WEBVTT 00:00:00.000 --> 00:00:02.500 You ever get that feeling, that little bit of 00:00:02.500 --> 00:00:04.919 friction when you open chat GPT to write something, 00:00:05.360 --> 00:00:07.559 but then you pause and you think, maybe Claude 00:00:07.559 --> 00:00:10.000 would structure this better, or maybe perplexity 00:00:10.000 --> 00:00:12.140 for the search part. Yeah, that feeling. It's 00:00:12.140 --> 00:00:14.419 like playing AI ping pong all day long. Exactly. 00:00:14.539 --> 00:00:17.579 Switching tabs, comparing outputs, just trying 00:00:17.579 --> 00:00:20.640 to get the best possible result for like one 00:00:20.640 --> 00:00:23.339 single task. It gets exhausting. Well, that friction, 00:00:23.539 --> 00:00:25.699 that's precisely the problem Genspark is setting 00:00:25.699 --> 00:00:28.000 out to solve. It's pitched as this all -in -one 00:00:28.000 --> 00:00:31.329 AI that, you know, does the switching for you. 00:00:31.329 --> 00:00:34.130 Yeah. Automatically. Interesting. So welcome, 00:00:34.229 --> 00:00:35.990 everyone, to the Deep Dive. Today we're digging 00:00:35.990 --> 00:00:38.829 into a pretty comprehensive guide on Genspark, 00:00:38.909 --> 00:00:42.689 and we're really focusing on its core idea, this 00:00:42.689 --> 00:00:44.990 multi -agent system. Right. Think of it like 00:00:44.990 --> 00:00:46.750 your own personal assistant. You give it one 00:00:46.750 --> 00:00:48.850 prompt, one question, and it kind of secretly 00:00:48.850 --> 00:00:51.210 goes and asks three experts in this case, three 00:00:51.210 --> 00:00:54.289 top AI models, and then it synthesizes the best 00:00:54.289 --> 00:00:57.630 bits into one answer for you. So for you listening, 00:00:57.909 --> 00:00:59.390 especially if you're trying to learn fast and 00:00:59.390 --> 00:01:01.350 stay informed without just drowning in tabs and 00:01:01.350 --> 00:01:03.729 tools, our mission today is simple. We need to 00:01:03.729 --> 00:01:06.689 figure out, is Ganspark actually the shortcut 00:01:06.689 --> 00:01:11.049 it claims to be? Does it deliver? Our roadmap 00:01:11.049 --> 00:01:12.989 today is pretty straightforward. We're gonna 00:01:12.989 --> 00:01:16.109 dive deep into that that multi -agent chat feature 00:01:16.109 --> 00:01:18.450 which sound like the main event Definitely then 00:01:18.450 --> 00:01:21.129 we'll explore its creative tools images video 00:01:21.129 --> 00:01:23.329 that kind of stuff We'll look at the productivity 00:01:23.329 --> 00:01:26.129 side like generating slides and sheets and finally 00:01:26.129 --> 00:01:29.629 there's this almost sci -fi feature The AI calling 00:01:29.629 --> 00:01:31.870 agent. Yes. Yeah, that was wild But the whole 00:01:31.870 --> 00:01:33.870 package the pitch is really compelling because 00:01:33.870 --> 00:01:36.650 of the value right it tries to do every chat 00:01:36.650 --> 00:01:39.450 video data analysis, even making phone calls 00:01:39.450 --> 00:01:42.269 for around 20 bucks a month. $20. Yeah. So straight 00:01:42.269 --> 00:01:44.829 away, it's positioning itself as like a way cheaper 00:01:44.829 --> 00:01:47.450 option than subscribing to maybe four or five 00:01:47.450 --> 00:01:50.409 specialized expensive AI tools separately. OK. 00:01:50.450 --> 00:01:52.370 But the real innovation here, it seems, isn't 00:01:52.370 --> 00:01:54.790 just bundling. It's how it uses those tools. 