WEBVTT 00:00:00.000 --> 00:00:02.140 It feels like every time you start to get a grip 00:00:02.140 --> 00:00:05.080 on just how fast AI is moving, something comes 00:00:05.080 --> 00:00:07.580 along that, well, really throws you for a loop. 00:00:08.380 --> 00:00:11.279 And I got to say this. latest release from Anthropic, 00:00:11.460 --> 00:00:14.859 this Claude 3 .7 Sonnet, has that feel like we're 00:00:14.859 --> 00:00:16.899 seeing a real shift happening right in front 00:00:16.899 --> 00:00:19.839 of us? Oh, absolutely. The amount of buzz and 00:00:19.839 --> 00:00:22.039 in -depth analysis we're seeing around this release, 00:00:22.160 --> 00:00:23.600 especially when you compare it head -to -head 00:00:23.600 --> 00:00:26.739 with the big players like GPT -3 and DeepSeq 00:00:26.739 --> 00:00:29.059 and, of course, Google's Gemini, it's a pretty 00:00:29.059 --> 00:00:30.940 clear sign that we're looking at something significant 00:00:30.940 --> 00:00:33.780 here. For sure. So for our listeners out there 00:00:33.780 --> 00:00:35.460 who are following along with these developments, 00:00:36.000 --> 00:00:38.679 think of this as us cutting through the noise 00:00:38.679 --> 00:00:41.320 and getting right to the core of what makes Cloud 00:00:41.320 --> 00:00:44.700 3 .7 so interesting. We've been digging into 00:00:44.700 --> 00:00:46.479 the comparisons, all the evaluations that are 00:00:46.479 --> 00:00:48.100 out there, and we're going to pull out the key 00:00:48.100 --> 00:00:49.840 insights. We want you to come away from this 00:00:49.840 --> 00:00:53.479 understanding why this particular model is generating 00:00:53.479 --> 00:00:56.079 so much excitement and what it really means for 00:00:56.079 --> 00:00:58.799 the future of AI. We're going to focus on the 00:00:58.799 --> 00:01:00.380 stuff that's going to have the biggest impact. 00:01:00.560 --> 00:01:02.960 You know, for anyone who wants to quickly grasp 00:01:02.960 --> 00:01:04.980 what's important, we're looking at some recent 00:01:04.980 --> 00:01:07.920 data that puts Cloud 3 .7 right up against its 00:01:07.920 --> 00:01:10.439 main competitors. OK. All right, let's get into 00:01:10.439 --> 00:01:11.920 it. The first thing that really jumps out at 00:01:11.920 --> 00:01:14.219 you is this whole concept of hybrid reasoning, 00:01:14.280 --> 00:01:16.540 right? Yeah. And what's so fascinating about 00:01:16.540 --> 00:01:20.140 it is the approach Anthropic has taken to solving 00:01:20.140 --> 00:01:23.000 a problem that's it's been a real sticking point 00:01:23.000 --> 00:01:25.200 in AI for a long time. And that's the trade off 00:01:25.200 --> 00:01:29.549 between speed. and the ability to do really deep 00:01:29.549 --> 00:01:32.370 complex reasoning. Traditionally, models have 00:01:32.370 --> 00:01:34.769 been optimized for one or the other. You've got 00:01:34.769 --> 00:01:37.650 your... models like GPT -3 that are, you know, 00:01:37.870 --> 00:01:40.390 super fast at generating responses. And then 00:01:40.390 --> 00:01:42.409 you have others like DeepSeq that are really 00:01:42.409 --> 00:01:45.250 powerful in very specific computationally intensive 00:01:45.250 --> 00:01:48.370 areas. And then there's Gemini, which, you know, 00:01:48.510 --> 00:01:51.269 aims for more general intelligence, but sometimes 00:01:51.269 --> 00:01:53.109 its performance can be a bit all over the place, 00:01:53.189 --> 00:01:54.989 depending on what you're asking it to do. It 00:01:54.989 --> 00:01:56.930 almost felt like you had to choose what you were 00:01:56.930 --> 00:02:00.010 going for, right? Like, do you want a quick... 00:02:00.250 --> 00:02:03.310 but maybe superficial answer, or are you willing 00:02:03.310 --> 00:02:05.129 to wait for something that's been more thoroughly 00:02:05.129 --> 00:02:08.789 thought out? But it seems like Claude 3 .7 is 00:02:08.789 --> 00:02:11.069 trying to change that whole dynamic with this 00:02:11.069 --> 00:02:14.229 hybrid approach. Exactly. Instead of having... 