Imagine for a second that you could take every
single Harry Potter book, all seven of them,
stack them up, hand them to someone. Now imagine
that person could read every single word just
instantly. But more importantly, imagine that
while they're reading the very last sentence
of the last book, they still perfectly remember
the first sentence of the first book. That is
the scale we're talking about here. It's a staggering
amount of information to hold in your head at
once, without getting fuzzy on any of the details.
It really is. And that concept, that ability
to hold all that information without losing the
thread, is technically called a context window.
It's pretty central to what we're exploring today.
Welcome back to the Deep Dive. Today we are unpacking
Google Gemini Advanced. It's good to be here.
This is a big one. It is, yeah. And looking at
the numbers, the interest is definitely there.
The user base for Gemini has, what, quadrupled
in just a year? That's massive growth. But digging
into the reports, there's this... this interesting
tension. We have millions of people using these
tools, but there's a massive gap between knowing
a feature exists, like, oh, I heard it can read
a PDF, and actually weaving that feature into
a daily workflow. That's the classic adoption
curve, right? We treat these tools like novelties
or just really big search engines. We ask a question.
We get an answer. It's transactional. But the
shift we want to talk about today and what the
source material really emphasizes is moving from
just chatting with a bot to building a persistent
personal assistant. Which is a heavy promise.
So to keep us on track, we have a bit of a roadmap.
We're going to look at three specific pillars
from the documentation. First, multimodality,
which is a fancy word we will definitely break
down. Second, the new thinking model versus the
fast model. And third, creating these custom
gems. And we'll wrap it all up with a master
class example, a gardening workflow that actually
ties all these disparate pieces together into
something really practical. So let's start with
that first pillar, multimodality. To me, this
often sounds a bit like marketing jargon. When
you strip it back, what are we actually talking
about here in terms of utility? It sounds complex,
but it's actually the most human part of the
AI. I mean, think about how you and I interact
with the world right now. We don't just read
text floating in a void. We see things. We hear
sounds. We watch movement. Multimodality just
means the AI can do that too. It isn't limited
to text anymore. It can process photos. It can
watch videos. It can listen to audio. So it's
effectively breaking that text -only barrier.
Exactly. And the efficiency gain here is massive.
Think about the friction of trying to describe
a photo to a computer. You're typing, OK, there's
a tree in the left corner, and the light is hitting
it this way. It's just tedious. With multimodality,
you don't describe the context. You just show
it. You just show it. And this ties back to that
opening hook we had, the context window. The
source material mentions Gemini Advanced has
a capacity of 1 million tokens. We use the Harry
Potter analogy, which is vivid, but technically,
what is a token? Good catch. A token is basically
a unit of text, roughly four characters. So a
million tokens is about 700 ,000 words. Which
is hard to wrap your head around. It is. But
practically, it solves the goldfish memory problem.
Most older models, if you gave them a long document
by page 50, they've kind of forgotten what was
on page one. With a million tokens, you can dump
a massive amount of data in, and it retains high
-fidelity recall from start to finish. Let's
look at a concrete example from the research
to see if this holds up. Say I have to learn
about quantum computing, and I have a dense,
79 -page PDF. It's dry, it's academic, it's painful.
The nightmare scenario. Right. So I drag that
PDF into Gemini, and because of that massive
context window, I can ask it, explain this to
me like I'm a five -year -old. And it works.
It synthesizes the whole thing. But here's my
pushback on that. It reads the text. It summarizes
it. That's great for saving time. But how does
that actually help us learn a complex topic?
Because just reading a summary isn't the same
as understanding the mechanics of it. That's
a crucial distinction. And if you just ask for
a summary, you are only getting surface -level
info. The power is in interrogation. The source
material suggests asking the AI to transform
that static text into interactive tools. Interactive
tools? Yeah. You aren't just getting a book report
back. You can ask it to generate an infographic
based on the text. Or, and this is where it gets
wild, you can ask it to create a simple interactive
simulation. Code, essentially. It lets you play
with the variables mentioned in the PDF. It turns
passive reading into active simulation. So I
could ask... Based on this paper, write me a
Python script that simulates particle spin. And
it'll do it. You run the code, and suddenly you're
seeing the concept in action rather than just
reading about it. It's bridging that gap between
information and real understanding. That's fascinating.
OK, so we've got text and documents covered,
but I want to pivot to the second pillar, which
I think is even more mind -bending. The ability
to watch. Video analysis. The source makes a
really crucial distinction here. When we say
Gemini watches a video, we aren't just saying
it reads the automated captions, are we? Because
I can do that. I can just read a transcript.
Correct. And that is the game changer. Most tools
just scrape the transcript text. Gemini is processing
the pixels. It's using its native vision capabilities,
analyzing the visual information frame by frame.
I have to pause on that because... Just imagine
the implication. It's not just hearing what is
said. It's seeing the editing speed. It's seeing
the color grading, the facial expressions. It
captures the whole texture of the video, not
just the content. So let's look at a use case.
