. If you can't write it in English,
you can't write it in Python. Okay, so we have
a why. Now we move to the U and BYLD underlying
data, the what. This is where we get into the
actual tools. Right. And for a lot of people,
this is the scary part. They think, I don't know
how to set up a server. But the source recommends
this, fastest combination for beginners. It's
a three -part harmony. Google AI Studio, Google
Antigravity, and SuperBase. Can you break those
down for us? I think people can get lives in
the brand names. For sure. Think of Google AI
Studio as your design lab. It's where you experiment
and set the vision. Then you have Google Antigravity.
That's the workspace. That's where the app actually
lives and where you talk to the code. And finally,
SuperBase. And SuperBase is the database. Think
of it as the long -term memory. or the vault.
Gemini is the brain processing the request, but
it needs to store facts somewhere permanent.
That's SuperBase. It's where the actual data
lives. That phrase, talk to the code, really
highlights how much things have changed. It feels
less like typing syntax and more like having
a conversation. It really is. The workflow starts
in Google AI Studio. You upload that SOP text
we just talked about. But here's a really cool
trick the source mentions. You also upload visual
inspiration. Like... Screenshots. Exactly. You
go to Dribbble or Pinterest, find a dashboard
that looks clean, pick one dark mode and one
light mode, and you upload those images along
with your text. You're telling the AI, I want
the logic from this text, but I want it to look
as professional as these images. Is that cheating,
or does it just create a derivative product?
I wouldn't call it cloning. I'd call it setting
a vocabulary. If you just say, make a dashboard,
you get a generic kind of 2015 looking website.
By providing the screenshots, you're giving the
AI a meta prompt. You're setting the aesthetic
bar high. You're saying use Tailwind CSS. Make
it look like a $5 ,000 corporate tool. You know,
I appreciate the vulnerability in the source
material here. The author admits to wrestling
with prompt drift. I still wrestle with prompt
drift myself. Getting the AI to stay focused
on the original plan is hard without these strict
guardrails. Yeah, it's a real thing. It needs
those guardrails. If you don't set the standard,
the AI will get lazy. It's like a contractor.
If you don't specify marble countertops, you're
going to get laminate. So to wrap up this section,
then. Why upload screenshots of other people's
designs? It forces the AI to mimic a professional
standard visually. Okay, let's unpack the next
letter. I. Information hierarchy. The where.
We have our data. We have our look. But how do
we arrange it all? The source mentioned something
called the 10 -second rule. This is all about
cognitive load. The 10 -second rule just means
a user should understand the state of their business
within 10 seconds of looking at the screen. And
to do that, you need a Herometric. The herometric.
The one number that tells the whole story. Exactly.
For a sauce company, maybe it's active users.
For our coffee shop, it's daily sales. And its
placement is non -negotiable. Top left corner.
Why so dogmatic about the top left? Couldn't
I put it in the center and make it huge? You
could, but you'd be fighting biology. We're talking
about the F pattern here. The F pattern. Yeah,
it's a classic user experience concept. Eye tracking
studies show that when we look at a screen, we
don't really read it. We scan it. Our eyes instinctively
anchor top left, scan across, then drop down
and scan across again. It forms a rough F shape.
If your most important number isn't in that anchor
spot, you're making the user hunt for it. That's
fascinating. So good design is really just anticipating
human efficiency. Or laziness. You want to reduce
friction. You make that number the biggest text
on the screen. Then you group related data together.
The guide suggests using Google Antigravity's
planning mode here. You can actually ask the
AI, based on design best practices, how should
I order these five metrics before you change
any code? That's a really smart use of the tool.
You're treating the AI as a consultant. And it
usually gives pretty good advice because it's
ingested millions of design patterns. It knows
what good looks like. So to pin this down, why
does the top left corner matter so much? It's
biological. That is where the human eye naturally
lands first. It's biological, unavoidable, and
speaking of things that are unavoidable. Sponsor.
Okay, we're back. Deep diving into the BUILD
framework. We've covered business purpose, underlying
data, and information hierarchy. Now we're on
to L layout, the how. And this section introduces
a concept I absolutely love, the museum principle.
