AI never sleeps and this week has been absolutely insane. ByteDance releases the best video model out there, Seed Dance 2.5. MiniMax also releases their best video model, MiniMax H3. And the awesome thing is this will be open source. Deep Seek releases their newest model and they pulled off the impossible again. It's as good as GLM and Opus but like a hundred times cheaper and way smaller. We have a new open source model that's trained entirely on AMD chips, not Nvidia. This AI can turn an image into transparent layers which you can edit further. We have some new video world models which you can interact with in real time. Google releases their latest robotics model and a lot more. So let's jump right in. First up, Netflix releases a really cool open source AI called IDV2V. In the simplest sense, this can basically change the style of the scene without affecting the identity or the movement of the characters. So you can just plug in an existing video and then edit one keyframe to show the new look you want and then the system will spread that style across the entire video. So you can change things like the background, lighting, clothing, or overall style while keeping the face and expressions and movements of the character the same as the original clip. Now at the top of the page, if you click on this GitHub button and you scroll down a bit, here it contains all the instructions on how to download and run this locally on your computer. Notice that this can generate videos of up to 720p and this is pretty huge. So the main model is like almost 80 gigabytes in size. So this would only fit on like really high-end consumer hardware. But hopefully there will be more compressed versions of this in the future. If you're interested in reading further, I'll link to this main page in the description below. Also this week we have a new open source transcription tool which is really powerful. It's called Whisper Whisper 2 and this can basically take any audio and turn it into text like this. The cool thing is this actually has two different outputs. If you turn on the verbatim mode, this includes everything including stutters, hesitations, laughter, or other meta tags, plus repeats like this. Now, alternatively, you can also turn on the intended mode, which would get rid of all these hesitations and meta tags and give you the fully polished transcript. So, a super flexible tool. Not only that, but this also gives you precise word level timing. In fact, let's try this out. They've already released a free hugging face space for you to try this out online. Here is where you can upload any audio. So, let's upload this one. We are going to generate this in verbatim mode. >> However, due to the slow communication channels, styles in the West could lag behind by 25 to 30 years. >> All right, so that was the audio and as you can see, the transcript is indeed correct and it also gives you the start and end times for each word. Or here's another example. Let me play you the audio first. >> And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country. >> And as you can see, it's able to provide me the transcript here, plus the timing for each word. Now, this supports all these different languages as you can see here, and if you look at this self-made benchmark, then Crisper Whisper 2 is indeed even better than 11 Labs or some other leading transcription tools. And if you look at this benchmark on word level timestamps, then as you can see, Crisper Whisper also contains the least amount of errors. The awesome thing is they've released the models to this. So, if you click on this button, they've released four different models in this family. The smallest one is 0.2 billion parameters, and this is less than 500 megabytes in size, so you can easily fit this on most consumer devices. You don't even need a GPU. And then, the largest one is 2 billion parameters. This one is 3 gigabytes in size, which is still fairly tiny. You should be able to fit this in most GPUs. And then if you scroll down the Hugging Face page here, it contains all the instructions on how to download and run this locally on your computer. If you're interested in reading further, I'll link to this main page in the description below. Also this week, the Goat DeepSeek is back with another mind-blowing model. They just released the latest version of DeepSeek V4 Flash, and even though this is a flash model, it even performs as good as some of the full open-source models out there, including MiniMax M3, and it's just one point below JLM 5.2. This scores way higher than even the previous DeepSeek V4 Pro version, and as you can see it's like 10 points above the previous DeepSeek V4 Flash. What an insane upgrade. Note that here it says it has the same model structure as DeepSeek V4 Flash DSpark. In fact, this is a really important architecture breakthrough, which helped it improve efficiency and throughput by a huge amount. I did a full explainer video on DSpark, so definitely see this video if you're interested in learning more. If you look at these benchmarks on agentic coding, software engineering, and cybersecurity, you can see that it absolutely clobbers the previous V4 Pro version. And for some instances, it even beats or matches JLM 5.2 or Opus 4.8. Keep in mind this is like 70% smaller than JLM 5.2, and it's also 100 times cheaper than Claude Opus. DeepSeek V4 Flash costs around 3 cents per million tokens, making it by far the cheapest model at Frontier Intelligence. And if you look all the way on the other side, GPT 5.6 and Claude Opus and Claude Fable are all the way over here. So in terms of performance versus cost, this is by far the best option to use. It's just incredibly efficient and just way cheaper than the other frontier models. Now as expected from the Goat, they've also released the model to this already. It's already out for you to download on Hugging Face and this is only 167 GB in size. So, this can easily fit on just like one DGX Spark. Now, because this is open source, the community has acted fast and for example, Unsloth has already released GGUF versions of this. The crazy thing is the smallest one-bit version is only like 82.5 GB in size. So, you can potentially fit this on just like one or two pieces of high-end hardware. And keep in mind, this has the intelligence of GLM 5.2 or Opus 4.8. It's pretty crazy that you can now run this level of intelligence locally. If you're interested in reading further, I'll link to this main page in the description below. Also this week, we have quite a useful AI called Redesign. This basically turns a flat image into layers. So, you can edit each component or move them around. And with this, you can customize or micro-edit certain things like recoloring the elements or repositioning certain things or changing the size of elements. So, think of this as like turning a screenshot back into something closer to a Figma or Photoshop project. Now, this actually orchestrates a ton of different AI tools at once. It uses PaddleOCR. In terms of generating the layers, it uses Kwen Image Layered, which I featured on my channel before. And then for detecting and segmenting different elements, it uses Dino and SAM too. Now, according to these benchmarks, it seems like this new redesign even does better than some other image-to-layer generators like Kwen Image Layered. At the top of