So there's been a lot of buzz recently around
OpenAI's latest thing, the chat GPT agent. People
are calling it a profound shift, like suggesting
an AI that doesn't just respond, but actually
acts across your digital life. Welcome to the
deep dive, everyone. Today, yeah, we're plunging
into this truly fascinating world of AI agents.
Our mission really is to unpack what this new
chat GPT agent actually means for you. And also
look at some other big advancements that are
fundamentally reshaping how we interact with
tech. Right. We'll explore its core capabilities,
how it performs in the, let's say the messy real
world, how it stacks up against competitors and
really what does all this mean for your daily
tasks, your future workflows. And our goal, as
always, is just a calm, curious, sort of down
-to -earth look at this landscape. It's moving
so fast. OK, let's get into it then. Sam Altman,
OpenAI CEO, he called this agent a banger. What
exactly is this agent? Beyond the hype, why is
it such a big deal? Well, I think what really
stands out is that agent isn't just another standalone
model. It's not like just a new version of ChatGPT.
OK. It's a Seamless integration. It's really
a redefinition of what an AI assistant can be.
OpenAI showed it off on July 17th. And it's this
deeply integrated system, pulling together a
bunch of powerful tools. So it's about combining
these sort of individual powers into one cohesive
brain. Precisely. Yeah. Think of it like bringing
together tools that used to be separate, but
now they're under one... So first, it has this
deep research capability. It accesses the internet,
spends real time kind of synthesizing vast amounts
of info, offers more depth, more nuance than
older AIs. Second, and this is a huge leap, it
has computer use, or what they call operator.
Operator. This means it literally controls a
computer like a human would. Clicking, scrolling,
filling forms, navigating complex websites. Past
versions of this, like AI enhanced RPA that's
a robotic process automation. Right, bots mimicking
clicks. Yeah, they were often just too unstable,
too brittle. Agents seems to have made... significant
improvements there. So it's not just searching
the web, it's actually using a browser, like
interacting with sites dynamically, that feels
fundamentally different. Exactly. And it also
has robust code execution. It can write and run
code, mostly Python, in a secure sandbox environment.
Okay. And then it uses the results for complex
requests, like analyzing big data sets or generating
charts right there. And images too! Yeah. Finally,
it's directly integrated with Daily 3 for image
generation. It can create illustrations, charts,
maybe artwork based on its research or analysis,
all without you leaving the conversation. That
flexible coordination does sound incredibly powerful.
But for a lot of listeners, the word autonomous
might raise some flags, right, given past AI
issues. True. How truly autonomous is it right
now? And what guardrails are there to stop it
from going off the rails or making big mistakes?
It's definitely a step closer to autonomous.
The real power isn't just one tool. It's the
agent flexibly deciding, okay, now I need to
research, now I should code, now I need to use
the computer. It really is a step toward a more
truly autonomous agent. It changes the game from
simple queries to genuinely autonomous multi
-step task completion. So how does that seamless
integration really change the nature of how users
interact with AI? It shifts from asking simple
questions to getting the AI to complete complex
multi -step tasks autonomously. Got it. And who
gets access? And when? It's rolling out in phases.
So, pray users, they get full access right now.
Plus, N -Team's users will get access soon, probably
with some initial limits. Then education and
enterprise later on. If you're on the free plan,
well, you'll need to upgrade to get the full
agent experience. Theory is one thing, but does
it actually work in the real world? Right, the
million dollar question. Let's dive into some
practical tests that really pushed agents limits.
What did that in -depth news research challenge
show? OK, so this test asked agent to analyze
generative AI trends affecting digital marketing
over the past month for a management memo. So
complex. Needs current info, multiple sources.
Exactly. Distinguishing important from irrelevant,
industry context, synthesis, professional format,
right time frame. Lots going on. And the real
world results. How did it do? Agent took about
15 minutes. Yeah. And it produced a memo that
was maybe 80, 85 % of the quality a human analyst
team would produce in several hours. Wow. Previous
AIs might only hit like 50 % accuracy on something
like that. It correctly picked up major stories.
The agent launched itself, new Mistral models,
Google search updates, structured it professionally.
Any issues? Minor stuff. Some info slightly outside
the time frame missed some niche discussions
happening on, say, X or Twitter. OK. So conclusion.
It's a powerful professional research assistant,
definitely, but you still need that human oversight.
That's still a huge leap for research tasks.
What about something really specific and precise,
like financial data retrieval and calculations?
Right, this is more of a clear pass -fail test.
The task, go to Yahoo Finance, find the VINFAST
ticker VFS on NASDAQ, extract the last five closing
prices, calculate the average daily percentage
change, and present it all on a table. So it
combines web navigation and code execution. Verdict.