00:02:13.840 --> 00:02:16.819 you know, separate models or different optimization 00:02:16.819 --> 00:02:18.860 pathways for different kinds of cognitive tasks, 00:02:19.300 --> 00:02:21.680 Claude 3 .7 has this ability to switch between 00:02:21.680 --> 00:02:23.860 different modes of thinking all within a single 00:02:23.860 --> 00:02:26.099 architecture. It's not just about being faster 00:02:26.099 --> 00:02:28.500 or being smart. It's about being able to understand 00:02:28.500 --> 00:02:30.819 what the task requires and then adapting its 00:02:30.819 --> 00:02:32.780 thinking to fit that task. And that's something 00:02:32.780 --> 00:02:35.539 that, you know, is much closer to how human intelligence 00:02:35.539 --> 00:02:37.639 actually works. I like that way of putting it. 00:02:38.039 --> 00:02:40.400 So can you give us like a real world example 00:02:40.400 --> 00:02:42.939 of how this plays out? Sure, think about a simple 00:02:42.939 --> 00:02:48.699 question like, what's 2 plus 2? Claude 3 .7 can 00:02:48.699 --> 00:02:50.979 process that and give you an answer almost instantly. 00:02:51.139 --> 00:02:54.199 It's incredibly fast. But now, imagine you ask 00:02:54.199 --> 00:02:56.639 it to plan a two -week trip to Italy, taking 00:02:56.639 --> 00:03:00.039 into account things like weather, budget, and 00:03:00.039 --> 00:03:02.180 even travel restrictions between different cities. 00:03:02.439 --> 00:03:05.099 In that case, the model shifts gears. It goes 00:03:05.099 --> 00:03:07.240 into this more deliberate step -by -step mode 00:03:07.240 --> 00:03:09.560 where it pulls in a much wider range of information 00:03:09.560 --> 00:03:11.919 and takes the time to really analyze everything 00:03:11.919 --> 00:03:14.360 before it gives you a comprehensive, well -reasoned 00:03:14.360 --> 00:03:17.699 plan. So it's like having a system that can pull 00:03:17.699 --> 00:03:21.539 a basic facts in a snap, but it can also engage 00:03:21.539 --> 00:03:23.520 in some really intricate planning and problem 00:03:23.520 --> 00:03:25.659 solving all within the same model. That sounds 00:03:25.659 --> 00:03:27.520 incredibly efficient if it works the way they 00:03:27.520 --> 00:03:29.620 say it does. And that's the key benefit for users, 00:03:29.759 --> 00:03:31.719 right? You no longer have to choose between speed 00:03:31.719 --> 00:03:34.780 and depth. You get both in one package. And this 00:03:34.780 --> 00:03:37.180 is something that seems to give Claude 3 .7 a 00:03:37.180 --> 00:03:40.919 real advantage over models that are more specialized 00:03:40.919 --> 00:03:43.400 or stuck in a single mode of thinking. And it's 00:03:43.400 --> 00:03:45.610 not just theoretical either, right? Anthropic 00:03:45.610 --> 00:03:48.389 has run its own tests, and the results seem to 00:03:48.389 --> 00:03:50.310 back up their claims pretty strongly. That's 00:03:50.310 --> 00:03:52.569 right. Their internal testing data shows that 00:03:52.569 --> 00:03:55.389 Claude 3 .7 is scoring higher in several key 00:03:55.389 --> 00:03:58.349 areas, like general problem solving, following 00:03:58.349 --> 00:04:01.310 complex instructions, and handling those really 00:04:01.310 --> 00:04:04.659 intricate multi -step reasoning tasks. all compared 00:04:04.659 --> 00:04:07.300 to their earlier Claude models. This suggests 00:04:07.300 --> 00:04:09.300 that their hybrid reasoning approach isn't just 00:04:09.300 --> 00:04:12.439 a cool idea, but that it's actually a real measurable 00:04:12.439 --> 00:04:15.319 improvement in how the model works. And this 00:04:15.319 --> 00:04:17.860 ability to handle complex tasks so efficiently 00:04:17.860 --> 00:04:20.019 has led to some really innovative applications. 