If you look at how software creation has changed,
I mean, even in just the last few years, it's
already dramatic. But I don't think anything
really prepared me for the actual demonstrated
reality that you could build a functional real
-time 3D ray tracing simulator just by typing
a single paragraph of text. Welcome to the deep
dive. That ability to turn conversation, or what
some people are calling vibe, into a complex
tool. That's the new reality check, isn't it?
It really is. And today we are cracking open
the documentation, the analysis of 12 different
applications that were built completely from
scratch using just natural language problems.
We'll stack apps in minutes. Exactly. Our mission
is pretty straightforward. We need to pull out
the most important lessons here about rapid prototyping,
handling, extreme complexity, and critically
understanding the real world limits. We're moving
past the hype. Right into the details. The short
version, the TLDR. is this. Gemini 3 .0 automates
these really specialized tasks that before required
such deep knowledge of coding frameworks. The
real value is just. It's drastically lowering
the barrier to entry for complex logic. And here's
your immediate actionable tip from the source
material. If you want to try and replicate this.
Always, always request standalone HTML files
in your prompt. Why is that? Because that guarantees
the app is immediately testable. It's portable,
ready to share or, you know, ready to debug.
That's such a critical constraint to put on the
AI. Because even just a year ago, if you suggested
we'd be creating a physics engine or a custom
board game just by typing. Yeah, you'd have been
left out of the room. It would have been dismissed
as science fiction. But now that shortcut is
undeniably real. The tests we reviewed, they
showed some shocking successes, you know, like
those physics demos. But we're also going to
look honestly at the limits. For sure. Especially
around browser lag and some of the visual artifacts.
It's an honest look at where vibe coding is today.
Okay, let's start with procedural generation.
This is essentially code writing code. We're
talking geometry and a little bit of fun with
this thing called the voxel creatures generator.
Right. It started with a really simple high level
prompt just asking for procedurally generated
voxel creatures and biomechanical vehicles. And
what's immediately fascinating here is that the
A .I. didn't just spit out spatic images. It
generated interactive physics. You can create
a creature and then you click a button to scrap
it and you watch the pieces just shatter. with
really realistic, tumbling physics and shadows.
It's like digital Lego blocks. And the system
retains conversational memory, which is key.
The source asked for a specific item, a retro
Polaroid camera. And Gemini just generated a
fully recognizable voxel camera in seconds. So
it's handling both random generation and specific
instruction at the same time. Yeah, inside a
3D space. It's pretty amazing. We also saw the
process of converting a flat 2D image. I think
a Michael Jackson photo was used in the test.
That's the one. And turning it into 3D voxel
assets. So the AI has to analyze the composition,
identify elements, figure out depth, and then
output three distinct 3D scenes. Setup. mid -event
and aftermath it's complex scene interpretation
so what was the quality like well the assessment
was interesting the results were described as
simplistic but actually really cool they weren't
photorealistic but they absolutely captured the
essence of the image and its movement Okay, so
let's unpack this a bit. When we look at that
voxel generator, the source noted a small sort
of rough edge. The app keeps the same color palette
from the last design until you do a hard refresh.
What does that color palette stickiness tell
us about where these generated apps are right
now? It tells us that generated apps often need
iterative refinement. The foundation is there,
but state management still needs a human review.
And now we have to move into the... uh the really
deep technical stuff we asked the ai to handle
a task that is just notoriously difficult and
computationally expensive real -time ray tracing
and ray tracing for anyone unfamiliar is the
rendering technique that simulates exactly how
light bounces through a scene it's what gives
you those hyper realistic reflections usually
requires high -end gaming hardware oh yeah and
historically weeks of highly specialized manual
coding to set up the geometry and the physics
The prompt was intense. It specifically requested
a 3D ray tracing app in a mirror maze using PBR
materials and high -end frameworks like 3JS.
Okay, let's quickly define that jargon. PBR.
