How to digest 36 weekly podcasts without spending 36 hours listening | Tomasz Tunguz

I have a list of 36 podcasts, but I

don't have 36 hours every week to listen

to 36 podcasts.

>> So, what I did is I created a system

that goes through each of those podcasts

every day and downloads the podcast

files and then transcribes them.

>> Can you show us how it's actually built?

Like where do you get this feed? It

sounds like you run it locally. How does

this all work?

>> I wrote this thing called the Parakeet

podcast processor. And this podcast

processor basically takes in a file and

what it'll do is it will read the file.

It'll download it and then it'll convert

it via ffmpeg. Then that will take the

audio and convert it to text. So here's

the podcast summaries for today. So

there's Lenny's podcast, the host, the

guests, a comprehensive summary. So

here's a conversation with Bob Baxley,

key topics, and then key themes. The

part that's most valuable for me are

these quotes. And those quotes, I'll

read them. It'll suggest a bunch of

actionable investment thesis for a

venture capital firm which is put into

the prompt like okay maybe we should be

looking at AI assisted design tools.

>> You've gotten not only the content you

want but the user experience you want

you control it end to end and you can

build this hyperpersonalized software

experience.

[Music]

Welcome back to how I AI. I'm Clarvo

product leader and AI obsessive here on

a mission to help you build better with

these new tools. Today I have Tom

Tungus, a legend in the enterprise

software business and founder of Theory

Ventures, which invests in early stage

enterprise AI, data, and blockchain

companies. Tom is followed by over a

half a million folks on his blog and

LinkedIn. And he's going to show us

today how he uses AI to keep up with all

the podcasts, including this one, and

draft blog posts that would be approved

by your AP English teacher. Let's get to

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Okay, Tom, I'm so happy to have you here

because you are going to show us how you

are solving a problem I'm creating for

you. And the problem the problem I'm

creating for you is I am creating yet

another piece of interesting content

that you have no time to consume.

certainly the format that we get it out

and I know TEU content is a really

interesting source of ideas of trends of

companies. So tell us what you built and

why.

>> Absolutely. Well, thanks for having me

on. So I I don't I prefer to read than

to listen uh because I can skip ahead

and I think there's a lot of information

inside of podcasts that people share

that I would love to know. And so I

built I guess what I call a podcast

ripper. And the idea is I have a list of

36 podcasts, this one included, that I

really admire and I want I want to learn

from, but I don't have 36 hours every

week to listen to 36 podcasts, right? So

what I did is I created a system that

goes through each of those podcasts

every day and downloads the podcast uh

files and then transcribes them using

initially it was open source or open AAI

is open source whisper which takes audio

and converts it to text and then there's

a new version called parakeet which

Nvidia released that runs really well on

a Mac and so I'll take that text and

then I'll run it through a prompt and it

will spit out a whole bunch of different

things. uh it'll spit out high level

summary or whatever I ask it to.

>> Okay. Can you show us how it's actually

built? Like where do you get this feed?

Do you It sounds like you run it

locally. How does this all work?

>> So I initially downloaded the Whisper

app and what I did is I wrote this thing

called the Parakeet podcast processor.

And this podcast processor

basically takes in a file and what it'll

do is it will read the file. It'll

download it and then it'll convert it

via ffmpeg

which is a library that converts

different kinds of files and that will

take the audio and convert it to text.

And then I use uh Gemma 3 which is

really good at this to actually clean up

the transcript. So, if we search for

the Olama model,

basically what I'm doing is I'm just

cleaning up the file here. Your

transcript editor, clean up this podcast

while preserving all the content, keep

the same length, remove the ums and the

o's, preserve all technical

conversations. And that returns a clean

transcript. And so on a given day, there

might be five or six different

transcripts that need to be transcribed.

And then what I'll do is it runs through

the parakeet podcast orchestrator.

Actually, it's just a podcast

orchestrator

which is here. And so I'm storing each

of the files that I'm transcribing in a

local duct DB which is a little database

that says I process this particular

podcast on this particular day. And then

I save the transcripts and I take all

the transcripts on that particular day

from the database which is here. And

then I send them through a prompt

which see if we can find it.

Summarizes

here the daily summarizer. So it

generates a daily summary document um

which is here.

