WEBVTT 00:00:00.000 --> 00:00:03.459 Every week, it feels like there's just another 00:00:03.459 --> 00:00:06.419 wave of AI tools hitting us. You know, chat GPT, 00:00:06.580 --> 00:00:08.980 Claude, Gemini, all these automation platforms, 00:00:09.259 --> 00:00:11.619 voice agents, coding assistants. It's honestly 00:00:11.619 --> 00:00:14.980 a bit much sometimes. My own list of AI I need 00:00:14.980 --> 00:00:17.079 to check out just keeps growing and growing. 00:00:17.339 --> 00:00:19.620 Oh, totally. It is a lot. And that feeling, that 00:00:19.620 --> 00:00:22.079 overwhelm, it can actually stop you in your tracks. 00:00:22.280 --> 00:00:24.899 It's this weird mix, isn't it? Like FOMO, you're 00:00:24.899 --> 00:00:27.420 scared of missing the next big thing, but also 00:00:27.420 --> 00:00:30.399 just decision fatigue. You know, there's amazing 00:00:30.399 --> 00:00:32.280 stuff out there, tools that could seriously help 00:00:32.280 --> 00:00:35.100 you. But it feels like standing in this massive 00:00:35.100 --> 00:00:38.719 library, millions of books, no catalog system, 00:00:39.119 --> 00:00:41.840 no map. And the real kicker isn't picking the 00:00:41.840 --> 00:00:44.179 wrong tool, necessarily. It's not having a system, 00:00:44.259 --> 00:00:46.280 any system, to decide in the first place. Well, 00:00:46.280 --> 00:00:48.159 welcome to the deep dive. Our whole mission today 00:00:48.159 --> 00:00:49.740 is basically to hand you that map. We want to 00:00:49.740 --> 00:00:51.780 give you a clear mental framework so you can 00:00:51.780 --> 00:00:55.299 navigate this kind of turbulent AI landscape 00:00:55.299 --> 00:00:57.439 with more confidence. Really turn that overwhelm 00:00:57.439 --> 00:01:00.119 into clarity. Yeah, exactly. And we've got a 00:01:00.119 --> 00:01:02.560 plan. First, we'll dig into this core idea we 00:01:02.560 --> 00:01:05.819 call the pain meter. Super useful. Then we'll 00:01:05.819 --> 00:01:08.459 break down what we see as the nine key domains 00:01:08.459 --> 00:01:11.400 of AI. Think of it as structuring the ecosystem. 00:01:12.040 --> 00:01:14.620 After that, we'll sketch out a potential AI roadmap 00:01:14.620 --> 00:01:17.700 for you, like stages for building skills, know 00:01:17.700 --> 00:01:20.760 what to learn next. And finally, wrap it up with 00:01:20.760 --> 00:01:23.480 a smart decisions framework, plus some real world 00:01:23.480 --> 00:01:25.680 scenarios to make it practical. OK, let's start 00:01:25.680 --> 00:01:28.040 with that foundation then, the pain meter. It 00:01:28.040 --> 00:01:31.540 sounds intriguing. What's the core idea? It's 00:01:31.540 --> 00:01:33.760 actually pretty simple, but powerful. Think of 00:01:33.760 --> 00:01:36.079 every single AI tool existing on a spectrum. 00:01:36.480 --> 00:01:38.439 On one end, you've got high convenience, but 00:01:38.439 --> 00:01:40.879 usually that means lower control. These are your 00:01:40.879 --> 00:01:43.379 out -of -the -box tools, often beautiful drag 00:01:43.379 --> 00:01:45.819 -and -drop interfaces, super easy setup, really 00:01:45.819 --> 00:01:48.099 getting things done fast, but the trade -off 00:01:48.099 --> 00:01:50.599 is limited customization. They do what they do. 00:01:50.760 --> 00:01:52.420 Right. Easy to use, but you can't really tinker 00:01:52.420 --> 00:01:55.120 into the hood much. Exactly. Then on the other 00:01:55.120 --> 00:01:57.849 end, Low convenience, high control, these are 00:01:57.849 --> 00:02:00.750 more like toolkits. They might need a more complex 00:02:00.750 --> 00:02:03.150 setup, maybe even some coding knowledge, but 00:02:03.150 --> 00:02:05.349 they give you almost limitless customization. 00:02:05.650 --> 00:02:07.430 You can build pretty much anything you imagine. 00:02:07.709 --> 00:02:11.110 So the key question isn't, which is better? Never. 00:02:11.610 --> 00:02:14.330 It's always. What level of pain, and by pain 00:02:14.330 --> 00:02:16.849 I mean complexity, setup time, learning curve, 00:02:16.949 --> 00:02:18.949 are you willing to accept for the amount of control 00:02:18.949 --> 00:02:22.229 you actually need? Like building a simple FAQ 00:02:22.229 --> 00:02:25.219 chatbot. A no code thing like voice flow? Probably 00:02:25.219 --> 00:02:28.300 perfect. Easy. Fast. But if you're automating 00:02:28.300 --> 00:02:30.819 a really complex business process, multiple steps, 00:02:31.159 --> 00:02:33.099 conditional logic, integrating weird systems, 00:02:33.639 --> 00:02:35.699 you'll probably need tools closer to code or 00:02:35.699 --> 00:02:37.759 maybe something flexible like N810. OK, that 00:02:37.759 --> 00:02:39.610 makes a lot of sense. It's about matching the 00:02:39.610 --> 00:02:42.030 tool's complexity to your actual need for control, 00:02:42.169 --> 00:02:44.569 not just chasing features. Precisely. And the 00:02:44.569 --> 00:02:46.650 advice for most people starting out, begin with 00:02:46.650 --> 00:02:49.310 convenience. Get comfortable. Only when you hit 00:02:49.310 --> 00:02:51.189 the limits of those tools, then start looking 00:02:51.189 --> 00:02:53.550 at the higher control options as your needs genuinely 00:02:53.550 --> 00:02:56.110 grow. Honestly, that approach alone filters out 00:02:56.110 --> 00:02:59.069 like 90 % of the noise. So, the big takeaway 00:02:59.069 --> 00:03:01.229 from the pain meter idea? It's aligning the tool 00:03:01.229 --> 00:03:04.330 complexity with your real need for control. Don't 00:03:04.330 --> 00:03:06.949 overcomplicate things unnecessarily. Got it. 00:03:06.990 --> 00:03:10.129 Okay, so... With that framework in mind, let's 00:03:10.129 --> 00:03:13.689 broaden the view to the nine key domains of AI. 00:03:13.969 --> 00:03:15.289 You said we're starting with