That's physically based rendering. Right. So
digital materials that react accurately to light,
like real metal or rough plastic. And 3JS is
just the standard library for creating 3D graphics
in a web browser. The AI had to seamlessly integrate
all of that. And what it delivered was, well,
a fully navigable 3D environment. You have these
floating metallic pieces where the reflections
update dynamically. Light bounces correctly between
three different light sources. That is a massive
computational load. Achieved via conversation.
Now, the source was honest. There were visible
artifacts, some smart alignment issues, a bit
of visual glitching. But for something generated
conversationally in minutes, the quality is truly
remarkable. I mean, that's the moment of wonder
right there. I genuinely did a double take when
I saw the metallic reflections. It looks like
a high -end tech demo from maybe five years ago,
but it was generated from a paragraph of text.
That's a huge leap. It is. Alongside this, we
saw the dot matrix image converter. You upload
a photo and the app turns it into thousands of
individual dots. And the interactivity is key
there. It is. You can apply effects like data
glitch or implosion and you watch the dots move
around in 3D space and then perfectly reassemble.
But if the output is visually glitchy, as the
source said, how much time does that really save
a developer? Is vibe coding just creating complex
but, you know, broken starting points? Well,
it showed AI can handle the advanced real -time
rendering physics. That complex structure, which
used to take weeks to set up, is now instantly
accessible. Next up, we tested complex multi
-body simulations. This is where you really stress
the math capabilities of the generated code.
We started with the particle collider simulator.
Right. The goal was an educational visualization.
A particle collider and a tokamak fusion reactor.
And Gemini created two separate simulators showing
particle acceleration, interaction, and detailed
collision paths. The physics here has to be mathematically
sound for it to be accurate. But this is where
we hit the performance reality check. The source
material really focused on this. It said the
simulation runs very slowly in my browser. But
that's because it's doing a lot of calculation.
That performance lag is so important. The source
noted that expecting a flawlessly optimized production
-ready app right away is the big mistake users
make. I still wrestle with this myself, actually.
You see this incredible functional output, the
whole collider visualized, and you kind of forget
that optimization is still a manual, time -consuming
step. It's easy to... expect immediate perfection.
Totally. And the interactive end -body gravity
simulator pushed this idea of complicity even
further. This challenges the AI to accurately
simulate Newtonian gravity with multiple bodies
at once. Meaning every planet is affecting every
other planet all the time. Constantly. And what
you can do with it is incredible. You can add
planets, moons, change their mass, and the simulation
just immediately recalculates all the gravitational
forces. Which is how you get those emergent chaotic...
behaviors like orbital decay or a gravitational
slingshot. And the AI responded perfectly to
requests for cosmic events. You could instantly
add a rogue star, a black hole, or even trigger
a supernova. And it all affected the physics
in real time. It proved the underlying engine
was robust, not just a visual trick. So when
dealing with these high computation tasks, like
simulating 10 bodies interacting, is that lag
a failure or is it the ultimate proof of concept?
It's proof of concept. The complex foundation
is sound, but performance optimization has to
follow the AI generation, not come before it.
All right. So moving away from visual physics,
we looked at how this generative capability applies
to pure data analysis and real world utility.
The AI bubble research storyboard is. It's a
fascinating case of macroeconomics meeting interactive
visualization. Yeah, this one was cool. The prompt
basically told the AI to act as a macroeconomist,
gather historical data on financial bubbles like
the dot -com era, the Great Depression, and then
build an animated public dashboard to analyze
the current AI market. And what it created was
an interactive macroeconomic analysis tool. It
had current AI market data, I think CapEx projections
of $405 billion in 2025. Yeah, and Minsky cycle
visualizations. Let's clarify that. CapEx, capital
expenditure. That's the money corporations are
spending on physical assets, right? Exactly.
Like infrastructure and chips. That number is
a critical proxy for massive corporate confidence.
