Original subtitles

I use around 10 AI tools for 90% of my

work, [music] and each one excels in one

specific area. But figuring out which

tool works best for what task usually

takes months of trial and error. So,

I'll share the one thing each tool does

better than alternatives, so you walk

away with a clear mental model for when

to use what. I've grouped these tools

into four categories across a two-part

series. There's just too much to cover.

This video covers everyday and

specialist AI, while part two covers the

remaining two categories. Let's get

started. Kicking things off with

everyday AI. These are your general

purpose chatbots. Chachi, Gemini, and

Claude. And while they seem

interchangeable, their quote unquote

moes, the specific things they do best

have actually become quite distinct.

Starting with the OG Chachet. While

Gemini and Claude are arguably just as

capable in raw power, Chachib still

holds the crown in one area. It's the

most obedient model. [music] In plain

English, Chachib drops fewer balls when

you hand it a complex checklist. Other

models might be just as smart, but give

them a lengthy set of instructions, and

they'll sometimes skip a step or decide

they know better. If you want proof of

this, just ask each model to optimize a

rough prompt for itself. Chacht will

generate a noticeably longer and more

detailed prompt because it knows it can

handle the complexity. And if you run

that optimized chachib prompt through

both chacht and gemini for example,

you'll notice two things. First, chachib

thinks longer because it's actually

checking every requirement and it

follows each instruction to the letter.

Gemini on the other hand often takes

shortcuts. Pro tip, I share the exact

prompt optimizer in the essential power

prompts template linked below, but you

can test this yourself with something as

simple as optimize this prompt for

Chachib insert model number here. Here's

my rough prompt. Diving into a real

world example, I gave both Chachet and

Gemini the same complex prompt, a hiring

rubric with a dozen requirements. Chachi

delivered every single one. Gemini's

output looked right at first glance, but

when I checked it against my original

list, it had quietly dropped a few

rules. That's the key difference.

Chachib doesn't decide which

instructions matter. It just follows

them. Here's a second simpler example.

Sometimes when you explicitly tell

Gemini to search the web, it just

doesn't, which is wild since Gemini and

Google search are both Google products,

right? Whereas with ChachiT, when you

enable web search, it performs the web

search every single [music] time. I know

this is a small example, but it's

downstream from Chachib's core

superpower. Obedience means you can

trust the behavior you ask for. So, as a

rule of thumb, if a task has a lot of

moving parts, and getting one wrong

breaks the whole thing, start with

Chachib. Next up, Gemini. Where ChachiT

wins on obedience, Gemini wins on

multiodality. In plain English, Gemini

is able to process a massive amount of

mixed media, video, audio, images, and

text natively. Taking a look at this

table, we see that only Gemini can

handle all four types of media natively.

It's able to quote unquote listen to

audio and quote unquote watch videos,

while Tragic and Claude use roundabout

ways to access that information. What's

more, Gemini's massive 1 million token

context window means it can handle large

video recordings, hour-long audio

recordings, full slide decks, all

together that would literally choke

other models. If you watch my latest

Gemini video, you'll remember the use

case where I screen recorded a messy

walkthrough of myself completing a task,

uploading that video onto Gemini, and

asking Gemini to turn it into a

readytouse SOP with perfect formatting,

which is an example of Gemini ingesting

video and turning it into text. Now,

let's take that a step further. Imagine

you just finished a weekly meeting. You

have a video recording of the call, a 20

slide deck, and a photo of a messy

whiteboard session. You can upload all

three and ask Gemini to summarize what

was discussed, pull out the key

decisions, and draft the follow-up

email. Gemini is the only tool that can

synthesize all three in one go. All that

said, I have to point out that Gemini's

raw reasoning capabilities sometimes

feels slightly behind CatchBT. But when

the task involves video, audio, or

massive files, the trade-off is

obviously worth it. Speaking of matching

the right tool to the task, today's

sponsor HubSpot put together a free

guide called the AI productivity stack

that covers 50 tools organized by use

case. Here's why I like it. While this

video focuses on my personal favorites,

your workflow probably needs something

different. Maybe you're in marketing and

need SEO specific tools or you manage a

team and want to build automated

workflows with reliable AI. This guide

breaks down tools across business

functions like research, design, and

marketing. And for each tool, it shows

you the best use case, key features,

pricing, and a step-by-step workflow.

What I found most useful is the decision

logic at the end of each section. So,

for example, the research category tells

you exactly when to use Perplexity

versus Claude versus Humatada based on

what you're actually trying to do. It's

a great way to quickly understand what

each tool does. [music] Well, I'll leave

a link to this free guide down below.

Thank you, HubSpot, for sponsoring this

video. Rounding out the everyday AI

category, Claude. Claude superpower is

producing higher quality first drafts

than the other models. In plain English,

that means Claude's first attempt is

usually closer to done. This superpower

shows up in two areas. First, coding.

Here's a fun fact. The latest version of

Gemini beat the older version of Claude

in every single benchmark score except

for the coding one, which is crazy. So

obviously Anthropic has figured out

something related to coding the others

haven't. And in practice, developers

universally agree that Claude writes

functional code on the first try more

consistently than alternatives. Here's a

real world example. I needed to bulk

export conversations from a customer

service platform, but their support team

said only developers could do it. I

described the problem and Claude not

only gave me step-by-step instructions

but also wrote a script in Go that

worked on the first try. I don't even

know what Go is nor can I write code.