00:04:20.360 --> 00:04:22.259 And maybe one of the most exciting is in the 00:04:22.259 --> 00:04:24.860 realm of software development with Claude Code. 00:04:25.160 --> 00:04:28.279 Oh yeah, Claude Code. Now this is something that 00:04:28.279 --> 00:04:31.029 could be a real game changer for... Pretty much 00:04:31.029 --> 00:04:34.649 anyone who writes or works with software. It's 00:04:34.649 --> 00:04:36.930 more than just suggesting the next line of code, 00:04:37.050 --> 00:04:38.509 isn't it? It's something much bigger than that. 00:04:38.649 --> 00:04:42.269 It is. Cloud Code is being described as an agentic 00:04:42.269 --> 00:04:45.410 AI coding tool. And that's a significant leap 00:04:45.410 --> 00:04:47.769 forward in the level of automation it brings 00:04:47.769 --> 00:04:50.889 to software development. Basically, what it means 00:04:50.889 --> 00:04:54.649 is the AI can act more independently and and 00:04:54.649 --> 00:04:57.149 proactively within the coding environment. It's 00:04:57.149 --> 00:04:59.389 not just passively responding to commands. It's 00:04:59.389 --> 00:05:01.490 actually taking initiative and doing things on 00:05:01.490 --> 00:05:03.509 its own. We're talking about capabilities that 00:05:03.509 --> 00:05:06.750 go way beyond simple code completion. OK, that 00:05:06.750 --> 00:05:08.430 makes the whole agentic thing a lot. clearer. 00:05:08.970 --> 00:05:11.129 So in practical terms, what can Claude code actually 00:05:11.129 --> 00:05:13.129 do? What does it bring to the table? Well, first 00:05:13.129 --> 00:05:15.810 of all, it can search and understand entire code 00:05:15.810 --> 00:05:18.269 repositories. Think about how much time that 00:05:18.269 --> 00:05:20.269 could save a developer who's trying to navigate 00:05:20.269 --> 00:05:23.110 a large or unfamiliar code base. But it goes 00:05:23.110 --> 00:05:25.569 even further than that. It can edit multiple 00:05:25.569 --> 00:05:29.029 files simultaneously, which is a huge step forward 00:05:29.029 --> 00:05:31.970 in AI -assisted code modification. It can write 00:05:31.970 --> 00:05:34.170 and run tests to make sure the code is working 00:05:34.170 --> 00:05:36.589 correctly. And it can even automate the process 00:05:36.589 --> 00:05:38.810 of committing and pushing changes to platforms 00:05:38.810 --> 00:05:42.029 like GitHub. And get this, it can even execute 00:05:41.899 --> 00:05:44.860 terminal commands, which opens up all sorts of 00:05:44.860 --> 00:05:47.480 possibilities for automated debugging and even 00:05:47.480 --> 00:05:49.779 deployment tasks. Wow, that's a lot more than 00:05:49.779 --> 00:05:51.800 just getting a suggestion for the next few characters 00:05:51.800 --> 00:05:53.560 you're typing. It sounds like a real partner 00:05:53.560 --> 00:05:55.899 in the coding process. That's the idea. It's 00:05:55.899 --> 00:05:58.180 about moving from just giving suggestions to 00:05:58.180 --> 00:06:00.540 actually participating in the whole process of 00:06:00.540 --> 00:06:03.480 building and maintaining software. For developers, 00:06:03.480 --> 00:06:06.480 this means less manual coding and editing, much 00:06:06.480 --> 00:06:10.279 faster development cycles, and a more efficient 00:06:10.279 --> 00:06:13.000 and integrated of working with AI. It's not just 00:06:13.000 --> 00:06:15.519 about isolated code snippets anymore. Claude 00:06:15.519 --> 00:06:18.860 3 .7, through Claude Code, can actually be part 00:06:18.860 --> 00:06:21.899 of the team, so to speak. So how does this compare 00:06:21.899 --> 00:06:25.500 to other tools that developers might be using? 00:06:25.579 --> 00:06:27.519 Because there are already other AI -powered... 