I want you to imagine a scenario. It's a Tuesday
morning. You are sitting at your desk. Maybe
you have your coffee in hand. Naturally. You
lean back in your chair. You fold your hands
in your lap. You are absolutely not touching
the keyboard. Right. And you are definitely not
touching the mouse. Kind of hard to work that
way. Exactly. But on your screen, the cursor
is moving. And it's not. It's not glitching out.
It's moving with purpose. Yeah. It clicks open
a web browser. It navigates to a vendor's website.
It's just by itself? Completely by itself. It
fills out a complex order form, checking boxes,
scrolling down to find the submit button. Wow.
Then it opens a spreadsheet and logs the confirmation
number. That's wild. It's not a recording? Not
a script you wrote line by line? No. It's an
AI literally using the computer interface. Just
like a human employee would. It sounds like science
fiction. Or maybe a hacker taking over your machine.
It really does. But this is actually the reality
we're looking at today. This is the computer
use feature. And it is the headline act for the
model we're discussing. Plaudsonnet 4 .6. Welcome
to the deep dive. Today, we are not just talking
about a chat bot that writes better poetry. Right.
We're looking at a fundamental shift in mechanics.
We are. Our source for this is a guide. It's
called Mastering Claude 3 .5 Sonnet. Though,
just to be totally clear for you listening, the
text focuses heavily on the upgraded version.
Sonnet 4 .6. Exactly. It's a fascinating document.
It frames this moment not just as an upgrade
in IQ, but as a shift in utility. Exactly. The
premise here is striking. We're looking at a
model that claims to be twice as fast as the
Heavy Opus model. Which was the previous King
of the Hill. Right. Yet it costs half the price.
Half the price. Half. And if you believe the
benchmarks, it's significantly smarter. That
is the holy grail of software, isn't it? Better,
faster, cheaper. Usually in engineering, you're
told to pick two. If it's fast and cheap, it's
usually garbage. Yeah, usually. And that is why
we need to unpack this carefully. Claims like
that usually come with an asterisk. So we have
a roadmap for today's deep dive. Let's do it.
First, we need to understand the brain. Specifically,
the context window and this spooky new ability
to control computers. Then we have to look at
the economics. For businesses, that is the bottom
line. Then we will walk through the stress tests.
Everything from fake operating systems to 3D
survival games. And finally, we'll look at agent
mode. Where the AI takes the wheel on browser
automation. Exactly. So let's start with the
engine. What is going on under the hood here?
What makes Sonnet 4 .6 distinct? Yeah. Well,
the first thing that jumps out is the sheer size
of the memory space. OK. This model comes with
a 1 million token context window. 1 million.
We hear these numbers thrown around a lot. 32k,
128k, now a million. Right. But let's ground
that. What is a token, basically? A token is
roughly a piece of a word. So what does a million
tokens actually feel like to you as a user? Think
of it this way. It's like giving the AI a very
thick or a massive disorganized folder of code.
In previous generations, the AI was like a sieve.
It's there, it might read the first few chapters,
but by the time you asked a question at chapter
10, it already forgot the character names from
chapter one. Exactly. The goldfish memory problem.
You have to keep reminding it. Like, hey, remember
that rule I gave you 20 minutes ago? Precisely.
With a one million token window, that sieve becomes
a vault. A vault. It holds that entire thick
book in its head at once. Wow. It remembers the
footnote on page three while working on page
500. That is a huge quality of life improvement.
It holds the state of the conversation perfectly.
But the guide suggests the bigger shish isn't
just memory, it's agency. This computer use capability.
Right. Let's talk about that. This feels like
a real departure. Until now, AI has been a text
generator. You type, it types back. But Sonnet
4 .6 is different. The guide describes this as
the ability to handle boring clicking tasks.
Boring clicking tasks. Yeah. It can read complex
spreadsheets, visually scan them, fill out long
forms without getting tired. It acts almost like
a real person sitting at the terminal. When you
say visually scanned, does it actually see the
screen? Or is it looking at the code behind the
website? That is a key distinction. It is actually
analyzing screenshots of the interface in real
time. Really? Yeah. It looks at the buttons,
the layout, the text fields, just like your eyes
do. The source mentions it's great at step -by
-step development. Is that just about writing
code? It's much broader. The model builds a project,
inspects what it made, and then fixes its own
errors. As it goes. Right. It's recursive. It
doesn't just shoot out an answer and hope for
the best. It manages the task from start to finish.
Exactly. Does this capacity for step -by -step
correction fundamentally change how we interact
with it? Yes. It moves from just answering questions
to managing entire workflows start to finish.
So we manage workflows now instead of just asking
questions. Exactly. You become the architect.
The AI becomes the builder. But usually, When
you get an agent that can replace a junior employee's
clicking capability, you expect a premium price
tag. And that is where the market is getting
aggressive. The guide breaks down the economics
very clearly. Let's hear the numbers. For Sonnet
4 .6, the input cost is $3 for every 1 million
tokens. OK. The output cost is $15 per 1 million
tokens. Contextualize that for me. How does that
stack up against the older versions? It is offering
top -level smarts at a middle -level price. It
is half the price of the heavy Opus model, but
it runs two times faster. And speed matters.
Waiting 10 seconds versus 20 seconds is the difference
between staying in flow and getting totally distracted.
It really is. Yeah. But let's look at the benchmarks.
Specifically, SW Bench. SW Bench. Explain that
in plain English for us. It is a test. checking
how well AI fixes code like a software engineer.
OK. So it's not just writing a simple function.
