You ever get that feeling, that little bit of
friction when you open chat GPT to write something,
but then you pause and you think, maybe Claude
would structure this better, or maybe perplexity
for the search part. Yeah, that feeling. It's
like playing AI ping pong all day long. Exactly.
Switching tabs, comparing outputs, just trying
to get the best possible result for like one
single task. It gets exhausting. Well, that friction,
that's precisely the problem Genspark is setting
out to solve. It's pitched as this all -in -one
AI that, you know, does the switching for you.
Yeah. Automatically. Interesting. So welcome,
everyone, to the Deep Dive. Today we're digging
into a pretty comprehensive guide on Genspark,
and we're really focusing on its core idea, this
multi -agent system. Right. Think of it like
your own personal assistant. You give it one
prompt, one question, and it kind of secretly
goes and asks three experts in this case, three
top AI models, and then it synthesizes the best
bits into one answer for you. So for you listening,
especially if you're trying to learn fast and
stay informed without just drowning in tabs and
tools, our mission today is simple. We need to
figure out, is Ganspark actually the shortcut
it claims to be? Does it deliver? Our roadmap
today is pretty straightforward. We're gonna
dive deep into that that multi -agent chat feature
which sound like the main event Definitely then
we'll explore its creative tools images video
that kind of stuff We'll look at the productivity
side like generating slides and sheets and finally
there's this almost sci -fi feature The AI calling
agent. Yes. Yeah, that was wild But the whole
package the pitch is really compelling because
of the value right it tries to do every chat
video data analysis, even making phone calls
for around 20 bucks a month. $20. Yeah. So straight
away, it's positioning itself as like a way cheaper
option than subscribing to maybe four or five
specialized expensive AI tools separately. OK.
But the real innovation here, it seems, isn't
just bundling. It's how it uses those tools.
Exactly. Instead of you manually copying your
prompt into GPT, then Claude, then Gemini, Gensmark
takes your one prompt and routes it to all three
models behind the scenes. Simultaneously. And
then this is the key part. It analyzes. or selects,
or maybe combines the results to give you just
one, hopefully better response. That seems to
be the core time saver. That's the promise, yeah.
Reducing that mental fatigue of constantly comparing.
So let's really unpack that core engine first.
The multi -agent chat workflow. Because the frustration
it aims to solve, I mean, it's real. You spend
time crafting this perfect prompt for, say, chat
GPT. Only to find the output structure is maybe
a bit weak. So then you take that same prompt
over to Claude. Maybe it's a little better, but
you still kind of feel you should check Gemini
2 just in case. Yeah, that whole dance. Gens
Park basically says, stop dancing. One prompt
goes out and it hits GPT -5, Claude Sonnet 4,
and Gemini 2 .5 flesh all at once. And then comes
the smart step, is the source called it, the
cohesion layer. It looks at all three outputs
and decides how to combine them. Or maybe just
pick the best one overall. It's leveraging the
unique strengths of each model, supposedly. Yeah,
and it's also efficient with your tokens. You
know, tokens are basically the currency AI models
use like the words they process, input and output.
Right, they charge based on usage. Exactly. So
because you only prompt once with Gansbark, you
only spend those tokens once, not three times
for the same result. Saves cost and time. OK,
let's make this concrete. The source had a really
complex example, a case study. We asked Genspark
to play the role of a senior marketing expert.
Okay. And the task was to analyze a competitor
called EcoBottle. This wasn't just like summarize
their website. No, this was a heavy lift. It
was actually a four part task rolled into one
prompt. Right. It had to do one, the role play
itself. Two, a full SWOT analysis, strengths,
weaknesses, opportunities, threats. three, come
up with three creative counter -marketing strategies,
and four, actually write content specifically,
a five -tweet thread based on those strategies.
