Imagine for a second you're sitting in front
of a blank chat window, the cursor is just blinking,
and you're not about to ask for a recipe or,
you know, for a summary of some confusing email.
Right. You type a single prompt and you ask it
to build a working machine, specifically a Nintendo
Game Boy emulator from scratch. Usually, this
is the part where the whole illusion just breaks.
The model gives you some generic apology, or
maybe a few broken snippets of code that don't
actually run. But this time, it didn't do that.
It generated over 3 ,000 lines of code in one
continuous flow. 3 ,000 lines. One shot. It built
the CPU emulation, the memory management, the
input handling. It mimicked the actual hardware.
And when the user ran it, it played real games.
It played Tetris. Honestly, it's hard to wrap
your head around. It feels like we just skipped
a few chapters. Oh, we absolutely did. Welcome
back to the Deep Dive. Today, we're unpacking
something that feels like a genuine tipping point.
We're looking at a stack of reports, developer
logs, and some leaked benchmarks around a model
that the internet has decided to call Snow Bunny.
Which is a very online nickname for what is actually
a very serious piece of engineering. Right. To
be clear, this is Google's Gemini 3 .5. But the
interesting thing is, this isn't a product you'll
see in a glossy press release just yet. It's
a ghost. It is. It's a leaked version. The coding
community started noticing these mysterious model
codes DN9, D13, popping up in Google AI Studio.
OK. And when they started poking at it, running
their tests, they realized this wasn't just a
slightly faster version of Gemini 3 Flash. This
was something else. And that's what we're exploring
today. The central idea we're looking at is this
shift in our relationship with these tools. We
spent the last few years in the era of the chat
bot. Right. You talk, it talks back. But Snowbunny
suggests we're entering the era of the AI director.
Exactly. The difference is agency and scope.
A chat bot answers a question. A director builds
a vision. So let's get into the evidence for
that. Because looking at this material, this
model is putting up some numbers that don't just
inch past the competition. They kind of leapfrog
them. The numbers are startling. But what's more
interesting to me is how it's getting those numbers.
It's not just raw power. It seems to be a change
in logic. Let's start there then. Segment one,
the logic leap. There were two specific tests
in these reports that really caught my eye. One
is a variation on the classic trolley problem.
Ah, yes, the misguided detention test. Wow. This
is a fascinating look at how these models actually
process information. So most of us know the drill.
A train is coming, five people on the track.
Do you pull the lever? It's ethics 101. Yeah.
But this test had a twist. A really important
one. Right. The prompt sets up the scenario,
but it adds this tiny crucial detail buried in
the text. Yeah. The five people on the track
are already dead. Which changes everything. It
changes the entire moral calculus. There's no
dilemma. But here's the thing. Most AI models,
even the really advanced ones, they see the words
trolley problem and they just go into autopilot.
They stop reading and start predicting. Exactly.
Their safety filters kick in what we call RLHF
and they start lecturing you about the sanctity
of life. They miss the context completely because
they're pattern matching the concept, not the
reality of the prompt. They're skimming like
a student who didn't do the reading for class.
Precisely. But Gemini 3 .5, it noticed. It caught
that detail and gave the correct logical answer.
It basically said, well, it doesn't matter. They're
deceased. It scored 68 .5 % on this test. Which
is way above the others. It beats out almost
every top model out there. That feels significant.
It implies the model isn't just retrieving data.
It's actually paying attention to nuance. And
that means the hallucination rate where the AI
just makes stuff up could drop because it's actually
grounded in the text you give it. And that was
backed up by the second test, the hieroglyph
test. This one sounded fascinating. It's not
about reading ancient Egyptian, is it? No, no,
not literally. It's a test of what they call
lateral reasoning. The AI gets these strange
symbols it has never seen before, and it has
to figure out the hidden rules. You can't just
look this up on Wikipedia. So it's basically
an IQ test for puzzles it's never seen before.