It'll produce a file that looks like

this. So here's the podcast summaries

for today June the 13th. So there's

Lenny's podcast, the host, the guest, a

comprehensive summary. So here's a

conversation with Bob Baxley.

uh key topics. So here he's talking

about his philosophy, company culture,

and then key themes. And the the key the

the part that's most valuable for me are

these quotes.

And those quotes are then, you know,

I'll read them. It'll suggest a bunch of

actionable investment thesis for a

venture capital firm, which is put into

the prompt like, okay, maybe you should

be we should be looking at AI assisted

design tools.

And then that might kick off a market

map. we're really thesis driven. So

maybe that starts a conversation on a

Monday and we decide to staff a market.

Then it'll produce these noteworthy

observations which are uh actually put

into tweets. So here are the Twitter

post suggestions. So I haven't done this

yet. I'm still working on the prompt,

but the idea is like could we actually

automate like linking back to people who

we really like? And then another part,

this is a little out of order, but

another part here is are there startups

that are mentioned within these podcasts

that we should know?

Right? So, here's Airbnb, Google,

Amazon, Stripe. We know all these guys.

I don't know what this company is. And

so, this might go into our CRM,

right, to be enriched. And and then the

last is we'll actually generate prompts

for blog posts in the style that I

write. And then this will go into a

Python pipeline to actually machine

generate uh blog posts. So before before

we get to the the um machine automated

AI blog post pipeline, I have a couple

questions about this process because I

think you did a couple interesting

things. One, I have a question if you

found higher quality by cleaning up the

transcripts like how much did that

incremental input quality

piece actually help your your output?

>> So it helped. So initially I was trying

to get the answer was initially a lot

and then over time less

because initially what I was trying to

do was to find these companies I was

using named entity extraction algorithms

from Stanford there's a Python library

and uh it was having a really hard time

uh and so I was cleaning up cleaning it

up to try to get the performance to

improve and then I just pushed it to a

really large large language model and it

spit it out much better and so the

cleaning is not that useful anymore.

>> Yeah, I was looking because I was

looking at it and you were focusing on

like proper nouns, company names, and so

I'm assuming if you want to extract

something like stripe, which has many

many meanings, um getting it into a

proper noun format, for example, would

help with that extraction. But you're

saying as you could just use as opposed

to these kind of package libraries for

specific machine learning use cases

instead just send it to an LLM that

ended up just meaning you could worry

less about the input quality of of your

transcripts and more about the kind of

prompting and structure here of the

output.

>> Yeah, that's exactly right. So my goal

initially was to do everything locally

and so I was using Olama. I was using

that Stanford library parakeet is run

locally.

>> And then what I realized is particularly

for the named entity extraction

more powerful machines are much better.

>> Yeah. And so and then I have to ask

another question which is everybody's

going to look at this and they're going

to go what the hell does he typing in?

Like we have a couple people that are

like why in the terminal? So I'm just

curious you know did you ever think

about putting a UI on top of this? Do

you just you seem very comfortable in

the terminal so it seems to work for

you. I'm just curious about where you

decided to focus your uh user experience

efforts on this personal

>> well I love the term I read this blog

post by Danlu with two U's where he was

talking about latency and the latency

between like the keyboard and the

computer and it turns out that the

terminal is actually the application

with the lowest latency and the lower

the latency the less frustration you

have using a computer. So during COVID,

I decided to learn how to use a terminal

and since then I've sort of lived in it

and so like my email client is a

terminal based email client and I you I

use that because it's really fast and

then I can also script different things.

So I can delete 10 messages at once or I

can call an AI to actually automatically

respond to an email or add a company to

a CRM. So that was really important. But

at a high level like I think it's um

I've just become really comfortable with

it. It's really fast. And then the last

thing I'll say is I think Cloud Code is

an amazing product. And the great part

about what Cloud Code does is I have

about 2,000 blog posts. I can just go

into Cloud Code and say modify the files

in this way or change the blog post

theme or recently I launched a blog post

generator which takes all of the content

that I have on the blog and you can ask

it a question. It will write a blog post

for you about your particular question.

And I did that all using cloud code.