00:06:27.930 --> 00:06:30.069 coding assistants out there? Ah, that's a good 00:06:30.069 --> 00:06:32.750 question. You know, models like GPT -3, they 00:06:32.750 --> 00:06:34.810 can generate code, but they usually can't do 00:06:34.810 --> 00:06:37.350 the deeper debugging or work across multiple 00:06:37.350 --> 00:06:39.970 files in a larger project. Gemini can also write 00:06:39.970 --> 00:06:42.589 code, but it's not always as seamless or robust 00:06:42.589 --> 00:06:45.529 as Claude code when it comes to real -world development 00:06:45.529 --> 00:06:47.750 workflows. And DeepSeq, while it's incredibly 00:06:47.750 --> 00:06:49.930 powerful in certain areas like scientific computing, 00:06:50.569 --> 00:06:53.170 it's not really designed for the wide range of 00:06:53.170 --> 00:06:55.569 tasks that software engineers face on a daily 00:06:55.569 --> 00:06:58.149 basis. It sounds like Claude code. is specifically 00:06:58.149 --> 00:07:01.129 designed to bridge that gap, to go beyond just 00:07:01.129 --> 00:07:03.709 generating code and actually becoming a truly 00:07:03.709 --> 00:07:06.189 integrated and helpful part of the entire software 00:07:06.189 --> 00:07:08.649 development process. Exactly. And the feedback 00:07:08.649 --> 00:07:11.689 from Anthropic's own internal testers has been 00:07:11.689 --> 00:07:13.689 incredibly positive, especially when it comes 00:07:13.689 --> 00:07:17.529 to using cloud code on large, complex projects. 00:07:17.930 --> 00:07:21.189 This level of automation and integration, it 00:07:21.189 --> 00:07:23.790 has the potential to really change how we think 00:07:23.790 --> 00:07:26.449 about AI -assisted coding and maybe even make 00:07:26.439 --> 00:07:28.720 less reliant on some of the existing tools that 00:07:28.720 --> 00:07:31.980 we use. Okay, so we've got this fundamental improvement 00:07:31.980 --> 00:07:34.339 in reasoning with the hybrid approach, and then 00:07:34.339 --> 00:07:37.399 we have this potentially revolutionary tool for 00:07:37.399 --> 00:07:40.360 software development with Claude code. Now, taking 00:07:40.360 --> 00:07:42.060 a step back and looking at the big picture, when 00:07:42.060 --> 00:07:45.899 we compare Claude 3 .7 directly to GPT -3, DeepSeq, 00:07:45.939 --> 00:07:47.879 and Gemini, what are the major differences we 00:07:47.879 --> 00:07:50.639 see? What stands out? When we look at those core 00:07:50.639 --> 00:07:54.000 capabilities, the advantages of Claude 3 .7 really 00:07:54.000 --> 00:07:57.779 start to come into focus. In terms of reasoning, 00:07:57.939 --> 00:08:00.160 the hybrid approach gives it a real edge. It 00:08:00.160 --> 00:08:02.220 can handle those quick, everyday information 00:08:02.220 --> 00:08:05.139 requests, but it can also tackle really complex, 00:08:05.420 --> 00:08:08.459 multi -layered analytical problems. GPT -3 is 00:08:08.459 --> 00:08:10.980 super fast, but it can sometimes struggle with 00:08:10.980 --> 00:08:13.120 logical consistency when you get into longer, 00:08:13.180 --> 00:08:16.240 more involved tasks. DeepSeek is amazing at things 00:08:16.240 --> 00:08:18.620 like math and computational science, but it's 00:08:18.620 --> 00:08:20.560 not as flexible when it comes to different types 00:08:20.560 --> 00:08:23.060 of reasoning challenges. And Gemini, well, it's 00:08:23.060 --> 00:08:25.360 got impressive multimodal understanding, but 00:08:25.360 --> 00:08:27.399 its structured reasoning can be a bit inconsistent, 00:08:27.399 --> 00:08:29.600 which can affect its reliability when you're 00:08:29.600 --> 00:08:31.540 dealing with purely logic -based questions. And 00:08:31.540 --> 00:08:33.799 what about when it comes to coding specifically? 00:08:34.059 --> 00:08:36.700 How does Quad 3 .7 stack up against the others 00:08:36.700 --> 00:08:38.799 in that area? Well, with Quad code, it's really 00:08:38.799 --> 00:08:40.919 in a league of its own. The ability to search, 00:08:41.159 --> 00:08:43.980 understand, modify, and test code across entire 00:08:43.980 --> 00:08:46.139 development development workflows, that's a major 00:08:46.139 --> 00:08:49.620 differentiator. GPT -3 can generate code snippets, 00:08:49.659 --> 00:08:52.860 but it can't do the deeper debugging or work 00:08:52.860 --> 00:08:55.360 across multiple files in a larger project like 00:08:55.360 --> 00:08:58.120 Cloud Code can. Gemini can produce functional 00:08:58.120 --> 00:09:00.720 code, but as we talked about earlier, it's a 00:09:00.720 --> 00:09:02.919 bit more prone to errors, especially in more 00:09:02.919 --> 00:09:06.080 complex situations. And DeepSeq is very specialized, 00:09:06.120 --> 00:09:08.600 so it's not as broadly applicable to the kinds 00:09:08.600 --> 00:09:10.940 of tasks that most software engineers are doing. 