00:01:54.790 --> 00:01:56.750 Exactly. Instead of you manually copying your 00:01:56.750 --> 00:02:00.010 prompt into GPT, then Claude, then Gemini, Gensmark 00:02:00.010 --> 00:02:02.349 takes your one prompt and routes it to all three 00:02:02.349 --> 00:02:05.129 models behind the scenes. Simultaneously. And 00:02:05.129 --> 00:02:07.659 then this is the key part. It analyzes. or selects, 00:02:07.659 --> 00:02:09.699 or maybe combines the results to give you just 00:02:09.699 --> 00:02:12.599 one, hopefully better response. That seems to 00:02:12.599 --> 00:02:15.620 be the core time saver. That's the promise, yeah. 00:02:16.000 --> 00:02:18.580 Reducing that mental fatigue of constantly comparing. 00:02:18.699 --> 00:02:21.219 So let's really unpack that core engine first. 00:02:21.439 --> 00:02:25.400 The multi -agent chat workflow. Because the frustration 00:02:25.400 --> 00:02:27.620 it aims to solve, I mean, it's real. You spend 00:02:27.620 --> 00:02:30.699 time crafting this perfect prompt for, say, chat 00:02:30.699 --> 00:02:33.759 GPT. Only to find the output structure is maybe 00:02:33.759 --> 00:02:35.560 a bit weak. So then you take that same prompt 00:02:35.560 --> 00:02:37.830 over to Claude. Maybe it's a little better, but 00:02:37.830 --> 00:02:39.389 you still kind of feel you should check Gemini 00:02:39.389 --> 00:02:41.870 2 just in case. Yeah, that whole dance. Gens 00:02:41.870 --> 00:02:44.770 Park basically says, stop dancing. One prompt 00:02:44.770 --> 00:02:47.509 goes out and it hits GPT -5, Claude Sonnet 4, 00:02:47.949 --> 00:02:51.750 and Gemini 2 .5 flesh all at once. And then comes 00:02:51.750 --> 00:02:54.889 the smart step, is the source called it, the 00:02:54.889 --> 00:02:57.990 cohesion layer. It looks at all three outputs 00:02:57.990 --> 00:03:01.430 and decides how to combine them. Or maybe just 00:03:01.430 --> 00:03:03.569 pick the best one overall. It's leveraging the 00:03:03.569 --> 00:03:06.270 unique strengths of each model, supposedly. Yeah, 00:03:06.270 --> 00:03:09.069 and it's also efficient with your tokens. You 00:03:09.069 --> 00:03:11.949 know, tokens are basically the currency AI models 00:03:11.949 --> 00:03:14.169 use like the words they process, input and output. 00:03:14.210 --> 00:03:17.009 Right, they charge based on usage. Exactly. So 00:03:17.009 --> 00:03:19.930 because you only prompt once with Gansbark, you 00:03:19.930 --> 00:03:21.969 only spend those tokens once, not three times 00:03:21.969 --> 00:03:24.469 for the same result. Saves cost and time. OK, 00:03:24.469 --> 00:03:26.770 let's make this concrete. The source had a really 00:03:26.770 --> 00:03:29.699 complex example, a case study. We asked Genspark 00:03:29.699 --> 00:03:31.919 to play the role of a senior marketing expert. 00:03:32.020 --> 00:03:34.159 Okay. And the task was to analyze a competitor 00:03:34.159 --> 00:03:36.719 called EcoBottle. This wasn't just like summarize 00:03:36.719 --> 00:03:39.259 their website. No, this was a heavy lift. It 00:03:39.259 --> 00:03:41.659 was actually a four part task rolled into one 00:03:41.659 --> 00:03:43.840 prompt. Right. It had to do one, the role play 00:03:43.840 --> 00:03:47.319 itself. Two, a full SWOT analysis, strengths, 00:03:47.620 --> 00:03:51.210 weaknesses, opportunities, threats. three, come 00:03:51.210 --> 00:03:53.430 up with three creative counter -marketing strategies, 00:03:53.729 --> 00:03:57.370 and four, actually write content specifically, 00:03:57.969 --> 00:04:00.289 a five -tweet thread based on those strategies. 