So, there's been an interesting
narrative playing out in the world of AI
right now and I've just been absolutely
fascinated by it and that's the whole
narrative of the openw weight models
versus the closed weight models and that
whole storyline sort of accelerated this
week. Earlier this week, OpenAI showed
off the first results from its Jalapeno
chip. This is an AI inference chip that
they're building to try to release
reliance on Nvidia. They announced that
they were working on it not too long
ago, but they finally showed off some
results of what it's capable of. We can
see here on openweight models, it's
getting upwards of 104 times the
performance. Now, they tested this on
public models on purpose because it's
much more verifiable. Other companies
can go and use other chips and see what
sort of performance they can get on GPT
OSS or Deep Seagar 1, etc. However, they
can't really go and test GPT 5.6 6 on
different chips since those all run in
OpenAI's cloud. They do however claim
that in their internal testing,
Jalapeno's advantage widened further on
their own Frontier Open AAI models. Now,
this is interesting to me because it
shows that OpenAI is actively taking
steps to release reliance on Nvidia
chips. Now, they recently partnered up
with Cerebrris to do ultraast inference
with their chips and now it even seems
like they're making like a competing
chip to what they were getting from the
Cerebrris chip. One thing that is
important to note though is that these
are specifically for inference. So
inference is essentially when you type a
prompt and you get the output. That is
the inference phase of AI. These chips
don't appear to be designed for actually
training brand new models which they
will likely still go to Nvidia to do at
least in the near term. However, this
story gets even more interesting.
According to the information, Nvidia
agreed to buy hugging face. Now, I
emphasized according to the information
because neither HuggingFace or Nvidia
have publicly made an announcement about
this as of the time of me recording it.
Now, if you're not familiar with Hugging
Face, Hugging Face is like the GitHub
for AI models. When companies go and
make open weight models and they go and
make them publicly available to anybody,
they typically go and store those
weights on HuggingFace. Now, HuggingFace
is also a GPU provider. You can go on
their site and essentially rent GPUs
from them to play around with the
various models that are available on
their site. Now, here's why I find this
super fascinating. You have OpenAI over
here starting to build their own chips
for inference chips that they would have
bought from Nvidia if they didn't start
making them themselves. You have Meta
starting to build their own chips
inhouse. You have Google who's already
building their own chips with their
TPUs. And as these AI companies get
bigger and bigger and start trying to
own more of the stack themselves, it
could start to shift some business away
from Nvidia. But Nvidia is going allin
on open-source. All of the models
Nvidia's been developing and releasing
have been these openweight models that
anybody can use. Now, most of them are
too big to run on a personal computer,
so you need to go and run them in a
cloud. hugging face is where people go
oftent times to run these models in the
cloud now. And if Nvidia owns all of
that infrastructure to run these models
in the cloud and more and more people
start switching to openw weight models,
well then Nvidia's no longer going to be
as reliant on the open AIs and the metas
of the world for this inference compute
because now everybody that's moving to
openweight models is now going to
directly go to an Nvidia company to get
their inference provided. So they're
betting that openweight models are going
to get bigger and bigger and bigger and
take away more and more market share
from the closed models and with owning
hugging face they're going to be the
dominant compute provider of everybody
running open models and there is some
data showing that we are seeing more and
more people and companies move to openw
weightight models. So check this out.
This is a post from the CEO of Verscell
and he's showing that 2 months ago most
of the tokens on Verscell's AI gateway
were going to closedweight models, 71.6%
with only 28.4% going to openweight
models. Fast forward to this week and
now open models account for 62% of the
token usage versus 38% for the closed
usage. Now there is a caveat to this.
When we go to their AI gateway model
leaderboard here, here's the graph that
he was showing showing the token volume.
So back here, blue is the open weight,
yellow is the closed weight, closed
weight models were getting most of the
token usage. However, there is a drop
down here that shows the amount of
requests. If we select requests that
tells us a little bit different of a
story. It tells us that most people that
are using this gateway are still making
their requests to closedweight models.
It's still 62% closed weight to 38% open
weight on actual requests to the models.
But the amount of tokens being used,
well, it's pretty clear that the open
models just use a lot more tokens.