The guide mentions learning from YouTube. Say
I want to make a video and I find a tutorial
on how to make viral AI videos, but it's super
fast paced. It's chaotic. Read the kind where
you're pausing every two seconds just trying
to catch up. Exactly. So you can paste that link
into Gemini. What happens next? You just say,
watch this and write a prompt for me to recreate
this exact style. Because it's watching the pixels,
it understands the visual language, the pacing,
the aesthetic, and it gives you a command you
can use to replicate it. It's like having a director
analyzing the film for you. There was another
example for creators that I thought was really
sharp. If you're a YouTuber or even just doing
marketing videos for work, you can upload your
top five performing videos. This is where it
becomes an analyst. You ask it, look at these
five videos. Why did they succeed? And because
it sees the video, it doesn't just say, oh, the
topic was good. It looks at the structure. It
might say, in all five videos, you used a rapid
visual hook in the first three seconds. Or your
editing pace accelerated at the one minute mark,
which kept retention high. It's spotting patterns
that you might not even realize you're doing.
Precisely. It's objective feedback based on visual
data. OK, now. This works great for immediate
analysis, checking a few videos, a PDF. But what
if I have a year's worth of scripts? What if
I have a huge archive of data that I don't want
to re -upload every single time? The context
window is big, but it's not infinite. That's
what you need to change tools. You connect it
to Notebook LM to give the AI that long -term
memory of your entire creative history. Notebook
LM is often mentioned alongside Gemini. Can you
just clarify the relationship there? Think of
Gemini as the processor. the brain that's thinking
right now. Think of Notebook LM as the library.
You store your deep archives, hundreds of PDFs,
old scripts, research notes in Notebook LM. Then
when you're chatting in Gemini, you can reference
that library without having to re -upload it.
It grounds the AI in your specific history. Okay,
moving on. I want to talk about something that
I think everyone can relate to. The pain of repetition.
Oh, absolutely. I still wrestle with this myself.
I find a prompt that works, but then I have to
type it out or copy paste it from a note. And
I have to remind the AI of the formatting rules
every single time. Don't use bullet points. Use
a table. Don't do this. Do that. It's just exhausting.
It's the friction that kills adoption. If it
feels like work to ask the AI to do the work,
you just stop doing it. So enter gems. Gems.
These are described as custom versions of the
AI, but how is that different from just, you
know, a saved chat? Think of a gem as a preset
persona. It's a version of Gemini where you have
preloaded a specific set of system instructions
that it never ever forgets. You're effectively
programming the AI, but you're using natural
language instead of code. The example in the
deep dive material is the accountant gem. I think
this illustrates it perfectly. It's so simple,
but so effective. The problem is universal. You
come back from a business trip, your pockets
are full of crumpled receipts. It's a mess. The
worst part of any trip. Easily. So the old way
is, you take a photo, upload it, and you type
a paragraph. Please extract the dates, amounts,
and vendors. Put them in a table, categorize
them. You have to type that every time. Which
is so annoying. Right. With a gem, you build
it once. You call it Expense Helper. And inside
that gem, you write those instructions one time.
Take numbers, format into an Excel table, categorize
it to food, travel, hotel. Do not wait for me
to ask. So the next time I open that gem, you
drop the photo, you say absolutely nothing. Silence.
Silence. It just does the job. It knows its role.
It's like walking into your office and handing
a file to an assistant who's worked with you
for 10 years. You don't need to explain what
to do with it. They already know the protocol.
That is the shift from prompting every time to
delegating to a system that you've built. Exactly.
And you could do this for anything. a coding
buddy that always comments your code, a writing
editor that always checks for passive voice.
You build the tool once, you use it forever.
But, okay, let's say we have the data organized.
The receipts are in a table, but that's just
data entry. Where do we actually do the work
of refining it? Where do we turn that data into,
say, a report or a script? We move to Canvas,
which the documentation describes as a hybrid
between a Google Doc and a code editor. Canvas
seems to be the answer to the chat interface
problem because chatting is actually terrible
for editing documents. It is. Chat is linear.
You ask, it answers. If you want to change one
sentence in the middle of a generated email,
you usually have to ask it to rewrite the whole
thing and then hope it doesn't mess up the parts
you liked. Or you just copy paste it into Word
and edit it yourself. Right. Canvas solves that.
It opens a separate window right next to the
chat. So you have your conversation on the left
and the actual document on the right. So it looks
more like a document editor. Yes. So let's say
you're writing a video script based on those
receipts. You generate the draft. It appears
in the Canvas window. Now, you can highlight
just the introduction and type, make this punchier,
or highlight a technical paragraph and say, remove
the jargon. It's surgical. It is. And crucially,
you aren't losing the context of the rest of
the document. It's acting like a collaborative
editor sitting next to you rather than a bot
just throwing text at you. Now, within this ecosystem,
there's also a distinction made between the models
themselves. We have the fast model and the thinking
model. I feel like most people just leave it
on default and never touch this. Which is a mistake.