No, it's much messier. Yeah. The test gives the
AI a real -world software repository and a bug
description. And it has to fix it. It has to
navigate the files, figure out what's broken,
write the fix, and not break anything else. A
test of troubleshooting. Exactly. In this test,
Sonnet 4 .6 scores an 80 .2%. That sounds high.
It is very high. It beats its big brother, the
Opus 4 model. So we are seeing an inversion where
the cheaper model is actually the more capable
one. Exactly. You no longer pay a premium for
intelligence, you pay for efficiency. So the
math is half the price, twice the speed, and
better performance. Impossible to ignore. Let's
move to the visual tests. Numbers are one thing,
but seeing what it builds is another. The guide
walks through a few specific test cases. One
of them was building a website for a local business,
a locksmith in New York. This was a test of adherence
to constraints. Right. The prompt was very specific.
A white background. Dark blue buttons. To build
trust. Exactly. And the key detail. An emergency
service price of exactly $99. Sounds simple,
but AI loves to hallucinate prices. Or they get
creative and make the button purple. Right. But
the result was spot on. Yeah. Professional fonts.
Correct layout. Exact adherence to the price
instruction. It looks like a human made it. It
did. But then they pushed it harder. They asked
it to simulate a Mac OS computer screen. Using
only web code. Yes. Building a system inside
a system. The first try was just okay. It was
static. A painting of a computer. Right. But
the tester asked it to improve. The second try
was amazing. What changed? It added drag and
drop folders. Wait, drag and drop? Yes. That
requires complex logic. Tracking mouse position,
updating elements instantly. It worked. You could
change backgrounds. It built a music player with
a moving time bar. I have to admit something
here, Beat. I still wrestle with prompt rift
myself. Oh, we all do. You ask an AI to fix one
thing, it breaks three others. It forgets the
button color. Yeah. Hearing it maintains the
state of a music player while dragging a folder
is so relieving. It shows real robustness. It
handles layout well, but does it have artistic
intuition? Not quite. Its SVG drawings, like
butterflies and robots, are just okay. Opus is
still the better artist. So it's an engineer,
not a painter. Exactly. Which leads us to the
gaming tests. Games are systems. Physics and
logic. This is where it really flexed. First
test was a marble board game. Using mouse movement
to tilt a wooden board. Yes. The marble rolls
faster or slower based on tilt. Black holes act
as obstacles. Vector math. Understanding the
connection between input and physics. And it
worked instantly. The marble rolled realistically.
But the next test was even bigger. A Minecraft
clone. Boxelcraft. Tested using KiloCode. Huge
prompt, I imagine. Massive. Health bars, food
bars. Breaking and placing blocks. And deep caves
under the ground. Yes. It included a proper start
menu, which is rare, and it generated those deep
caves. Whoa! Two -sex silence. Think about that,
generating deep caves under the ground. They're
crazy. The AI built a hidden part of the world
just because the prompt asked for it, simulating
depth we can't initially see. It really is a
moment of wonder. Did the browser actually handle
a full 3D survival game written by AI? Mostly
yes, though the graphics were heavy. The logic
held up, even if the browser struggled. So the
underlying system worked flawlessly, even if
rendering lagged. We're going to take a quick
break for our sponsors and when we come back,
we'll talk about agent mode. Sounds good. And
we're back. Let's look at the agent workflow,
the big data task. This is about automation,
treating the AI as an autonomous agent. The prompt
was a long chain of commands. Create a folder,
write a Python script, open a browser. Search
Google for news, save to file, build a dashboard.
That is where things usually break down. The
AI forgets step three. But this time, it utilized
Selenium. Define Selenium for us. It's browser
automation software. code that clicks buttons
on websites. Usually very finicky. Very. But
the AI wrote the script and executed it. Wait,
it ran the code? Yes. The computer physically
opened the browser by itself, typed in the search
bar, found top links, populated the dashboard.
Is the main advantage here capability, or is
it reliability? Reliability. Unlike others that
get lazy, this model finishes long lists of steps
without stopping. It doesn't get lazy and quit
halfway. Exactly. It follows through. If someone
listening wants to actually use this, how do
they get access? The official chat is free, but
has limited messages. The dreaded rate limit.
Yeah. There's the API, which is pay as you go.
$3 in, $15 out. Right. Then there's LMSYS for
free, side -by -side testing. And KiloCode. Open
source platform offering $25 in free credits.
Great for testing. OK, what is the tactical advice
for prompting this thing? Three tips. First,
be specific. Give us an example. Don't just say
write an ad. The guide uses the AI fire newsletter.
OK. Specify the hook, the audience busy office
workers, and the promise to save five hours a
week. Give it constraints. Second tip, iterate.
Build in small pieces. Like stacking Lego blocks
of beta. Log in screen first, then dashboard.
It stops the AI from getting confused. And third
tip. feed the memory, use that 1 million token
window, paste in whole documents. Don't be shy.
Exactly. Let's recap the big idea here. We are
seeing a shift toward high intelligence, low
price. Yes. It excels at doing coding, clicking,
automating. Rather than just knowing facts. It
lacks artistic flair for SVGs, but makes up for
it with strict adherence to rules and massive
memory. It's a highly skilled engineer. So here's
my challenge to you listening. Try the marble
game prompt. or use kilo code credits to build
a small tool. The source mentions knowing how
to use these tools reduces work stress. We are
moving away from reading every line of code to
orchestrating the system. It's a totally different
kind of work. Focus on the what and why. Let
the AI handle the how. Exactly. Thanks for diving
in with us. Always a pleasure. See you in the
next deep dive.