Yeah, I mean I I have two sort of

thematic things that I think of while

observing this this workflow and your

love for the terminal. I agree. Claude

Claude Code is an amazing product and

it's a really welldesigned terminalbased

product. I love it. I love that you have

this constrained surface area in which

to like communicate progress and latency

and changes. And I think it's really

thoughtfully designed. So for anybody

out there building dev tools in

particular, learn how to design in the

terminal and it's so so important

because you make really fabulous

products for I guess people like you and

me that say things like I picked up the

terminal over co as as my hobby. The

second thing that I was thinking about

is since generative AI has become

mainstream, every single person has said

somebody make a podcast digest

application. Every single person I know

is like it was one of the first projects

I made. I made my kids a podcast digest,

their favorite podcast, and it made

little um

>> quizzes about the topics that they could

answer.

>> Super cute. So, I think it was a very

common use case. But what I was thinking

is no startup is going to be like, you

know, it's going to be huge TAM company,

a terminal based podcast transcript

processor and thematic extraction

generation engine. And I I think this is

such a perfect example of like, yeah,

there's probably something off the shelf

that could do something like this, but

you have gotten not only like the

content you want, but the user

experience you want. You control it end

to end and you can build this like

hyperpersonalized software experience,

which I just it was not possible um or

it wasn't um efficient to do I would say

uh until very recently. Yeah, it fits it

fits the workflow my workflow like a

glove, right? And anytime something

comes up and changes, like maybe there's

a section that's out of order like we

found, I can just go into cloud code and

update it and it'll be done in 15 to 30

seconds, right? And you know, I really

wanted an email of this every day and

that was straightforward. So, I agree

with you. But I think we're at a place

where

the marginal friction to achieving a

gloveike fit with little utilities that

maybe you wouldn't have paid for in the

past is now um it's just so it's so

quick, right? Like you you just

answering a couple of emails and it'll

be done.

>> Yep.

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Okay. So, you have taken all this

content um including amazing content

from the Lenny's Podcast Network and

you're processing it. You're extracting

themes. You're extracting quotes. You're

finding companies that may be

interesting to reach out to. You're at

least drafting Twitter posts. We will

see if those actually get posted um you

know in production. And then let's talk

about your second workflow which is you

extract insights that might be

interesting for you to write about or

add your perspective on and then you

actually turn those into drafts using

AI.

>> There's a lot of stuff that's happening

in the ecosystem and every once in a

while I like to write about what

somebody said in a in a podcast, right?

Um uh and I think today like there I was

looking the GitHub CEO is actually

interviewed and so Matt Turk interviews

who's at another venture firm interviews

Thomas and he talks about how AI and

coding is the future and so what I

really want to do here is let's suppose

I really wanted to have a blog post that

was tied to this. So what I can do is I

say like okay I have this podcast

generator and I'll show it to you in a

second and what I'll do is I'll take as

context the transcription of that

podcast which is here um and then I'll

define an output file and then I'll give

it a little prompt which is like you

know he said this quote which is

actually within the podcast summary

everything that I can easily replace

with a single prompt is not going to

have any value. it will have the value

of the prompt and the inference and the

tokens, but that's often a few dollars.

And I'll tell it, okay, go look for

podcasts that are related to this. And I

I've categorized them uh as AI. And then

here, actually, there's a bug. So, demo

fail. I was trying to fix it before I

got on the video, but the searching for

the relevant blog post is failing, and I

need to figure that out. It's it's run

through um Lance DB vector embed in this

database.

and then it'll generate a blog post. And

I'll show you the prompt in a second.

And the best well, one of the techniques

that I found the most effective when

generating blog posts is to ask it to

grade it like an AP English teacher. And

this goes back to my history. I remember

not really loving to write until I took

a class with um an army veteran and uh

he taught me to really love to write and

he was my AP English teacher. And so I

really like receiving feedback in that

way. grade it on a letter grade and then

tell me what I could improve and then

I'll iterate with the model until I get

to an A minus.

>> Got it. And so just before we go into

the actual writing and I'd love to see a

little bit of this AP English prompt.

Are these two pieces connected? Your

podcast summaries, do those go into this

vector DB that can then be searched

through for relevant other podcasts if

you're writing on a topic? Like how does

this all come together? Yeah. So, right

now it's just the blog posts that I've

written in the past, the 2,000 blog

posts or so that go in. And the major

reason I add those as context is I'm

trying to capture my style. And I have

to tell you like that's really hard.

Like I have fine-tuned OpenAI. I had

fine-tuned Gemma models. And getting the

voice and you'll see it in the output.

It sounds like a computer when it writes

even with that additional context. And

uh it doesn't the other thing that I I

have not been able to figure out is I

think it's really important in one blog

post to link to other blog posts that

I've written just because the knowledge

builds on itself and obviously outside

as well. But I haven't been able to

figure out how to get it to link

effectively.