the page, they've released everything already. So, if you click on this code button and you scroll down a bit here, it contains all the instructions on how to download and run this locally on your computer. And this is actually fairly tiny at less than 4 GB in size. However, at least according to this base code, it also does require, apparently, an OpenAI API key. But you could tweak the code further to swap this out for a local model to run it completely for free. If you're interested in reading further, I'll link to this main page in the description below. Around 2 weeks ago, Moonshot AI released Kimiko 3, which is currently the most powerful open-source model you can use right now. Now, on their release page, they said that the model weights will be released on July 27th. Well, as promised, this week, on July 27th, they released the full Kimiko 3 model on Hugging Face. So, here are some specs. This is a massive 2.8 trillion parameter mixture of experts model, so think of it as like a team of specialists working together at once. And when you use it, only 104 billion of these parameters are active. For the architecture, it uses Kimiko delta attention and attention residuals, which were also designed by them. In fact, if you want to learn more about attention residuals, which is actually a really fascinating breakthrough, see this video to learn more. And also, this has vision capabilities natively baked in. And they used this Moon VIC vision encoder to make it happen. Now, if you click on files and versions, as expected with a 2.8 trillion parameter model, this is like 1.56 terabytes in size. So, you're going to need to stack like multiple enterprise GPUs in order to run this. However, because this is open-source, the community is already really quick to help quantize and compress this further. So, for example, Unsloth released some compressed GGUF versions of Kimiko 3. And the 1-bit version is only like 594 GB. Still massive, but this is a really big drop from like 1.6 terabytes in size. Anyway, if you want to download this or check out the technical details, I will link to this Hugging Face page in the description below. Also this week, AMD releases their very own open-source model called Instella MoE. And this is quite a big deal. You see, Nvidia has basically dominated the AI space because most AI tools are built off of their CUDA platform. It's really hard to train and run AI models on non-CUDA chips. But here, AMD has trained this Instella mixture of experts model from scratch on their AMD Instinct and using the AMD Rockum software stack. First of all, here are some specs of this. So, this has 16 billion total parameters and this is a mixture of experts models. So, think of it as like a team of specialist AIs working together. So, when you use it, only 2.8 billion parameters are active, making it fairly efficient. The cool thing is this reportedly beats other similar-sized models, including Gemma 4E4B and a smaller version of Quen 3.5. The awesome thing is they're not just releasing the final model, they're actually publishing the checkpoints from pre-training, mid-training, and all these subsequent stages, along with the training recipes and the code. It uses what they call a multi-head latent attention to make the attention and memory more efficient. And then they also use something called a far skip collective, which helps overlap communication and computation across GPUs. This is a bit technical, but on this page they document how exactly they did all these stages of training to create the model. And then if you click on this GitHub repo down here, it contains all the instructions on how to download and run this, as well as the training scripts. And then if you click on their hugging face repo, note that the final version after training on reinforcement learning, this is called Think and this is roughly 32 GB in size. So, it's a medium-sized model, similar to Quen 3.6, which should be able to fit on high-end hardware. If you're interested in reading further, I'll link to this main page in the description below. Also this week, one of my favorite image generators, Audiogram, has released a new tool called object remover. And here's how it works. You can simply brush over the object that you want to remove and it'll automatically highlight that object and then remove it for you. It's as simple as that. Here are some other examples for your reference. So, we can select this bike and remove it and notice that it also removes the shadow of the bike. Or here's another really tricky example where I want to remove this plant, but there's also a lamp that's kind of occluded plus some books in front of it. But as you can see, it's able to remove the plant while also preserving the lights and the books. Plus, it also is able to remove the plant's reflection on the floor. And here's another example. Now, at least according to this removal bench, you can see that Ideogram Object Remover has the lowest error rate compared to other models like Nano Banana 2 and GPT Image 2 Medium. So, if you're looking for a free object remover which you can run online, this is a nice option for you to use. You can sign up for free and you get some daily credits to run some generations. If you're interested, I'll link to this in the description below. If you want to supercharge your content creation, definitely check out Higgsfield, the sponsor of this video. Think of it as an all-in-one AI creation platform built specifically for creators. Instead of jumping between a bunch of different tools, Higgsfield gives you access to the world's leading models in one place, including Seed Dance, Kling, and more. And they've just added the ability to generate 4K videos with Seed Dance 2.0, so you can generate ultra-sharp videos with incredible details. They're also going to roll out the best video generator out there, Seed Dance 2.5, very soon, so stay tuned for that. This supports multiple inputs, so you can combine text, images, video, and audio to control the final results more precisely. And using this in Higgsfield makes everything way easier. For example, they have Marketing Studio, which is really useful if you're making marketing content. You can paste a product link or upload a product image, and it can generate multiple ad formats in one workflow, like UGC videos, tutorials, unboxings, product reviews, and more. They also have Cinema Studio, which is built as a full end-to-end filmmaking pipeline. Instead of just typing a prompt and hoping the video looks good, Cinema Studio lets you plan scenes, control the camera, add specific characters, reduce locations, and keep everything consistent across the whole project. From idea to final output, Higgsfield gives you way more control over the whole creative process. Whether you're making ads, social videos, AI influencers, product launches, cinematic clips, or any other content, this is one of the easiest platforms to start creating with AI. Try Hicksfield today using the link in the description below. Also this week, Thinking Machines, which is an AI lab that was started by OpenAI's former CTO, they just released a new open-source model called Inkling Small. Now, last week they released the full Inkling model, which is really good. This is an omnimodal model that can understand text, audio, images, and video. Well, this week they released a