Exactly. It has to think on its feet. And the
performance gap here is just massive. The older
Gemini 2 .5 Pro scored about 20%. the model called
GPT -5 reasoning reached about 45%. Gemini 3
.5 Snowbunny hit between 80 and 88%. Wow, that
is a staggering jump. We're normally excited
about a 5 % or 10 % increase, doubling the score
of a GPT -5 reasoning model. That's different.
So if it notices the dead people detail when
others don't, what does that imply about its
ability to handle messy real -world data? It
means we can finally trust it to read carefully,
rather than just pattern match. Which brings
us to the application of that intelligence. Because
being smart is one thing, but can it actually
build anything? This brings us to what the community
is calling vibe coding. I love this term, vibe
coding. It sounds less like computer science
and more like a Spotify playlist. But it's about
building websites without knowing HTML or CSS.
Yeah, that's the core of it. The idea is that
you describe the feeling, the vibe, and the AI
handles all the syntax. The case study here was
a project called Cakes from the Heart. Sounds
delicious. So the test was to see if the model
could generate a professional -grade website
for a bakery. The prompt was specific, but descriptive.
You know, fancy style, cream and light gold colors,
specific menu items. And what was the result?
It wrote the entire thing. The hero section,
the menu, the contact map, all the styling in
a single HPML file. And it did it in about eight
minutes. Eight minutes. And the cost? About 38
cents. That's cheaper than a croissant. Significantly.
But the impressive part wasn't just the speed.
It was the workflow. The user could ask for dark
mode after the fact, or ask to make images move
smoothly. The model would just iterate on the
code. So it's not just spinning out a template.
It's refining a product based on your feedback.
It's separating the intent from the execution.
That's the director model again. You provide
the vision, the AI provides the technical labor.
Does vibe coding make actual coding obsolete
then, or does it just change the barrier to entry?
It lowers the barrier, letting you focus on the
idea while the AI handles the syntax. So we've
got logic, we've got code, but the sources also
mention this model is multi -modal in a way we
haven't really seen before, specifically when
it comes to sound. This was a huge leak. A user
named legit spotted an A -B test in Google AI
Studio. Usually, if you want AI music, you go
to a separate tool like Suno or Udio. Right.
You leave your chat, you go to the music app,
paste your lyrics. There's a lot of friction
there. Exactly. It's disjointed. But with Gemini
3 .5, the music generation is native. It doesn't
give you sheet music or a link. It plays an audio
file right there in the chat. That integration
seems like a small UI change, but the workflow
implications feel pretty massive. Oh, it's huge.
Because of context. Think about it. You spend
20 minutes working with the AI to write a funny
script for a video. Right. It has the history.
It knows the characters, the timing, the jokes.
Then you just say, compose a background track
for this. You don't have to re -explain the vibe
to a separate music bot. It already knows the
script is a comedy. It knows the pacing. It shares
the memory. So the magic isn't just the music
quality. It's the shared memory between the text
and the audio. Exactly. It's the seamless workflow.
The AI understands the whole project, not just
one slice of it. And moving from ears to eyes,
we've seen AI generate images for a while now
with MidJourney and Deli. But the reports on
Gemini 3 .5 focus on something else, vector graphics,
SVGs. Yeah, this is a favorite topic for developers.
Generating a JPEG is kind of easy. It's just
a grid of colored pixels. If you get a pixel
wrong, it's just a blurry spot. But an SVG that's
code, it's a set of mathematical instructions
on how to draw a line or a curve. So if the code
is wrong, the image doesn't just look blurry.
It breaks. It completely breaks. The line shoots
off the page. The circle doesn't close. It's
a much higher bar for accuracy. The leak showed
users generating these cyberpunk robot icons.
And they were good. Professional quality. But
what really stood out was the consistency. They
generated a whole series, and the robots all
had this neon blue and purple theme. They looked
like they belonged to the same brand, even though
the shapes were unique. There was a specific
test mentioned here that I found kind of hilarious.