00:09:11.159 --> 00:09:14.039 Got it. And finally, what about speed? and efficiency, 00:09:14.360 --> 00:09:16.059 because that's always a practical consideration, 00:09:16.080 --> 00:09:18.940 right? Yeah, for sure. And Claude 3 .7 seems 00:09:18.940 --> 00:09:20.759 to strike a really nice balance there. It can 00:09:20.759 --> 00:09:22.980 be super fast when you need a quick answer, but 00:09:22.980 --> 00:09:26.419 it can also shift into a more in -depth thinking 00:09:26.419 --> 00:09:29.279 mode when the task calls for it. It's constantly 00:09:29.279 --> 00:09:31.659 adapting and allocating its resources based on 00:09:31.659 --> 00:09:34.620 what's needed. GPT -3 is undeniably fast, but 00:09:34.620 --> 00:09:37.259 sometimes that speed comes at the cost of accuracy 00:09:37.259 --> 00:09:40.840 or depth of understanding. Gemini can be a bit 00:09:40.840 --> 00:09:43.059 slower, especially when you have those back and 00:09:43.059 --> 00:09:45.720 forth multi -turn conversations, which can be 00:09:45.720 --> 00:09:48.080 a problem in some real -time applications. And 00:09:48.080 --> 00:09:50.200 DeepSeq, again, it's great in its niche, but 00:09:50.200 --> 00:09:52.179 it doesn't have the same level of flexibility 00:09:52.179 --> 00:09:55.659 or adaptability as Cloud 3 .7. So it sounds like 00:09:55.659 --> 00:09:58.279 across the board, Cloud 3 .7 is presenting a 00:09:58.279 --> 00:10:00.960 really strong and well -rounded set of capabilities. 00:10:02.109 --> 00:10:04.110 Earlier you mentioned the transparency of Claude 00:10:04.110 --> 00:10:05.950 3 .7, and I think that's something that deserves 00:10:05.950 --> 00:10:07.509 a closer look because it seems like a really 00:10:07.509 --> 00:10:10.070 important differentiator. What makes it so transparent? 00:10:10.289 --> 00:10:12.610 Why does that matter? It's one of the few models 00:10:12.610 --> 00:10:15.009 out there that actually lets you see its thought 00:10:15.009 --> 00:10:17.590 process, its reasoning behind the answers it 00:10:17.590 --> 00:10:19.309 gives. Right, so that's a big difference from 00:10:19.309 --> 00:10:21.149 what we're used to with, well, a lot of the other 00:10:21.149 --> 00:10:24.350 big AI models. Exactly. With models like GPT 00:10:24.350 --> 00:10:27.490 -3, Gemini, and DeepSeq, you get the output, 00:10:27.490 --> 00:10:30.090 but you don't really know how the AI got there. 00:10:30.210 --> 00:10:32.590 It's like a black box. And that can make it really 00:10:32.590 --> 00:10:35.730 hard to check if the information is accurate, 00:10:35.929 --> 00:10:39.009 to debug errors, or even to just understand if 00:10:39.009 --> 00:10:41.210 the AI is interpreting your request the way you 00:10:41.210 --> 00:10:43.990 intended it to. So what are the benefits of having 00:10:43.990 --> 00:10:46.950 this transparency, this ability to see how the 00:10:46.950 --> 00:10:49.389 AI is thinking? Well, for one, it builds trust. 00:10:49.629 --> 00:10:52.149 When you can see how the AI arrived at a conclusion, 00:10:52.470 --> 00:10:54.330 you're more likely to trust that the result is 00:10:54.330 --> 00:10:56.490 accurate. And for developers and researchers, 00:10:56.629 --> 00:10:59.190 it makes debugging much easier. If there's an 00:10:59.190 --> 00:11:01.450 error, you can trace it back to the specific 00:11:01.450 --> 00:11:03.529 step where things went wrong, instead of just 00:11:03.529 --> 00:11:06.909 having a wrong answer with no explanation. And 00:11:06.909 --> 00:11:09.490 it also helps with ethical considerations and 00:11:09.490 --> 00:11:12.289 making sure the AI is aligned with human values 00:11:12.289 --> 00:11:15.429 and intentions. By seeing the reasoning process, 00:11:15.470 --> 00:11:19.750 it's easier to spot potential biases or contradictions 00:11:19.750 --> 00:11:22.029 in how the AI is thinking, which is important 00:11:22.029 --> 00:11:24.570 for building safer and more reliable AI systems. 