Another example, I asked all three

models to turn a static image into an

interactive chart and Claude performed

the best on the first try. So basically,

anything that requires generating

working code tends to favor Claude. Pro

tip, when it comes to diagrams, you can

ask Claw to generate mermaid code, which

you can then paste directly into tools

like Excaliraw to get clean visuals in

minutes. Area two, polishing copy.

Beyond code, Claude produces written

drafts that sound human and need fewer

revisions. When you need to tighten an

argument or match a specific voice,

Claude just gets it. Put simply, it's

exceptionally good at style matching.

Once you share examples of your existing

work, it replicates your tone almost

perfectly. When I was in corporate, I'd

shared previous documents so Claude

could replicate that voice across

presentations and performance reviews.

And now, as a creator, I feed it my

existing YouTube scripts to help refine

new drafts. At this point, you might be

wondering how I use all three everyday

AI tools together. In a nutshell,

Chachip or Gemini usually handles the

beginning of my work, ideation,

research, drafting the outline of a

presentation. Claude then handles the

last mile, turning that rough output

into something I'm ready to present or

publish. Quick note on Grock. A lot of

people ask why I don't use it. It's

actually very simple. Uh Grock's

superpower is its direct access to the

Twitter/x fire hose, right? So it's the

best option for people who need to

analyze breaking news events in real

time. I never needed that. And as a rule

of thumb, we should never use tools just

for the sake of using tools. We should

only add them to our toolkit when they

solve an actual problem we have. Here's

a quick recap of the three models and

when to use them. And if you're

wondering whether you need all three,

the short answer is no. Most people

should stick with the paid version of

ChachiBT and get really good at it. But

if you can afford multiple subscriptions

and your workflow can take advantage of

their individual superpowers, mix and

match as needed. Fun fact, according to

this study on open router data, models

from different labs like Chadypt and

Gemini expand the pie of AI use cases

precisely because they excel at

different things. Onto the second

category, specialist AI. Before diving

in, let's clear up a very common

misconception. Tools like Perplexity are

not foundational models. Here's a simple

visual. OpenAI, a Frontier AI lab,

develops the GPT family of models. They

also created ChatGpt as the userfriendly

app

>> [music]

>> layer. Perplexity is different. It

fine-tunes existing foundational models

for speed and accuracy and is optimized

for search. Their own sonar model, for

example, is just a fine-tuned version of

Meta's openweight llama model. So, on

that note, Perplexity superpower is

finding accurate information fast. In

plain English, the general purpose

chatpots are built for reasoning. You

use them to help you think, brainstorm,

or write a draft. Perplexity is built

for fetching. You need a specific fact,

and you need it now. Starting off with a

simple real life example, I used chachib

to plan a trip to Japan with my brother

because that is a creative task. It

requires weighing trade-offs, building a

narrative, and for that kind of task,

I'm happy to wait while the model

thinks. But when I need grab-and-go

information, like whether a specific

restaurant is foreigner friendly because

we don't speak Japanese, I'd want

Perplexity to give me accurate and

update information within seconds.

Second example, going back to how I use

the three everyday AI tools, let's say

Gemini or Chachet helps me brainstorm

and structure my newsletter. Claude

produces the final draft. Perplexity in

this case is the search scalpel that

verifies information like whether

Gemini's contact window is 1 million or

2 million tokens. In case you're

curious, consumers get 1 million,

enterprises get 2 million. Pro tip, you

can use Google style search operators

like [music] site colon reddit.com to

narrow your results to a specific

source. [music] I have an entire video

on the most useful Google search

operators, so I'll link that down below.

As a rule of thumb, think of perplexity

as a replacement for Google AI mode.

They're both for fetching information

and not as a replacement for general

purpose chatbots. Actually, let me know

if you want an entire video breaking

down the AI search apps like Perplexity,

Google Search, Google AI overviews,

Google AI mode, because they're all made

for different things. Rounding out

Specialist AI, Notebook LM superpower is

that it only answers from the sources

you give it, meaning it won't make

things up. Think of it like a walled

garden. You upload your sources and

Notebook LM answers questions using only

those documents. It can't really

hallucinate because it has no outside

knowledge to draw from. Going back to

the visual around how perplexity is

optimized for search, Notebook LM uses a

fine-tuned Google Gemini model that

minimizes hallucinations. For instance,

when I was at Google before publishing

marketing materials, I would upload the

final draft alongside the source

documents and ask Notebook LM if the

draft made any claims that contradicted

the sources and it would catch these

tiny discrepancies other AI might have

missed. I use a similar workflow today

for my videos. Before I start filming, I

upload my script and all my research

into Notebook LM and ask it to flag

anything not directly supported by the

source material. The obvious caveat here

is that the output is only as good as

the sources we give it. So if the

sources are incorrect, Notebook LM is

going to be confidently incorrect. So as

a rule of thumb, if the accuracy matters

more than creativity and you have source

materials to check against, use Notebook

LM. There are a few more specialist AI

tools I use but didn't make this list

because I don't use them every day. But

to quickly go through them, Gamma for

presentations, 11 Labs for voice

cloning, Zapier and N for automation,

and Excaliraw and Napkin AI for quick

visuals. As a reminder, I'll cover the

remaining two categories in part two, so

keep an eye out for that. See you on the

next video. In the meantime, have a

great one.