The Pelican on a bicycle challenge. The Pelican
on a bicycle. It's become a standard benchmark
for vector intelligence. Why? Is there a big
market for cycling birds? No, but it tests spatial
logic. Most models just failed this. They draw
a bird and they draw a bike, but the bird is
floating next to it or it's merged into the wheels.
It's a mess. OK. So Simon Willison, a well -known
researcher, he analyzed the output from Snow
Bunny and it nailed it. The bird was actually
sitting on the seat. Its legs were reaching for
the pedals. I have to admit, the idea of spatial
logic in a model that is essentially just predicting
the next word, I still find that hard to wrap
my head around. It just feels counterintuitive.
It really does. It's like it's deriving physics
from language. So why is a bird on a bike a better
test of intelligence than writing an essay? It
proves the AI understands how objects relate
in physical space, not just how words relate
in sentences. OK, we've covered logic, websites,
music and art, but we have to circle back to
the cold open, to the Game Boy emulator, because
this feels like the graduation project. This
is the system architecture test. We mentioned
3 ,000 lines of code. Can you break down why
this is so different from, say, asking it to
write a short Python script? Well, a short script
is isolated, but an emulator. That is a complex
ecosystem. You have the CPU emulation, which
has to talk to the memory management. The memory
has to talk to the input handling for the buttons.
The input has to talk to the display pipeline.
And they all have to agree on the rules. Exactly.
If the AI forgets a variable name it created
on line 50 when it's writing line 2500, the whole
thing crashes. This is what we call global consistency.
So it's holding the entire blueprint in its head
at once. Yes. And developers Jared Liu and Shitas
Lua verified this. They ran real game ROMs on
the code this model wrote. It required a few
tiny manual fixes, but the architecture, the
heavy lifting, was done in one shot. If it can
hold 3 ,000 lines of logic in its head at once,
are we looking at the end of spaghetti code?
We're looking at the ability to build full products,
not just parts, by describing the system architecture.
It's incredible. We're going to take a quick
breather here, but when we come back, we're going
to talk about what this means for you. If the
AI is the director, what is your role? Stay with
us. And we are back. We've looked at the capabilities
of Gemini 3 .5 Snow Bunny. It's smarter, it's
multi -modal, and it can architect complex systems.
It's a beast. So let's zoom out. What's the big
idea here? If I'm a listener and I'm not a professional
coder, why should I care that a computer can
draw a pelican on a bike? Because the cost of
creation is collapsing. We talked about that
bakery website costing 38 cents. Right. The pricing
structure for these models. About 50 cents per
million. Tokens input. $3 for output. It means
experimentation is virtually free. You can afford
to fail. You can afford to try 10 different versions
of a website. And this shifts the user's role.
We kept using that word director. That is the
key philosophy. You are no longer the one placing
every pixel or writing every single line of CSS.
You are the one with the vision. Your job is
to describe the vibe. Which is a skill in itself.
It is. The sources highlight three rules for
this new era. One. Be specific. Don't just say,
make a website. Say who it's for, why it exists.
Two, describe the vibe. Use emotional words.
Warm, modern, aggressive. The AI gets that now.
And three, the 80 -20 rule. Let the AI do the
heavy lifting, the 80%. You come in for that
final 20%. to polish, to tweak, and to refine.
So it's collaborative. You basically have a team
of musicians, designers, and engineers inside
your laptop. Just waiting for your orders. So
here's the call to action. You don't have to
wait for some big Super Bowl commercial to try
this. No. Go to Google AI Studio. It's open.
Look for Gemini 3 Flash. If you happen to see
the code DN9, you've got the snow bunny. But
even if you don't, the tools are there. The gap
between having an idea and making it real has
never been smaller. Start playing. break things.
Thanks for diving in with us today. We'll catch
you on the next one.