00:11:25.070 --> 00:11:27.570 And finally, it just creates a better user experience 00:11:27.570 --> 00:11:30.330 overall. Whether you're using the model for coding, 00:11:30.429 --> 00:11:32.990 research, or just general problem solving, being 00:11:32.990 --> 00:11:34.789 able to see its thought process gives you more 00:11:34.789 --> 00:11:37.110 control, more confidence in the accuracy of the 00:11:37.110 --> 00:11:39.090 information, and it just makes the whole interaction 00:11:39.090 --> 00:11:41.570 more satisfying and insightful. It really underscores 00:11:41.570 --> 00:11:44.450 the importance of being able to show your work, 00:11:44.629 --> 00:11:47.679 even for an AI. Now let's talk about some of 00:11:47.679 --> 00:11:49.460 the mind -blowing performance gains that have 00:11:49.460 --> 00:11:52.860 been reported with Claude 3 .7. What kind of 00:11:52.860 --> 00:11:55.059 improvements are we seeing? The benchmarks across 00:11:55.059 --> 00:11:57.600 a whole range of tasks are really impressive. 00:11:57.779 --> 00:12:00.200 From math and physics to coding and following 00:12:00.200 --> 00:12:02.960 complex instructions, it looks like Claude 3 00:12:02.960 --> 00:12:05.799 .7 is a significant step forward in terms of 00:12:05.799 --> 00:12:08.740 both efficiency and accuracy. Can you give us 00:12:08.740 --> 00:12:10.980 some concrete examples of where these improvements 00:12:10.980 --> 00:12:14.320 are most noticeable? Sure. In areas like advanced 00:12:14.320 --> 00:12:17.000 math and physics, Claude 3 .7 is showing a much 00:12:17.000 --> 00:12:19.440 higher level of precision when working with complex 00:12:19.440 --> 00:12:21.620 equations compared to its predecessor, Claude 00:12:21.620 --> 00:12:25.440 3 .5. That means fewer errors, better accuracy 00:12:25.440 --> 00:12:27.700 in those multi -step problem -solving scenarios, 00:12:27.840 --> 00:12:30.100 and just overall better logical reasoning in 00:12:30.100 --> 00:12:32.399 those demanding fields. And what about the problem 00:12:32.399 --> 00:12:34.139 of hallucinations, which is something we hear 00:12:34.139 --> 00:12:35.960 about a lot with these large language models? 00:12:36.519 --> 00:12:38.899 The tendency for them to make stuff up or give 00:12:38.899 --> 00:12:41.700 inaccurate information. Has Claude 3 .7 made 00:12:41.700 --> 00:12:45.200 any progress in that area? It has. It seems that 00:12:45.200 --> 00:12:48.279 Claude 3 .7 has significantly reduced its rate 00:12:48.279 --> 00:12:51.039 of hallucination, and that makes it much more 00:12:51.039 --> 00:12:53.559 reliable for tasks where you need accurate information 00:12:53.559 --> 00:12:56.179 like research or following step -by -step instructions. 00:12:56.700 --> 00:12:59.820 And when you compare it directly to GPT -3, DeepSeq, 00:12:59.820 --> 00:13:02.799 and Gemini, Claude 3 .7 consistently performs 00:13:02.799 --> 00:13:04.659 better when it comes to structured reasoning 00:13:04.659 --> 00:13:07.000 and handling those multi -step problems. problem 00:13:07.000 --> 00:13:08.919 -solving tasks. It sounds like they've really 00:13:08.919 --> 00:13:11.419 focused on making the model more reliable and 00:13:11.419 --> 00:13:14.980 less prone to generating nonsensical or factually 00:13:14.980 --> 00:13:17.360 incorrect information. Now, I have to ask about 00:13:17.360 --> 00:13:19.860 this Pokemon test I read about. It sounds like 00:13:19.860 --> 00:13:22.259 a pretty unusual way to evaluate an AI. Yeah, 00:13:22.259 --> 00:13:24.600 it is a unique approach. Basically, the researchers 00:13:24.600 --> 