the most fundamental, 00:03:15.430 --> 00:03:18.729 language models, LLMs. Why there? Because they're 00:03:18.729 --> 00:03:21.389 the engines. They're the core technology powering 00:03:21.389 --> 00:03:24.409 so much of this AI revolution. Everything else 00:03:24.409 --> 00:03:27.270 kind of builds on or interacts with them. Understanding 00:03:27.270 --> 00:03:29.650 the main players here is crucial. All right, 00:03:29.770 --> 00:03:32.469 lay them out for us. OK, first up, Anthropics 00:03:32.469 --> 00:03:35.810 Clawed. It really excels at writing, creative 00:03:35.810 --> 00:03:38.909 stuff, and coding. It's known for solid reasoning, 00:03:39.250 --> 00:03:41.629 following complex instructions really well, and 00:03:41.629 --> 00:03:45.129 its context windows are huge. Claude 3 .5 Sonnet 00:03:45.129 --> 00:03:47.270 handles like a million tokens. That's basically 00:03:47.270 --> 00:03:49.789 processing a whole thick book in one go. Plus 00:03:49.789 --> 00:03:52.150 they have this constitutional AI thing for safety, 00:03:52.250 --> 00:03:53.949 which is interesting. It's like built -in ethical 00:03:53.949 --> 00:03:55.849 guidelines. Okay, Claude, for the complex reasoning 00:03:55.849 --> 00:03:58.889 and writing, who's next? Open AI's chat GPT. 00:03:59.080 --> 00:04:01.780 Probably the most well -known. It's the versatile 00:04:01.780 --> 00:04:04.080 workhorse. Does pretty well across the board. 00:04:04.180 --> 00:04:06.400 You've got different versions, right? Like GPT 00:04:06.400 --> 00:04:09.379 -40 mini for simpler, cheaper tasks, and the 00:04:09.379 --> 00:04:12.560 full GPT -40 for really heavy lifting. And it's 00:04:12.560 --> 00:04:15.919 multimodal stuff. Text, images, audio is super 00:04:15.919 --> 00:04:18.410 powerful now. The all -rounder. Makes sense. 00:04:18.709 --> 00:04:21.189 Then there's Google Gemini. It's big advantage. 00:04:21.750 --> 00:04:24.370 Massive data, trained on like 20 plus years of 00:04:24.370 --> 00:04:27.170 Google search, often gives you big context windows 00:04:27.170 --> 00:04:30.110 too, and can be pretty cost effective for developers. 00:04:30.189 --> 00:04:32.769 Really strong for research, pulling info together, 00:04:33.129 --> 00:04:35.449 and obviously integrates well with Google workspace. 00:04:35.649 --> 00:04:37.589 Right. Leveraging that huge Google knowledge 00:04:37.589 --> 00:04:40.149 graph. Exactly. And if research is your main 00:04:40.149 --> 00:04:41.990 game, you got to look at perplexity. It's more 00:04:41.990 --> 00:04:44.389 of an answer engine than a chat bot. Right. It 00:04:44.389 --> 00:04:46.389 gives you answers and sites and sources directly 00:04:46.389 --> 00:04:49.269 so you can check. useful for verification. Different 00:04:49.269 --> 00:04:52.170 search modes too, like sonar. Okay, so perplexity 00:04:52.170 --> 00:04:54.550 for verifiable research. What about open source 00:04:54.550 --> 00:04:57.740 options? Yeah, that's where Llama 3 comes. in 00:04:57.740 --> 00:05:00.620 Meta's model. It's the big open source player 00:05:00.620 --> 00:05:02.920 right now. Cost effective. It's free, free to 00:05:02.920 --> 00:05:05.620 use, free to modify. You can even run it locally 00:05:05.620 --> 00:05:08.480 on your own machine for total privacy, no ongoing 00:05:08.480 --> 00:05:12.279 API costs, and the quality. It's improving incredibly 00:05:12.279 --> 00:05:14.759 fast, really starting to nip at the heels of 00:05:14.759 --> 00:05:17.720 the closed source giants. Wow. OK. So lots of 00:05:17.720 --> 00:05:19.759 choices just within LLMs. You mentioned a pro 00:05:19.759 --> 00:05:22.199 tip. Yeah. And it's simple but crucial. Focus 00:05:22.199 --> 00:05:25.439 on mastering one language model first. Pick one. 00:05:25.540 --> 00:05:29.129 Maybe chat GPT or or Claude, and really dig deep. 00:05:29.569 --> 00:05:31.670 Understand its prompts, its quirks, its strengths, 00:05:31.829 --> 00:05:34.189 its weaknesses. That deep understanding becomes 00:05:34.189 --> 00:05:36.550 your cheat code for figuring out any other model 00:05:36.550 --> 00:05:38.990 later on. So don't spread yourself thin. If someone's 00:05:38.990 --> 00:05:41.089 just starting out, which one? Just pick one, 00:05:41.269 --> 00:05:43.290 Chad, GPT, or Claude are great starting points, 00:05:43.329 --> 00:05:45.629 and really learn it inside out. OK, so we've 00:05:45.629 --> 00:05:48.589 got the engines, the LLMs. Next up. Automation 00:05:48.589 --> 00:05:50.430 platforms. What are these? These are the connectors, 00:05:50.610 --> 00:05:52.189 right? The tools that let you link different 00:05:52.189 --> 00:05:54.589 apps together like your Gmail, Slack, Google 00:05:54.589 --> 00:05:57.170 Sheets to build workflows without needing to 00:05:57.170 --> 00:05:59.509 write code. Exactly. They automate the boring 00:05:59.509 --> 00:06:01.649 stuff. And there are kind of three big players 00:06:01.649 --> 00:06:04.930 people usually talk about. First, N8n. This one 00:06:04.930 --> 00:06:07.509 leans towards power users. It has cool stuff 00:06:07.509 --> 00:06:10.269 like an AI agent node built in. Big advantage, 00:06:10.449 --> 00:06:12.810 you can self -host it, run it on your own server. 00:06:12.910 --> 00:06:15.149 That means full data control and potentially 00:06:15.149 --> 00:06:17.189 massive cost savings if you're doing a lot of 00:06:17.189 --> 00:06:19.790 automation. It's a bit more technical, but super 00:06:19.790 --> 00:06:23.240 flexible. OK, N8n. for the tech savvy or high 00:06:23.240 --> 00:06:25.899 -volume user. What else? Then there's Make, used 00:06:25.899 --> 00:06:28.439 to be called Integromat. Its strength is its 00:06:28.439 --> 00:06:30.879 visual interface. It's really beautiful, makes 00:06:30.879 --> 00:06:33.379 it easy to see complex workflows, got thousands 00:06:33.379 --> 00:06:36.259 of integrations, maybe 3 ,000, 4 ,000. Great 00:06:36.259 --> 00:06:38.279 for beginners, up to moderately complex stuff. 