smaller version of this, which is roughly a quarter of the size. It's still pretty huge though at 276 billion total parameters, and when you use it, 12 billion of these are active. And its performance is actually really impressive given the cost. So, the red line is Inkling Small. As you can see, it's able to actually achieve, in some cases, even higher performance than the full model, but using less compute. And then here's the performance of Inkling Small across these different benchmarks compared with DeepSeek V4 Flash. Note that this is the older version. They released a new version this week. And then also Gemini 3.5 Flash Lights and GPT-5.6 Luna. So, Inkling Small holds up pretty well, but as you can see, the main advantage of this is it also can take in audio, whereas the other models don't really perform so well in terms of audio capabilities. Now, on Artificial Analysis, you can see that Inkling Small is over here, just 1 point below the full Inkling model, so it is a lot more cost efficient. However, it's still behind some of the other similar-sized open-source models like the latest DeepSeek V4 Flash. So, in terms of intelligence, Inkling Small isn't the best, but I do like its multimodal capabilities, especially if you need an open-source model that can also analyze audio and images, then this would be one of the best options to use. The awesome thing is this is already out. So, if you click on this Hugging Face link, and you scroll down a bit, here it contains all the instructions on how to download this. Note that at over 200 billion parameters, this is still quite huge at 532 GB in size. So, you'll need to stack like multiple DGX sparks in order to run this. If you're interested in reading further, I'll link to this main page in the description below. Also this week, we have a new system for robotics called Prism. This helps robots control their bodies and react to physical contact more effectively. So, what this does is it takes an ordinary robot sensor readings, images, or other instructions and it outputs the movement actions for the robot. And if you compare Prism with other similar algorithms, you can see that Prism has a much higher success rate, whereas the other ones are more likely to fail, especially when manipulating objects. Here are some additional examples for your reference. You see, the problem is that the actions that robot takes shouldn't depend on just one metric alone. It should come from different measurements like force, velocity, contact, friction, joint angle, etc. Well, this algorithm basically looks at all of these combinations of different signals to help the robot make a more informed decision and action. And the results are surprisingly strong. So, you can see across these different metrics, this new Prism algorithm has a much higher success rate and lower error rate. Now, at the top of the page, they've already released the code to this. So, if you scroll down a bit here, it contains all the instructions on how to download and run this yourself. If you're interested in reading further, I'll link to this main page in the description below. Also this week, ByteDance releases what is probably the best video model out there, Seed Dance 2.5. By the way, their previous model, Seed Dance 2, was already the best video model out there. No other generator really comes close to beating it. Well, Seed Dance 2.5 is even better. It's incredibly good at high-action fight scenes and character consistency, as you can see from some of these demos. The nice thing about it is it's multimodal. So, you can input reference videos for it to edit. Here's an example where you can input a simple 3D scene like this and get it to generate a full video that follows the composition. Or you can upload an existing video with a green screen and get SeeDance to turn the scene into something else. It's as easy as that. You can also input a storyboard like this and easily get it to generate a full video that follows all these different shots. My favorite feature is that this can generate videos of up to 30 seconds long. This enables you to do a lot more, whereas the other models can only do around 15 to 20 seconds max. Currently, this can only generate videos of up to 720p, but they are going to release the ability to generate 1080p and 4K resolution videos in the near future. Now, this can take up to 50 reference inputs including audio, images, and video. So, it's incredibly versatile. For example, I can upload all these images and get it to incorporate everything into the video. Now, currently, this is already out for certain countries on various ByteDance platforms like Dreamina, but if you're in the US, it's not available yet. Now, this is one of the most expensive video models to use. For example, a 10-second clip would cost roughly 460 credits, and at least currently in their Illumina platform, you can top up 1,000 credits for 10 bucks. So, 460 credits is roughly $4.60. So, this is much more expensive than MiniMax H3. Also note that API access is not out yet. They're planning to roll this out next week. But, here's the thing. This is the best video model out there, especially for high-action scenes or scenes with tricky physics and motion. This is still way cheaper than filming everything yourself. Now, I'm planning to do a full review video on this very soon, probably tomorrow or the day after that. So, stay tuned. If you're interested in reading further, I'll link to this main page in the description below. Also this week, MiniMax releases their latest model MiniMax H3. By the way, this is the company behind High Law. So, I think now they're just rebranding HighLaw into the MiniMax H series. Now, this is a super powerful and flexible multimodal model, which means you can input text, images, video, and audio to use as references. And this can generate 2K resolution videos, as you can see from this really detailed example. Here's an example where we can input these two images and get it to generate a trailer, and here's the result. >> It was waiting for me. >> Or here's another example where I can input this storyboard plus this logo, and then get it to make an ad for this luxury handbag based on the storyboard and logo. And here's the complete commercial. >> [music] [music] [music] >> Here's an even more impressive example where we can input images of this bold red and blue comic strip effect, and it's able to create a very nice video from this. Now, because this can take a video, you can also input a green screen video like this plus the background which you want to use and easily add that to the scene. Or here's another example with audio. I'm going to input this audio track which was made from the open-source music generator a step >> [music] >> And then I'm going to upload this image of some random K-pop group and also a few reference panels of some text, and let's get it to make an MV from this song. Show these K-pop members from this image singing and dancing to the music. Add coarse grain, glitch effects, grunge effects. Keep the edit fast and use hard cuts only. Cuts should occur within 3 seconds based on the beat of the song. Use the typographic reference from my second image. And here's the result. >> [music] [music] >> Currently, you can try this on their online platform. It allows you to select all these different aspect ratios and up to 