Okay, that's a massive multi -layered request
for any AI. Hold on a sec, though. This idea
of combining outputs from three different AIs,
it sounds potentially, well, messy. How does
Ginspark make sure you don't just get a Frankenstein
answer that kind of loses the point? What did
the source say about that cohesion part? That's
a fair question. The idea isn't just mashing
text together. It's about extracting the best
component from each model suiting for that specific
part of the task. OK. So for that eco bottle
example, it probably pulled the really structured
logical SWOT analysis maybe from Claude because
Claude's good at reasoning. Right. Then maybe
it grabbed the most out there creative marketing
ideas from Gemini. And then it might use GPT
-5, which is often great at writing, to actually
polish the language and stitch the whole thing
together into a cohesive professional response.
Ah, okay. So it's like having a specialist team
working on the different parts of the problem,
not just three generalists shouting answers.
That makes more sense. Exactly. And looking at
the results from that example, the level of detail
was impressive. Like the AI note of the competitor,
EcoBottle, is tapping into this huge eco -friendly
bottle market. It even pulled a specific stat.
projected to hit $18 .49 billion by 2032. Oh!
That specific data point alone adds a ton of
value to the market analysis section. Absolutely.
And those counter strategies it came up with?
Some were genuinely clever, like one called the
Lifetime Impact Dashboard. What was that? Instead
of just making vague sustainability claims, it
proposed giving customers a real -time dashboard
showing the positive environmental impact of
using their bottle over time. Kind of gamified
sustainability. That's smart. And the other one
you mentioned, strategy two. Yeah, that tackled
the circular economy idea head on. It was called
the Trade In and Transform program. The AI suggested
accepting any brand's old bottle, not just their
own, for recycling or repurposing when a customer
buys a new one. That's really smart competitive
positioning. Okay, so beyond just efficiency,
how does Genspark ensure that synthesis, that
combining of the three models, actually improves
the overall quality of the answer? Well, like
we discussed, it leverages the unique strengths
of each model simultaneously for different parts
of the task. Okay, got it. Let's shift gears
now to the creative side. The multi -agent tools
for images and video, is the process similar
there? Pretty much, yeah. Same principle. Instead
of relying on just one image generator, it runs
your prompt through several heavy hitters. They
mentioned NanoBanana by Dan C. Dream. That's
from the TikTok parent company. Right. And GTT
Image, which uses a daily technology. So you
get results influenced by different training
data and styles. And the example prompt they
used was really specific, wasn't it? That Hanoi
sidewalk cafe scene. Oh yeah, super detailed.
They wanted photorealistic, but also a specific
16 .9 aspect ratio. Visual details like condensation
on the iced coffee glass, raindrops on the window
pane. And even a specific mood. Quiet, cinematic,
nostalgic. That level of control is what pros
need, definitely. And the guide gave a good tip.
Always try to use a reference image if you have
one. Or, if you just have a vague idea, use their
auto prompt feature to flesh it out into a more
detailed description for the AI. Good advice.
Now on to video. The key insight here was great
AI video starts with a great starting image.
You can't just skip that composition step. Makes
sense. Garbage in, garbage out applies to the
starting frame, too. Right. And for video generation,
it uses models like VO3, C -dance light, Pixverse
V5, again, pulling from different specialized
engines. And the prompt for the video was layered
on top of the image prompt. Exactly. They took
that perfect Hanoi coffee image and then commanded
multiple precise layers of motion. Things like
a slow camera zoom, a dolly zoom effect, raindrops
slowly sliding down the window. and those blurry
neon lights in the background. They wanted them
to gently flicker. Wow, that's intricate. Yeah.
And they even added a specific negative constraint.
Absolutely no steam or smoke. Because remember,
it was an iced coffee, right? Details matter.
Whoa. I mean, just imagine scaling that level
of precise, multi -layered motion control, doing
that across hundreds of marketing assets every
single day. That's... That's a huge capability
boost right there. It really is. But let's play
devil's advocate again. Is the trade -off in
quality acceptable? I mean, integrating image,
video, chat all in one place is convenient. But
are these the absolute best -in -class models
for each specific task or just good models? That's
where. For rapid content creation? For budget
-conscious teams? Small businesses? Yeah, probably.
