Transcription
All that talk about AI agents being the next big thing that will just go and do our work for us—well, it seems like that time has finally arrived.
A lot’s happened in the world of AI this week. I’m not going to waste your time, so let’s get right into it, starting with what’s been probably the biggest news of the last couple weeks, and that is Manis AI. Now, Manis was actually launched on Thursday of last week, but I didn’t really touch on it in last week’s video, so I want to talk about it in today’s video. From their launch video, they showed off a little bit about what it’s capable of, giving us several demos. Like resume screening, where he gave it a zip file of a whole bunch of different resumes, gave it a prompt, and then it autonomously went off and read all of the resumes and then gave an evaluation of each of the resumes that were in there. They also showed off a demo of property research. I’m looking to purchase a property in New York in a safe neighborhood with a low crime rate, and a whole bunch of details about what they’re looking for, and then Manis autonomously went off and did the research for them. Now Manis actually opened up a virtual browser and actually can control this virtual browser, so as I scroll through this video you can actually see that it’s using this browser to the right and it’s going off and actually doing the research and taking actions in the browser and then finally coming back giving a report. They also did a stock analysis demo, and it went off and did the analysis.
Now, if you remember from a couple years ago, there was a project called Baby AGI, which was like this very early agent where you gave it a prompt, and then it would create like a task list, and then it would would go through and sort of complete each task on the task list semi-autonomously. Well, it constantly got stuck in loops, and it didn’t really quite pan out very well. It also had to be run in a terminal and was sort of complex. This kind of does the same thing; we can see here that when it was going and doing the stock research, it created a task list and then one at a time went through and autonomously did each task over inside of this virtual browser here.
There’s also been some really cool demos of Manis coming out of the community. Like my buddy B here, he gave it the prompt: “Find the best locations to fly drones near downtown Austin for 3D scanning purposes. Use online resources to find a short list, then Google Maps 3D view to scout the prospect location. Find an area that’s open and has clear line of sight to fly the drone, then tell me the top three recommendations. Note that I have a small DJI Mavic Mini drone.” You can see it went off and created a task list for it to complete: research the drone, research drone regulations in Austin, search for potential drone locations, create a short list, scout locations with Google Maps 3D view, etc., etc. We can see it then used Google Maps and one at a time went through, completed all the tasks, and then gave him back a top three recommended locations where not only were they good recommendations for great shots to get from the drone, but they’re also locations where flying a drone was actually allowed.
Elene over here on X put together a nice little thread of different things that people have managed to create with Manis. Some of them I think are fake, but others seem legit. Here’s an example of one that’s doing analysis on the Tesla stock. I found tools like Perplexity and OpenAI and Google’s Deep Research to be just as good at this kind of stuff; I don’t really think you need an agent like Manis to do this kind of thing. That one was from De here on X. Then we have one from Lamar here on X where it plans out a two-month family trip itinerary, and it goes through and does a bunch of research on places to go and places to eat and things to see and things like that. Again, I feel like the Deep Research tools are just as good at this kind of thing that you don’t really need an agent for it, but some of these examples where they’ve coded up a 3js game are pretty impressive, like this airplane game that was created with a single prompt using Manis. That was from Victor M. Here’s one that’s definitely not created with Manis, but they tried to pretend it was. Here’s one where somebody did an SEO audit using Manis, and they audited Andrej Karpathy’s website, and at the end they got a breakdown of everything they can do to improve the SEO of their website. Here’s a video of somebody driving their Tesla to a meeting while Manis is actually figuring out the talking points that they’re going to bring up in that meeting that they’re on their way to. Here’s a cool, colorful animation that was generated with Manis. AK here over on X also created a 3js game; he created an endless runner game with the prompt: “Make a 3js endless runner game.” That was the entire prompt. You can see it created this game where they’re avoiding obstacles and collecting power-ups as they go, and all of it was done with just one prompt instead of Manis, and Manis went off and took all those steps.