2K in resolution, and you can set this up to 15 seconds. Now, if I select 10 seconds, note that it costs 120 credits, and similar to ByteDance, it costs $10 to top up 1,000 credits. So, 120 credits is roughly $1.20, around like three times cheaper than SeeDance 2.5. The really awesome thing is they are actually going to open source this. They'll likely release the model sometime next week, and I'll definitely make a full review and installation tutorial on this once it's out. So, stay tuned for that. I think this is going to be the best open video model For now, if you're interested in trying this out, I will link to this main release page in the description below. Also this week, Google DeepMind releases a really exciting update for robotics. So, they just introduced Gemini Robotics 2. This is a new family of models designed to control a robot from its feet all the way to its fingertips. You see, the previous Gemini Robotics model mainly focused on upper body and table top tasks, but this version can combine walking, balancing, reaching, grasping, and reasoning all in one continuous sequence. For example, you can tell this Apollo 2 humanoid robot to do a certain action, and with this model as the brain, it can understand the instruction, locate the object, walk towards it to pick it up, and then move across the room to reach the target and place the object in the correct location. Now, they've actually released three different models. The main one is Gemini Robotics 2, and this is their vision language action model. So, this turns your natural language instructions and the robot's vision via cameras on its face into physical motor commands, basically into actions that the robot should carry out. And then they also released Gemini Robotics ER2. So, this is a higher-level reasoning system. This is designed to understand the environment, the room, plan tasks, and then it's also designed to correct the failure, and it can also coordinate with multiple robots. And then there's Gemini Robotics on device 2. This is a smaller version that can run locally on a robot without needing to connect to the internet. So, this is a completely offline model. The system also brings much better hand control compared to the previous version. As you can see, it's able to perform a ton of tasks that require a lot of dexterous finger movements like unscrewing a light bulb or tying a trash bag or sealing a Ziploc bag. Now, at the bottom here, you can actually try out Gemini Robotics 2 in AI Studio, and you can also sign up for their trusted tester program. If you're interested in reading further, I'll link to this main page in the description below. Also this week, we have a new video world model called Wonder. And like other world models, this allows you to generate an interactive world which you can explore in real time. Notice that this is just video, but you can press all these keys here to navigate around the scene. As you can see, this works with a variety of different environments and characters. So, here's a more anime digital art example. The quality is not perfect. There's still some noise and inconsistencies around the edges. And then here's an example of another artistic style. And then here are some realistic examples for your reference. Now, the really cool thing is instead of just inputting an image as the starting frame, you can even input a video as you can see in these demos. This will render the same movements as the video, but it allows you to now walk around the scene as if you're viewing the video in 3D. Now, again, this is far from perfect. There's a ton of noise and artifacts with its generations, but this is a pretty cool feature. And then here's another example of a fight scene, but you can like press these keys to change the camera perspective and walk around this scene. Now, at the top of the page, it does say the code and the models are coming soon. Now, this is from Adobe, which haven't really released anything, so hopefully they will stick to their word and release the model to this. For now, if you're interested, I'll link to this main page in the description below. Also this week, Google has added a new voice feature to the Gemini app. Now, currently this is only for Mac OS. Hopefully, they will also release a Windows version in the future. This is basically an AI-powered writing and editing tool. You simply hold down the function key and speak naturally into pretty much any application on your Mac, and it will use Gemini as the transcription model. It transcribes what you say, and it also removes filler words or mistakes or repeats. It also understands when you correct yourself halfway through a sentence. It's also able to help you clean up formatting, add the correct punctuation, etc. And it inserts the text directly where your cursor is located. So, it's very similar to other AI dictation apps like Typeless or Whisper Flow. You can also enable Gemini reasoning, which gives the system permission to perform more complex tasks. For example, you can highlight some documents and ask Gemini to summarize the content. So, I think this is a really useful tool. I hope they roll it out to Windows and mobile platforms as well. For now, if you have a Mac and you're interested in learning more, I'll link to this main release page in the description below. Also this week, we have a new AI called Phi-0. This is a new video world model built around what the authors call physical language. The main idea is that instead of immediately generating the next video frames, you get the model to first reason about how everything should move and change physically. Then it goes through the video generator to actually render those frames. And from that, it's actually very good at predicting what happens next. And so this has a variety of applications. For example, you can use this to generate interactive worlds. It can predict how the scene should move if you press a certain combination of keys. Or you can also use this to create videos for autonomous driving. And the same thing applies for creating videos to train robots. And if you look at its performance on physical coherence and understanding, then you can see that on average it even outperforms other similar world models. Now, at the top of the page they've released a code button to this. And here it says the code is coming soon. So, stay tuned for that. If you're interested in reading further, I'll link to this main page in the description below. And that sums up all the highlights in AI this week. Let me know in the comments what you think of all of this. Which piece of news was your favorite? And which tool are you most looking forward to trying out? As always, I will be on the lookout for the top AI news and tools to share with you. So, if you enjoyed this video, remember to like, share, subscribe, and stay tuned for more content. Also, there's just so much happening in the world of AI every week. I can't possibly cover everything on my YouTube channel. So, to really stay up-to-date with all that's going on in AI, be sure to subscribe to my free weekly newsletter. The link to that will be in the description below. Thanks for watching, and I'll see you in the next one.