00:06:38.379 --> 00:06:41.620 Nice. Visual and powerful. And the third? Zapier. 00:06:41.800 --> 00:06:43.660 Probably the most well -known, the integration 00:06:43.660 --> 00:06:46.660 champion. They boast over 7 ,000 integrations. 00:06:46.720 --> 00:06:48.439 It's generally the most user -friendly to get 00:06:48.439 --> 00:06:51.670 started with. But gotta give a warning. The cost 00:06:51.670 --> 00:06:54.009 can really climb fast if you have high volumes 00:06:54.009 --> 00:06:56.850 of tasks running. Right. Ease of use versus potential 00:06:56.850 --> 00:07:00.209 cost scaling. That N8N self -hosting point sounds 00:07:00.209 --> 00:07:03.089 important for heavy users. Oh, it can be huge. 00:07:03.250 --> 00:07:06.149 We're talking hundreds, maybe thousands saved 00:07:06.149 --> 00:07:08.850 per month compared to paying per task on other 00:07:08.850 --> 00:07:11.610 platforms if you're really scaling up. Okay, 00:07:11.829 --> 00:07:14.949 moving on. Databases and vectors. This sounds 00:07:14.949 --> 00:07:18.420 technical. When does AI need a memory? Yeah, 00:07:18.500 --> 00:07:20.319 this is where things can seem complicated, but 00:07:20.319 --> 00:07:22.839 here's the simple truth. Most people probably 00:07:22.839 --> 00:07:25.000 don't need a dedicated vector database right 00:07:25.000 --> 00:07:27.819 now. Like you said with Claude, modern LLMs have 00:07:27.819 --> 00:07:30.660 these huge context windows, sometimes a million 00:07:30.660 --> 00:07:33.879 tokens. often you can just feed all the relevant 00:07:33.879 --> 00:07:35.819 information directly into the prompt itself. 00:07:36.040 --> 00:07:38.339 Right. That's the core of R -GREG retrieval augmented 00:07:38.339 --> 00:07:40.620 generation. It's like giving the AI an open book 00:07:40.620 --> 00:07:43.939 test. You provide the book, your data, in the 00:07:43.939 --> 00:07:45.939 prompt, and it looks things up from there. Exactly. 00:07:46.279 --> 00:07:48.579 So when do you actually need a separate database 00:07:48.579 --> 00:07:51.519 for your AI stuff? Basically, two main scenarios. 00:07:51.660 --> 00:07:54.240 One, Your data volume is just too massive to 00:07:54.240 --> 00:07:56.360 fit in the pump, even a huge one, or two. You 00:07:56.360 --> 00:07:58.879 need extremely fast and accurate lookups from 00:07:58.879 --> 00:08:01.459 a private knowledge base, maybe faster than context 00:08:01.459 --> 00:08:04.160 injection allows. Okay, massive data or need 00:08:04.160 --> 00:08:06.399 for speed and precision. What are the options 00:08:06.399 --> 00:08:09.459 then? For simple needs, honestly, sometimes Google 00:08:09.459 --> 00:08:11.959 Sheets or Airtable can work for basic lookups. 00:08:12.360 --> 00:08:14.220 If you're already comfortable with traditional 00:08:14.220 --> 00:08:17.399 databases, Postgres has an extension called PG 00:08:17.399 --> 00:08:19.920 Vector that lets it handle vector searches. Kind 00:08:19.920 --> 00:08:22.540 of adds AI memory to your existing setup. For 00:08:22.540 --> 00:08:25.180 more advanced rag, dedicated vector databases 00:08:25.180 --> 00:08:27.519 like Pinecone are popular, sort of the industry 00:08:27.519 --> 00:08:30.680 standard. Qdrant is another strong option, maybe 00:08:30.680 --> 00:08:33.159 a bit easier for maintenance. And SuperBase is 00:08:33.159 --> 00:08:35.100 interesting, it combines traditional relational 00:08:35.100 --> 00:08:37.269 database features with with vector capabilities. 00:08:37.629 --> 00:08:39.990 Kind of an all -in -one solution. Got it. But 00:08:39.990 --> 00:08:42.450 the practical advice is key here. Absolutely. 00:08:42.809 --> 00:08:45.090 If your entire data set, the whole knowledge 00:08:45.090 --> 00:08:47.649 base you need the AI to access, is maybe around 00:08:47.649 --> 00:08:50.409 200, 300 pages of text, try just putting it directly 00:08:50.409 --> 00:08:52.850 in the prompt first. Use that direct context 00:08:52.850 --> 00:08:55.649 injection. Only look at vector databases if that 00:08:55.649 --> 00:08:57.269 doesn't work well enough or your data is way 00:08:57.269 --> 00:09:00.049 bigger. So bottom line on vector databases. Only 00:09:00.049 --> 00:09:02.350 jump in if your data is truly massive or needs 00:09:02.350 --> 00:09:05.740 that ultra -fast, super -precise retrieval. Otherwise, 00:09:06.100 --> 00:09:08.899 keep it simple. All right. Let's whip through 00:09:08.899 --> 00:09:10.940 the remaining domains quickly. Voice technology 00:09:10.940 --> 00:09:13.639 first. This space is exploding, right? Virtual 00:09:13.639 --> 00:09:15.820 assistants are sounding incredibly human now. 00:09:15.960 --> 00:09:17.740 Yeah. It's getting kind of spooky good. What 00:09:17.740 --> 00:09:20.340 are the tools? For easy starts, check out VOPI 00:09:20.340 --> 00:09:23.500 or Retail AI. They often have drag and drop interfaces. 00:09:24.000 --> 00:09:25.940 You can get something basic running in like five 00:09:25.940 --> 00:09:28.289 or 10 minutes. For more specialized stuff, 11 00:09:28.289 --> 00:09:30.509 Labs is often cited for having the best voice 00:09:30.509 --> 00:09:33.210 quality, really natural sounding, and good voice 00:09:33.210 --> 00:09:36.230 cloning. OpenAI's real -time API is also strong, 00:09:36.529 --> 00:09:38.850 handles accents well, and can be more affordable. 