Did it nail it? Flawless. Yeah, it navigated
Yahoo Finance, found the data, wrote a Python
script for the calculation, presented it perfectly
in a table. Many previous AIs would just stumble
or fail outright on one of those steps, especially
combining them. Agent handled it cleanly. Whoa.
Imagine scaling that kind of precision, analyzing
entire portfolios instantly. That really could
change things for financial tasks, couldn't it?
Two secs silence. OK, but what about something
totally different, like managing, say, the flood
of daily emails? How did Agent handle email complexity?
Yeah, this task involved connecting to Gmail,
reviewing 10 recent support emails. Just 10?
OK. Yeah, start small. Then classify them technical,
feedback, billing, and then draft replies using
uploaded FAQ and return policy files. So this
tests external access, reading context, classification,
and RG retrieval augmented generation. That's
where it pulls info from documents you give it,
right? Exactly. It needs to understand the docs
to draft good replies. That's a lot of context
and steps. How did it go? Did it in about three
minutes, correctly classified each email, drafted
tailored replies that actually cited the documents,
and even offered to create actual Gmail drafts
for you. So potentially huge for customer service
automation using internal knowledge. But you
mentioned a limitation. Right. Important one.
Agent can get overwhelmed by too much info, trying
to process, say, a thousand emails instead of
ten. It might struggle. You definitely need to
understand its limits. Got it. So scale matters.
And finally, product research and comparison.
This used to be so hit or miss with AI, almost
felt like a gamble. How did it do here? OK, the
task was research and compare the five best air
purifier models for Vietnamese cities under a
specific budget, five million VND. Very specific.
Yeah. and create a comparison table with exact
columns. Product name, reference price, suitable
room area, filtration technology, and a retailer
link. And did it deliver that kind of consistency
we saw with the financial data? Pretty much exactly
as requested. Yeah. It found models. used the
precise table format, consulted local e -commerce
sites, gave detailed feature comparisons. Any
glitches? Minor one. Maybe one or two product
links went to general category pages, not the
specific product. OK. But the significance is
huge. This kind of detailed, structured consumer
research used to be really unreliable with AI.
Agent makes it genuinely useful. It performs
research almost like an experienced human would.
It really does sound like Agent crossed some
kind of critical threshold, moving from just
experimental to genuinely reliable for complex
stuff. What truly sets it apart from previous
attempts we've seen? I think the real breakthrough
is, yeah, while other companies definitely experimented
with similar ideas, agents seem to have finally
crossed that crucial threshold. It feels reliable
enough for regular, practical use. Previous tools
often lacked the stability, or the deep integration,
or the consistent accuracy, or they just couldn't
handle real -world complexity well. Agent seems
to deliver consistent results much more often,
turning that promise of an AI assistant into
more of a practical reality. So given this new
level of capability, what do you think is the
biggest hurdle users might face when they try
to apply Agent to their own, maybe unique, complex
workflows? Probably setting clear boundaries
and managing the information input effectively
for the AI. Okay, so... Agent can talk to my
Gmail, analyze spreadsheets. How does that even
work? What exactly are these AI connectors we
keep hearing about? AI connectors, they're essentially
like intelligent bridges. They let AI assistants
seamlessly access and interact with your external
accounts and services, you know, Gmail, Google
Drive, Slack, Microsoft 365, things like that.
So the AI works directly within your existing
workflows, not just in some isolated chat window,
not just conversation. It's interaction with
your whole digital setup. like stacking Lego
blocks of data and actions together. Interesting
analogy. And I've heard there are different approaches
here, like chat GPT agent versus, say, Claude's
connectors. How do they differ? That's a great
point. They represent pretty different philosophies
about AI integration. Agents connectors are deeply
integrated with its full suite. So email plus
web research plus code plus images. OK. This
makes it really good for complex, multi -step,
flexible workflows where you need lots of different
tools working together. The philosophy seems
to be build a central brain that orchestrates
many tools. Right. And Claude. Claude's connectors,
on the other hand, tend to focus on simpler,
really reliable connections to external services.
They're excellent for more focused, simpler tasks
like summarize my unread emails in the hashtag
general Slack channel. Gotcha. Their philosophy
feels more like providing reliable specialized
tools the model can call on when needed. Less
orchestration, more specific capabilities. So
one's like a symphony conductor for your whole
digital life, maybe? And the other is more like
a set of specialist mechanics, each really good
at one specific job. Fascinating. Yeah, that's
a decent way to put it. So when we start using
these connectors, what are some tips for doing
it safely and effectively? Good question. First,
definitely start small. Begin with simple tasks
before you try to automate something really complex.