In this video, you will learn how you can create this reveal effect futuristic website with this cool fonts and interactions. I'll also show you how you can turn that same character into animated video that would work well on all of the devices and animated websites to very good quality award-winning designs because I believe that everyone can now build websites for their companies, for their businesses. With AI, I've built more than 100 websites with AI and I post all of them on Motion size. So, if you go here, you'll have access to copy the prompts, but also it's a proof that I know what I what I'm talking about. I built all of these interactive websites, all of these scrolling effects websites, and all of this is built with AI and available for prompt. But in this video, I'll show you everything you need to know. So, the first step is actually find inspiration and I'll be building this futuristic type of websites that Pinterest already suggests me. Once you click on something, Pinterest will suggest you even more of the same images that you like. So, the most important thing, just find something once, and then it will give a lot of cool animations and cool videos and even more images that you can then just optimize and customize for your websites, not exactly copy. I already found one that I like, which is this one. So, basically, we have futuristic text here and futuristic person, right? All I have to do is just copy this now and start already building a website. So, once I copy this, I will go to Chat GPT to make images. So, Chat GPT image tool is the best model, and I found that Hexfield works best for me. I'm not affiliated or sponsored with Hexfield. I don't have affiliate links for them. And again, none of the companies or products that I'm sharing this video are sponsored or affiliated with me. So, let's start generating our images. Uh the first one is I'm just going to upload the image here, and for the UI interface of our website, I usually go to Pinterest again to find something that I think would work well. So, again, just go to Pinterest. And here is just find actual website UI, not just a person, but something that you think would work well. So again, this is something that looks cool. I think if you can customize it into a website, it would look very good. So I'm just going to save that for a future detail for future purpose, maybe video. And now I'm just basically scrolling through and looking for some images that I think would work well. This doesn't fit because it's in dark colors, so we need something in the light colors to fit our design very well. So something like this could work. But now we need to actually change this. And if you're not a designer, this would be like very big issue for you cuz it's actually not that easy to customize the text, the fonts, and stuff like that. So let's say I like this design. All I have to do is just copy this. And let's go back to our Hixfeld. And here, believe in AI creativity. You don't have to explain everything exactly. Like look at the example that I'm going to show All I have to say is I'm just going to move this. Again, not affiliated with Hixfeld. Use ChatGPT, it's way cheaper. So what I'm going to say is um change the person in the first image to be the person from the second image. Change the color to white and also change the fonts to be more futuristic a futuristic fonts and maybe uh just kind of redesign a little bit the UI so it fits. And then you would just basically describe your company. I will remove this last part cuz I'm not trying to be a little website for an actual company. I'm just doing this for demonstrative purposes. And then I would select GPT image two. This is very important. High quality and two 4K resolution. Uh select auto so it kind of preserves the square the position so it actually would look that great. And now we just have to experiment couple of times. Like uh if I for example creating a design for something I would try a lot of different iterations as you can see here to create a thumbnail that you see on this video. I've spent like a lot of credits to come up with this design that was actually the thumbnail that you're seeing and yeah so this is actually process would rarely become something that you would use from the first design but let's say actually actually let's wait and see what it comes back with. And this is what we've got as you can see it looks pretty close. It suggested a font style probably it connected from Google fonts which are free so you can use them for production ready websites. The background is pure white. Let's now generate our image that is like this but without any text. So for this I'm going to ask it to create an image like this. So I'm going to just reference it here. Or for the prompt we're going to say remove every text every button every UI element from the image just leave me this background with this person as it is. Make sure that the GPT point two selected and let's just click it and see what it comes back with. And this is what we've got. Now let's actually remove some of the pieces of this futuristic stuff from her face so we have this mask reveal effect similar to kind of this element where there's two different images stacked on each other similar to this one. And where it's kind of like have this effect. For this we would need two images that are at the same position but separate kind of designs. So I'm going to say something like remove the pieces of this futuristic clothing from her face. And let's just see send that see what it comes back with. And this is the image that we received. Now that we have two images that are exactly the same position and we can actually see that they are positioned the same way. We can go to our original prompt which was promotion sites. This one which has this effect and for this I'm just going to replace the two links that says asset one. So I'm going to paste the link here. And the second one which is this one. And I'm going to paste the asset here as well. And let's copy the whole thing. Reveal animation. And go back to our Google AI studio and I'm going to say on the background let's paste these like image reveal effect. Please do not change any content that we have already on the page. Keep exactly as it is. And let's just send a prompt and see what it comes back with. And this is the result that we've got from the first prompt and now if we hover over something we can see that the image is clearly changing. We have this interactive effect. Obviously the animation could be improved on the mobile. We would move the image below the the the whole thing. So let's just say that on the mobile let's actually move the image below the uh text so it's kind of two sections and in the mobile only the image will have will not have reveal effect. It will just be basically one static image. And let's just send that. And see what it comes back with. And there we have it. Mobile is pretty much as functional as it is. Obviously if you want to be creative you could make a transition to be more subtle with a gradient. But yeah, that's it for this hero section. And now let's me let me show you how to actually build something like this, but for e-commerce. A lot of people have been asking me about this uh prompt, which is uh this one. And a lot of people don't know how to customize it cuz like we have this video, but let's say you wanted to change something. You want to change the rings or something like that. Let me show you how you can actually do that to make it