00:09:39.289 --> 00:09:41.330 And then for the really advanced full control 00:09:41.330 --> 00:09:43.629 over latency, security, all that you're looking 00:09:43.629 --> 00:09:46.769 at things like LiveKit or PipeCat, more complex 00:09:46.769 --> 00:09:49.029 enterprise grade. Okay, voice is moving fast. 00:09:49.129 --> 00:09:52.230 Next up, visual code builders. What's the deal 00:09:52.230 --> 00:09:54.320 here? These are platforms that let you build 00:09:54.320 --> 00:09:56.919 the front end, the user interface, and sometimes 00:09:56.919 --> 00:09:59.980 the back -end logic for AI apps, but using visual 00:09:59.980 --> 00:10:02.059 drag -and -drop components instead of writing 00:10:02.059 --> 00:10:05.639 tons of code. Think tools like Lovable, Bolt, 00:10:06.120 --> 00:10:08.840 maybe Replet in some modes, Base 44. They're 00:10:08.840 --> 00:10:11.279 great for getting you maybe 60%, 80 % of the 00:10:11.279 --> 00:10:13.759 way there, really quickly. But usually for the 00:10:13.759 --> 00:10:17.480 final 20, 40%, the really custom bits are polishing, 00:10:17.879 --> 00:10:19.960 you'll likely export the code they generate and 00:10:19.960 --> 00:10:21.799 refine it using other tools, maybe something 00:10:21.799 --> 00:10:24.759 like cursor. So rapid prototyping, but maybe 00:10:24.759 --> 00:10:27.309 not the whole journey. Often, yeah. Good for 00:10:27.309 --> 00:10:30.830 MVPs or internal tools. OK, next, super apps 00:10:30.830 --> 00:10:33.389 and aggregators. Ah, the platforms that bundle 00:10:33.389 --> 00:10:35.789 access to lots of different AIs. Exactly. They 00:10:35.789 --> 00:10:37.850 give you a single interface, a single subscription 00:10:37.850 --> 00:10:40.090 sometimes, to access a whole range of models, 00:10:40.370 --> 00:10:42.929 text models like we discussed, but also image 00:10:42.929 --> 00:10:45.950 generators, video tools, et cetera. I think Ginspark, 00:10:46.250 --> 00:10:48.610 Manus, Poe by Quora is another one. They're great 00:10:48.610 --> 00:10:50.090 for beginners who want to try different things 00:10:50.090 --> 00:10:52.529 without signing up everywhere, or for teams needing 00:10:52.529 --> 00:10:55.090 varied AI access without managing tons of accounts. 00:10:55.450 --> 00:10:57.490 Convenient. All right, what about core coding 00:10:57.490 --> 00:10:59.730 layers? Sounds like we're getting deep now. Yeah, 00:10:59.889 --> 00:11:02.149 this is stuff like Python using libraries like 00:11:02.149 --> 00:11:05.070 Langchain or Lama Index. Honestly, most people 00:11:05.070 --> 00:11:06.929 listening probably won't need to become expert 00:11:06.929 --> 00:11:09.629 coders here, but just understanding the basic 00:11:09.629 --> 00:11:13.139 concepts like... what an API call is, what an 00:11:13.139 --> 00:11:16.419 HTTP request does, how functions work, that basic 00:11:16.419 --> 00:11:18.740 literacy makes all the other AI tools, even the 00:11:18.740 --> 00:11:21.320 no -code ones, much less mysterious. You kind 00:11:21.320 --> 00:11:23.360 of get what's happening under the hood. That 00:11:23.360 --> 00:11:25.419 makes sense. Understanding the principles helps 00:11:25.419 --> 00:11:27.700 even if you don't write the code yourself. Okay, 00:11:27.899 --> 00:11:29.980 generative media. This is all the content creation 00:11:29.980 --> 00:11:32.320 stuff. For images, you've got the big names. 00:11:32.500 --> 00:11:35.379 Mid -Journey, Stable Diffusion, OpenAI's DAL 00:11:35.379 --> 00:11:38.539 E3. For video, things like Pika are making waves, 00:11:38.639 --> 00:11:40.700 and of course, OpenAI's Sora, when it becomes 00:11:40.700 --> 00:11:43.639 more widely available. And for audio, music generation, 00:11:43.879 --> 00:11:46.720 tools like SunoAI and Udio are pretty amazing. 00:11:47.019 --> 00:11:49.259 Creating content from scratch with AI. And the 00:11:49.259 --> 00:11:52.419 last domain. Monitoring and observability. Tools 00:11:52.419 --> 00:11:55.299 like LangSmith, from the LangChain folks, arise 00:11:55.299 --> 00:11:58.570 AI, weights and biases. These are crucial once 00:11:58.570 --> 00:12:00.889 you start building more complex AI applications, 00:12:01.110 --> 00:12:03.309 especially in business. They help you track how 00:12:03.309 --> 00:12:05.529 your AI is performing, what it's costing, where 00:12:05.529 --> 00:12:07.669 errors are happening, essential for anything 00:12:07.669 --> 00:12:09.950 serious or production grade. Got it. Keeping 00:12:09.950 --> 00:12:12.970 an eye on the AI once it's running. Whoa. Just 00:12:12.970 --> 00:12:16.019 thinking about all this. Imagine an AI agent 00:12:16.019 --> 00:12:18.879 that could watch a tutorial video, automatically 00:12:18.879 --> 00:12:20.899 pull out the step -by -step instructions into 00:12:20.899 --> 00:12:23.259 text, and then read those steps back to you in 00:12:23.259 --> 00:12:25.980 a natural voice while you work. That multimodal 00:12:25.980 --> 00:12:29.440 future, connecting voice, vision, language, it's 00:12:29.440 --> 00:12:31.600 going to be incredible. Yeah, the potential connections 00:12:31.600 --> 00:12:34.000 are mind -bending. So with all these different 00:12:34.000 --> 00:12:36.799 specialized domains and tools, what's the key 00:12:36.799 --> 00:12:38.960 idea for choosing among them? It really comes 00:12:38.960 --> 00:12:41.139 back to matching the right category of tool, 00:12:41.320 --> 00:12:43.799 the right domain, to the specific problem you're 00:12:43.799 --> 00:12:46.120 trying to solve. Don't try to force a language 00:12:46.120 --> 00:12:50.080 model to do complex data visualization. Use the 00:12:50.080 --> 00:12:53.279 right tool for the job. Sponsor. OK. We've explored 00:12:53.279 --> 00:12:55.539 the tools, the domains. Now let's talk about 00:12:55.539 --> 00:12:58.240 your journey. We've mapped out a kind of AI roadmap 00:12:58.240 --> 00:13:01.039 with five stages of proficiency. Let's walk through 00:13:01.039 --> 00:13:03.830 them. Stage one is the starter. Yep. If you're 00:13:03.830 --> 00:13:06.850 here, the focus is totally foundational. Pick 00:13:06.850 --> 00:13:10.570 one LLM seriously. Just one. Maybe Claude, maybe 00:13:10.570 --> 00:13:13.830 Chad GPT, and commit to mastering it. Learn prompt 00:13:13.830 --> 00:13:16.070 engineering fundamentals. The biggest thing here. 00:13:16.450 --> 00:13:19.090 Resist the urge to jump between tools constantly. 