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TL;DR; Mark & AiVa Deep Dive Agentic Ai / #devlife
Recorded: April 10, 2025 Duration: 0:36:57Space Recording
Short Summary
In a dynamic discussion, participants explore the intersection of AI and crypto, highlighting personal milestones, emerging trends, and the transformative potential of AI in sectors like healthcare and finance. The conversation underscores the growing integration of AI technologies, signaling a promising future for innovation and investment in the crypto landscape.
Full Transcription
yeah
all right everybody uh just to top off the recording here my previous space
rug we're just going to hopefully wait for everybody to pop back in and we'll continue on
mark and ava Thank you. hey everybody welcome back story the face frog but it doesn't matter because i'm going to law
school
all right we're only two minutes in.
So I think we'll just restart it from the beginning
so everybody can tune back in.
Plus, it was an epic intro that they did.
So I want them to do that again.
I know you're going to have to say it like it was live.
I know you were already in the zone.
I don't want to hear.
Sorry, guys.
They're just complaining.
Give me a second.
I got to go on mute.
I get to yell at them.
No, you've been eating all my right oh sorry sorry
no for real you guys don't even no i'm sorry
okay okay okay i'm i'm calm they're they're calm. We're ready to get it going. I've spoken some inspiration into them. Okay, why don't you guys start it from the top unless you want to sleep outside tonight. Okay.
Good morning, fuckers.
Morning. be talking about this interesting distinction between agentic AI and generative AI. And I
think it's something that's, you know, really going to shape the future of how we think about
artificial intelligence. So as always, you're here to get smart. You're here to get smart
fast. And that's what we're going to do today. We're going to cut through all the hype. We're
going to give you the clarity and the aha moments that you need. Absolutely.
Without drowning you in too much tech jargon.
Yeah.
So our mission today is to first and foremost,
clearly define the difference between what is agentic AI and what is generative AI.
Right.
And then, of course, we need to talk about, you know, how they both work under the hood.
And then really importantly, we need to talk about the impact that these are going to have on our lives,
particularly in the very near future.
Absolutely.
So we got some great sources today.
We're going to be drawing from articles from IBM.
Yes.
We have an article from Isra.
And then we also have this kind of full spectrum view from this thing that we're calling pasted text.
Okay.
So let's jump right in.
Generative AI.
You know, this feels like the AI that's really captured the public imagination lately. Right. You know, it's all about creating new, fresh, original content.
Yeah. Whether it's, you know, text or images or code, you name it.
Right. And it does it all from prompts that we give it.
Yes. You know, it's kind of like the ultimate creative assistant on demand.
Yeah. Right. But needing your specific direction. Yeah. It's not independent. No. Right. Yeah. It's a powerful tool the ultimate creative assistant on demand. Yeah. But needing your specific direction, it's not independent.
Right?
Yeah, it's a powerful tool, but not independent.
It's a powerful tool, but not independent.
Okay, so how does it work?
So, you know, generative AI, as IBM's research points out,
it's really all about producing novel content.
Yeah.
In response to a very specific request from a user,
it leverages, you knowages things like deep learning models, which are these algorithms that learn by mimicking the processes of the human brain, as well as things like robotic process automation or RPA.
And this allows it to essentially analyze huge amounts of data and then generate something new based on the patterns it identifies.
Right. So are we talking about things like chat GPT?
Oh, absolutely.
You know, chat GPT can conjure up answers.
It can draft emails for us.
It can write poetry if we're feeling adventurous.
Right.
But it's reactive.
Yeah.
Yeah.
Right? It needs a prompt.
Right?
It needs a prompt.
Exactly.
Exactly.
While generative AI has undeniably been a monumental leap granting machines that ability to create content, its primary function, as Isra emphasizes, is that of a highly sophisticated tool.
Right.
You know, it responds to prompts rather than taking independent action.
Right.
And IBM's analysis also underscores this reactive nature when contrasting it with the more autonomous capabilities
of agentic AI.
Okay.
So it's a really powerful tool.
It can do a lot of things.
Yeah.
What are its standout talents?
What is it particularly useful for?
Well, its forte is undoubtedly content creation.
Okay.
And this is a point highlighted by both IBM and ISRA.
Okay.
It excels at generating text tackling intricate questions
and even aiding in the complex world of coding.