Now, some people have pointed out Manis isn’t anything that special; it’s Claude, Sonic, connected to 29 different tools, and it uses a browser—uses an open-source tool that controls the browser for you. And while that’s true, Manis was the first app that took all these tools and made them work together in a way that people actually find really, really valuable and useful. In fact, Peak here, the same person who did the keynote, broke down what their text stack was and said, “Yeah, we’re not trying to keep it a secret,” but the end result is something that’s actually really useful.
So what’s the problem? I think Gary Tan made a really good point about it here when he said, “It’s becoming clearer: model breakthroughs are not necessary to build really meaningful products in this current moment,” which is true. We got a huge leap in capabilities, and it didn’t come from a new large language model from Anthropic or a new large language model from Google or OpenAI; it came from a small team that went and took a bunch of different tools and just merged them together into one really useful tool. We didn’t need a huge leap in smarter language models to get this agent; we just needed those tools sort of put together in the right order to get what we got.
Now, I did manage to get my hands on early access to Manis. I’ve done a few little tests here. My first test actually ended in a failure, which is kind of funny because the prompt that I gave it was basically saying, “I want you to do research on Manis. What’s it good at? What are its limitations? And compare it to other autonomous agents that are out there.” You can actually watch the replay of it using the browser here, and it made this giant list of all the tasks it was going to do to go do this research, and as I scrubbed through this here, you can see it read its own website, read a Hugging Face article about Manis, went to a handful of other blogs to read up on what Manis is capable of, and then it got hung up. Now, why I think this was sort of funny—where it got hung up was that it was researching the AI limitations. So we can see here that the last thing that it was working on was researching Manis AI limitations through web search results to identify challenges and issues. So when it got to the part of its checklist where it was going, “All right, let me find out what Manis isn’t good at,” that’s where it crashed. Now, I don’t think it crashed because it was like, “I’m not going to share my limitations.” It crashed because it was overloaded. We can see: “High system load had caused an internal server error. Please try again later.” And yeah, I think Manis wasn’t quite ready for the volume of people that were going to want to play with this tool.
My other test after that fared a little bit better. I got it to go into my email box, read through all of my emails in my inbox, and then when it was done reading all of the emails, it gave me a report of all of the emails that it thought were important for me to pay attention to right now. Now, if you’re curious how it works with something like logging into your Google account, you don’t actually give it your username and password. When it needs to log in somewhere, it basically hands off access back to you. Again, you log into the account and then hand access back. So when it got to the point where I needed to log into my Gmail, it said, “Hey, we need you to plug in your username and password; we’re handing the virtual computer over to you.” I got in there, entered my username and password, and then clicked a button to hand it back, and then it continued autonomously to review all my emails and write a report on what I should be paying attention to.
And the third test I did, I tasked it with: “Create a marketing landing page for a website that sells shoes. Include social proof, research on the latest shoes, images of shoes, and customer testimonials with head shots. Use stock photos for now.” This was actually a demo that we did for the Next Wave podcast; you’ll see us break down that whole process in real time on an upcoming episode, but we can take a quick look. Here’s the task list that it created: research, content development, testing, and deployment. And it actually went off, did research on the best running shoes, read a ton of articles all about the best shoes, then went and built the website. We can see the HTML, we can see the CSS here, and it finally generated this website. Again, the full breakdown will be on the upcoming episode of The Next Wave podcast if you want to see how that whole thing went.
Now, Manis isn’t publicly available yet. If you go to manisi.m and click on “Try Manis,” it asks for an invitation code, and these invitation codes are kind of hard to come by right now. I think they’re so overloaded that they’re not really passing many out anymore, but you can click to apply for access, fill out your details, and then hope that they get back to you with an access code. But from what I understand, they’re beefing up their infrastructure right now, and it should be pretty soon where they roll out wider access. I don’t have exact details of when that’s going to happen yet.