00:13:27.799 at Anthropic wanted to see if Clawed 3 .7 could 00:13:27.799 --> 00:13:30.379 actually think strategically and make progress 00:13:30.379 --> 00:13:33.159 in a classic Pokemon video game. It was a way 00:13:33.159 --> 00:13:35.720 to test its capabilities beyond the usual language 00:13:35.720 --> 00:13:38.039 under and coding challenges. Wait, they actually 00:13:38.039 --> 00:13:40.720 let it play the game? Yep. They wanted to see 00:13:40.720 --> 00:13:42.500 if it could understand the game's objectives, 00:13:42.700 --> 00:13:45.340 make decisions, plan ahead, and adapt to different 00:13:45.340 --> 00:13:47.799 challenges. And the results were pretty interesting. 00:13:48.159 --> 00:13:50.700 The previous version of the model, Claude 3 .5, 00:13:50.820 --> 00:13:53.379 really struggled. It couldn't even get past the 00:13:53.379 --> 00:13:55.460 starting area of the game. It seemed to have 00:13:55.460 --> 00:13:58.779 trouble coordinating actions and making effective 00:13:58.779 --> 00:14:01.980 decisions. Oh, wow. That's not a great sign for 00:14:01.980 --> 00:14:04.360 its strategic thinking abilities. Yeah. So how 00:14:04.360 --> 00:14:07.370 did Claude 3. It was a night and day difference. 00:14:07.610 --> 00:14:10.429 Claw 3 .7 was able to understand the game, make 00:14:10.429 --> 00:14:12.870 progress, and even defeat several gym leaders. 00:14:13.549 --> 00:14:15.990 And that's a pretty clear sign that it's capable 00:14:15.990 --> 00:14:18.210 of strategic planning, adapting to different 00:14:18.210 --> 00:14:20.509 situations, and making longer -term decisions. 00:14:20.590 --> 00:14:22.350 That's pretty impressive when you think about 00:14:22.350 --> 00:14:24.889 it. But why is its performance in a video game 00:14:24.889 --> 00:14:27.129 like Pokemon significant in the grand scheme 00:14:27.129 --> 00:14:30.129 of AI development? Because video games, especially 00:14:30.129 --> 00:14:33.389 a game like Pokemon with its exploration, battles, 00:14:33.570 --> 00:14:35.889 and character progression, they offer a really 00:14:35.889 --> 00:14:38.149 good testing ground for some of the key cognitive 00:14:38.149 --> 00:14:40.730 abilities that we want to see in AI. Things like 00:14:40.730 --> 00:14:43.450 problem solving, memory, and long -term planning. 00:14:43.720 --> 00:14:46.440 The fact that Claude 3 .7 could learn from its 00:14:46.440 --> 00:14:49.179 actions and adjust its strategy, it shows that 00:14:49.179 --> 00:14:51.820 AI is starting to move beyond just reacting to 00:14:51.820 --> 00:14:54.460 individual prompts. It's a step towards more 00:14:54.460 --> 00:14:57.440 structured, adaptive, and goal -oriented thinking. 00:14:57.860 --> 00:14:59.919 That makes sense. So it's not just about answering 00:14:59.919 --> 00:15:02.840 questions correctly. It's about being able to 00:15:02.840 --> 00:15:05.259 understand a complex system and operate within 00:15:05.259 --> 00:15:07.779 that system intelligently. over an extended period 00:15:07.779 --> 00:15:10.539 of time. Exactly. And a lot of AI models, even 00:15:10.539 --> 00:15:12.440 some of the most advanced ones, still struggle 00:15:12.440 --> 00:15:14.960 with maintaining consistent knowledge and making 00:15:14.960 --> 00:15:17.279 good decisions over multiple interactions or 00:15:17.279 --> 00:15:20.399 in complex environments. But Cloud 3 .7's ability 00:15:20.399 --> 00:15:23.740 to plan ahead, learn from its mistakes, and actually 00:15:23.740 --> 00:15:26.019 improve its performance based on its experience, 00:15:26.279 --> 00:15:28.500 it suggests that we're seeing real progress in 00:15:28.500 --> 00:15:31.220 how AI can handle those more intricate, dynamic 00:15:31.220 --> 00:15:34.659 tasks. So this all points to some pretty big 00:15:34.659 --> 00:15:38.000 implications for the ongoing AI race and the 00:15:38.000 --> 00:15:40.500 competition between the different players. Where 00:15:40.500 --> 00:15:44.340 does the