Saying that, we are seeing more and more
requests recently going to more of the
openweight models. So, yeah, I think
that Nvidia acquiring hugging face, if
true, is actually a pretty big deal
because again, it is Nvidia betting that
open weight is more of the future. And
to round this whole discussion out,
there was another thing that happened
this week that, you know, kind of adds
to the story as well, and that's the
fact that Apple just introduced some new
chips. This week, they announced the M6
and the M5 Ultra, and these are designed
for AI compute. These are designed to be
devices that live on your desktop that
can run your compute completely locally.
And this M5 Ultra chip is insane. I'm
currently running an M3 Ultra and this
has 4.5 times the peak GPU compute for
AI and my computer right now is an
insanely good AI computer. They're also
releasing it in versions all the way up
to 512 GB of unified memory, which means
pretty much most of that 512 GB can be
used as essentially VRAM for running AI
models. Now, they're not going to be
cheap. an M5 Ultra with only a 1 TBTE
hard drive is still going to cost you
almost 11 grand. And that's for the 256
GB version. They haven't released the
price of the 512 GB version yet. But the
bigger point being here is that we're
starting to get more and more devices
that are very capable of running really
good models at home. Like you're going
to be able to run these really good
models coming out of ZAI, like the GLM
models. You're going to be able to run
some of the DeepSeek models. you're
going to be able to run some of these
bigger Quinn models on this thing. And I
mean, it's only a matter of time before
we're going to get like Fable level GPT
5.6 soul level models that will run on a
computer like this. But I've been
finding this whole story just
fascinating watching the open source
versus closed source battle. If you
would have asked me 3 or 4 years ago, I
would have told you I don't see
openweight models catching up to the
closed weight anytime soon. And here we
are a few years later and like we're
this close. Speaking of openweight
models, we got two new models this week
that I want to talk about real quick.
First, we got GLM 5.3 Flash. And this
model kind of flew under the radar and
launched in stealth like a week or two
ago under the name Ox Alpha. And when we
look at the benchmarks here, again, Deep
Suite is kind of the one that I pay the
most attention to because I think it's
the most accurate to like actually what
we'll feel when we're coding with these
models. And this one scored a 63.4,
which outperforms Opus 4.8. and falls
just shy of Google's like most recent
model Gemini 3.7 Flash. I also think
this chart right here is very very
telling. So on the left side you've got
the artificial analysis intelligence
index. This is sort of a combined score
across a whole bunch of different
benchmarks to kind of pick the
objectively smartest model. On the
bottom you've got your compute cost.
This red dot here is where GLM 5.3 Flash
sits. So, as far as intelligence goes,
it's right around Quinn 3.8 max level,
but way cheaper to run. And notice how
some of these other models aren't even
that big of a jump. Claude Opus 5,
Claude Fable 5, GPT 5.6 Soul. Like, not
a massive intelligence leap over what
you're getting over GLM 5.3 Flash.
Everything that falls into this red box
that you're seeing here is basically
both dumber and more expensive than GLM
5.3 Flash. And that includes models like
Gemini 3.7 Flash, Claude Sonnet 5,
Gemini 3.1 Pro, DeepSeek V4. These
models are all more expensive and less
intelligent, which is why this model
release is kind of a big deal. It just
made a big chunk of models kind of
obsolete. And if you're curious how it
performed on Beautybench, here's what we
got. Now, it was during a period where I
was able to use the model for free. So,
I'll probably run it again to get an
actual accurate cost. And it also ran it
when it was called ox alpha. So the name
needs to be updated here. But it took 12
minutes to run, used 46,000 tokens, and
came in at a 5.4, which on my very, very
subjective leaderboard here puts it at
number 25 overall. But we got another
open- source model this week as well. We
got Quinn 3.8 Flash. Now, recently, we
got Quinn 3.827B, 827B, which was a
really good model for running locally on
high-end consumer GPU. This one is a
little bit bigger. It's 125 billion
parameters. Like that new M5 Ultra would
run this model probably, but most likely
you're going to want to run it in a
cloud. It does decent on the Deep Suite
benchmark, and on artificial analysis,
it falls at about a 56 compared to a 57
from the model we were just looking at,
GLM 5.3 Flash. Quinn 3.8 Flash generated
this on beauty bench. It took a little
over a penny to generate, just under 5
minutes and used 26,000 tokens.
Interestingly, this one got ranked at
number 19, which came in below what my
AI ranking thought of Quinn 3.827B.