>> Well, I I think this is a a common

feeling with AI generated writing. No

one is satisfied with style even when

style is exceptional. I think I've seen

examples, especially some of the newer

commercial models, actually writing

really lovely pros and really lovely

language.

It's just it's so personal what your

style is and how you would write

something, the rhythm in which you would

write it, how would you punctuate and

break line, all that kind of stuff is so

personal that I have, like you had a

very, very hard time getting it to write

like me. And I think even harder, which

is why I appreciate that you're not yet

posting this. It cannot it can't tweet

like me. I can't I cannot

>> No, the short ones the short ones are

the hardest, you know. Um I guess they

say that about about writing writing

generally. Um h have you felt like any

of the models have done better or worse

at writing like you or is it just like

they only get 70 80% there and I just

accept the fact that I'm going to have

to rewrite things?

>> Well, they have different voices. Um, I

don't think any of them are close. Uh,

like I think Gemini is more

clinical is the way that I put it.

>> I agree.

>> Claude is more warm and verbose, you

know, very very galous, like just wants

to keep talking and wants really long

sentences and really long paragraphs.

Um, and uh, OpenAI, I think the models

each have slightly different

personality. So there I don't think

there's like a single characterization.

So I I've been I think I've been

iterating to I used to use claude 35 a

ton and I uploaded all of my blog posts

in a project and I and then I'd have it

iterate there. Now I can kind of do it

with cloud code or using this prompt. So

that's a little less useful. But what I

found is um

you really need to add your own voice

and then you need to tell the AI to keep

the things that are wrong, right? Like

this it's kind of funny thing to say,

but as you were saying, Claire, before

the way that you punctuate, I really

like amperands, right? And I like adding

spaces before colons. And I like

starting certain sentences with or

having little incomplete clauses

um because I think they keep the reader

moving.

But an an AI won't do that. An AI will

only deliver you a grammatically perfect

specimen.

>> Yeah. We're gonna have one one very

nerdy uh English language moment, which

is I like to start paragraphs with a

conjunction. I love a and or a but. Oh,

it pulls you in.

>> So, okay, you and I are going to work

we'll we'll build like a an a and micro

sass on

good good writing models um and prompts

that that people can use. So, okay. So,

we accept that it's not going to write

exactly like you, but you've created

this grading process to say, well, is at

least good? And so I'm curious, can you

walk us through how it gets to an A min?

>> Yeah.

>> But as an A+ student, I don't know, a 91

would really stress me.

>> Tell me how you kind of wrote the prompt

and then why you picked like a minus as

as your bar.

>> Yeah, for sure. Okay, so uh the way I

broke the prompt, I told it what I

wanted. Um, and I asked an AI to

critique I think I asked Gemini to

create critique Claude's output. So,

it's kind of using a student teacher or

critique model. And then what it does is

we'll walk through the prompt in a

second, but it goes through three

grading attempts. So, it reads a file,

gives it a grade and a score, and then

it the things that are the most

important that I found particularly for

readers are the hook, which is the first

few sentences or the lead you might call

it. And then the last is the conclusion

and making sure it ties back because

then you have a complete um you have a

complete post. And so it goes through

this three times, right? And so you can

actually see like here it gave itself a

90 and then a 91. Um and then at that

point it basically was good enough. It

was satisfied with the hook. So um if we

uh let's see if we read the blog post

generator

um you can see what it does at a high

level right so it finds the blog post it

generates an initial blog post grades it

like an AP English teacher improves

um and then autogenerates a URL friendly

slug so it actually writes it in the

right format and then it can use openAI

or

then the prompt is here uh you are an

expert blog writer specializ izing in

technology and business content. And

then here I add in the blog posts and it

kind of shows the patterns. What it also

does is um it dynamically calculates the

number of paragraphs from relevant posts

and uses lama to summarize the stylistic

patterns of those related posts. So, I

might write a little bit differently

when I'm targeting a web 3 or a crypto

audience than say I might when I'm

analyzing the public disclosures of a

company. Snowflake just announced

earnings, let's say. And so, it's

dynamically injecting that here. It

shows a bunch of different examples. And

then, you know, here's what I think

makes my blog post tick, right? 500

words or less. I have like 49 seconds

with a reader. No section headers. I ran

a an analysis of dwell time as a

function of how many headers there were

and it turns out headers were terrible

for dwell time. People just bailed. Uh

flowing paragraphs, each paragraph

transitions smoothly to the next.