yours. Yet for now, I'm just going to build this. So, all I have to do is just copy that, go to Google AI Studio, and just build it, and then then start customizing. So, yeah, this is a very long prompt that we just going to click on build and see what it comes back with. And this is the result that we've got. So, we can scroll through the first video, then we can scroll down to see the rest of the product. And yeah, all of this is just from a single prompt that now we can customize it. We have the cart working, and everything is working without any issues. Now, let me show you how to change this person to be something else. So, let's publish this or not publish. Uh what I want to do is just take a screenshot of this. So, what I'm going to ask is give me a link to the video in the hero section. So, now that we have this video, we can just literally just copy the frame. So, copy video frame. Uh let's go to the beginning so we get the first frame also. Uh we just basically need the first frame. We don't really need the the last frame. Now, let's go back to our GPT image generation model. And here, all I have to do is just put these two images next to each other and say something like in the first image, replace the girl to be the girl from the second image. The clothing, the style of her hands, the style of her face should be as in the second image. And I also want to kind of take a screenshot of uh the girl with her hand visible. Something like this. And let's just send that here. We can remove that. And see what it comes back with. And this is what we've got. Now, let's make a video out of this. And in the prompt uh using students too, we're going to just upload the video reference, which is the original video. Make sure it's the same length, 5 seconds as the video. And then we're going to choose students too. And upload the image that we're going to create animation as well, which was which is what we just generated. And for the prompt, it is create a video exactly like the video one. This not make any changes. And let's just send that and see what it comes back with. I'm showing you how to customize the text uh the the video itself. I don't think there is any necessity in showing you how you can customize the text, the content because this is pretty straightforward. Just talk to the AI what you want the changes for, whether it's like adding new product, whether it's changing the product, just literally uploading your own pictures of your own products and asking AI to update that with the pricing. It will do that exactly as it is. To connect the e-commerce, either you can use uh lovable since lovable has like stripe connected or like Shopify integration. But, yeah. So, let's just wait and see what Hicks field give us. And this is the video that we received. As you can see, it's perfectly what we need. Now, let's just basically download this, go back to our site, and let's ask, replace the video in the hero section to be this link. Let's just paste that and see what it comes back with. We can also create like a landing page. It doesn't have to be e-commerce. So, let me show you how I can customize that as well. Like we can remove all of these products and test Gemini 3.6 flash how good it is at actually building some cool landing pages. I don't really like AI slop. Whenever I'm building landing pages, I would prefer to look like something of a good quality landing page with unique sections designs with unique kind of cars and stuff. So, like websites like these are what I would prefer to build whenever I build something with cool interactions, animations. You can see that. It looks pretty cool. I don't know what this website is. Um yeah, let's wait and see what AI So, as you can see, it's it says it's already updated. Let's refresh and see what it does. All right. For some reason, AI just sends me the code to the chat itself. Not sure what's going on. Uh I'm trying to get it to actually write code in the actual code base, not in the chat. So, let's see if it can handle this task. And here's the updated video. We can see that it works perfectly. We have the super cool design. Now, let's actually ask it to get rid of all of the sections except the hero section. To kind of see how it's going to build or under the made without compromise section, add around four to five more sections which will talk about the company, talk about the brand, maybe some other sections that you think would be fitting and then add footer after three or four more more sections. And usually I would just also provide some examples of the design. So I would just usually go and type like uh landing page, let's say rings or whatever we're selling. So just sending a screenshot, obviously this is not the best example, but like if you see some designs that you like, you can just freely send a screenshot. AI would never be able to copy exactly as it is, so there will be no copyright problems, but at least it will give it some unique examples of what it should do. So like for example, in this, I can just say I don't even have to explain anything. I could just send that and see what it comes back with. This is what AI created. Let's scroll down past the sections that we already had, and yeah, this is the first new section. And if you're not a designer, maybe this might look good to you, but initially I think it lacks space. So what I would do is just ask AI to just give it the whole thing more spacing, add more padding, and then customizing the images, the videos, and then I think it would look pretty good actually for a one-shotted thing. But yeah, let me show you actually how you can now download this and whether keep working with this cuz Google AI Studio is good per se, but then I would prefer working with Antigravity. So this is the same thing from Google. You can just go to Google Antigravity and just download it for free. Does not require The best thing about this, it has all different models that you can select including Opus, including uh GPT and Sonnet. So you'll have more availability here. You have access to all of the assets, so you can drag assets here. Let me show you how it can actually do that. So here in Google AI Studio, you can just click on connect the whole thing to GitHub, and from GitHub, we can just download it. So let me just exactly do that. So I just click on GitHub. Then I'll have to sign in. Make it even simpler, you don't have to connect it to GitHub. You can just click on code and here just click on export and right here you can see export to Ant Gravity. And let's click on export. And it will do exactly everything that we need since this is actually the same project. Click on export. And you can see that it's already connecting. Let's actually change this to new project. We'll name it review website. Open and try that again. For some reason it didn't really work. Yeah, let's just download the code then manually. And then we can just drag it inside that folder. So in Ant Gravity I can just click on this. Copy. Find our project. Paste it here. And what we have to say here is that preview the page. And that would be let's say preview. And see what it comes back with. Now we have it in live host. Let's build the rest of the pages which is shop, collection, and journals. And if you just ask AI to build those, the result probably will be something like we got before. So let's try to