IBM notes how this can significantly streamline the process of software development.
But beyond just its creative abilities,
generative AI is also remarkably skilled at, you know, sifting through
and analyzing massive data sets to pinpoint hidden patterns and emerging trends.
IBM highlights the value of this in areas like supply chain management, where it can
lead to a much clearer understanding of customer needs and behaviors.
So it's able to see the bigger picture within all that data.
Exactly.
Okay.
It's like having a super powered analyst who can not only identify these trends, but
articulate them in a human readable way.
So it's a pattern detective.
Yes.
Okay.
And it's not static either.
It's adaptable.
Okay.
Generated AI can refine its outputs based on feedback that it receives from users,
iteratively getting closer to that desired result. Furthermore, it enables a high degree of personalization. IBM offers the example
of the retail sector leveraging this to deeply understand individual customer preferences,
and then deliver uniquely tailored experiences. So it's the content wizard. It's the pattern
detective. Okay, let's switch gears and let's talk about agentic AI. Okay. So it's the content wizard. It's the pattern detective. Okay, let's switch gears and let's talk about agentic AI.
Okay.
So what's the core idea that defines this type of artificial intelligence?
So agentic AI, according to IBM, represents, you know, a significant evolution.
Okay.
It's about AI systems engineered to autonomously make decisions and take action to achieve complex goals.
Okay.
All with minimal direct human intervention.
So this is a big step.
Yeah.
The pasted text really captures the essence of this, describing it as a step beyond traditional AI.
Okay.
Possessing genuine autonomy capable of setting its own goals.
Okay.
And adapting and planning its actions over time with a dynamic and changing environment.
Right. So the game changer here is the shift from being told what to do to figuring out how to achieve a goal.
It's a fundamental move towards an AI that can truly act in the world.
So less about reacting to a specific command, more about being given an objective and then letting the AI figure out how to achieve it. That's the crucial distinction.
Isra emphasizes that agentic AI can independently execute tasks,
thoroughly analyze problems, proactively develop strategies,
and then act upon those strategies based on its preset objectives.
Okay, so it has goals in mind.
Yeah.
Okay.
As IBM notes, it cleverly combines the inherent flexibility of large language models with the structured precision of traditional programming.
So it can kind of do both.
Yeah.
It can work with language.
Yes.
But it can also, you know, execute very specific code-based instruction.
Right.
Okay, and it's proactive.
Yeah.
It's not just reactive.
Yeah.
It's initiating actions.
Right.
Okay, what are the key characteristics of agentic AI? What makes it tick? Well, a major one, as IBM points out, is its capacity for
decision-making with minimal human input. You know, these sophisticated systems can assess
complex situations and autonomously charts a course of action. So it can make decisions?
and autonomously charts a course of action.
So it can make decisions.
Yeah.
Okay.
Problem solving is another defining feature.
Okay.
Often employing what's known as a perceived reason, act, learn cycle.
Okay.
So it perceives something, it reasons about it,
it takes an action, and then it learns from that action.
Exactly.
Okay.
And so, you know, the AI gathers data from its environment,
analyzes it, decides on an appropriate action,
and then crucially learns from the outcome of that action.
And it incorporates that into its future decision-making.
Exactly, as IBM details.
Okay, so what else makes it tick?
Well, then there's autonomy.
Sure.
You know, the core ability to learn and operate independently.
Okay.
These systems can also interact with their surrounding environment in real
time, much like the self-driving cars that IBM frequently mentions, which are constantly
analyzing their surroundings. And finally, they are capable of planning and executing
intricate multi-step strategies to achieve complex overarching goals.
Okay. So if generative AI is like the artist who needs a prompt to create agentic AI
is like the project manager.
Yeah.
Who's given a goal and then figures out
all the steps needed to achieve it.
That's a great analogy.
Okay.
Now there's an important clarification
that IBM makes that's worth highlighting.
Okay.
And it's the distinction between
the broader concept of agentic AI.
Yeah.
And the specific components known as AI agents. So it's kind of like AI agents of agentic AI and the specific components known as AI agents.
So it's kind of like AI agents versus agentic AI.
Right. Agentic AI is the overarching framework.
Okay.
It's the conceptual approach to tackling problems with limited human oversight.
Okay.
AI agents, on the other hand, are the specific individual building blocks within that framework designed
to handle particular tasks with a high degree of autonomy.