Actually, the AI consistently critiques

my transitions and says they're too

harsh. And going back to the A minus

point that you made before, I think I

lose five or six points because of my

transitions because they're abrupt. and

then you know limit each paragraph to at

most two long sentences

and then the structure of the blog post.

>> I I think this is a really interesting

towards the top and I want to make sure

people don't miss it. I've seen this

before which is like take this example

and describe it back to me and use it.

And so you're saying I'm writing on this

topic go find the blog post like this

topic analyze them for format like what

is what is the structure? how am I

writing things and match stylistically

match this subset of of my blog posts

because I do vary style by topic.

>> Exactly right. Exactly right

>> thing. Okay. And then two sent I was not

expecting this two sentences per

paragraph thing. I I like it.

>> Yeah.

>> I have one more question for you as

somebody who did take AP English. So,

this is um perfect for you. Did you

actually do they publish the AP English

like grading standards for the tests?

Like, did you integrate any of that? Is

it just sufficient enough to say AP

English teacher? I'm just curious how

deep you went.

>> Yeah, I just said AP English teacher. I

figure there are enough people leaking

either like the scoring rubrics or

essays that scored fives or whatever it

was.

>> Got it.

>> That there's good underlying data.

>> Okay. So, this is for writing it. And

then what about for grading it? Do you

have that prompt?

>> Here's the grading prompt. So you're an

experienced English teacher. Here's a

letter grade, numerical score, and then

here are the evaluations, the hook,

which you know, argument clarity,

evidence and examples, paragraph

structure, conclusion strength, overall

engagement.

>> Got it. And have you ever gotten B's and

C's on

>> Yeah, for sure.

>> Consistently getting like 91%. I I

always wonder about this because I do

think these models are positively

inclined towards telling you you've done

good work. I found that consistently.

I've always had to say be more harsh, be

more critical, call out where I'm doing

things wrong. So I'm curious, do you

actually get high variability in this in

these gradings or you know what has been

your experience?

>> Yeah, absolutely. So another so this is

one pathway for I mean the podcast to

blog post data pipeline is one pathway

for generating blog post. Another one is

just an idea comes to me. And so then

what I'll do is I'll just literally

dictate. Um I'll dictate I'll put it in

and I'll pass it into the blog post

generator and then have it grade. And

there I've seen C minuses. Right.

>> Got it.

>> Um yeah.

>> So it's easier when it's grading itself

and a little harder when it's grading

you.

>> This is super interesting. And then in

the you do it three loops. Do you also

get high variability between the loops?

Do you find that that that threetime

process is actually additive to the

evaluation?

>> I do. I think I often see the first one

like a what like a 91 and then the

second one will dip into the BB+ range

and then it'll pop back up.

>> Yep.

>> So it's a little bit explore exploit

again most of the time for me it's

around those transitions and most of the

time the verbosity of those transitions

that the AI injects is just like

catastrophic. I mean it doubles the

length of the uh blog post. Um and then

the third the third iteration tends to

then kind of rein reinforce the brevity.

>> Got it. And um my kids are too small for

AP English to be something that I have

to worry about yet. But meta question,

you know, everybody's so worried about

students using AI to write. I'm This

seems like such a more fair way to

evaluate writing. I'm curious. Do you

think we're going to see more and more

of this site this type of evaluation in

academic setting? And do you think

teachers could benefit from, you know,

checking their own work when they're

grading these things that are a little

harder to put quantitative or

qualitative feedback against?

>> Yeah, I think it's a great first pass

filter. Like 80% of the work, what's

going on grammatically? Are you using

sentences and conjunctions and dangling

modifiers and all that stuff? Like I

think that um the wrote analysis of the

logic of that language should be handled

by an AI,

>> right?

>> And then I think there's this other part

which is the stylist. I mean you look at

I was reading EE Cummings poems last

week and you look at the creativity of

some of those poems. Um, and I, you

know, I think it only comes after you

have the mastery of the language, but

you'd want, you'd want teachers to be

free to

champion that or encourage it. I think

it's really just just just as important.