00:04:00.949 --> 00:04:03.849 Okay, that's a massive multi -layered request 00:04:03.849 --> 00:04:06.569 for any AI. Hold on a sec, though. This idea 00:04:06.569 --> 00:04:09.849 of combining outputs from three different AIs, 00:04:09.849 --> 00:04:13.629 it sounds potentially, well, messy. How does 00:04:13.629 --> 00:04:16.110 Ginspark make sure you don't just get a Frankenstein 00:04:16.110 --> 00:04:18.170 answer that kind of loses the point? What did 00:04:18.170 --> 00:04:20.350 the source say about that cohesion part? That's 00:04:20.350 --> 00:04:22.350 a fair question. The idea isn't just mashing 00:04:22.350 --> 00:04:24.449 text together. It's about extracting the best 00:04:24.449 --> 00:04:26.990 component from each model suiting for that specific 00:04:26.990 --> 00:04:29.290 part of the task. OK. So for that eco bottle 00:04:29.290 --> 00:04:32.850 example, it probably pulled the really structured 00:04:32.850 --> 00:04:35.230 logical SWOT analysis maybe from Claude because 00:04:35.230 --> 00:04:36.910 Claude's good at reasoning. Right. Then maybe 00:04:36.910 --> 00:04:39.410 it grabbed the most out there creative marketing 00:04:39.410 --> 00:04:42.620 ideas from Gemini. And then it might use GPT 00:04:42.620 --> 00:04:45.060 -5, which is often great at writing, to actually 00:04:45.060 --> 00:04:47.519 polish the language and stitch the whole thing 00:04:47.519 --> 00:04:50.160 together into a cohesive professional response. 00:04:50.740 --> 00:04:53.220 Ah, okay. So it's like having a specialist team 00:04:53.220 --> 00:04:55.019 working on the different parts of the problem, 00:04:55.040 --> 00:04:56.959 not just three generalists shouting answers. 00:04:57.000 --> 00:04:59.000 That makes more sense. Exactly. And looking at 00:04:59.000 --> 00:05:01.480 the results from that example, the level of detail 00:05:01.480 --> 00:05:04.100 was impressive. Like the AI note of the competitor, 00:05:04.439 --> 00:05:07.319 EcoBottle, is tapping into this huge eco -friendly 00:05:07.319 --> 00:05:10.060 bottle market. It even pulled a specific stat. 00:05:10.170 --> 00:05:15.370 projected to hit $18 .49 billion by 2032. Oh! 00:05:15.990 --> 00:05:18.910 That specific data point alone adds a ton of 00:05:18.910 --> 00:05:21.069 value to the market analysis section. Absolutely. 00:05:21.230 --> 00:05:23.230 And those counter strategies it came up with? 00:05:23.430 --> 00:05:25.470 Some were genuinely clever, like one called the 00:05:25.470 --> 00:05:28.069 Lifetime Impact Dashboard. What was that? Instead 00:05:28.069 --> 00:05:30.870 of just making vague sustainability claims, it 00:05:30.870 --> 00:05:33.250 proposed giving customers a real -time dashboard 00:05:33.250 --> 00:05:35.490 showing the positive environmental impact of 00:05:35.490 --> 00:05:38.170 using their bottle over time. Kind of gamified 00:05:38.170 --> 00:05:40.189 sustainability. That's smart. And the other one 00:05:40.189 --> 00:05:42.170 you mentioned, strategy two. Yeah, that tackled 00:05:42.170 --> 00:05:44.709 the circular economy idea head on. It was called 00:05:44.709 --> 00:05:48.389 the Trade In and Transform program. The AI suggested 00:05:48.389 --> 00:05:51.009 accepting any brand's old bottle, not just their 00:05:51.009 --> 00:05:53.870 own, for recycling or repurposing when a customer 00:05:53.870 --> 00:05:56.480 buys a new one. That's really smart competitive 00:05:56.480 --> 00:05:59.420 positioning. Okay, so beyond just efficiency, 00:05:59.879 --> 00:06:02.600 how does Genspark ensure that synthesis, that 00:06:02.600 --> 00:06:05.139 combining of the three models, actually improves 00:06:05.139 --> 00:06:08.060 the overall quality of the answer? Well, like 00:06:08.060 --> 00:06:10.120 we discussed, it leverages the unique strengths 00:06:10.120 --> 00:06:12.259 of each model simultaneously for different parts 00:06:12.259 --> 00:06:14.540 of the task. Okay, got it. Let's shift gears 00:06:14.540 --> 00:06:17.639 now to the creative side. The multi -agent tools 00:06:17.639 --> 00:06:20.600 for images and video, is the process similar 00:06:20.600 --> 00:06:23.060 there? Pretty much, yeah. Same principle. Instead 