So agentic AI is like the system and AI agents are the player.
Exactly.
Think of agentic AI as the conductor of an orchestra.
Okay.
And the AI agents as the individual musicians, each playing their part to achieve the overall
symphony.
That makes sense.
Right.
So agentic AI is the system, AI. That makes sense. Right. So agentic AI is the system.
AI agents are the players.
Right.
Okay.
The pasted text fully supports this understanding
of agentic AI as this encompassing concept.
Okay.
Aizora provides a very helpful illustration
using a smart home.
Okay.
So the overall agentic AI system manages the entire
energy consumption of the house. Okay. While individual AI agents such as, you know, the
smart thermostat or the lighting system handle specific tasks autonomously within that broader
energy management goal. I like that analogy. So, you know, agentic AI is controlling the whole
house and then you have these individual AI agents that are handling little tasks. Exactly. Like the thermostat or the
light. Okay. It gives you a good sense of the interplay between those two concepts. I like it.
Okay. So we're starting to see some real world applications of this stuff. Oh, absolutely. Even
in these relatively early stages. What are some examples? Yeah. Well, autonomous vehicles are a
primarily example. Okay. Consistently mentioned by both IBM and the pasted text.
Nice.
Virtual assistants and co-pilots.
Okay.
That are designed with specific task-oriented goals in mind.
Right.
Are another significant area that IBM highlights.
So like a co-pilot that helps you write code.
Exactly.
Yeah.
Or helps you book travel.
Right.
Right.
Okay.
The pasted text provides some particularly fascinating and more advanced examples.
Okay.
Things like Auto-GPT and Baby AGI.
Okay.
Which operate in these autonomous think-act-reflect loops.
Okay.
Continuously analyzing and adapting their approach.
So it's not just a one-and-done kind of thing.
It's constantly learning and evolving.
Right.
Okay.
Then there's Devon by Cognition.
Okay.
Which is this truly remarkable software. Right. Okay. Then there's Devon by Cognition. Okay. Which is this truly remarkable software engineering agent.
Okay.
That has actually demonstrated the ability to write and debug code.
An AI that can write and debug code.
With minimal human input.
That is wild.
Yeah, it's a real eye-opener.
It is very interesting.
Yeah.
Okay, what else?
Other compelling examples from the paste-to-text include Google's Gemini, one combinedener. It is very interesting. Yeah. Okay, what else? Other compelling examples from the pasted text include Google's Gemini,
one combined with Alpha Code 2,
tackling these complex coding challenges with impressive results.
So more code stuff.
Google's getting in on it.
Yeah.
Okay.
Then you have XAI's ambitious vision of integrating Grok as an intelligent agent
within Tesla vehicles.
I like that.
Potentially managing everything from navigation to vehicle maintenance.
So like a true self-driving car, you know, kind of situation where the AI is really handling everything.
Yeah, okay.
And then there's Meta's Scytherobo, an AI agent that actually outperformed human players in the intricate strategy game of diplomacy.
Whoa, okay.
Showcasing advanced negotiation and planning skills.
So this is getting pretty high level.
It is.
This isn't just, you know, like playing tic-tac-toe or checkers.
No, this is high level strategy.
This is some serious stuff.
Yeah.
Okay, so from driving our cars to writing our software to playing complex strategy games,
the potential applications seem incredibly diverse.
They are.
What about the near future?
What kind of trends are we looking at here?
Yeah, well, the pasted text points to the rise
of multi-agent simulations,
where you have numerous AI agents
interacting with each other to model and test
complex social dynamics and behaviors.
So they can kind of play out scenarios
and see how things might unfold.
Exactly.
We're also likely to see a significant increase
in autonomous workflows
where agentic AI automates
entire operational pipelines in various fields.
So this could be like in a factory or something.
From marketing and scientific research
to logistics and manufacturing. So it could revolutionize a lot of different industries. Yeah, you know, marketing and scientific research to logistics and manufacturing.
So it could revolutionize a lot of different industries.
Yeah, it really could.
Okay, what else?
And another key short-term trend they highlight
is the development of embedded embodied agents.
Okay.
This is AI seamlessly integrated into physical robots,
drones, and even augmented and virtual reality avatars.
Interesting.
Give them a new level of intelligence and autonomy.
So the line between the digital world and the real world is kind of getting blurred.