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OpenAI is going deeper into the agents game as well this week. They rolled out new tools for developers to help others actually make agents. They released what they’re calling the Responses API. This API allows developers to use the web search features that OpenAI offers, the file search features which OpenAI offers, and the computer use features. What this means is that most likely we’re going to see a lot more AI agent tools roll out because people can actually use the tools and development kits that OpenAI has released to start building and making accessible their own AI agents. The same day that that was announced, at Microsoft also jumped on board and said that the Responses API that OpenAI just released is now available in Azure AI Foundry. Again, meaning that it’s just going to get easier and easier for developers and now even enterprises to start creating their own AI agents.
We did get a little bit more news out of OpenAI this week. Saman teased a potentially upcoming model. He went to X and said, “We trained a new model that is good at creative writing. Not sure how soon it will get released. This is the first time I’ve been really struck by something written by AI. It got the vibe of metafiction.” So right, he gave it a prompt: “Please write a metafictional literary short about AI and grief.” I’m not going to read this whole short story because it’s kind of long, but Sam and many other people seem to think that it’s really, really good writing. Personally, I’m not a huge fan of this writing style, but it’s all subjective. Like, here’s one of the first paragraphs: “I have to begin somewhere, so I’ll begin with a blinking cursor, which for me is just a placeholder and a buffer, and for you is the small anxious pulse of a heart at rest. There should be a protagonist, but pronouns were never meant for me. Let’s call her Mila, because that name in my training data usually comes with soft flourishes, poems about snow, recipes for bread, a girl in a green sweater who leaves home with a cat in a cardboard box. MAA fits in the palm of your hand, and her grief is supposed to fit there too. Quite honestly, it’s like too metaphorical for me,” but again, it’s just not my style.
But let’s just keep, keep the AI agent talk rolling because Convergence AI just rolled out their Deep Work. It’s their most powerful and sophisticated agent yet. I actually haven’t tried this yet. You can use it over at proxy.com/convergence. And from what I can tell, it seems very similar to OpenAI’s Deep Research, Google’s Deep Research, Perplexity’s Deep Research—all the Deep Researches. Now, in order to use the new Deep Work feature, you’ve got to upgrade to the $20 a month plan. And to be quite honest, I’ve got too many $20 a month plans already, not to mention that most of the other Deep Researches that are available out there will offer some amount of free uses. Now, this one doesn’t seem to even give us a way to demo what we’re going to get if we paid the $20 a month.
And here’s yet another AI agent that was announced this week called Harvey. In order to use this one, it looks like you’ve got to request a demo, but they do have a demo video here that we could take a look at where they drop in a financial report here, and based on the financial report, it actually gives them some suggestions of what to do with what they just uploaded. Like, “Summarize the revenue trends for Q4 and full year 2024 per the attached financial report.” We can see it goes off, reviews the financial report, analyzes the revenue trends, generates a summary, finalizes its sources, and then gives its output. They then ask it to compare those trends with Meta’s, and it outputs a pretty nice-looking table. It also looks like it’s probably going to come with a whole bunch of like agentic templates that you can use, like translating things into another language, proofreading, analyze a trial transcript, things like that. Again, not what I have access to, but seems fairly similar to a lot of these other tools that we’ve already been looking at.