00:06:23.060 --> 00:06:25.160 of relying on just one image generator, it runs 00:06:25.160 --> 00:06:27.279 your prompt through several heavy hitters. They 00:06:27.279 --> 00:06:29.439 mentioned NanoBanana by Dan C. Dream. That's 00:06:29.439 --> 00:06:31.879 from the TikTok parent company. Right. And GTT 00:06:31.879 --> 00:06:34.500 Image, which uses a daily technology. So you 00:06:34.500 --> 00:06:36.540 get results influenced by different training 00:06:36.540 --> 00:06:38.990 data and styles. And the example prompt they 00:06:38.990 --> 00:06:41.709 used was really specific, wasn't it? That Hanoi 00:06:41.709 --> 00:06:44.250 sidewalk cafe scene. Oh yeah, super detailed. 00:06:44.589 --> 00:06:47.129 They wanted photorealistic, but also a specific 00:06:47.129 --> 00:06:51.910 16 .9 aspect ratio. Visual details like condensation 00:06:51.910 --> 00:06:54.410 on the iced coffee glass, raindrops on the window 00:06:54.410 --> 00:06:59.470 pane. And even a specific mood. Quiet, cinematic, 00:06:59.750 --> 00:07:02.949 nostalgic. That level of control is what pros 00:07:02.949 --> 00:07:05.310 need, definitely. And the guide gave a good tip. 00:07:05.579 --> 00:07:07.420 Always try to use a reference image if you have 00:07:07.420 --> 00:07:10.060 one. Or, if you just have a vague idea, use their 00:07:10.060 --> 00:07:11.959 auto prompt feature to flesh it out into a more 00:07:11.959 --> 00:07:14.439 detailed description for the AI. Good advice. 00:07:14.889 --> 00:07:17.449 Now on to video. The key insight here was great 00:07:17.449 --> 00:07:19.069 AI video starts with a great starting image. 00:07:19.250 --> 00:07:21.110 You can't just skip that composition step. Makes 00:07:21.110 --> 00:07:23.250 sense. Garbage in, garbage out applies to the 00:07:23.250 --> 00:07:25.550 starting frame, too. Right. And for video generation, 00:07:25.709 --> 00:07:29.170 it uses models like VO3, C -dance light, Pixverse 00:07:29.170 --> 00:07:31.290 V5, again, pulling from different specialized 00:07:31.290 --> 00:07:33.189 engines. And the prompt for the video was layered 00:07:33.189 --> 00:07:35.009 on top of the image prompt. Exactly. They took 00:07:35.009 --> 00:07:37.389 that perfect Hanoi coffee image and then commanded 00:07:37.389 --> 00:07:39.529 multiple precise layers of motion. Things like 00:07:39.529 --> 00:07:42.350 a slow camera zoom, a dolly zoom effect, raindrops 00:07:42.350 --> 00:07:45.750 slowly sliding down the window. and those blurry 00:07:45.750 --> 00:07:48.089 neon lights in the background. They wanted them 00:07:48.089 --> 00:07:50.629 to gently flicker. Wow, that's intricate. Yeah. 00:07:50.910 --> 00:07:53.290 And they even added a specific negative constraint. 00:07:53.949 --> 00:07:56.269 Absolutely no steam or smoke. Because remember, 00:07:56.389 --> 00:07:58.750 it was an iced coffee, right? Details matter. 00:07:59.069 --> 00:08:02.370 Whoa. I mean, just imagine scaling that level 00:08:02.370 --> 00:08:05.810 of precise, multi -layered motion control, doing 00:08:05.810 --> 00:08:08.389 that across hundreds of marketing assets every 00:08:08.389 --> 00:08:10.959 single day. That's... That's a huge capability 00:08:10.959 --> 00:08:13.079 boost right there. It really is. But let's play 00:08:13.079 --> 00:08:15.500 devil's advocate again. Is the trade -off in 00:08:15.500 --> 00:08:17.779 quality acceptable? I mean, integrating image, 00:08:17.879 --> 00:08:20.699 video, chat all in one place is convenient. But 00:08:20.699 --> 00:08:23.420 are these the absolute best -in -class models 00:08:23.420 --> 00:08:26.300 for each specific task or just good models? That's 00:08:26.300 --> 00:08:28.699 where. For rapid content creation? For budget 00:08:28.699 --> 00:08:31.259 -conscious teams? Small businesses? Yeah, probably. 