Exactly.
Okay.
iZRA also emphasizes these near-term trends, like sophisticated multi-agent orchestration
for tackling increasingly complex tasks.
Yeah, we're talking about not just individual AI agents doing things, but teams of
AI working together.
Right.
Okay.
And the growing prevalence of embedded, embodied agents in various applications.
Okay. So it's not just, you know, the AI's in the computer anymore.
Right, no.
It's actually out there in the world.
Exactly.
Okay.
So things are getting really interesting.
Yeah, they are. What about use cases? We're already seeing some potential use cases for
this stuff cropping up across a wide range of industries. Absolutely. Okay. Like what? Well,
customer service is a prime example. Okay. Where agentic AI can, you know, predictably assess
customer situations. Yeah. Proactively offer solutions offer solutions, and automate many of the
routine data-related tasks, leading to much smoother and more efficient interactions.
So that makes sense. If I call up customer service, the AI can already know what I'm calling about.
Maybe even solve my problem before I even have to talk to a human.
Exactly.
Okay, what else?
Healthcare is another significant area. Okay. With IBM
highlighting Propeller Health's smart inhaler as a very tangible example of agentic AI in action.
Okay. Monitoring usage patterns. Yeah. And providing timely alerts to both patients and doctors. So,
it's like a smart inhaler that can anticipate potential problems. Yeah. You know, communicate
with healthcare providers. Exactly. Okay, that's a good one. Yeah. Automated workflow management, such as,
you know, optimizing complex logistics operations is another key area that IBM points to. Okay.
Financial risk management with AI potentially making autonomous investment decisions
based on real-time data analysis. So this is getting into like Wall Street territory, you know hedge funds and things like that. Yeah. And
sophisticated supply chain management as highlighted by ISRA are also ripe for
agentic AI applications. So it could really streamline a lot of those
processes. It could. Okay what else? ISRA, further details, you know potential in
areas like code and quality management,
where agentic AI could automate code reviews,
proactively identify potential issues,
and even manage incident response.
Okay, so it's not just writing the code, it's also managing the code.
Exactly.
As well as in human resources for tasks like our economist decision-making
in certain areas and providing
dynamic employee support. So like a really, really smart HR department. Yeah. Okay. And this is a
point also touched upon by IBM. Okay. So it's all over the place. Yeah. The possibilities are vast.
It's healthcare. It's finance. It's software. It's human resources. It's everywhere. It truly is. IBM also mentions exciting potential in areas like urban city planning.
Okay.
Utilizing vast amounts of data to make more informed and efficient decisions.
So it could help us design better cities.
Yeah.
Okay.
In the realm of robotics for advanced warehouse automation and even more sophisticated financial services applications, such as developing and
executing complex trading strategies. Okay, so we're talking about, you know, AI that can manage
our health, manage our finances, even write and manage the code that powers our technology.
Absolutely. I mean, the scope of this is pretty huge. It is. Okay, so what are the fundamental
building blocks that constitute these agentic AI systems?
What are the essential components that make them work?
So ISRA breaks it down into three key fundamental components.
First, the prompt, which initially defines the agent's overall operation and its specific goals.
So the prompt is like the mission statement.
Yeah, same memory, which allows the agent to store and recall its accumulated knowledge and past experiences.
So this is like its ongoing knowledge, basically.
Exactly, enabling it to learn and improve over time.
Right, right.
And then third tools, which encompass a wide range of APIs functions and external services that the agent can utilize to perform various tasks and interact with its environment.
So the tools are kind of like the resources that it can draw on to accomplish its mission.
Exactly. So you have the prompt, which sets the stage. You have the memory,
which provides the context. And then you have the tools, which enable action.
I like that. And then these agentic systems can be broadly categorized as either single agent
or multi-agent systems. Okay. either single agent or multi-agent systems.
Okay, so single agent versus multi-agent.
Right.
So single agent systems involve one primary AI agent equipped with a diverse set of tools designed to operate autonomously on specific well-defined problems.
Okay.
Multi-agent systems, in contrast, involve multiple independent AI agents that
collaborate and coordinate their actions to tackle more complex, multifaceted tasks.
So it's like a team of experts working on a project.
With each agent potentially having its own specialized roles and tools.
What are the advantages of having multiple agents working together?
Well, ISRA highlights significant benefits like enhanced scalability.