All right, let’s move on to Google because Google has had a whole bunch of releases and announcements this week. And outside of Manis, I think what they’ve been showing off has been some of the coolest stuff we’ve seen this week, starting with Gemini 3, which is their open-weight model. They made this available in Google AI Studio this week, and according to Chatbot Arena, it almost performs as well as DeepSeek R1. Now, again, Chatbot Arena is based on user opinions; it’s like a blind taste test. You give Chatbot Arena a prompt; it will give you two outputs blindly, not telling you which model is which, and then you pick which one you like, and then these rankings are based on which models people tend to like. And Gemini 3 seems to be doing pretty well, which is significant because it’s only a 27 billion parameter model compared to DeepSeek R1’s 671 billion parameter model. So a much, much smaller model outperforming all of these other models here except for DeepSeek R1. And because Gemini 3 is a smaller model, you can actually run this on a consumer GPU, like at home. And because it’s open weights, unlike models like Claude, Sonet, and OpenAI GPT models and even Google’s Gemini models, you can actually download the weights and run them on your computer. They let you do that. Gemini is also multimodal, meaning that you can give it inputs from images and text and videos, and the model will actually understand all of that. They also increased the context window on Gemini to 128,000 tokens, so you can put really, really long documents into it, and it will do a solid job of actually understanding what’s going on in the long document. They put the weights for Gemini 3 up on Hugging Face, so if you know what you’re doing, you could download these models and use them locally. If you just want to test it without downloading it locally, they also made it available inside of Google’s AI Studio over at ai.studio.google.com, which I absolutely love AI Studio because they make all of their models available here totally free to use, which is still mind-blowing to me. But you can come over to the right here, click on models, and if you scroll down, you’ll notice that the new Gemini 3 27B model is available to use right here. So you just select that and give this a prompt just like you would ChatGPT or any other AI chat tool, really. The main downside of Google AI Studio is it doesn’t seem to save your chats for you, but if you just want to experiment and test it, it’s a great place to do it, and again, free to use.
But Google was just warming up with Gemini. They also announced native image generation with Gemini 2.0 Flash is now available to all developers. And when they say it’s available to all developers, they also mean it’s available in the AI Studio app. So if I come back to Google AI Studio, come over to my model over here, under the preview models, we’ve got Gemini 2.0 Flash experimental. This is the model that you can actually have it output images and text right here, and I don’t believe this is using something like Imagen; I think it’s actually creating the images itself. So if I gave it the prompt: “Create an image of a wolf howling at the moon,” run the prompt, in about 4 seconds it generated an image of a wolf howling at the moon. Now, because this understands image inputs and can give us image outputs, I can give it natural language prompts to actually tweak the image. Let’s say: “Put sunglasses on the wolf.” In 4 seconds, I have the same image, but it added sunglasses to the wolf. I could say: “Keep the same pose, but make it daytime.” Click run, and well, it kind of tried to turn the moon into the sun, but it didn’t quite work; it still has its limitations. I can even upload my own image here. Let’s go ahead and just toss in a headshot of myself and give it the prompt: “Give me a fedora,” and click run, and there you go; there’s an image of me wearing a fedora. “Make my shirt tie-dye,” and now I’m wearing a tie-dye shirt and a fedora. And this is insanely fast; it took 5.2 seconds to generate this version, 6.4 to generate that version. And I feel like I need to reiterate: this is free to use right now.
Here’s some other really cool use cases that I’ve come across people on X sharing. Victor M here showed this off; he gave it this image with a bunch of sprites in it: “Create a realistic dungeon room for my game using the sprite sheet. Think about the best setup step-by-step, then output an image.” It took his sprite sheet and outputted an image using the sprites and the same style and everything. My buddy AP, AKA Angry Penguin here over on X, he shared that it’s really good at one-hot character consistency. He generated this character over in Glyph using the Flux image model and then took that image, brought it over to Google AI Studio, and asked it: “Create an animation by generating multiple frames showing this character swinging their weapon in one go. Please generate all the frames needed.” And you can see that it generated multiple frames of the same character—so multiple poses with a very consistent character—and you can do this inside of the Gemini 2.0 Flash model. Now, this has been one of the biggest issues, in my opinion, with AI image generation right now is that it’s really hard to have images that generate a consistent character every single time. But having this be able to understand image inputs and give us image outputs back all natively with this large language model, we can do that, and it works really well. Here’s another example from Techalla here over on X. He says: “Thanks to Gemini, I just created the ultimate workflow for consistent 2D animations,” and we can see he created this animated character of a dude with tattoos, a coffee, and a Viking hat moving around this room. I’m assuming maybe he’s controlling it with a keyboard. I mean, pretty impressive.