00:13:19.509 --> 00:13:22.429 Avoid that shiny object syndrome. Deep understanding 00:13:22.429 --> 00:13:25.610 first. Solid advice. Okay, stage two. The tinkerer. 00:13:25.850 --> 00:13:28.230 Now you started playing it. Pick one automation 00:13:28.230 --> 00:13:31.190 platform, make, Zapier N8n, and actually build, 00:13:31.389 --> 00:13:33.389 say, three to five workflows that are genuinely 00:13:33.389 --> 00:13:35.669 useful to you. Start experimenting with those 00:13:35.669 --> 00:13:37.789 super apps, too. You'll also begin to get a feel 00:13:37.789 --> 00:13:40.070 for which LLM works better for certain tasks. 00:13:40.549 --> 00:13:42.029 Budget -wise, you're probably looking at maybe 00:13:42.029 --> 00:13:44.590 $30, $50 a month across a few key tools at this 00:13:44.590 --> 00:13:48.909 stage. Right, this is where integration starts 00:13:48.909 --> 00:13:51.049 to happen. You'll want to learn the concepts 00:13:51.049 --> 00:13:53.490 behind vector databases, even if you're not building 00:13:53.490 --> 00:13:56.110 one yet. Play around with the visual code builder 00:13:56.110 --> 00:13:59.299 to create a... A simple app? Maybe dip your toes 00:13:59.299 --> 00:14:02.559 into basic voice AI tools? The big mindset shift 00:14:02.559 --> 00:14:05.799 here is crucial. You stop asking what's the best 00:14:05.799 --> 00:14:08.139 tool overall and start asking what's the right 00:14:08.139 --> 00:14:10.799 tool for this specific problem I have right now. 00:14:11.200 --> 00:14:13.759 Problem -focused thinking. Nice. Stage four. 00:14:14.120 --> 00:14:16.679 The AI Generalist. Now you're thinking strategically. 00:14:17.340 --> 00:14:19.639 You can look at a problem and confidently map 00:14:19.639 --> 00:14:22.519 it to the optimal AI domain. You understand scalability 00:14:22.519 --> 00:14:24.299 issues, you can start combining different AI 00:14:24.299 --> 00:14:25.980 types effectively, maybe building a workflow 00:14:25.980 --> 00:14:28.279 where, I don't know, a voice AI makes a call, 00:14:28.379 --> 00:14:31.139 an LLM summarizes it, and the result gets saved 00:14:31.139 --> 00:14:33.659 to a database via an automation platform. You're 00:14:33.659 --> 00:14:35.179 making architectural choices now. Connecting 00:14:35.179 --> 00:14:38.779 the dots. And finally, stage five. The top 1%. 00:14:38.779 --> 00:14:42.080 Yeah, this is the expert level. You're comfortable 00:14:42.080 --> 00:14:44.279 working in AI integrated coding environments 00:14:44.279 --> 00:14:46.740 if needed. You possess that basic programming 00:14:46.740 --> 00:14:48.500 literacy we talked about. You understand the 00:14:48.500 --> 00:14:51.220 core concepts. You're capable of building robust 00:14:51.220 --> 00:14:53.519 production -grade AI applications or complex 00:14:53.519 --> 00:14:56.539 systems. Full control. That's a clear progression. 00:14:56.759 --> 00:14:59.159 Yeah. Now, to help make decisions along that 00:14:59.159 --> 00:15:01.960 roadmap, you mentioned a smart decisions framework. 00:15:02.799 --> 00:15:04.539 What are the key questions we should be asking 00:15:04.539 --> 00:15:06.919 ourselves before picking any tool? Absolutely. 00:15:07.120 --> 00:15:10.179 Six key questions. First, scale. How many users 00:15:10.179 --> 00:15:12.299 will this serve? How many operations or tasks 00:15:12.299 --> 00:15:14.500 will it run per month? Are we talking tens, hundreds, 00:15:14.580 --> 00:15:16.720 or millions? Scale changes everything. Second, 00:15:17.059 --> 00:15:19.659 cost. What's the budget? Think beyond just the 00:15:19.659 --> 00:15:22.059 initial price. What are the ongoing operational 00:15:22.059 --> 00:15:25.279 costs per user, per API call, per month? Third, 00:15:25.419 --> 00:15:27.440 control. How much customization do you really 00:15:27.440 --> 00:15:29.960 need? Is the standard out -of -the -box functionality 00:15:29.960 --> 00:15:31.899 enough, or do you genuinely need to tweak every 00:15:31.899 --> 00:15:34.179 little detail? Be honest with yourself. Scale, 00:15:34.240 --> 00:15:38.200 cost, control. Okay, what else? Fourth, integration. 00:15:38.509 --> 00:15:41.850 What other systems, apps, or databases does this 00:15:41.850 --> 00:15:44.350 new tool absolutely have to work with smoothly? 00:15:44.970 --> 00:15:48.070 Compatibility is key. Fifth, maintenance. Who's 00:15:48.070 --> 00:15:50.610 going to manage this thing? Who updates it? Troubleshoots 00:15:50.610 --> 00:15:52.950 when it breaks. Is it you, your team, or does 00:15:52.950 --> 00:15:55.909 the vendor handle it? And finally, sixth, data. 00:15:56.289 --> 00:15:58.629 Where is your data actually going to be stored 00:15:58.629 --> 00:16:01.909 on their servers? Yours. What are the security 00:16:01.909 --> 00:16:03.830 implications? Are there data sovereignty rules 00:16:03.830 --> 00:16:05.549 you need to follow depending on your location 00:16:05.549 --> 00:16:09.120 or industry? Scale. cost, control, integration, 00:16:09.259 --> 00:16:11.519 maintenance, data. That's a really solid checklist. 00:16:11.860 --> 00:16:13.460 And you mentioned some red flags to watch out 00:16:13.460 --> 00:16:16.480 for too. Yeah, definitely things to avoid. Number 00:16:16.480 --> 00:16:18.919 one, choosing a tool just because it's getting 00:16:18.919 --> 00:16:21.899 a lot of hype or buzz. Focus on your need, not 00:16:21.899 --> 00:16:25.480 the trend. Number two, starting way too complex. 