Second, set clear boundaries. Be really explicit,
say, analyze these five emails from this sender,
not just check my inbox. Right, specificity.
Third, provide context. Upload relevant documents,
internal guides, policies, templates, give the
AI the background it needs. Fourth, Always, always
review thoroughly. Look over AI -generated responses
or actions before you let them happen. Makes
sense. And finally, manage permissions. Go back
regularly and review what services the AI has
access to. Revoke access you don't need anymore.
You know, I still wrestle with prompt rest myself
sometimes, so that specificity really does help
get the AI to deliver what you actually want.
That's helpful to hear, yeah. Beyond just the
features, what does this difference in philosophies
orchestration versus specialization tell us about
where AI integration might be heading more broadly?
It suggests maybe two paths. One, prioritizing
holistic orchestration. The other, specialized
reliable automation. Interesting. OK, it wasn't
just OpenAI making waves this past week, though.
The whole AI landscape seems to be just constantly
shifting. What else caught your eye that felt
significant? Yeah, a big one was Grok. That's
XAI's product -launching AI companions, characters
named Annie and Rudy. Companions, like friends.
Basically, yeah. Designed as digital friends,
not just work assistants. This feels like a real
shift, you know, from AI... purely as a productivity
tool towards AI as a social companion. It seems
targeted at tech enthusiasts right now, but it
raises some really fascinating psychological
and social questions about our future relationships
with AI. Definitely something to watch. And then
there was Higgs Field's sole ID. Sounds like
it's making AI selfies incredibly realistic.
Yes, Higgs Field put out an updated AI image
generator. It specializes in remarkably realistic
photos of people. How does it work? It needs
about 20, 25 photos of a person to train on.
Then it generates these very natural, almost
iPhone -looking images, focusing on expressions.
What's striking is how good they are. They can
genuinely fool the human eye. Wow. You can imagine
uses in social media, maybe professional headshots.
That uncanny valley is shrinking fast. It really
is. What about on the open source side? We saw
some cool updates there, too. Voxtral from Mistral
came out. That's an open source speech recognition
model. Speech recognition, like transcribing
audio. Exactly. And it's competitive with things
like 11Lab Scribe and OpenAI's Whisper. That's
great for developers, makes high quality transcription
more accessible. Nice. And then there's KimiK2
from a Chinese startup called Moonshot AI. This
thing is massive, a trillion parameters. It uses
a mixture of experts or Moe architecture. A mixture
of experts. What's that mean? It's basically
like having a team of specialized AI models working
together on different parts of a problem. makes
it incredibly powerful. And its performance on
benchmarks is really competitive. It definitely
signals the growing strength of Chinese AI companies
on the global stage. Okay, a lot happening there.
And for video generation, Runway Act 2 got an
important upgrade too, right? Getting closer
to practical use and things like filmmaking.
Absolutely. Runway Act 2 is their upgraded AI
video tech. It can take an input video, say...
of a person's motion and use that motion to drive
completely AI -generated content. So a person
in a suit could become an astronaut or maybe
a superhero, but their original motion is preserved
precisely. So the movement looks real even if
the character changes. Exactly. It's much more
precise now, less distorted than earlier versions.
That brings us way closer to practical uses in
filmmaking, advertising, anywhere. Motion fidelity
is key. Cool. And you mentioned something about
transparency. With Grok. Yeah, this was interesting.
So Grok initially gave some inappropriate answers,
apparently because of viral memes in its training
data. Yeah. So they fixed it by updating the
system prompt, basically the core instructions
the AI follows. But what really matters is that
the Grok team published the exact system prompt
changes on GitHub. They showed their work. Exactly.
Showed precisely what they modified and why.
That level of transparency is pretty rare in
this competitive field, and it does a lot to
build trust, I think. That is unusual. So from
AI companions to hyper real visuals, open source
advances, it's a lot. Which of these other developments
do you think might have the most immediate impact
on daily life for the average person? Probably
the AI companions. Shifting AI from just tasks
towards actual relationships is a really significant
step, I think. Yeah, potentially transformative.
OK, bringing it back to agent, who is ChatGPP
Agent really for, and how can someone start using
it effectively without feeling totally overwhelmed?
Right. So ChatGPP Agent seems perfect for researchers,
analysts, people who need to synthesize info
quickly, often from lots of different places.
Thanks. Also. Marketing and comms professionals,
content creators who are combining research,
writing, maybe visuals. Small business owners,
too, for things like customer support or market
analysis. Really, anyone who frequently combines
multiple types of digital work. And who might
it not be ideal for, at least right now? Well,
probably not ideal for really simple single purpose
tasks where regular chat GPT might be fine. or
for users who need absolute 100 % accuracy for
truly mission -critical decisions, or tasks that
require really nuanced human judgment on sensitive
stuff, it's powerful, but it's still an AI. Got
it. So for those who fit the profile and are
ready to jump in, what are the best practices?