The museum principle. Let me guess, the dashboard
should be a work of art. Actually, the total
opposite. Think about a modern art museum. What
do the walls look like? White. Plain. Empty.
Exactly. They're invisible. Why? So you only
look at the art. In a dashboard, the art is the
data. The design, the containers, the background
should be the museum walls. If a user is noticing
your cool gradient menu, you've probably failed.
Good design should be invisible. That's a fantastic
analogy. The interface should just get out of
the way. But that's hard to do, isn't it? We
have a tendency to want to fill empty space.
We do. We want to clutter it. And the biggest
offender is usually the sidebar. The source compares
it to a steering wheel. If you have 50 buttons
on a steering wheel, the driver's going to crash.
Cognitive overload again. Right. The rule here
is strict. Keep the sidebar to five to seven
items max. Dashboard, analytics, customers, settings,
help. That's it. If you have more. Nest them.
It's about subtraction, not addition. And here's
another great way to use AI that I hadn't thought
of. You can run an AI audit. You literally prompt
the AI to act as a senior UI UX designer. You
ask it to critique your work. You say, look at
this code. Suggest items to remove to reduce
clutter. That's brilliant. You're role -playing
with the model. Usually you'd have to hire someone
to get that kind of feedback. It works so well.
It catches things you miss because you've been
staring at the screen for six hours. It helps
you get that expensive look without the agency
price tag. So what is the ultimate test of a
good layout? If the user doesn't notice the design,
you succeeded. That brings us to the final letter.
D -Dynamics. The feel. And honestly, this is
where so many tools fall apart. They look fine,
but they feel Dead. Dead is the perfect word.
Silence creates distrust. Explain that. Why does
silence create distrust in software? Well, imagine
we're having a conversation. I say something,
and you just stare at me, stone -faced, for three
seconds. That's awkward. I'd think you didn't
hear me, or you're angry or something. Exactly.
Software is a conversation. If you click a button
and nothing happens, you panic. You think, did
it work? Did I break it? The dashboard needs
to feel alive. So how do we manufacture that
life? Visual feedback. It's the micro interactions
hover effects when your mouse goes over a button
It should change color slightly to say I see
you Loading states never show a blank screen
show a spinner so the user knows thinking is
happening success messages It all ends up. It's
the software nodding back at you. Yes. And this
is also where we connect to the real world. We
move from fake placeholder numbers to real API
data using SuperBase. This is the part that always
feels like magic to me. Oh, it's a total moment
of wonder. Whoa. Imagine just handing the AI
and AP and watching it wire up the entire database
instantly. It used to take days of writing Fed
requests and error handling. Now it's just connected.
But once it's connected, it has to be fast, right?
That's the snappy test. Page transitions must
be smooth. Buttons must react instantly. If it
feels sluggish, people subconsciously assume
the data is unreliable. It's irrational, but
it's human nature. So why do things like hover
effects and spinners really matter? Responsiveness
builds psychological trust that the data is accurate.
So let's zoom out. We've walked through the whole
framework. BUILD. Let's do a rapid -fire recap.
Okay, let's hit it. B, business purpose. Solve
a real problem with an SOP. U, underlying data.
Connect clean data with the right stack. I, information
hierarchy. Spotlight that hero metric top left.
L, layout. The museum principle. Keep it invisible.
D -Dynamics. Make it feel alive to build trust.
It's a powerful framework. And what I find most
compelling here is the philosophical shift it
implies. It really emphasizes that you, the listener,
you're no longer just a viewer of technology.
You are a creator. That's the big shift. We're
moving away from an era where you had to spend
years learning to code syntax. We are moving
toward an era where you need to learn to define
the problem. The AI handles the how. The syntax,
the database queries, but you have to master
the why. You're the director. It's a call to
action really. Don't worry about perfection.
The barrier to entry has never been lower. Exactly.
Open antigravity. Build one screen. Just one.
Do it today. The best way to learn this isn't
to listen to us talk about it, but to actually
struggle through that first prompt. I think that's
the perfect place to leave it. Go build something.
See what happens. Thanks for diving in with us.
Always a pleasure.