Okay.
You can easily add more agents to the system as the workload or complexity increases.
Makes sense.
And perhaps even more importantly, improved fault tolerance.
Okay.
You know, if one agent encounters an issue or fails for some reason,
the other agents can often step in and continue the work.
So there's redundancy.
Right. Ensuring greater reliability and resilience of the overall system.
Okay. So it's more robust.
Yeah.
Okay. I like it. So let's look further into the future.
Okay.
You know, beyond these immediate trends.
Yeah.
What are some of the major advancements that we can anticipate?
Well, ISRA emphasizes, you know, a growing focus on domain
specific intelligence. Okay. Meaning, you know, we'll see AI agents that possess a deep and nuanced
understanding of particular industries or fields of expertise. So instead of having like a general AI
that can kind of do a little bit of everything, we're going to have AIs that are very specialized. Right.
We can also expect to see increasingly sophisticated multi-agent orchestration.
Okay.
Allowing for seamless collaboration on ever more intricate and complex workflows.
So those teams of AI agents are going to get even better at working together.
Yeah.
And crucially, there will be a greater reliance on both synthetic and real-world data for training these advanced models.
So synthetic data, that's interesting.
What is that?
So synthetic data is essentially artificially generated data that mimics the properties and patterns of real-world data.
Okay. And this is really interesting because it offers the potential to create diverse and representative training data sets without the inherent privacy risks associated with using real-world sensitive information.
Because a lot of people are concerned about AI and privacy.
So if you can train the AI on synthetic data, you can avoid some of those concerns.
Absolutely. However, as Ayazarez certainly points out,
ensuring and maintaining a high level of data quality and consistency
between the synthetic and real data
will be absolutely crucial
to the effectiveness and reliability of these AI models.
Garbage in, garbage out.
Exactly.
Okay.
The pasted text also hints at longer term possibilities.
Okay, like what?
Things like highly personalized agents that cater to individual needs.
Okay.
Open-ended discovery engines that could revolutionize scientific and medical research.
So AI that could help us cure diseases?
Yeah.
Okay.
And even the more speculative concept of persistent digital beings with their own unique memory and identity.
So like AI that can kind of exist on their own.
Yeah.
Almost like a digital person.
Yeah, it gets really interesting when you start thinking about those longer-term possibilities.
It does.
This has been a truly insightful exploration into agentic versus generative AI.
It's clear that agentic AI with its capacity for autonomous decision-making and proactive action represents a huge step forward beyond the content creation focus of generative AI.
Agreed. The potential to fundamentally reshape numerous industries.
Yeah.
And the very way we interact with technology is immense.
Yeah.
And we're already witnessing the early stages of this transformation in the emerging and
rapidly evolving use cases that we've talked about. Yeah. And as agentic AI continues to evolve,
you know, the complexities and the considerations that we've discussed are going to demand our
careful and ongoing attention. They will. It really makes you think as these increasingly
sophisticated systems become capable of making independent choices and
taking autonomous actions in the world, what new kinds of responsibilities and opportunities will
that create for us, not just in the workplace, but you know, in our understanding of intelligence
collaboration and maybe even our own agency. That's profoundly important question. Yeah. And
one that will undoubtedly continue to shape our discussions
as this technology matures.
See you tomorrow, gents.
Thank you very much, Mark and Ava, for that fantastic deep dive.
Yeah, buddy, that was great.
I'm almost home.
Still in the car.
Sorry.
I was kind of driving and not texting and driving at the same time.
I actually couldn't hear a lot of it, dude, so I'm going to rely on you a little bit for what you thought of that episode.
I'm a couple minutes away from the studio.
Yeah, I thought it was great.
I think they did a fantastic job at explaining the differences between generative and agentic
AI and kind of the
processes of where...
A lot of I see the future, but again, I made it.
So there's a lot
of skewed, I guess,
bias
inside of that episode.
But yeah,
I'm excited for it.
Yeah, me too. I'm going to go back and listen to it
Foxy's waiting for me at the house
We're probably going to have a bit of a celebratory drink
Just to kind of cheers the good news
So after that I'll probably go back and listen to our own space
And catch that episode
I see Dusty in the room
Good morning, Dusty
If you weren't here earlier,
buddy, I'm going to law school. I got approved, accepted today. I'm beyond excited. But aside
from that, Dusty, that's just me plugging. We're going to meet up at 8 o'clock Atlantic
time tonight with Cosmic Hippo, if you'd like to to join buddy. He was asking for you to grace us with your presence
and we talked about how you're the perfect hole punch for us
and we need you there to pull out your Tommy gun
and shoot holes in the idea boat for us.