But again, the rankings here don't
really matter. You can just use your own
eyes and determine which one you think
did a better job. Here's another cool
update that's been out for a little
while now, but I just came up with a
cool new way to use it. I'm going to use
chatbt sites inside of the Codex app to
turn my spreadsheet of social media data
into a live performance dashboard that
my whole team can access and use. So
inside the Codex app, I'm just going to
type at sites and say build me a social
media performance dashboard that tracks
all my platforms and that I can share
with my team. And then sites will create
the website plus handle everything else
like the hosting and the authentication
and even a built-in database. Now, the
underlying data is coming from this
spreadsheet that tracks my audience
across YouTube, the Future Tools
newsletter, Instagram, LinkedIn, Tik
Tok, X, Threads, and even Facebook. So
now my team can quickly see our
audience, weekly growth, and which
platforms are moving the fastest. And
now we can even build and refine the
site together and connect it to a custom
domain if we want. With site analytics,
I can see basic performance metrics like
visitors and page views. And the really
interesting part is this new web MCP
support. You can build predetermined
actions into the site that compatible AI
agents can use instead of the agent
blindly clicking around the page. This
is a really easy way to build tiny
pieces of custom software for your team
without dealing with all of the
infrastructure that you normally need.
So if you want to try sites out for
yourself, head to the link in the
description box to download the Codex
app. And thanks so much to OpenAI for
supporting my channel and sponsoring
this portion of today's video. We also
got like a flurry of new announcements
out of Google this week. They released
an updated version of their video model
called Gemini Omni 1.1 Flash. I'm
guessing their VO model is pretty much
done for cuz everything seems to be this
new Omni model. We can see this new
model is the same price as the previous
model. 10 cents for a 720p output. We
can also do a 360p output, 1080p, and a
4K output. The 360p is nice if you want
like a quick generation just to like
test to see how something might come
out. And if you think it does a good
job, I guess you can always do a new
generation at a higher resolution. The
new model can analyze up to 10 seconds
of prior context. You can specify start
and end frames so that you can have them
animate between or do like cool
transitions and things. Do drast
upscale to 4K. Add video reference.
Seems like a lot of the same stuff we
were able to do with the original Omni.
It's just we have better quality
versions and a little bit more context.
Here's some examples with the prompts on
the screen. [music]
Start and end frame.
A microscopic view of iridescent marine
dios displaying integrate glass silica
shells with breathtaking natural
symmetry
etc. Interestingly over on arena.ai
under the texttovideo arena Gemini Omni
1.1 Flash is the new top model there. If
you don't know what Arena is, it's
basically like a blind test. You give
Arena a prompt. It gives you two outputs
from two different video models, not
telling you which model is which. It can
be any model that's in their database.
You pick which one is your favorite
between the two and then after enough
people have done their voting, that's
how you get the ranking. So, it's a
blind test of user preference. And Omni
1.1 Flash is winning in text to video.
And in image to video, it is second only
behind Miniax and not by a wide margin.
But you can also see not a ton of votes
yet. It's still early. Now, their
announcement here says you can build in
AI Studio using your API key. Or if
you're a AI Plus Pro or Ultra
subscriber, you could use it inside of
Google Flow. So, first I went to AI
Studio and have tried to prompt multiple
times and I keep getting an internal
server error. I don't know if I'm doing
something wrong. I even went and
pre-added money in. So, I put $25 in
here so I have credits and I still
couldn't get it to work. So, I took my
prompt over to Flo because I am an ultra
subscriber. And this was the prompt I
gave it. A video of a robot wearing a
hat that says future tools on it. Break
dances in front of a castle where a
dragon flies overhead.
[music]
My initial thought is okay. Again, I've
talked about this a bit lately. I'm less
and less impressed by video models these
days. And this one, I do see some
wonkiness in it. The castle in the
background looks fine, but my robot, for
some reason, the head just pops off,
falls on the ground, and then apparently
it's got the same hat on both sides and
the same face on both sides. Although,
when you look at it here, it clearly
doesn't. So, that's a little funky. And
then there's some wonkiness when he
starts doing his like ground moves here.
[laughter] Like, yeah, it's just a
little awkward. The consistency of the
castle and the dragon are really good
between scenes, though. That's one thing
that's gotten a lot better over time is
consistency. When it switches scenes and
then comes back to the same scene,
things look really good. That's
something that's really, really improved
with AI video models. Google also
released a new transcription model,
Gemini 3.5 Transcribe. Now, this is
pretty much exactly what it sounds like.