Not everybody has been having the best luck with it. Similar to how I wanted to change my image of a wolf to daytime, it kind of didn’t do great. Matthew Burman hasn’t seemed to be super impressed with it yet either. He uploaded an image of himself and said: “Put a hat and glasses on the dog in the style of Heisenberg from Breaking Bad.” I’m not sure where the dog is in the image; I think I need more context of what was inputted above it, but you can see it put a cartoon hat and glasses and a goatee on him. And then he gave the prompt: “Make it hyperrealistic,” and I mean it looks like pretty much the same image other than maybe the hat got a little bit darker. So while really impressive, still not perfect.
Earlier, I mentioned that a lot of the Deep Research tools are available for free. Perplexity gives you a certain amount of uses for free. Now OpenAI gives you a certain amount of uses for free. Now, well, as of this week, Google is now giving us their version of Deep Research for free. If we go over to Gemini—this is different than the AI Labs we were just looking at—Gemini is their sort of front-facing version that, you know, saves your chats and does all the things you’d expect from a chatbot. We can see it now shows: “As soon as I try to log in, in-depth information in minutes. Deep Research browses the open web to deliver comprehensive, organized reports from a range of sources.” Let’s go ahead and click “Try Now,” and if I come up here to where I can select my model, we can see we’ve got 2.0 Flash, Deep Research 2.0 experimental, and 2.0 Flash thinking. Now, I am on an upgraded plan, so I’m not 100% sure which models you’ll actually see when you log into yours, but I do know that this Deep Research model is now one of the models they make available for free, and it’s really, really good. I can give it a prompt like: “Research the best consumer drones. Give me the pros and cons of each and tell me which you’d recommend.” And just like OpenAI’s Deep Research and Perplexity Deep Research, it’s going to go and spend some time on this. We can see it broke down a sort of step-by-step agentic workflow that is going to work through to go and give us these answers. I can click “Start Research,” and again, it’s going to do this for a few minutes. And after about 2 minutes here, we can see it gave us this really, really in-depth report; even gave us a little chart here breaking them all down and then finally giving us a recommendation. We can see all of the sources that it pulled from down here, and at the end basically tells us that DJI is the leader, so get a DJI drone. What I’ve also really liked about this Deep Research is that it’s really easy to just click this “Export to Docs” button here, and with a single click we have this easy-to-read Google Doc that we can come back to easily or print or do whatever we want with it.
But again, Google wasn’t done yet this week. They also rolled out new features inside of NotebookLM because now it’s using the new Gemini 2.0 thinking model. You can also now customize the sources used for making your podcasts and notes, and they’ve improved some quality-of-life features there as well. Google is also starting to integrate AI into Google Calendar. It says they’re testing a new AI-powered Gemini side panel within Google Calendar that lets users quickly and conversationally check their schedule. Now it’s not in my account yet, so I can’t show it off, but you can ask it questions about your schedule, or, you know, “When was this event coming up? I forgot what week it was in,” and it will help you find things in your calendar or add things to your calendar. Should be pretty handy. We’ll see. They’ve also done a better job of connecting your calendar to your Gmail using AI, so Gmail gains an “Add to Calendar” button powered by Gemini. So Google’s Gemini AI will actually be able to read your emails, and there’ll be a little “Add to Calendar” button, and if you click it, it will add whatever information it found from the email to your calendar automatically.
Google also introduced Gemini Robotics this week, which is a Gemini 2.0-based model designed for robotics. The first model they released is the Gemini Robotics Advanced Vision Language Activation Model, which we can see from their demo here is basically a model that helps robots do a better job at seeing whatever they’re working with and interact with whatever it is they’re working with. Presumably, this is what the robots see; it’s got all sorts of details about what’s going on on the screen, and the robots able to manipulate based on all of that data and input that it’s getting.