emergence of Cloud 3 .7 leave the likes 00:15:44.340 --> 00:15:47.419 of OpenAI, Google, and DeepSeek? Are they feeling 00:15:47.419 --> 00:15:50.269 the pressure? I think it's safe to say that, 00:15:50.269 --> 00:15:52.590 yeah, they've been put on notice. Cloud 3 .7's 00:15:52.590 --> 00:15:54.870 hybrid reasoning model, the way it can switch 00:15:54.870 --> 00:15:57.929 between fast processing and deep analysis so 00:15:57.929 --> 00:16:00.730 seamlessly, it makes it potentially much more 00:16:00.730 --> 00:16:03.870 versatile and adaptable than GPT -3, DeepSeq, 00:16:03.990 --> 00:16:05.990 and even Gemini to a certain extent. Then you 00:16:05.990 --> 00:16:09.029 have Cloud Code, which is arguably the most advanced 00:16:09.029 --> 00:16:11.750 AI coding model out there right now. It's got 00:16:11.750 --> 00:16:13.830 features and capabilities that its competitors 00:16:13.830 --> 00:16:16.049 haven't really matched yet. So it's not just 00:16:16.049 --> 00:16:18.970 like excelling in one or two areas. it's making 00:16:18.970 --> 00:16:21.340 waves across the board. Yeah, that's right. It's 00:16:21.340 --> 00:16:23.320 not just about coding. Claude 3 .7 is showing 00:16:23.320 --> 00:16:26.240 real strength in reasoning, understanding, and 00:16:26.240 --> 00:16:28.500 following instructions and general problem solving. 00:16:29.139 --> 00:16:31.899 And the fact that it's so transparent in its 00:16:31.899 --> 00:16:33.679 reasoning, that's something that none of its 00:16:33.679 --> 00:16:35.960 main rivals can really match right now. So if 00:16:35.960 --> 00:16:38.019 the other players don't step up their game soon, 00:16:38.019 --> 00:16:40.559 they could find themselves falling behind. And 00:16:40.559 --> 00:16:43.440 there's already talk of Claude 4 .0 in development, 00:16:43.480 --> 00:16:46.259 so it's clear that this race to innovate in AI 00:16:46.259 --> 00:16:48.759 is far from over. Well, this has been a fascinating 00:16:48.759 --> 00:16:52.210 deep dive into Claude 3 .7 Sonnet and everything 00:16:52.210 --> 00:16:54.509 it brings to the table. I think the key takeaway 00:16:54.509 --> 00:16:58.690 here is that this new model represents a pretty 00:16:58.690 --> 00:17:00.750 significant leap forward in the evolution of 00:17:00.750 --> 00:17:03.950 AI. It's this potent combination of speed, deep 00:17:03.950 --> 00:17:06.630 reasoning, those advanced coding capabilities 00:17:06.630 --> 00:17:09.890 with Claude Code, and this unprecedented level 00:17:09.890 --> 00:17:11.910 of transparency that really sets it apart. I 00:17:11.910 --> 00:17:13.309 couldn't have said it better myself. It's not 00:17:13.309 --> 00:17:15.250 just a minor improvement. It's addressing some 00:17:15.250 --> 00:17:16.970 of the fundamental limitations that we've seen 00:17:16.970 --> 00:17:19.190 in previous generations of these large language 00:17:19.190 --> 00:17:23.140 models. listeners out there. As you're thinking 00:17:23.140 --> 00:17:27.049 about all of this, consider this. Given these 00:17:27.049 --> 00:17:29.730 rapid advances in AI, especially with the arrival 00:17:29.730 --> 00:17:33.230 of models like Claude 3 .7, what areas do you 00:17:33.230 --> 00:17:36.329 think will be most impacted by this new generation 00:17:36.329 --> 00:17:39.150 of intelligence systems? What new opportunities 00:17:39.150 --> 00:17:41.450 or possibilities might this unlock in your own 00:17:41.450 --> 00:17:44.049 work, your hobbies, or even just the way we interact 00:17:44.049 --> 00:17:46.589 with technology every day? The pace of innovation 00:17:46.589 --> 00:17:48.710 is just incredible, and models like Claude 3 00:17:48.710 --> 00:17:51.089 .7 are really pushing the boundaries of what 00:17:51.089 --> 00:17:53.069 we thought was possible. It's definitely something 00:17:53.069 --> 00:17:56.039 to keep a close eye on. Absolutely. time to be 00:17:56.039 --> 00:17:57.359 following these developments, that's for sure.