00:08:31.600 --> 00:08:34.159 The output they showed was solid. It was robust. 00:08:34.500 --> 00:08:36.360 Definitely good enough for quick prototyping, 00:08:36.379 --> 00:08:38.980 social media posts, internal stuff. Maybe not 00:08:38.980 --> 00:08:41.559 Hollywood VFX, but very usable. So it's solid 00:08:41.559 --> 00:08:43.960 and good enough for fast content creation needs. 00:08:44.159 --> 00:08:46.940 Pretty much, yeah. OK. Let's transition, then, 00:08:46.940 --> 00:08:49.460 from the creative tools to pure productivity, 00:08:50.039 --> 00:08:53.840 starting with AI slides. Right. The utility here 00:08:53.840 --> 00:08:56.759 seems pretty clear. taking an idea, maybe even 00:08:56.759 --> 00:08:58.879 just a structured outline, and quickly turning 00:08:58.879 --> 00:09:01.539 it into a presentation slide deck. Apparently 00:09:01.539 --> 00:09:03.299 it's also helpful just for clarifying your own 00:09:03.299 --> 00:09:05.720 thinking. Yeah, forcing structure onto an idea. 00:09:05.909 --> 00:09:08.169 We tested this with another detailed prompt, 00:09:08.690 --> 00:09:11.470 creating a 15 slide internal training deck for 00:09:11.470 --> 00:09:14.409 new B2B sales hires. OK. And critically, we didn't 00:09:14.409 --> 00:09:16.450 just say make a sales training. We gave it a 00:09:16.450 --> 00:09:19.149 very structured nine part outline, everything 00:09:19.149 --> 00:09:22.049 from what is B2B and understanding the sales 00:09:22.049 --> 00:09:25.389 funnel through to using CRM software and ending 00:09:25.389 --> 00:09:27.690 with a quick quiz. And that structure is key. 00:09:27.950 --> 00:09:30.409 It turns the AI from being a guesser, where results 00:09:30.409 --> 00:09:32.549 can be kind of hit or miss, into more of an efficient 00:09:32.549 --> 00:09:34.110 worker filling in the blanks you've already defined. 00:09:34.350 --> 00:09:36.830 Exactly, but there was a limitation noted here, 00:09:37.070 --> 00:09:39.970 too, right? Yeah, the feedback was the structure 00:09:39.970 --> 00:09:42.009 and content of the slides were functional, often 00:09:42.009 --> 00:09:45.220 pretty good, but the design, not so much. The 00:09:45.220 --> 00:09:47.519 slides weren't beautiful. They'd likely need 00:09:47.519 --> 00:09:49.700 polishing afterwards in something like PowerPoint 00:09:49.700 --> 00:09:52.440 or Keynote. OK, so great for a first draft of 00:09:52.440 --> 00:09:54.559 the content and flow, but expect to do design 00:09:54.559 --> 00:09:56.620 work. That's an important caveat. Definitely. 00:09:56.860 --> 00:09:59.200 AI sheets. Doing market research in minutes. 00:09:59.980 --> 00:10:01.620 The source mentioned something interesting here, 00:10:01.679 --> 00:10:03.679 that a lot of users are kind of scared of complex 00:10:03.679 --> 00:10:06.179 spreadsheets. Yeah, that spreadsheet intimidation 00:10:06.179 --> 00:10:08.740 is real. So this feature tries to bridge that 00:10:08.740 --> 00:10:12.210 gap. The test prompt was pretty ambitious. asking 00:10:12.210 --> 00:10:14.929 the AI to research and gather competitor data, 00:10:15.029 --> 00:10:17.950 putting it directly into a spreadsheet. OK. The 00:10:17.950 --> 00:10:20.409 specific market was mid -range smartphones in 00:10:20.409 --> 00:10:24.490 Vietnam, looking ahead to 2025 data. Highly specific. 00:10:24.549 --> 00:10:26.090 And you could specify the columns you wanted. 