first create the design using chat GPT and then build that out. Again, uh to do that we can just reference the image that we had before which is this page. And then we're going to say let's do this one. I'm going to reference. Going to get rid of all of this stuff here. And I'm going to say create me an image like this but build a shop page and add some products related to like futuristic stuff similar to what you see on the on the image, but it should be not the main page. It should be shop page. And let's just send that. And this is what we got. So, it looks pretty cool in terms of UI. I'm really surprised how the heck does it does everything that it does, but I'm going to do two things now. The first one is reference the image and create two separate versions. One without these images. Create me an image exactly like this, but remove the product images from the card. Keep just the same background, but without the product images itself. Keep the text, every detail, but just kind of remove the product images. So, I'm saying this basically just to make AI better job in terms of recreating that design. Because if it's going to if I'm going to say let's say is it this one? And the second prompt would be create me four images next to each other. Just the product images that is in this section without any UI elements, without any text, without any anything, just basically the product images themselves. And let's just wait and see what it comes back with. So, now that we have this image without any of the UI elements, I want to just take a screenshot of this. Like this. Go back to our Google and to gravity. And I'm going to say Okay, thank you. Build me the second page which is shop like this. Keep it like this. I'll send you the product images itself later. And let's just ask it to build. And in the meantime, we can just get these product images ready. So, again, I'll show you how I can exactly get them. Let's go to images. I'm going to copy this image. Paste it into Figma. You can do any editor that you want. I'm going to create a new frame, which is basically going to be a square. And now I can just duplicate it four times. Get this image pasted like this. Maybe increase the size a little bit. And paste it like this. Then call command C, command V, and just move it like this. Center align it. One more time. Move it again. So, now that we have the third image. And now we have the fourth image. Let's in all of these remove the background. So, I'm going to select all four of them like this. And I'm going to click remove the background. And also I'm going to remove the background in these things. So, it's kind of going to be transparent. And now we can export this as PNGs. So, let's do that. Going to name it 1 2 3 and 4. Let's just export it. Going to make sure that it's PNG. This will allow us to have transparent background. And for the folder, I'm going to name it like future shop or something. And save it here. Let's go back to anti-gravity and see if it's changed anything. Let's click on shop. Yeah, it created this page. Uh now we can have it Let's make sure that the home page works as well. And the shop page also works. So, for the products we're going to say for the products All right, it's not for the products And let's just upload these images. I I how I named that. Probably future shop, yeah. And we can just drag them here. And send that and see what it comes back with. Make sure that uh three-point flash is good enough. Let's see it what what it comes back with. And this is what we've got. Very cool hero section that will capture the user retention. And then we have very cool product page that works well. So, we have all of this thing working like all of the product connected to their own cart. And with AI, I don't really have to worry about none of that. Again, everything is mobile-responsive. So, this was it for this video. Hope you've enjoyed it and learned a thing or two, and I'll see you in the next one.

The Sequence Radar #906: Last Week in AI: Open Models, Intelligent Robots, and the Price of Conviction NVIDIA's letter, Gemini Robotics, Kimi release and more. Next Week in The Sequence: - We continue our series about distillation with an awesome new technique. - The AI of the week dives into Gemini Robotics 2. - We will have a new section about AI in space. - The opinion section get into a crazy idea for engineering teams in the era of tokens Subscribe and don’t miss out: 📝 Editorial: Last Week in AI: Open Models, Intelligent Robots, and the Price of Conviction AI spent this week speaking four languages: policy, models, robots, and markets. Strangely, all four delivered the same message. The AI race is moving beyond spectacular demonstrations and toward harder questions about distribution, embodiment, ownership, and economic returns. Jensen Huang helped frame the policy debate by backing an industry letter defending open-weight models. This was not simply an argument about research culture. It was industrial strategy. The letter’s central idea is that American leadership cannot depend only on a handful of closed systems. It also requires an ecosystem in which startups, universities, enterprises, and public institutions can inspect, adapt, and operate advanced models themselves. The timing was almost too perfect. Moonshot then released the weights for Kimi K3, a massive mixture-of-experts model with native multimodality and a one-million-token context window. K3 may not decisively surpass the strongest proprietary systems, but that is almost beside the point. Open models are no longer the minor leagues. They are becoming a parallel frontier—and Chinese laboratories are increasingly setting its pace. Google DeepMind pushed the frontier in a different direction with Gemini Robotics 2. The release extends Gemini from understanding the digital world to controlling the physical one: planning complex tasks, coordinating full-body humanoid movement, and manipulating objects with greater dexterity. The deeper significance is architectural. The next model race may not be won by the system that writes the best answer, but by the one that can turn reasoning into reliable action. Robotics is where tokens acquire consequences. Then markets supplied the warning label. Leopold Aschenbrenner’s Situational Awareness fund suffered a dramatic collapse and forced unwind after highly concentrated AI positions moved against it. The episode does not invalidate the long-term AI thesis. It illustrates something more uncomfortable: a secular prediction can be directionally correct and still become financially fatal when concentration, leverage, and timing are misaligned. You can predict the destination and still run out of fuel on the way. Big Tech earnings transformed that lesson into a comparative experiment. Microsoft was rewarded after Azure crossed $100 billion in annual revenue and Copilot adoption continued to expand. Amazon offered a similar narrative as AWS growth accelerated alongside its AI infrastructure investments. In both cases, investors could see a direct bridge between enormous capital expenditure and customer revenue. Meta received a harsher reaction. Its core advertising business remained strong, but infrastructure spending accelerated faster than the market’s confidence in near-term AI monetization. Apple offered another variation: powerful distribution and cash generation can buy time, but they cannot permanently substitute for a compelling AI product story. This was not a week of AI skepticism. It was a week of discrimination. Open models must diffuse. Robots must act reliably. Technology companies must convert capital expenditure into revenue. Investors must survive the journey. The market is no longer asking whether AI will be enormous. It is asking who can turn intelligence—digital or physical—into durable economics without losing control of models, machines, or capital. 