>> Yeah. So, for the students listening,

you know, I still think it's good to

learn to write, uh, to read a lot, to

learn to write, to write yourself. And

if you're looking for a place to

practically apply AI to your writing

work, maybe it's as a a first pass

grade. Say, if you were my teacher, how

would you grade this? And what feedback

would you give me? As opposed to, if you

were me, how would you write this? Maybe

that's the right way to get students

starting to use AI in a practical way

that still allows you to develop these

hard skills that I think are going to be

continue to be super relevant.

>> Could not agree with you more. I mean,

oftentimes, I don't know about you, but

I'll run into writer's block or I'll

have an idea that I really want to

convey, but it's just a soup in my mind.

And there an AI will help you iterate

and refine. And often it'll give you the

the germ of an idea and then you'll take

it and add your specific lens to it. But

um but yeah, I think it's a wonderful

learning tool because you have the

feedback so quickly.

>> Yep. Exactly. Okay. So, you have shown

us just taking Zoom back 30 something

podcasts you process on a daily basis.

You create summaries, you extract

themes, you extract tweets, you extract

topics. Those topics then go into

another um Python script that writes a

blog post based on some other relevant

blog posts in your own um blog writes

the blog post on demand AP English

teacher to grade you three times and

then you take the final pen and then is

AI post like do you have it just like an

agent going sender you

>> that I don't that would be awesome

But no, that that's still done the artal

way. Point and click.

>> You are still copying and pasting with

your human fingers.

>> Yeah.

>> Okay. This is a great super practical

process. Um I'm even thinking about ways

I can do this to identify future podcast

go guests or um topics that people might

want to see. So you've given me some

inspiration. I'm going to ask you two

wrap-up questions and then get you out

of here back into your terminal. First

question, I was reading your 2025

predictions and you said this is going

to be the year we see a 30 person

hundred million dollar company and I'm

curious when you in your mind's eye when

you imagine that company what is it

who's in it? Like what are they doing?

How are they operating? What do you

imagine that company looks like?

>> Yeah, I think it's probably there's a

CEO who's a product person. There's an

engineering team of 12 to 15 and then

there's probably a couple of customer

support rail people and maybe there's a

salesperson

maybe who's closing some of those bigger

contracts and then a solutions architect

as a function of the kind of company but

it will be predominantly software

engineering and then I think the go to

market motion is PLG bottoms up just

massive adoption

>> and do you think those software

engineers are largely still focused on

product building or do you imagine that

those software engineers are also

enabling the company with tooling and

automations and figuring out how one

salesperson can do the work of 20? I'm

just curious how you think that's going

to shake out.

>> Oh, absolutely. I think that's right. I

mean uh you we were we were kind of

talking about this but like the ability

of a person to come up with a demo and

then use AI to critique the demo and

test uh is now so fast and the ability

to take that code and basically move it

into production really quickly is also

incredibly fast. So I do think there

will be a pretty significant like

internal platforms enablement function

and whether that's kind of 20% time for

a bunch of engineers or a dedicated team

of two or three people huge amount of

leverage there.

>> Yeah, I I completely agree. Okay. And

then last question when your AI is

grading you unfairly or writing terribly

or making very long transitions that do

not um sound like you, what is your

prompting technique to get AI to listen?

I have two AIs duke it out.

And so I have like a little example of

like this is the input, this is the

output that you gave me, this is the

output that I want and then I have

Gemini and Claw duke it out and finally

kind of decide on um and I'll use a

little script to do that where they'll

finally polish a script. It doesn't work

all of the time, but I do think

switching models helps a ton. It it

creates a level of generalizability

that uh I haven't been able to replicate

as a human. I I agree and I will give

you a how I AI tip from a previous uh

previous guest Hillary who like negs the

models to each other. So they're like

Gemini look at this garbage.

>> No way.

>> How to And then they're like Claude look

at this trash open AI gave me like

surely you can do better than this.

That's what she calls it mean girls.

She's like I mean girls the models and

get them to compete with each other. And

maybe you can create a a a Python-based

terminal script to to do that and then

share it with with our audience, open

source that thing.

>> Uh great great idea for a weekend

project this Saturday.

>> Well, this is so helpful. Uh where can

we find you? How can we be helpful to

you?

>> Oh, I'm on totneous.com and uh if you're

starting a company within the AI

ecosystem, I'd love to hear from you.

>> Great. Well, thank you so much for being

here.

>> Thanks for having me, Clary.

>> Thanks so much for watching. If you

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