00:10:26.230 --> 00:10:28.590 Yep. We asked for columns like brand, popular 00:10:28.590 --> 00:10:31.070 model in that category, the price range, specifically 00:10:31.070 --> 00:10:34.549 5 to 8 million Vietnamese don key specs, estimated 00:10:34.549 --> 00:10:36.710 market share, their marketing slogan, and user 00:10:36.710 --> 00:10:39.889 ratings. Wow. So you basically get a mini market 00:10:39.889 --> 00:10:42.470 research report generate into a usable table 00:10:42.470 --> 00:10:45.909 in like under a minute That's the idea and the 00:10:45.909 --> 00:10:48.409 utility doesn't stop there You can then apparently 00:10:48.409 --> 00:10:50.529 ask gen spark follow -up questions like turn 00:10:50.529 --> 00:10:52.690 this data into charts or even use it for some 00:10:52.690 --> 00:10:54.309 basic financial planning based on the market 00:10:54.309 --> 00:10:56.850 numbers Okay, that's powerful. But given the 00:10:56.850 --> 00:11:00.549 complexity there Synthesizing research data into 00:11:00.549 --> 00:11:03.950 specific columns. Mm -hmm. How critical is fact 00:11:03.950 --> 00:11:07.409 -checking on the data that AI sheets generates? 00:11:07.960 --> 00:11:09.860 especially with market share estimates and things 00:11:09.860 --> 00:11:12.019 like that. Yeah, the advice was clear. Always 00:11:12.019 --> 00:11:14.080 fact check important data, especially if you're 00:11:14.080 --> 00:11:15.740 going to present it publicly or make decisions 00:11:15.740 --> 00:11:17.779 based on it, treat it as a powerful starting 00:11:17.779 --> 00:11:20.360 point. So always fact check important content 00:11:20.360 --> 00:11:23.039 before public presentation. Got it. All right. 00:11:23.100 --> 00:11:25.179 Final main segment. This is about automation 00:11:25.179 --> 00:11:28.240 and connecting to the real world, starting with 00:11:28.240 --> 00:11:31.679 something called MCP. MCP. model context protocol. 00:11:31.840 --> 00:11:33.159 The guide explained it pretty well. Think of 00:11:33.159 --> 00:11:37.179 them like secure digital bridges. Or maybe like 00:11:37.179 --> 00:11:39.440 stacking Lego blocks, but with data from different 00:11:39.440 --> 00:11:42.759 apps. They let the AI, if you give it permission, 00:11:43.399 --> 00:11:46.860 access external software you use. Right. And 00:11:46.860 --> 00:11:49.059 GenSpark apparently offers connections to hundreds 00:11:49.059 --> 00:11:51.440 of these. Things like Gmail, Google Calendar, 00:11:52.019 --> 00:11:55.519 Notion, even ex -Twitter. Yes, 631 connections 00:11:55.519 --> 00:11:57.960 were mentioned. It's a lot. So we give it a task 00:11:57.960 --> 00:12:00.320 that used these connections, a cross -checking 00:12:00.320 --> 00:12:03.129 task. Scan my Gmail for the last seven days and 00:12:03.129 --> 00:12:05.309 my Google Calendar for the next seven days. Okay. 00:12:05.370 --> 00:12:07.629 And find any potential scheduling conflicts or 00:12:07.629 --> 00:12:09.970 maybe urgent tasks mentioned in emails that I 00:12:09.970 --> 00:12:11.929 might have missed putting on the calendar. Whoa, 00:12:12.049 --> 00:12:14.149 okay, that is a true personal assistant task. 00:12:14.169 --> 00:12:15.809 Yeah. Finding those things that fall through 00:12:15.809 --> 00:12:17.809 the cracks. Yeah. Like an email mentioning a 00:12:17.809 --> 00:12:19.649 deadline that isn't actually blocked out on your 00:12:19.649 --> 00:12:22.090 calendar yet. Or maybe spotting a double book 00:12:22.090 --> 00:12:24.389 meeting where you only accepted one invite. The 00:12:24.389 --> 00:12:26.970 potential time management benefit there is huge. 00:12:27.350 --> 00:12:30.690 Massive. But, you know. I'll admit I still wrestle 00:12:30.690 --> 00:12:32.669 with prompt drift myself sometimes trying to 00:12:32.669 --> 00:12:35.309 get the AI to do exactly what I want and connecting 00:12:35.309 --> 00:12:39.389 my private data my actual email my calendar