It's, you know, their version of
Whisper, but apparently more accurate
than everything else that's out there.
And it's available in the Gemini app on
MacOSS, Rambler on Android, and coming
soon to Chrome, but you also have it in
the various enterprise platforms, and
via the API if you'd want to use it.
Google also added some new AI features
for travel inside of the AI mode in
search. It can track flight prices for
you now. It'll show you in both price
and points, and you can apparently book
hotels directly inside of AI mode in
search now, as well. There's also a new
update to Gemini notebook, previously
known as Notebook LM. You can now
incorporate books that you own directly
into your notebook. So, they say, "We've
built the ability to add select ebooks
you've purchased from Google Playbooks
directly to a Gemini notebook." So, you
would have had to purchase it through,
you know, Google's book platform. It's a
pretty cool feature, but personally,
when I do buy ebooks, it's mostly on
Kindle. And if you use anti-gravity to
do your coding, they finally added a
remote control feature. This is a
feature that pretty much every other
sort of AI enabled IDE has had for a
while now, but now it is also inside of
anti-gravity. Remote control allows you
to connect and drive your anti-gravity
sessions running across your different
machines like laptop, servers, and
desktops. All you need is a web browser.
while you have your web browser handy.
You're able to remotely connect to each
of your different machines running
anti-gravity and retain full access to
your files, workspaces, build tools,
credentials, etc. Again, this is
something we've had in Codeex for a
while now. We've had it in Claude Code
for a bit now. It's been in cursor for a
bit now. And well, you could do it in
anti-gravity now, too. Now, it has been
a really, really busy week, and I've
shared a lot already. And I still have
quite a few more things I want to share,
but I'm just going to run through them
super quick in a rapid fire.
We got a couple additional updates out
of OpenAI. ChatGPT work can now use its
computer and browser to sign into
websites on both web and mobile. Now, if
you're using the ChatGpt app, it has a
built-in browser which can remember
passwords and things like that. But this
is now available on the web version.
Here's an example they give to log into
DMV and help me replace my driver's
license. And you can see it logs into
the site and then asks you to continue
with your password and it can log in
with things like one password or your
built-in password manager. And it never
actually knows your passwords. Now,
personally, I still like using the chat
GPT app better cuz it will just open the
browser and then log in for you using
whatever saved credentials you have
inside of the chat GPT browser. But if
you like to use the web browser version,
this is a pretty cool new quality of
life update feature as well. There's
another new feature inside of chat GPT
work on web and mobile. If you're on a
plus or a pro plan, you can now have
tasks run based on things changing in
places like Slack, Gmail, and GitHub.
So, in the past, you've got this
scheduled tab here, and you can have it
schedule a task to run, you know, every
day at 2:00. Now, you can say schedule a
task every time I get this Slack message
or schedule a task every time this Gmail
message comes across. So, more ways to
have automations fire off on your
behalf. OpenAI also dropped the price on
their most powerful model. So their GPT
5.6 Soul model is actually 20% cheaper
for the next three months. So that's
pretty cool as well. Moving on to
Anthropic news. Claude is getting its
own browser inside of Co-work. You know,
we saw this happen in Codeex and
Anthropic likes to steal OpenAI's
homework and whatever a feature looks
cool that OpenAI does, Enthropic seems
to roll it into their products these
days as well. Codex has its own browser.
Couple weeks later, Claude is like,
"Yeah, us too." Although I don't believe
it's available quite yet. Yeah. So, we
see the built-in browser is rolling out
over the coming weeks to Pro, Max, and
Teams plans on Claude desktop app on
MacOSS, Windows, and Linux. They
announced that it's coming, but it's not
here yet. But they also have Claude in
Chrome, which is now generally
available, which is a Chrome extension
that will basically, you know, summarize
articles for you on the web. It will
fill in details for you. it can actually
click around, but it is pretty slow. So,
I'm hoping when they actually build the
browser into their app, that will be a
much faster way to do a lot of those
things. Enthropic also announced this
week that Claude's memory works
everywhere. So, starting today, the
memory you use in chat is the same as in
Claude Co-work. Co-work now has memory,
and it's the same one you use in chat,
leading to less reexplaining and more
picking up where you left off. When
Co-works a task in the cloud, what
Claude remembers from your chats is
there, and vice versa. So you can sort
of plan out something you want to do in
a regular chat. Jump over to co-work and
have co-work execute it and it will
remember what you chatted about in a
previous chat. By default, Claude does
not store topics related to personal or
sensitive subject matter like health,
race, ethnicity, religious beliefs,
politics, etc. But you can go in and
change what it does save about you and
what you don't want it to remember about
your conversations. Jumping back to the
conversation about AI running locally,
Perplexity's computer now has a portable
computer which is local. So the model
harness conversation and trajectory all
live on the user's machine. However,
work that needs the outside world such
as web search connectors or escalation
to a stronger advisor model on the cloud
is invoked only when necessary and
always gated by the user. Local models
carry no inference fee. So the system is
private and cost-effective by
construction. So they're using small
efficient models like Nvidia's Neotron,
Quinn 3.6, Quinn 3.8, and it does say
systems such as the Nvidia DGX Spark can
now run these models locally. So you
must need a pretty decent system to be
able to run this local computer model.