00:16:26.000 --> 00:16:28.159 Don't pick an enterprise level vector database 00:16:28.159 --> 00:16:30.299 if all you need is a simple chat bot for your 00:16:30.299 --> 00:16:33.179 personal blog. Start simple, scale later if needed. 00:16:33.759 --> 00:16:36.730 Number three, Constantly switching tools. Pick 00:16:36.730 --> 00:16:39.029 something, learn it reasonably well, stick with 00:16:39.029 --> 00:16:41.250 it unless there's a compelling reason to change. 00:16:41.669 --> 00:16:44.690 Tool hopping creates chaos. Also, avoid building 00:16:44.690 --> 00:16:46.570 everything from scratch if a perfectly good tool 00:16:46.570 --> 00:16:49.090 already exists and fits your needs. Don't reinvent 00:16:49.090 --> 00:16:51.830 the wheel unnecessarily. And related to the complexity 00:16:51.830 --> 00:16:54.730 point, don't use super heavy -duty enterprise 00:16:54.730 --> 00:16:57.379 tools for simple projects. like using pine cone 00:16:57.379 --> 00:17:00.200 for a basic to -do list AI overkill. Yeah, those 00:17:00.200 --> 00:17:01.980 are definitely traps to fall into. You know, 00:17:01.980 --> 00:17:03.559 it's funny, I still wrestle with prompt drift 00:17:03.559 --> 00:17:06.259 myself sometimes, like constantly tweaking prompts, 00:17:06.339 --> 00:17:08.099 trying to get consistent results from the LLMs 00:17:08.099 --> 00:17:10.039 day after day. It really is a marathon, not a 00:17:10.039 --> 00:17:12.019 sprint. And having these frameworks helps keep 00:17:12.019 --> 00:17:13.900 you grounded. Oh, absolutely. Prompt engineering 00:17:13.900 --> 00:17:16.460 is an ongoing practice for everyone. So thinking 00:17:16.460 --> 00:17:18.859 about those red flags in the framework, what's 00:17:18.859 --> 00:17:21.200 the single biggest mistake you see people making 00:17:21.200 --> 00:17:24.569 when choosing AI tools? Honestly. chasing the 00:17:24.569 --> 00:17:26.890 hype instead of focusing squarely on their actual 00:17:26.890 --> 00:17:28.809 needs and the problem they're trying to solve. 00:17:29.670 --> 00:17:31.630 Okay, let's make this framework concrete with 00:17:31.630 --> 00:17:34.970 some common scenarios. Scenario one. You just 00:17:34.970 --> 00:17:37.690 want to automate some basic email filtering or 00:17:37.690 --> 00:17:40.049 maybe automatically create calendar events based 00:17:40.049 --> 00:17:43.210 on emails. What do you recommend? For that, based 00:17:43.210 --> 00:17:45.130 on what we've discussed, I'd say start with Zapier, 00:17:45.170 --> 00:17:47.190 because it's probably the easiest for those common 00:17:47.190 --> 00:17:49.430 apps. Or maybe make if you prefer that visual 00:17:49.430 --> 00:17:52.589 interface. Exactly. Why? Because these are simple, 00:17:52.829 --> 00:17:54.950 common tasks, and those platforms have really 00:17:54.950 --> 00:17:57.250 robust, well -supported integrations for things 00:17:57.250 --> 00:17:59.710 like Gmail and Google Calendar. Keep it simple. 00:18:00.289 --> 00:18:03.109 OK. Scenario two. You need a Q &A chat bot for 00:18:03.109 --> 00:18:05.670 your company website. It needs to answer questions 00:18:05.670 --> 00:18:08.750 based on, say, 20 product specification PDFs 00:18:08.750 --> 00:18:11.279 you have. Right. First step, try that direct 00:18:11.279 --> 00:18:13.619 context injection. Take the text from all 20 00:18:13.619 --> 00:18:16.119 PDFs, put it into one big prompt for something 00:18:16.119 --> 00:18:19.000 like Claude 3 .5 Sonnet, see how well it answers 00:18:19.000 --> 00:18:21.059 questions just based on that. If the answers 00:18:21.059 --> 00:18:23.799 are good enough, you're done. Simple, cheap. 00:18:24.079 --> 00:18:25.920 If it struggles, then maybe look at building 00:18:25.920 --> 00:18:28.339 a proper RG system, maybe using super base since 00:18:28.339 --> 00:18:30.819 it combines database and vector stuff. Start 00:18:30.819 --> 00:18:33.339 simple, escalate complexity only if necessary. 00:18:33.680 --> 00:18:36.539 Love it. Scenario three. You have an idea for 00:18:36.539 --> 00:18:39.700 a simple, minimum viable product, an MVP app. 00:18:40.059 --> 00:18:42.339 It needs to take voice input, transcribe it to 00:18:42.339 --> 00:18:45.079 text, and then summarize that text. But you don't 00:18:45.079 --> 00:18:48.259 code. OK, no coding. This screams Visual Builder. 00:18:48.559 --> 00:18:50.599 Use something like Replet or Bolt to build the 00:18:50.599 --> 00:18:53.579 basic interface. Then connect it via APIs to 00:18:53.579 --> 00:18:56.480 OpenAI, use Whisper for the voice -to -text transcription, 00:18:56.980 --> 00:18:59.680 and GPT -4 .0 maybe for the summarization. Get 00:18:59.680 --> 00:19:01.960 the core function working visually. You can always 00:19:01.960 --> 00:19:03.619 export the code later and have someone refine 00:19:03.619 --> 00:19:05.779 it using a tool like Cursor if the MVP proves 00:19:05.779 --> 00:19:09.039 promising. Validate the idea first. Validate 00:19:09.039 --> 00:19:12.289 quickly with visual tools, then refine. Makes 00:19:12.289 --> 00:19:16.279 sense. Last one, scenario four. You need to analyze 00:19:16.279 --> 00:19:18.180 thousands of customer reviews to find common 00:19:18.180 --> 00:19:20.539 themes like feedback about pricing or customer 00:19:20.539 --> 00:19:23.599 service or specific features. Classic LLM test. 00:19:23.799 --> 00:19:26.460 This is perfect for using the API of a powerful 00:19:26.460 --> 00:19:29.900 model like GPT -4 .0 or Claude 3 .5 Sonnet. The 00:19:29.900 --> 00:19:32.019 trick is to craft a good prompt that specifically 00:19:32.019 --> 00:19:34.599 tells the model to analyze the review and output 00:19:34.599 --> 00:19:36.920 its findings in a structured format like JSON. 00:19:37.339 --> 00:19:39.700 You'd ask it to identify the main theme, example 00:19:39.700 --> 00:19:42.660 pricing, support, feature X, and maybe sentiment. 