it 00:12:39.389 --> 00:12:42.379 It still feels inherently risky obviously Yeah. 00:12:42.500 --> 00:12:44.759 But you can see the potential, right? If you 00:12:44.759 --> 00:12:47.340 can get past that initial hurdle of trust, the 00:12:47.340 --> 00:12:50.039 assistant capability is just enormous. I totally 00:12:50.039 --> 00:12:52.860 get that hesitation. Which brings us nicely to 00:12:52.860 --> 00:12:55.919 maybe the most out there feature. Right. The 00:12:55.919 --> 00:12:58.519 AI calling agent. Right. This thing actually 00:12:58.519 --> 00:13:01.820 makes phone calls to real people. Yeah. makes 00:13:01.820 --> 00:13:04.399 the call, holds a conversation, apparently a 00:13:04.399 --> 00:13:06.740 pretty natural sounding one, and then it transcribes 00:13:06.740 --> 00:13:08.580 the key results back to you in the chat. Okay, 00:13:08.740 --> 00:13:10.360 the example task for this was pretty involved 00:13:10.360 --> 00:13:12.639 too, a multi -step negotiation. What was it? 00:13:12.700 --> 00:13:15.299 The prompt was basically, call this specific 00:13:15.299 --> 00:13:17.960 electronics store. Ask if they have a Sony Bravia 00:13:17.960 --> 00:13:21.519 X90L 55 -inch TV in stock. Tell them a competitor 00:13:21.519 --> 00:13:24.539 is selling it for $2 ,500. Request a price match. 00:13:25.159 --> 00:13:27.559 And then critically ask exactly what documentation 00:13:27.559 --> 00:13:29.720 or proof you need to bring into the store to 00:13:29.720 --> 00:13:31.919 actually get that match price. OK, that's not 00:13:31.919 --> 00:13:33.879 just asking a simple question. That's navigating 00:13:33.879 --> 00:13:35.940 a whole interaction, handling potential objections, 00:13:36.159 --> 00:13:39.059 asking clarifying questions. Exactly. And the 00:13:39.059 --> 00:13:41.440 AI supposedly handles that entire thread. It 00:13:41.440 --> 00:13:43.500 overcomes the initial let me check hurdles to 00:13:43.500 --> 00:13:45.600 the negotiation part and then reports back. OK. 00:13:45.740 --> 00:13:48.000 they agreed to $2 ,500, and you need to bring 00:13:48.000 --> 00:13:50.820 a printout of the competitor's ad. Managing that 00:13:50.820 --> 00:13:53.279 kind of multi -step, real -world communication 00:13:53.279 --> 00:13:55.980 completely autonomously, that's pretty sci -fi, 00:13:56.019 --> 00:13:58.740 like you said. It really is. But linking back 00:13:58.740 --> 00:14:02.220 to the MCPs and security, what's the single most 00:14:02.220 --> 00:14:04.639 crucial piece of advice for users thinking about 00:14:04.639 --> 00:14:07.220 connecting sensitive data like email or calendars 00:14:07.220 --> 00:14:10.200 to something like Gansburg? Based on the dyed, 00:14:10.360 --> 00:14:12.639 it's straightforward. Only connect tools and 00:14:12.639 --> 00:14:15.320 data sources that you absolutely trust and where 00:14:15.320 --> 00:14:17.840 you understand the security implications. Start 00:14:17.840 --> 00:14:20.779 small, maybe. So only connect tools you absolutely 00:14:20.779 --> 00:14:24.600 trust to maintain security. Makes sense. Okay, 00:14:24.639 --> 00:14:26.240 let's wrap this up. Bring it all together. The 00:14:26.240 --> 00:14:28.580 final verdict, the value proposition. What's 00:14:28.580 --> 00:14:30.919 the takeaway? Well, the best feature, hands down, 00:14:31.179 --> 00:14:33.200 seems to be that multi -agent workflow for chat. 00:14:33.440 --> 00:14:35.840 giving just one prompt and getting back this 00:14:35.840 --> 00:14:38.480 synthesized best of breed answer from GPT -5, 00:14:38.659 --> 00:14:41.000 Claude and Gemini simultaneously. That saves 00:14:41.000 --> 00:14:44.000 a massive amount of time and frankly mental energy. 