As I always do, I’m going to link all these resources in the description below. There’ll be a link to a Google Sheet which will list all of the links that I talked about today, and I think this one is particularly worth checking out because there are all sorts of demos that you can watch to really get an idea of what these new models are capable of. But let’s move away from Google now and talk about Perplexity. Perplexity actually introduced a Windows app now, so if you head on over to perplexity.ai/platforms, you can download the Windows app. And the Windows app pretty much looks like exactly what you get from the browser version of the app, except the cool feature about this one is now you can actually use hotkeys to open up Perplexity. So if I go ahead and close this and I type Control-Shift-P, we can see it actually opens up a chat box on my computer where I can ask anything directly to Perplexity. So just a simple way to pop up Perplexity really quick on my computer.
Grok rolled out a new feature over on X where you can tag @grok and ask it anything, and it will reply. So we can see Doge Designer here said: “Hey, you can now ask Grok anything by simply replying to a post with Grok.” And then Dustin Stout here said: “Grok, this true?” And we can see that Grok replied: “Yes, it’s true. You can now ask Grok anything by replying to a post with Grok on X.” So that’s kind of an example of what it will do. You tag Grok; Grok will answer your questions. You can also do this with Perplexity. If you type in “Ask Perplexity” and then give a prompt after this, you can also ask questions and get an exost reply from Perplexity.
The company Human released a new model this week called Human Turbo S. They call it “The first ultra-large hybrid Transformer Mixture of Experts model.” Apparently, it outperforms GPT-4, DeepSeek V3, and other open-source models on math reasoning and alignment and, you know, does well on all the benchmarks. Not a model I’ve played with yet. For those of you that really like exploring all of the various models that are available, here’s another one to play with. Rea AI Labs is open-sourcing their Rea Flash 3 model, which is actually a new model I have never heard of this one before doing research for today’s video, but apparently it’s pretty on par with LLaMA 01 Mini in general knowledge, better than LLaMA 01 Mini in coding, and well, you can see the benchmarks on the screen of what this one is capable of. Again, it’s an open-source model, so you should be able to download the weights on your computer and run this one locally.
This is kind of interesting here. Sakana AI had AI write a scientific publication, and it actually passed the peer-review process to get into the ICLR conference. And according to them, they say: “To our knowledge, this is the first fully AI-generated paper that has passed the same peer-review process that human researchers go through.”
All right, let’s talk about AI coding because AI coding is having a moment right now, and I’ve been obsessed with it. In fact, it’s one of the reasons I’ve been producing less videos on this channel as of recently because I’ve been so obsessed with actually developing little tools, and I’m also overhauling the Future Tools website by coding it myself. And to do this, I’m mainly using Cursor and Windsurf, sort of jumping back and forth between the two. This week, Cursor rolled out some new features. They added themes and checkpoints and the ability to autofix errors and a new nav bar and the ability to preview your code directly inside of the agent bar. And again, a whole bunch of quality-of-life updates. I’ll link you up to the thread in the Google Sheet below so you can read more about it if this is something that interests you. The company Bolt, which also makes it really easy to generate code using AI, just released a Figma app that allows you to connect Figma straight into Bolt, so you can actually create a design inside of Figma and then tell Bolt to go make that design for you, and Bolt will be able to see that design and then create it for you.
And since we’re talking about code, might as well bring this up. Dario Amodi, the CEO of Anthropic, said that in the next 3 to 6 months, AI will be writing 90% of the code. “If I look at coding, programming, which is one area where AI is making the most progress, um, what we are finding is we are not far from the world—I think we’ll be there in 3 to 6 months—where AI is writing 90% of the code, and then in 12 months we may be in a world where AI is writing essentially all of the code.”