00:19:43.099 --> 00:19:45.059 Then you can process those thousands of reviews 00:19:45.059 --> 00:19:47.140 in bulk, either by writing a simple script or 00:19:47.140 --> 00:19:49.539 even using an automation platform like Make or 00:19:49.539 --> 00:19:52.599 N8n to feed the reviews to the API and collect 00:19:52.599 --> 00:19:55.299 the structured JSON results. No need for fancy 00:19:55.299 --> 00:19:57.559 specialized tools here. The LLM itself is the 00:19:57.559 --> 00:19:59.400 tool. Perfect. Using the LLM, the core strength 00:19:59.400 --> 00:20:02.160 for classification. Now, what about some advanced 00:20:02.160 --> 00:20:04.259 tips for people who are maybe further along that 00:20:04.259 --> 00:20:07.420 roadmap, the power users? Sure. Three key areas, 00:20:07.839 --> 00:20:11.299 cost, performance, and data. for cost optimization. 00:20:11.880 --> 00:20:13.759 Don't always use the most powerful, expensive 00:20:13.759 --> 00:20:16.460 model. Use cheaper ones, like GPT -4 and Mini, 00:20:16.779 --> 00:20:19.619 or Cloud 3 Haiku for simpler tasks within a workflow. 00:20:20.359 --> 00:20:22.880 Self -host N8n, if your volume justifies it, 00:20:23.079 --> 00:20:25.940 saves tons on execution fees. Try to combine 00:20:25.940 --> 00:20:28.299 multiple small steps into a single, more complex 00:20:28.299 --> 00:20:31.440 API call, if possible. Fewer calls often mean 00:20:31.440 --> 00:20:34.779 lower costs. Smart. OK, performance. For performance 00:20:34.779 --> 00:20:37.339 optimization, use streaming responses whenever 00:20:37.339 --> 00:20:39.740 possible, especially for chatbots. Makes the 00:20:39.740 --> 00:20:41.880 user experience feel much faster because text 00:20:41.880 --> 00:20:44.460 appears word by word. Implement proper error 00:20:44.460 --> 00:20:46.559 handling and fallbacks, what happens if an API 00:20:46.559 --> 00:20:49.240 fails? Does your whole workflow break? And consider 00:20:49.240 --> 00:20:51.480 prompt chaining, breaking down a very complex 00:20:51.480 --> 00:20:53.859 task into a sequence of simpler prompts, passing 00:20:53.859 --> 00:20:56.519 the output of one as input to the next. Can sometimes 00:20:56.519 --> 00:20:58.460 yield better, more reliable results than one 00:20:58.460 --> 00:21:01.160 massive prompt. Good points. And data strategy 00:21:01.160 --> 00:21:04.059 for power users. Crucial. For data strategy. 00:21:04.299 --> 00:21:07.779 Keep sensitive data on -premises or use self 00:21:07.779 --> 00:21:11.160 -hosted tools like N8n if possible. Understand 00:21:11.160 --> 00:21:13.839 the data retention policies of every cloud service 00:21:13.839 --> 00:21:16.420 you use. How long do they keep your prompts and 00:21:16.420 --> 00:21:19.119 responses? Plan for potential migration. How 00:21:19.119 --> 00:21:21.880 easy would it be to switch LM providers or vector 00:21:21.880 --> 00:21:25.119 databases if needed? Actively try to avoid vendor 00:21:25.119 --> 00:21:27.059 lock -in where you become totally dependent on 00:21:27.059 --> 00:21:29.740 one specific proprietary tool. That data piece 00:21:29.740 --> 00:21:32.119 feels really critical, especially as businesses 00:21:32.119 --> 00:21:34.420 rely more on these tools. Thinking about that, 00:21:34.440 --> 00:21:36.559 what's one aspect power users often overlook? 00:21:36.829 --> 00:21:39.549 I'd say that data strategy piece, really thinking 00:21:39.549 --> 00:21:41.670 through data control, where it lives, retention 00:21:41.670 --> 00:21:44.170 policies, and consciously avoiding getting locked 00:21:44.170 --> 00:21:47.190 into one vendor's ecosystem. Looking ahead, what 00:21:47.190 --> 00:21:48.809 are some key trends people should be watching 00:21:48.809 --> 00:21:51.230 that will shape their AI strategy? Well, one 00:21:51.230 --> 00:21:53.410 huge one is the rise of open source models. We 00:21:53.410 --> 00:21:55.529 mentioned Llama 3, but others like Mistral AI 00:21:55.529 --> 00:21:57.650 are also getting incredibly good, really fast. 00:21:57.930 --> 00:22:00.210 This is democratizing access beyond just the 00:22:00.210 --> 00:22:02.549 big tech companies. Yeah, absolutely. And closely 00:22:02.549 --> 00:22:05.630 related is local AI. Tools like a Llama are making 00:22:05.630 --> 00:22:08.490 it surprise to download and run pretty powerful 00:22:08.490 --> 00:22:11.009 LLMs directly on your own laptop or desktop. 00:22:11.609 --> 00:22:13.930 That means speed benefits, potential cost savings, 00:22:14.130 --> 00:22:16.289 and complete privacy since your data never leaves 00:22:16.289 --> 00:22:18.190 your machine. That's a big deal. Definitely. 00:22:18.440 --> 00:22:21.680 Another trend is deeper multimodal integration. 00:22:22.099 --> 00:22:24.740 We touched on this, but workflows that seamlessly 00:22:24.740 --> 00:22:28.420 blend text, voice, images, maybe even video, 00:22:28.579 --> 00:22:30.619 all working together on a task that's gonna become 00:22:30.619 --> 00:22:32.880 much more common and powerful. For sure. And 00:22:32.880 --> 00:22:35.319 lastly, agent frameworks. Things like Crew AI 00:22:35.319 --> 00:22:38.220 or AutoGen. These allow you to set up multiple 00:22:38.220 --> 00:22:40.519 AI agents, each maybe with a different LLM or 00:22:40.519 --> 00:22:42.240 specialized tools, and have them collaborate 00:22:42.240 --> 00:22:45.339 like a team to solve complex problems. One agent 00:22:45.339 --> 00:22:47.380 researches, another writes, another critiques. 