Basically, my understanding is that
Perplexity built their own version of an
OpenClaw or Hermes agent that will just
run on your computer and do tasks that
you need it to do on your computer. It
will run local models. It will go off
and use cloud models when necessary, but
it'll ask your permission first. But
it's kind of designed for really decent
computers that have a lot of compute
capacity, seeing as they called out a
DGX Spark, which is, you know, a roughly
$4,000 box for 128 GB of VRAM. So, not
quite accessible to everybody yet, but
like this is their local version of a
openclaw Hermes kind of agent harness
thing. Let's talk about AI music for a
second. Dr. Andre admitted that he uses
AI to produce songs, which is a very
risky thing for an artist to do these
days. He said, "I don't see it as a
threat. I think the only people that see
it as a threat are the people who have
trouble creating." I had a discussion
with a few people a few days ago. They
were against AI. And I'm like, okay, you
sound like the person that would have
been against the drum machine when it
came out or synthesizers right now. I do
have my own take on AI music. And I
agree with Dr. Dre. I think AI is a
great tool to assist with music. Where I
think the line should be drawn and where
I'm not a fan of AI music is just like
prompting a whole song into existence,
right? Like going to Sunno, creating a
song that sounds like any other band,
and then putting it up on Spotify and
saying, "I created this." No, you just
prompted that song. However, I've played
around with Sunno a lot. I actually
think it's a really, really fun tool.
It's a blast to make silly songs for my
own personal use and listen to them and
play them for my family and we laugh
about it. But I'm not a big fan of
publishing them and trying to fool
people into thinking that they're real.
Something that is really, really fun
though is I've grabbed my guitars before
and started to play like a little riff,
put that into Sunno play drums over the
riff that I made. Now it's like a
collaborative thing between me and AI.
And that's really really fun. So I
definitely think there's like levels to
AI music. And I think just prompting a
song into existence and saying, "Look,
I'm a musician now."
I don't know about that. but using AI
tools to assist in making cool music
where it's like a collaborator with you.
I'm actually pretty cool with that.
That's just my take. And speaking of
music, this is really interesting. The
company Stability AI just raised a new
round of funding and they actually have
music generators under their umbrella of
tools they've built. And the investors
that just invested are comprised of
Electronic Arts, Sony Music Group,
Universal Music Group, Warner Music
Group, and more. So, I think this kind
of tells us that the music industry, the
big studios, aren't necessarily against
AI. They're actively investing in it.
They just, you know, don't want to be
cut out of the loop of making money on
AI music. Time magazine released their
list of 100 most influential people at
AI, and it's an absolute joke.
[laughter]
Now, don't get me wrong, there's a ton
of people that I do think deserve to be
on this list. I definitely think the
OpenAI team, Elon Musk, Daario, Peter
Steinberger, Mikey Scholamman, Fay Lee,
Ilia Suskiver, Mirror Morati, these are
all people that I think absolutely
deserve to be on that list. However,
Jensen Hong, the person who's making
this happen through all of this compute
that he's providing to all of these
companies, one of the people that's
probably shaped the like AI revolution
the most. Deis Habibus, the ex CEO of
DeepMind, who literally has a Nobel
Prize for his AI with AlphaFold. Andre
Carpathy who's literally the most
followed voice on X around AI and even
like Mark Zuckerberg who's trying to be
like the voice of openweight models and
push that forward. All of these people
are like pretty influential and they
didn't make the list yet somehow Paris
Hilton is on the list. Joseph Gordon Lev
is on the list. Bernie Sanders is on the
list. Like what? And the reason Paris
Hilton is on the list is that she's an
advocate against deep fakes. I mean,
nothing against any of those people.