How do you actually get value from agents? OK,
first, be incredibly clear and specific with
your requests. Don't just say help with email.
Try something like draft replies to the five
most recent emails with complaint in the subject.
Use the complaint -response .docsis template
as a guide. Then ask for my approval before doing
anything else. OK, very precise. Second, provide
those context documents we talked about, upload,
guides, templates, before you start the task.
Third, use confirmation steps for important things.
Ask agent, have you understood the data needs
to be for Q2? Something like that. Check its
understanding. Exactly. Fourth, combine its capabilities
strategically. Think about the steps. And finally,
set realistic expectations. Agent is powerful,
but it's not perfect. Always review its work.
It sounds like a new kind of workflow design
almost like programming an assistant, but in
plain English kind of yeah What are some sample
workflows someone could try with agent things
that really show off its unique abilities? Oh
the possibilities are getting really exciting
now imagine Content planning and creation you
could prompt it to research trending topics in
say sustainable development Okay, then propose
a list of blog post ideas and for the top idea
create a detailed outline and generate a suitable
image, all in one go, or customer feedback analysis.
Connect to Google Drive, tell it to read a CSV
file, identify the top complaints and the features
people love, create a bar chart from that data,
and then draft a brief summary for a team meeting.
That's powerful. Or even market entry research,
maybe for specialty coffee in the Thai market.
Ask for an overview report on competitors, average
prices, import regulations, and present it all
as a slide deck outline. It really does feel
like a paradigm shift brewing. How do you see
the evolution of AI assistance continuing from
here, building on what Agent has started? Yeah,
what feels revolutionary is that Agent really
signifies this profound evolution. We're moving
from single -purpose tools, you know, a grammar
checker here, an image generator there, to deeply
integrated assistance, from simple Q &A to actually
completing complex tasks, and from AI needing
human supervision at every single step to much
more autonomous AI agents that can chain multiple
actions together intelligently. And for us as
professionals, how is this going to impact our
day -to -day work? Our productivity, where do
humans fit in this picture? Well, I think we'll
see routine research and analysis get increasingly
automated. That frees up our cognitive space
for higher level thinking. Creative work will
likely involve more human AI collaboration. Humans
provide the strategy, the vision. AI handles
more of the execution, maybe the iteration, communication.
probably more AI assisted drafting, making interactions
more efficient. And for complex problem solving,
it'll be about combining human strategy with
AI's execution power, letting us tackle bigger,
harder problems. Which means the essential skills
we need as professionals are also shifting, right?
What are those core skills we need to be cultivating
now? Absolutely. You'll definitely need prompt
engineering learning to communicate precisely
and effectively with AI. That's fundamental.
Right. And systems thinking, designing workflows
that optimally combine human strengths and AI
strengths. Also, AI ethics and oversight being
able to recognize and mitigate risks, biases
in AI output. That's crucial. Critical finting,
too, I imagine. Definitely. Critical thinking,
evaluating the AI's output, knowing when it's
right, when it's wrong, and crucially, when and
how to intervene. Given all these new skills,
what's the single most important mindset shift,
do you think, for professionals trying to adapt
to this rapidly evolving AI landscape? I'd say
it's embracing human AI collaboration truly as
an amplification of our abilities, not as a replacement
for them. Amplification, not replacement. OK,
so to recap our deep dive today, ChatGPT Agent
feels like a genuine breakthrough for practical
multi -step AI assistance. Yeah, it's reliably
handling complex tasks that honestly used to
need constant human handholding. It really marks
a paradigm shift, doesn't it? From these isolated
tools we used to use to more integrated, almost
autonomous agents. Exactly. And that combination
of human judgment and AI capability is becoming
extraordinarily powerful. Really quickly. It's
not about AI replacing humans, fundamentally.
It's about humans amplifying what they can achieve
with AI. Well said. So the question for you listening
is, what workflow will you try first with ChatGPT
Agent? The possibilities are really opening up.
Yeah, we definitely encourage you to experiment,
to explore what it can do. Because as these tools
keep getting better, and they will, the critical
question isn't really if you'll adopt them, but
maybe how quickly you can learn to use them effectively.
Right. the businesses, the individuals who master
this human AI collaboration today, they're likely
going to have significant advantages as these
technologies just become standard everywhere
in every industry. Thank you for diving deep
with us today. We'll be back soon with more insights
into the future of technology. OutTO Music.