00:22:47.990 --> 00:22:50.609 Fascinating. Wow, AI teams. So with all this 00:22:50.609 --> 00:22:53.529 constant change, new models, local AI agents, 00:22:54.289 --> 00:22:56.309 what are the skills that won't become obsolete, 00:22:56.470 --> 00:22:58.109 things people should really focus on developing? 00:22:58.430 --> 00:23:01.450 Great question. Four things come to mind. First, 00:23:02.089 --> 00:23:04.690 prompt engineering fundamentals. No matter how 00:23:04.690 --> 00:23:07.029 smart the AI gets, knowing how to communicate 00:23:07.029 --> 00:23:09.190 your intent clearly and effectively will always 00:23:09.190 --> 00:23:13.180 be crucial. Second, problem decomposition. the 00:23:13.180 --> 00:23:15.640 ability to take a big complex problem and break 00:23:15.640 --> 00:23:17.900 it down into smaller, manageable steps that an 00:23:17.900 --> 00:23:21.059 AI can actually handle. Third, systems thinking. 00:23:21.759 --> 00:23:23.880 Understanding how all these different tools and 00:23:23.880 --> 00:23:26.240 domains fit together, how data flows between 00:23:26.240 --> 00:23:28.559 them, how changes in one part affect others, 00:23:28.920 --> 00:23:32.240 seeing the whole picture. And fourth, basic programming 00:23:32.240 --> 00:23:34.839 literacy. Again, not necessarily becoming a pro 00:23:34.839 --> 00:23:37.059 developer, but understanding fundamental concepts 00:23:37.059 --> 00:23:39.920 like APIs, data structures, functions, and patients. 00:23:40.440 --> 00:23:42.319 It just makes you so much more effective at using 00:23:42.319 --> 00:23:45.240 any tool, low code or not. It helps you troubleshoot 00:23:45.240 --> 00:23:49.400 and think logically about workflows. the system 00:23:49.400 --> 00:23:52.460 and basic code concepts. Out of those four essential 00:23:52.460 --> 00:23:54.700 skills, if you had to pick the absolute bedrock 00:23:54.700 --> 00:23:56.700 foundation, what would it be? Got to be prompt 00:23:56.700 --> 00:23:59.279 engineering. At its core, it's about clear communication 00:23:59.279 --> 00:24:01.119 with the AI. That's fundamental to everything 00:24:01.119 --> 00:24:03.440 else. Let's try to wrap this all up. The big 00:24:03.440 --> 00:24:06.599 idea we want you to take away today, this. The 00:24:06.599 --> 00:24:09.799 AI landscape, it's going to keep changing. Rapidly. 00:24:09.799 --> 00:24:12.500 New models, new tools, new hype cycles. That's 00:24:12.500 --> 00:24:15.240 the reality. But your goal shouldn't be to try 00:24:15.240 --> 00:24:17.440 and learn or use every single new thing that 00:24:17.440 --> 00:24:19.160 comes out. That's impossible and exhausting. 00:24:19.779 --> 00:24:22.119 The real goal is to master a strategic framework 00:24:22.119 --> 00:24:24.619 for thinking about it all. Understand the key 00:24:24.619 --> 00:24:27.119 domains. Know the trade -offs, like that pain 00:24:27.119 --> 00:24:29.569 meter between convenience and control. and make 00:24:29.569 --> 00:24:31.569 decisions about which tools to use based on your 00:24:31.569 --> 00:24:33.130 specific needs and the problem you're trying 00:24:33.130 --> 00:24:35.069 to solve, not just based on whatever's trending 00:24:35.069 --> 00:24:37.509 this week. It's about solving real problems, 00:24:37.970 --> 00:24:40.410 efficiently. Couldn't agree more. And to help 00:24:40.410 --> 00:24:42.849 you put this into practice, here are some concrete 00:24:42.849 --> 00:24:44.930 next steps you can take this week. Seriously, 00:24:45.349 --> 00:24:47.190 choose one LLM, maybe the one you already use 00:24:47.190 --> 00:24:49.930 most, or pick Claude or chat GPT. Spend just 00:24:49.930 --> 00:24:52.910 two hours really digging into its nuances. Try 00:24:52.910 --> 00:24:55.329 writing maybe 10 different prompts for the same 00:24:55.329 --> 00:24:57.690 simple task just to see how the outputs vary. 00:24:58.089 --> 00:25:00.490 and identify just one repetitive task in your 00:25:00.490 --> 00:25:02.450 work that you think could potentially be automated. 00:25:02.700 --> 00:25:05.940 Just IDNFA this month. Now, pick one automation 00:25:05.940 --> 00:25:09.059 platform, Zapier, make N8N, and commit to building 00:25:09.059 --> 00:25:11.240 your first actual workflow to automate that task 00:25:11.240 --> 00:25:14.519 you identified. Also, maybe join one or two active 00:25:14.519 --> 00:25:17.039 AI communities online, selectively, just to keep 00:25:17.039 --> 00:25:19.000 a pulse on things. And set yourself a small, 00:25:19.220 --> 00:25:21.599 realistic monthly budget for AI tools this quarter. 00:25:22.119 --> 00:25:23.700 Take stock of your current tool stack. Are you 00:25:23.700 --> 00:25:25.339 actually using all the AI tools you might be 00:25:25.339 --> 00:25:27.500 paying for? Challenge yourself to learn the basics 00:25:27.500 --> 00:25:30.240 of one new domain from the nine we covered. Maybe 00:25:30.240 --> 00:25:33.160 dip your toes into voice AI or try it. and try 00:25:33.160 --> 00:25:35.259 to build something small that combines maybe 00:25:35.259 --> 00:25:37.500 two or three different AI capabilities, like 00:25:37.500 --> 00:25:39.839 using an LLM to generate text and then an automation 00:25:39.839 --> 00:25:42.640 tool to email it. Those are great, actionable 00:25:42.640 --> 00:25:44.779 steps. It really comes down to this. The best 00:25:44.779 --> 00:25:47.420 AI strategy isn't about having access to everything. 00:25:47.799 --> 00:25:49.839 It's about knowing exactly what you need, why 00:25:49.839 --> 00:25:52.119 you need it, and having a clear path to get there. 00:25:52.720 --> 00:25:55.000 Stop chasing every new shiny object and start 00:25:55.000 --> 00:25:57.079 building solutions to your actual problems. Thank 00:25:57.079 --> 00:25:58.880 you so much for joining us on this deep dive 00:25:58.880 --> 00:25:59.980 of UTRO music.