They just don't deserve to be on a list
of the most influential voices in AI.
Take it from me, somebody who is
literally in the weeds of AI every
single day. I read every bit of news. I
read all the articles. I read the
research papers. I watch the YouTube
videos. I scroll the threads. I try the
tools. I am in the thick of it every
single day. These are not influential
people in the space. They're just not.
And I know I'm getting worked up over
something that's probably designed to
work people up, right? Like Time
magazine is probably purposely rage
baiting us knowing that this is an
incomplete list and like names like
Paris Hilton are on it to get people to
go, "What the hell is Paris Hilton doing
on this list?" and get people like me
and other like people on social media
talking about how bad this list is just
because it drives more eyeballs to time.
And I played right into it. But okay,
I'm done ranting. I just think this list
is absolutely ridiculous. Let's end with
a cool robot. So, Skilled AI introduced
S1, which is a new foundation model for
robots. It can be taught 10-minute long
tasks that it has never seen before from
just one video prompt without any
fine-tuning. So, in this example, you
can see them wearing like little wrist
things with cameras on it doing the
task. And then when they're done doing
the task, the robots know how to operate
their hands and recreate the task
because they can watch the cameras,
right? There's a camera on each wrist
and there's two robot arms and now based
on what they saw from the cameras, they
could now do that. That's pretty cool. I
love seeing advancements in robotics
and, you know, making it easier and
easier to train robots. So, yeah, this
one's pretty awesome. Oh, and by the
way, if you haven't watched some of the
videos from the World Humanoid Robot
Games, you should definitely look them
up. They're kind of awesome. And that's
what I got for you today. Now, before I
sign off, this week was a really big
milestone for me personally as well.
This was the week that I actually passed
a million subscribers on YouTube, and I
can't thank you enough for being a part
of that. Everybody who watches these
videos, has hit the subscribe button,
who has liked the videos, which has
helped me get more reach on these
videos. I appreciate you more than you
know. It has been an awesome ride and
I'm excited to keep on going and seeing
how big this thing could get and how
much more helpful I could try to make
these videos as I go. It's my goal every
week to drink from the fire hose and
keep up with all the AI news. I'm not
going to try to pretend that everything
is the biggest deal that's ever
happened. I'm just going to share my
thoughts as they come out about the news
that I think is the most important to
the most amount of people. And if that's
something that's interesting to you,
maybe consider liking this video. And if
you haven't already, subscribe to this
channel. A million of you have already
done it. And again, thank you so much. I
do record these videos on Thursdays and
publish them on Friday. So, if anything
came out sort of late Thursday evening
or on Friday, it'll usually make it into
next Friday's video. But again, I'll do
my best to keep you looped in and
hopefully make you feel like you don't
have to keep up with it on a daily
basis. I got a lot of cool thank yous
and shout outs from a lot of different
people over the last few days as I
crossed that million subscriber
milestone. And so to wrap this video up,
I thought it would only be appropriate
to share a few of those messages that
people sent to me just because I think
it's pretty cool and like I'm very
grateful that people took the time out
of their day to send me some of these
messages. So, thank you so much for
tuning in. I really really appreciate
you. It's been so much fun nerding out
over the last 17 years, but really five
and a half years about AI. And uh I'm
going to keep on going with this. Thank
you again for tuning in. I really,
really appreciate you. And hopefully
I'll see you in the next one.
>> Congratulations on a million
subscribers.
>> Congratulations on a million on YouTube.
That is amazing.
>> Congrats, buddy. Well earned.
>> Congrats on 1 million subscribers.
>> Congratulations on the million
subscribers.
>> Congratulations on the 1 million
subscribers.
>> Matt, congrats on 1 million subscribers.
This is such a big milestone and so
welld deserved.
>> Congrats on reaching 1 million.
>> Very happy for you and uh onwards and
upwards. Let's do it.
>> I wanted to wish you a huge
congratulations on reaching those seven
digits.
>> I wish you way more success than you
could ever imagine and I hope that this
is just the beginning of your journey.
>> Just wanted to wish you a heartfelt
congratulations on reaching 1 million
subscribers. Big boy.
>> Congrats.
Congrats
on 1 million subscribers.
>> We love you.