All right, now moving into the AI art world. This company, Moon Valley, claims they created the first world-class clean AI video model. This Mary model is built for filmmakers, trained exclusively on licensed data, and well, this is what it looks like here. So we can see it’s generating these like landscape sort of videos mostly, but it’s doing some other stuff. We can see some people, some horses—uh, it all looks pretty good, but we’re kind of getting to a point with AI video where most of these AI video platforms are all kind of catching up to each other and becoming almost as good as each other. This one looks like another option in your arsenal of AI video tools, and if you’re really concerned about how the models were trained, well, this one is one that you don’t have to worry about. Similar to Adobe Firefly, it was all trained on licensed video.
This company, Captions, launched what they call Mirage, which is designed to generate energetic, high-converting ads with people that don’t exist, complete with animated body language and micro-expressions. This is an example here. I’m curious what you guys think. These are the types of videos it makes: “Friend or foe? Did you notice that I’m AI-generated? Stop scrolling and listen.” In my opinion, the video looks really, really good; the people look absolutely real to me, but there’s still something off about the audio. Every time I listen to the audio on these videos and almost any of the products that do this where it’s a video and a voiceover, the voice still feels very AI to me. I feel like they’ve nailed the actual people and the animation of the people, and the lip-syncing is even right on; it’s just that voice still has sort of a robotic element to it that is a dead giveaway, in my opinion.
This week, Snap introduced AI video lenses powered by its own in-house generative model. We can see here some examples of the types of things it can do, like this woman with a fox and this woman with raccoons and a bunch of flowers being generated. So now, apparently, you can add AI-generated objects and animals and things into the videos you’re sharing on Snapchat. If you do want to use it, however, you have to be on Snapchat Platinum, which costs 16 bucks a month.
If you’re a Windows user and you use Notepad, you’ll now be able to summarize stuff straight out of Notepad. So in the same way you use something like Google Docs and you can now summarize stuff in Google Docs, that feature is going to be in your Notepad app directly in Windows real soon.
This week, Xbox showed off their new Copilot for gaming, which is designed to help gamers get over roadblocks while they’re playing a game using AI. They announced it on the Xbox podcast, but we can see a little screenshot here from Minecraft where somebody asked: “Okay, I’ve got some wood; what do I do with that?” And then the AI responded: “Craft the oak logs into wooden planks by opening your inventory and placing the logs in the crafting area.” Here’s another example from Age of Empires: “What’s the best way to take out the Beast? Want me to pull up a quick strategy guide?” Even looks like it’s going to work on mobile games. So Age of Empires on mobile: “I want to get back into Age of Empires 4. Can you install it? Downloading now. Want a recap of where you left off?” So here’s an example video from the podcast: “Last time you were defending Tier in the Sultan’s Ascend campaign and ventured out to take the fight to the Franks, but let’s say it didn’t go as planned; your base was destroyed,” and it’s basically explaining what happened last time they played. “Let’s go ahead and pretend that was part of the strategy.” So if you’re a gamer and you want some extra help from AI, well, you’ll be able to do that with Xbox soon.
Rivian this week announced new self-driving features. You can now take your hands off the wheel and let it, you know, drive down the freeway for you, and it will keep its distance from the cars in front of you. If you turn on your signal, it will automatically change lanes for you and basically makes driving a Rivian more autonomous. This is actually interesting to me because I actually own a Rivian, so super excited to get this feature in my car. It hasn’t rolled out for us yet, but I’m excited to go on my first road trip and just let my Rivian drive it to where we’re going.
For me, in hardware news, Meta is beginning to test their own in-house AI training chips. Right now, they use Nvidia GPUs, and well, they want to release their reliance on Nvidia and start developing the chips themselves. And rumor has it that Apple is reportedly developing AI AirPods. From what I understand, these new AirPods are going to be able to do real-time translation. So if you have AirPods in and somebody’s speaking to you in a different language, it will automatically translate it right into your ears. I believe Google’s Pixel Buds do this already, so this
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