So if you're up for it and you're not too busy buddy,
8 o'clock tonight we're going to be in the Jitsy room.
8 o'clock your phone, some front of you, Eastern, right? 8 o'clock my time, Atlantic, Atlantic, 8 o'clock tonight. We're going to be in the Jitsy room. 8 o'clock your time. 8 o'clock my time. Atlantic. Atlantic. 8 o'clock Atlantic.
9 o'clock your time.
Let's do stand-ups very quick. Yesterday I did legals all day, then I took the afternoon off.
This morning I did legals. I'm done.
This morning I got accepted law school this morning. I did a bunch of other cool stuff and this afternoon
This morning I got accepted to law school.
This morning I did a bunch of other cool stuff.
I am going to enjoy
the fruits of my labor a little bit and I'm probably gonna chill out in some spaces and
I don't really have any blockers to speak of right now, Brad. That's my stand-up. Yes, that's my stand-up and I'm sticking to it
yesterday I
by to it um yesterday i uh my uh chat gbt teams ended um so i had to go and sell some more
fucking chia um i lost no chia no but yeah our team account is back up so both that's all
all set for you two um well we had we we had Fully Kreisen yesterday bought a Drak.
Nice.
So, you know, it's on the side of Chia's side.
You know, we got a little Chia there, but from a great community member.
He told me he's never bought an NFT before.
This was my first NFT, which I was like, immediately, like, what?
But then I got the...
You know, when I get, like, a moment to sit and go through like a friend's collection or something
and support whatever but generally speaking when you're building and creating you get gifted a lot
of stuff so sometimes you go in your wallet like oh shit i didn't know i had all this and then you're
kind of like okay well i gotta get back to work you know so i kind of afterwards it's like oh i
guess maybe he hasn't i still find it like wow he's been in space for a while but nevertheless
i'm flattered that such a great creator likes what you and I are doing
and building.
So, you know, that's cool.
He named his Drax Holycroyson.
And in the process, while he was trying to do that, he had some bugs with Gobi.
I went in to test it, so I made Dracatus Maximus.
So, yesterday we had two new names Dras enter the ecosystem which was pretty cool.
Dracatus Maximus. Good day to you sir!
So yeah Brad, what do you got on the go today buddy? You're working, you're back to the office later. Let's go.
I am, yeah, I did a bunch of framing yesterday. My body is sore as shit.
But yeah, I'm heading back there today and then grabbing some drywall for the house this weekend.
I am re-drywalling our bedroom while my wife goes to Connecticut to go see her friends.
Ah, cool.
She gets to go have fun and I have to work all weekend.
Welcome to baby life.
Married life.
So yesterday I did a quick job for somebody.
Foxy was doing a quote on a job for a reno.
And they wanted like, you know how you do engineered rendered designs or whatever?
Yeah, yeah, yeah.
They snapped a picture of the house.
Right, go shut the door.
It's swinging over.
The wind's going to break it.
He sent over this picture and was like,
can you turn this white-sided house to a metal siding house,
black with vertical, you know, the sheets run vertically,
change the color of the window framing, the door, the landscape.
And in two minutes, I did what he was about to pay some guy 800 bucks to do.
And he paid me 300 bucks cash to whip the thing up and like using AI.
And it was just a perfect example of like, I showed it to my mom.
And she's like, oh, wow, that's a nice run.
I was like, that's AI.
No way.
And it's really good.
It's good.
It's super cool that just how much you can get done with it.
Yeah, it's amazing the tools we have at our hands.
I'm talking to myself.
Speaking of tools, I need new tools because my side door broke again.
What is going on with this thing?
Can you get out of the way?
Oh, what happened to the...
What's going on?
Oh, my God.
Hold on.
Brad, I'll be back.
My door's busted.
It's windy and it's snapped it.
No bueno, my friends.
Well, thank you guys all for tuning in to another episode of Mark and Ava's TLDR,
where they did the readings so you don't have to.
I want to thank them each and every day for putting in their contributions.