And the Minsky cycle just explains the five standard
stages of speculative bubble, from displacement
all the way to panic. So the AI didn't just plot
data. It applied complex financial theory. It
did. And the highlight was the bubble simulator
game that was built into the dashboard. Users
could make business decisions, raise funding,
hire people, and the chart would update tracking
risk until the demo day bubble popped. And the
tool even provided its own strategic assessment.
It did. It said something like, productive buttle,
infrastructure survives, speculators die, banks
safe, stocks at risk. That's predictive application,
not just visualization. So moving from macro
trends to micro analysis, we have the basketball
shot analyzer. This is a total game changer for
coaching and skill development. The workflow
seems simple. You just upload a short clip of
a jump shot and the system processes it frame
by frame. Right, using computer vision. And the
technical advantage is just so clear. It delivers
this granular biomechanics, breakdown joint angles,
wrist snap, foot placement. It analyzes the entire
kinetic chain and gives you a shot score and
even a pro comparison mode with skeleton overlays.
So how does that economic bubble game really
show the difference between just raw analysis
and genuine application? it turns static data
and historical frameworks into a dynamic educational
and most importantly a playable scenario for
the user to learn from our final segment looks
at the ai's ability to model large -scale systems
and integrate real -time inputs we're starting
with the earth simulator with weather control
the prompt asked for a perfect 3d earth globe
realistic interactive cloud layers high quality
rendering and it delivered a rotatable 3d earth
where you can actually adjust environmental parameters,
CO2 levels, temperature. The satellite feature
was particularly impressive, I thought. It really
was. The system calculated and displayed the
correct orbital paths for communication and weather
satellites, all moving in their actual trajectories.
Then that's hard. That requires accurate geospatial
calculation with real -time physics. It's not
just a visual. And you could use natural language
commands. You can type trigger a Category 5 hurricane,
and the system updates the visual state. Amazing.
Then we have the hand -tracking fluid dynamics
simulator. This one is simpler. It just tracks
your hands with a webcam to create these swirling
fluid dynamic effects. appeal there is just demonstrating
that real -time computer vision integration with
generative effects it's the basis for future
gesture based interfaces and finally the power
of 10 animator this tested the scaling challenge
generating an animation zooming all the way from
a cosmic scale down to an atomic scale. And Gemini
did this by coordinating with another model,
NanoBanana, to blend the imagery across those
different magnitudes. And that ability to coordinate
specialized subtasks, telling one AI to handle
image generation and another to handle the animation,
that's the next huge leap. Though we do have
to acknowledge the honest check, the content
accuracy. It varied. There was an odd, unexplained
hand artifact that appeared at the cellular level.
Right. So the concept of AI directing other AI
worked, but the final polish still needed a human
eye. So what's the overarching lesson from combining
vision, physics, and image generation across
all these different scales? That the AI can successfully
coordinate specialized subtasks and separate
models to achieve these really complex multimodal
results on a grand scale. When you look back
at All 12 of these applications, I mean, from
the Monopoly board generator, which we didn't
even have time to get into, to that high -end
ray tracing demo, the impressive part isn't any
single one of them. No. It's the sheer totality
of it, the diversity and the complexity. 12 functional
applications built through conversation in a
matter of minutes. That amount of work would
have required significant development time and
specialized expertise just a few months ago.
Multiple teams, probably. The ultimate unlock
here isn't just speed. It's the democratization
of high -end tooling. The code is still complex,
but the barrier to accessing that complexity
has been, you know, just vaporized by conversation.
So the real question then isn't whether AI can
build perfect applications today. It's whether
AI can make building useful, structurally sound
things accessible to significantly more people.
And based on these results, the answer is, it's
an undeniable yes. So if the AI handles the complexity,
does the value of the human developer now shift
entirely to optimization? Or does this finally
mean we can move beyond the screen and start
building truly custom interfaces applications
that just weren't worth the coding effort before?
The tools are here today. What kind of complex
logic or creative idea that you previously thought
was impossible to code are you now ready to prototype?
That's something fascinating to consider as we
wrap up this deep dive.