The output they showed was solid. It was robust.
Definitely good enough for quick prototyping,
social media posts, internal stuff. Maybe not
Hollywood VFX, but very usable. So it's solid
and good enough for fast content creation needs.
Pretty much, yeah. OK. Let's transition, then,
from the creative tools to pure productivity,
starting with AI slides. Right. The utility here
seems pretty clear. taking an idea, maybe even
just a structured outline, and quickly turning
it into a presentation slide deck. Apparently
it's also helpful just for clarifying your own
thinking. Yeah, forcing structure onto an idea.
We tested this with another detailed prompt,
creating a 15 slide internal training deck for
new B2B sales hires. OK. And critically, we didn't
just say make a sales training. We gave it a
very structured nine part outline, everything
from what is B2B and understanding the sales
funnel through to using CRM software and ending
with a quick quiz. And that structure is key.
It turns the AI from being a guesser, where results
can be kind of hit or miss, into more of an efficient
worker filling in the blanks you've already defined.
Exactly, but there was a limitation noted here,
too, right? Yeah, the feedback was the structure
and content of the slides were functional, often
pretty good, but the design, not so much. The
slides weren't beautiful. They'd likely need
polishing afterwards in something like PowerPoint
or Keynote. OK, so great for a first draft of
the content and flow, but expect to do design
work. That's an important caveat. Definitely.
AI sheets. Doing market research in minutes.
The source mentioned something interesting here,
that a lot of users are kind of scared of complex
spreadsheets. Yeah, that spreadsheet intimidation
is real. So this feature tries to bridge that
gap. The test prompt was pretty ambitious. asking
the AI to research and gather competitor data,
putting it directly into a spreadsheet. OK. The
specific market was mid -range smartphones in
Vietnam, looking ahead to 2025 data. Highly specific.
And you could specify the columns you wanted.
Yep. We asked for columns like brand, popular
model in that category, the price range, specifically
5 to 8 million Vietnamese don key specs, estimated
market share, their marketing slogan, and user
ratings. Wow. So you basically get a mini market
research report generate into a usable table
in like under a minute That's the idea and the
utility doesn't stop there You can then apparently
ask gen spark follow -up questions like turn
this data into charts or even use it for some
basic financial planning based on the market
numbers Okay, that's powerful. But given the
complexity there Synthesizing research data into
specific columns. Mm -hmm. How critical is fact
-checking on the data that AI sheets generates?
especially with market share estimates and things
like that. Yeah, the advice was clear. Always
fact check important data, especially if you're
going to present it publicly or make decisions
based on it, treat it as a powerful starting
point. So always fact check important content
before public presentation. Got it. All right.
Final main segment. This is about automation
and connecting to the real world, starting with
something called MCP. MCP. model context protocol.
The guide explained it pretty well. Think of
them like secure digital bridges. Or maybe like
stacking Lego blocks, but with data from different
apps. They let the AI, if you give it permission,
access external software you use. Right. And
GenSpark apparently offers connections to hundreds
of these. Things like Gmail, Google Calendar,
Notion, even ex -Twitter. Yes, 631 connections
were mentioned. It's a lot. So we give it a task
that used these connections, a cross -checking
task. Scan my Gmail for the last seven days and
my Google Calendar for the next seven days. Okay.
And find any potential scheduling conflicts or
maybe urgent tasks mentioned in emails that I
might have missed putting on the calendar. Whoa,
okay, that is a true personal assistant task.
Yeah. Finding those things that fall through
the cracks. Yeah. Like an email mentioning a
deadline that isn't actually blocked out on your
calendar yet. Or maybe spotting a double book
meeting where you only accepted one invite. The
potential time management benefit there is huge.