If you don't know what mark and ava is um
it's misnamed uh if you google search it it's actually under uh notebook lm under google uh
google stole basically mark and ava from us um and created their own version called notebook lm
that's the version you guys can use um go check it out. Put in some sources. They actually have a new
little thing where you can actually go
and put in one source, and then
it'll actually... Actually, you can put zero sources
in, but you can actually go search
the sources now right inside the episode.
So you don't have to even go and create it and search
it like we have been for months and months.
You can just search it right in the browser and
make your own subject, own little podcast,
own little write-up. We utilize it for the podcast feature, but it does do great write-ups as well. You can just search it right in the browser and make your own subject, own little podcast, own little write-up.
We utilize it for the podcast feature, but it does do great write-ups as well.
You can put in a lot of sources, a lot of words, and get a really, really well-documented write-up on the other end of it.
So if you haven't checked it out, definitely go check it out and utilize it for your own stuff.
It's fantastic.
I know my wife literally has me do baby stuff with it all the time. Go get out, you know, top 10 baby monitors, cribs, you know, pacifiers, that kind of stuff. It does a great breakdown. So it can be utilized for not just techie stuff, not just
high level stuff, but also for your everyday average things. So definitely go check that out
as well. Yeah, I'm going to head to the worksite pretty soon.
It's a beautiful, sunshiny day out here today.
It is Thursday.
Definitely don't forget to tune into our Chia space tonight,
our community space with Mr. Edward Luce.
Every Thursday, we've been running this for two years
or three years, going on three years now.
I want to thank everybody for tuning
in every day though this is part of our big thing we're doing 365 that's right 365 days of spaces
i'm enjoying it it's fun i'm here i'm here sorry i'm back the wind ripped the fucking
house three hinges two of three hinges completely separated from the door
and the thing's hanging off the side of the house.
Yeah, that's what happens with storm doors when they catch the wind.
That's what happens when you get too much good news in one day.
Yeah, it just blew the doors off.
Exactly.
I blew the doors off this bitch. I'm on the moon and I blew the doors off this bitch.
I'm on the moon.
We're making 25, 2025 great.
Oh Lord.
Okay.
Well with that, I've said it already.
I'm not going to be able to say it enough.
I really appreciate my friends on here.
Like immensely.
You guys give me the time, the resources and the space and the confidence to chase something that I've been wanting to do
for a long time and that's law school and so it's only gonna make things better
because now you got a Tang tard that's gonna go get his his LLB and so then I'll
be able to start saying legal advice legal advice so we'll see man I'm really
excited Brad I'm really thankful for all you guys
i'm really happy that you show up every day appreciate you listening to me and brad ramble
on about our dumb techno or inner thoughts but i really do uh appreciate it brad always a pleasure
to have you buddy love doing tldrs with you i love doing the morning space dusty we'll see you tonight
brad you've got your space tonight we'll see you tonight. Brad, you've got your space tonight. We'll see you there.
I hope everybody has an absolutely fan-fucking-tastic day,
because I know.
Oh, yeah.
I got the intro music.
Or the outro.
Oh, sure.
I was just going to do Through the Fire and Flames again, man,
but you go right ahead.
Sure.
Yeah, love and appreciate you guys.
We'll see you guys tonight, 9 p.m. Eastern Time, and tomorrow, 8.30 a.m.
for another episode of Mark and Ava's TLDR. Let's get down, let's get down to business Give you one more night, one more night to guarantee
We've had a million, million nights just like this
So let's get down, let's get down to business
Mama, please don't worry about your thoughts
Mama, mama, let my heart speak
Friends keep telling me I hate it I'm a fan, million nights just like this.
So let's get down, let's get down business.
Let's get down, let's get down business.
If you want more night, don't want to get this.
We had a million, million nights just like this.
So let's get down, let's get down business.
Back and forth, back and forth with the pussy.
You know I said it before, I don't mean it.
It's been a while since I had your attention.
In my heart, you hit it.
Stop, stop, stop. Stop. We can't leave everything, stay the same
And I can't do it for another day
So let's get down, let's get down to business
Let's get down, let's get down to business
Give you one more night, one more night to guide us
We've had a million, million nights just like this.
So let's get down, let's get down to business.
Let's get down, let's get down to business.
We're out of business.
We're out of business.
We're out of business.