00:14:44.279 --> 00:14:46.500 It directly tackles that core pain point we started 00:14:46.500 --> 00:14:48.899 with, the AI ping -pong. Exactly. And then there's 00:14:48.899 --> 00:14:51.320 the value, 20 bucks a month for that advanced 00:14:51.320 --> 00:14:53.899 chat plus the image generation, the video tools, 00:14:54.100 --> 00:14:56.299 the slides, the sheets, the calling agent, and 00:14:56.299 --> 00:14:58.649 all those hundreds of MCP connections. Yeah, 00:14:58.649 --> 00:15:01.090 you really can't get that breadth of functionality 00:15:01.090 --> 00:15:03.350 without stacking up several different subscriptions, 00:15:03.789 --> 00:15:05.990 which would definitely cost way more than $20. 00:15:06.289 --> 00:15:09.190 For sure. Now, we did note limitations, right? 00:15:09.649 --> 00:15:12.889 The calling agent, while cool, apparently needs 00:15:12.889 --> 00:15:15.110 some refinement. There can be slight delays in 00:15:15.110 --> 00:15:17.889 the conversation flow still. OK. And those AI 00:15:17.889 --> 00:15:21.250 slides, functional structure, good content start. 00:15:21.809 --> 00:15:23.889 But yeah, you'll need to polish the look and 00:15:23.889 --> 00:15:26.590 feel in another tool. So knowing all that, who 00:15:26.590 --> 00:15:29.330 is this really for? Who gets the most value here? 00:15:29.529 --> 00:15:32.330 I think it's ideal for content creators who need 00:15:32.330 --> 00:15:34.269 to generate different types of stuff quickly. 00:15:34.850 --> 00:15:37.250 Entrepreneurs, maybe small teams who are juggling 00:15:37.250 --> 00:15:40.769 lots of tasks on a tight budget. And fundamentally, 00:15:40.909 --> 00:15:42.769 it's for anyone who's just plain tired of having 00:15:42.769 --> 00:15:45.830 15 browser tabs open. Copy and pasting prompts 00:15:45.830 --> 00:15:47.990 between different AI models all day. Yeah, the 00:15:47.990 --> 00:15:50.990 tab switchers. So the big idea recap. The main 00:15:50.990 --> 00:15:54.009 takeaway is that Genspark really consolidates 00:15:54.269 --> 00:15:56.669 the utility of many different AI models into 00:15:56.669 --> 00:16:01.090 one place. It aims to provide good or solid capability 00:16:01.090 --> 00:16:03.850 across a whole range of tasks, which should reduce 00:16:03.850 --> 00:16:06.190 the complexity and the cognitive load for the 00:16:06.190 --> 00:16:10.289 user. It's about practical application. And if 00:16:10.289 --> 00:16:12.549 you, listening, decide you want to try it out, 00:16:12.809 --> 00:16:15.809 the advice was spend some time on day one or 00:16:15.809 --> 00:16:18.759 two. setting up your profile context, defining 00:16:18.759 --> 00:16:21.960 your preferred style, your goals. Personalize 00:16:21.960 --> 00:16:24.179 it. And then maybe use those detailed structured 00:16:24.179 --> 00:16:26.120 prompts we talked about today, like that Hanoi 00:16:26.120 --> 00:16:28.700 coffee prompt or the B2B sales training outline. 00:16:28.879 --> 00:16:31.299 Use those as templates to get the best results 00:16:31.299 --> 00:16:33.700 early on. Good starting points. So final thought, 00:16:33.840 --> 00:16:35.799 then. We just talked about an AI that can potentially 00:16:35.799 --> 00:16:38.740 handle a multi -step price negotiation over the 00:16:38.740 --> 00:16:41.179 phone with a real person. If it can do that today, 00:16:41.689 --> 00:16:44.750 What's the actual limit for AI assistance accessing 00:16:44.750 --> 00:16:47.429 and managing our real -world tasks maybe next 00:16:47.429 --> 00:16:49.250 year or the year after? Where does this capability 00:16:49.250 --> 00:16:51.450 lead? That's the big question, isn't it? Something 00:16:51.450 --> 00:16:52.529 to definitely keep an eye on.