Massive. But, you know. I'll admit I still wrestle
with prompt drift myself sometimes trying to
get the AI to do exactly what I want and connecting
my private data my actual email my calendar it
It still feels inherently risky obviously Yeah.
But you can see the potential, right? If you
can get past that initial hurdle of trust, the
assistant capability is just enormous. I totally
get that hesitation. Which brings us nicely to
maybe the most out there feature. Right. The
AI calling agent. Right. This thing actually
makes phone calls to real people. Yeah. makes
the call, holds a conversation, apparently a
pretty natural sounding one, and then it transcribes
the key results back to you in the chat. Okay,
the example task for this was pretty involved
too, a multi -step negotiation. What was it?
The prompt was basically, call this specific
electronics store. Ask if they have a Sony Bravia
X90L 55 -inch TV in stock. Tell them a competitor
is selling it for $2 ,500. Request a price match.
And then critically ask exactly what documentation
or proof you need to bring into the store to
actually get that match price. OK, that's not
just asking a simple question. That's navigating
a whole interaction, handling potential objections,
asking clarifying questions. Exactly. And the
AI supposedly handles that entire thread. It
overcomes the initial let me check hurdles to
the negotiation part and then reports back. OK.
they agreed to $2 ,500, and you need to bring
a printout of the competitor's ad. Managing that
kind of multi -step, real -world communication
completely autonomously, that's pretty sci -fi,
like you said. It really is. But linking back
to the MCPs and security, what's the single most
crucial piece of advice for users thinking about
connecting sensitive data like email or calendars
to something like Gansburg? Based on the dyed,
it's straightforward. Only connect tools and
data sources that you absolutely trust and where
you understand the security implications. Start
small, maybe. So only connect tools you absolutely
trust to maintain security. Makes sense. Okay,
let's wrap this up. Bring it all together. The
final verdict, the value proposition. What's
the takeaway? Well, the best feature, hands down,
seems to be that multi -agent workflow for chat.
giving just one prompt and getting back this
synthesized best of breed answer from GPT -5,
Claude and Gemini simultaneously. That saves
a massive amount of time and frankly mental energy.
It directly tackles that core pain point we started
with, the AI ping -pong. Exactly. And then there's
the value, 20 bucks a month for that advanced
chat plus the image generation, the video tools,
the slides, the sheets, the calling agent, and
all those hundreds of MCP connections. Yeah,
you really can't get that breadth of functionality
without stacking up several different subscriptions,
which would definitely cost way more than $20.
For sure. Now, we did note limitations, right?
The calling agent, while cool, apparently needs
some refinement. There can be slight delays in
the conversation flow still. OK. And those AI
slides, functional structure, good content start.
But yeah, you'll need to polish the look and
feel in another tool. So knowing all that, who
is this really for? Who gets the most value here?
I think it's ideal for content creators who need
to generate different types of stuff quickly.
Entrepreneurs, maybe small teams who are juggling
lots of tasks on a tight budget. And fundamentally,
it's for anyone who's just plain tired of having
15 browser tabs open. Copy and pasting prompts
between different AI models all day. Yeah, the
tab switchers. So the big idea recap. The main
takeaway is that Genspark really consolidates
the utility of many different AI models into
one place. It aims to provide good or solid capability
across a whole range of tasks, which should reduce
the complexity and the cognitive load for the
user. It's about practical application. And if
you, listening, decide you want to try it out,
the advice was spend some time on day one or
two. setting up your profile context, defining
your preferred style, your goals. Personalize
it. And then maybe use those detailed structured
prompts we talked about today, like that Hanoi
coffee prompt or the B2B sales training outline.
Use those as templates to get the best results
early on. Good starting points. So final thought,
then. We just talked about an AI that can potentially
handle a multi -step price negotiation over the
phone with a real person. If it can do that today,
What's the actual limit for AI assistance accessing
and managing our real -world tasks maybe next
year or the year after? Where does this capability
lead? That's the big question, isn't it? Something
to definitely keep an eye on.