Because they are fundamentally different tools.
How so? The fast model is... Well, it's fast.
It uses what psychologists call system one thinking.
It's instinctive, rapid, pattern matching. Great
for brainstorming or quick chats. But the thinking
model uses system two. System two being that
slow, deliberative, logical part of the brain.
Exactly. When you switch to the thinking model,
you'll actually notice the AI pauses. It might
take 10 or 15 seconds before it even starts typing.
What's it doing during that pause? It's reasoning.
It's mapping out a chain of thought. If you ask
it a complex logic puzzle or a math problem,
or to plan a travel itinerary with five different
constraints, the thinking model maps out the
steps before it generates the final answer. It
actually checks its own work. The guide compares
it to choosing the right tool to fix a car. You
don't use a hammer for everything. Exactly. My
recommendation. Keep thinking as the default.
for anything complex. If you're doing real work
coding, planning, analysis, the extra 10 seconds
of wait time is worth the accuracy if you're
just chatting about a movie. Use fast. So if
we zoom out, we have multi -mobility, which is
the eyes and ears. We have the thinking model,
the brain. We have gems for the specialized training.
And Canvas is the workspace. The full stack.
It feels like a lot of separate tools when we
list them like that. I want to see how they fit
together. The source material lays out a smart
garden workflow that I think perfectly illustrates
the stacking concept. This is my favorite part
because it takes someone with zero knowledge
and gives them a result that looks like an expert
produced it. OK, so let's walk through it. Step
one, you want to grow a garden. You're in California.
You have a balcony. You know nothing about plants.
What do you do? Step one is multimodality. Don't
type, I have a balcony that faces south. Just
go outside, take a photo, upload it to Gemini,
ask what grows here in California weather. So
the AI analyzes the light, the space, the context
right from the pixels. Right. It suggests, say,
strawberries and lettuce because it sees you
have Limited floor space, but good railing space.
Okay. Step two? Visualization. This is the motivation
step. You ask, show me a picture of what this
will look like when it's fully grown. It uses
image generation to show you a lush green balcony.
Now you're excited. You can actually see the
goal. I'm motivated, but I still don't know how
to keep anything alive. Step three is deep research
with a thinking model. You ask for a detailed
plan. Create a weekly schedule telling me when
to plant, when to fertilize, when to harvest.
It digests all that agricultural data into a
structured timeline for you. Now this is where
most people would stop. They have the plan, they
print it out, they lose it, and the plants die.
Exactly. But we're going to build a system. Step
four. Create a gem. You call it garden expert.
You upload that plan and that original photo
of your balcony into the gem's knowledge base.
So the gem knows your specific garden. It knows
your garden and the instruction you give it is,
you are the caretaker of this balcony. When I
send a photo of a sick plant, tell me how to
fix it naturally based on the plants I own. That
is brilliant. So two months later, the strawberry
leaves are turning yellow. You don't Google yellow
leaves. No. You snap a photo, you drop it in
your gem, and it says, hey, remember those strawberries
in the South Rail? They're nitrogen deficient.
Add coffee grounds. It's context -aware troubleshooting.
And finally, step five. Management via Canvas.
You ask it to create a visual dashboard, a tracking
table for watering and harvesting that you can
edit. You just keep that open to track your progress.
So in minutes, you went from zero knowledge to
a personalized plan, a visual goal, a custom
troubleshooter, and a management system. That's
the power of the stack. You aren't just chatting.
You are orchestrating. So the core shift here,
it really is moving from just asking the AI a
question to stacking the AI skills into a workflow.
Exactly. It's not a robot you chat with. It's
a multi -skilled assistant you build workflows
for. You're the architect. The AI is the builder.
I love that framing. It makes it feel much more
active. But we've thrown a lot of features at
you today. If you take away just one thing, what
is the big idea here? The big idea is a mindset
shift. It's resisting the urge to be overwhelmed.
Don't try to change your entire work life tomorrow.
Don't try to build the accountant gem and the
garden gem and a coding gem all at once. Right.
Start small. Pick one thing. One repetitive,
boring, daily task. Maybe it's meal planning.
Maybe it's summarizing that weekly meeting no
one pays attention to. Maybe it's a good best
report. It's one thing that annoys you. And build
one simple gem for it. That's it. And once you
save time on that one thing, the utility of the
other features, multimodality, canvas, the thinking
model, it all becomes obvious. It just clicks.
You realize, oh, I can use this for that other
thing, too. And suddenly, you aren't just a user
anymore. You're a master of the tool. That is
a great place to leave it. So here is our challenge
to you listening right now. Identify one boring
task today, just one, and attempt to create a
gem for it. See if you can delegate that friction
to the machine. And let us know how it goes.
Thanks for diving deep with us today. We'll see
you next time. Safe travels.