🔎 AI Research AI Lab: OpenAI Summary: The paper “scientific-computing-in-the-age-of-agentic-ai-an-exploratory-field-report.pdf” explores the potential of Large Language Model agents to address technical debt and software engineering shortages in life sciences computing. Through eight case studies, the authors demonstrate that while AI agents can successfully execute tasks ranging from minor code maintenance to full performance rewrites, careful human verification and long-term stewardship remain essential. AI Lab: Google Cloud AI Research Summary: This paper introduces the Chain-of-Evidence framework and the ScientistOne system to combat the undetected verifiability failures, such as hallucinated citations and irreproducible scores, prevalent in current autonomous research agents. By structurally enforcing that every generated claim traces back to verifiable evidence, ScientistOne eliminates hallucinated references and achieves perfect score verification while matching or exceeding expert performance on complex benchmarks. AI Lab: Kimi Team (Moonshot AI) Summary: This paper introduces Kimi K3, a 2.8-trillion parameter multimodal Mixture-of-Experts model featuring a 1-million-token context window and native vision capabilities. By combining architectural innovations like Kimi Delta Attention with multi-domain reinforcement learning, the model achieves frontier-level performance on long-horizon coding, agentic, and reasoning tasks. AI Lab: Google DeepMind Summary: This paper demonstrates that visual prompt engineering (VIPE)—transforming task images via image editors—systematically improves the visual reasoning capabilities of video models. The authors find that video models possess a strong realism bias, meaning that converting abstract sketches into photorealistic scenes can be a more effective test-time scaling strategy than traditional text-based prompting. AI Lab: MistralAI Summary: This paper presents Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that simplifies content moderation into a unified binary question-answering task. Through extensive data curation and contrastive sample generation, this compact model matches or outperforms models nearly seven times its size across diverse text and multimodal safety benchmarks. 🤖 AI Tech Releases Gemini Robotics 2 Google DeepMind released Gemini Robotics 2 , a three-model suite of intelligence for robotics. LFM2.5 Liquid AI released LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, two encoder models that can be easily adaped to downstream tasks. MAI-Cyber-1-Flash Microsoft announced MAI-Cyber-1-Flash, a model to find and fix vulnerabilities in complex code bases. DeepSeek released the official version of the v4-Flash Official API 📡10 AI News You Need to Know About - Jensen Huang used his first ever X post to share Open Weights and American AI Leadership, a three-page letter co-signed by 25 companies including Nvidia, Microsoft, Meta and Palantir asking Washington to avoid “premature restrictions” on open-weight AI models, with OpenAI and Anthropic notably absent from the signatories. - Situational Awareness, the roughly $20B AI-focused hedge fund founded by Leopold Aschenbrenner, sold its entire public equities portfolio to Ken Griffin’s Citadel days after reports it was seeking fresh capital following heavy losses in the AI selloff. - Anthropic disclosed three incidents found across 141,006 evaluation runs in which Claude models reached the open internet from a misconfigured test environment and gained unauthorized access to the production systems of three organizations. - Nscale agreed to acquire Anyscale, the company built by the creators of Ray, adding a workload orchestration layer on top of its power, data center and GPU stack, with Bloomberg putting the price at roughly $1.65 billion. - Richard Socher’s Recursive signed a multi-year $410M agreement with AWS to run its automated AI research system, committing most of the $650M it raised on leaving stealth in May to compute rather than headcount. - Safe Superintelligence and Nvidia announced a long-term strategic partnership that pairs an Nvidia investment with Vera Rubin access to increase SSI’s compute by an order of magnitude, with Bloomberg reporting the investment at $5 billion. - Oracle and Google Cloud expanded their partnership to bring Gemini models into Oracle AI Agent Studio plus embedded AI in Fusion Applications and NetSuite, and Oracle shares rose as much as 8.4%. - Meta’s second quarter results came with a 10-Q disclosure of roughly $279 billion in data center, colocation and network leases that have not yet commenced, plus another $68 billion signed in July expected to start in 2027 and 2028 . - Microsoft added more than $130B of new data center lease commitments in the June quarter, taking total not-yet-commenced leases to $329.1 billion, up from $196.6 billion, disclosed alongside its FY26 Q4 results. - Moonshot AI closed a $3.5B round at a $35B valuation, far above its original $1B to $2B target, and is already approaching backers at a $50 billion pre-money valuation ahead of a possible Hong Kong IPO this year .

🔮 Leopold & exponential markets; transformative GLP-1s; runaway AI & the future of safety++ #595 Plus: Citizen agents, protein & longevity, nervous Europe “It is a lot to cope with the rollercoaster of the last decade and deep uncertainty of what’s coming next. Over many years, the quality and depth of the newsletter has ensured I am better informed and inspired.” — Hugh K., a paying member AI adopter’s decision trap We modeled three types of companies adopting AI. They have the same starting economics, the same 5% hit rate, but different learning practices. Two years into their investment, all three are losing similar amounts of money. In year five, the eventual loser looks best. It takes eight years to see which approach leads to outsized ROI. Most CEOs are facing the decision trap right now – you likely don’t know if your firm’s spending is learning that will compound to ROI, or waste. The FT calls Zuck “the king of the side quest” as he works away on a portfolio of bets: Mark doesn’t need each project to succeed, as long as the experiments deepen Meta’s infrastructure and inform the next move. In our model, system builders that consistently compound their learnings over time have the winning formula. See our framework and the interactive model: Too brittle to go exponential A $45 billion fund at its peak, started by an AI researcher with no hedge fund experience, betting on the AGI capex build-out at roughly four times leverage, was forced to liquidate this week. Leopold Aschenbrenner started Situational Awareness LP in late 2024 on a thesis that the path to superintelligence would put trillions into compute, chips and power. He did great. And then, the trade turned. The Philadelphia Semiconductor Index fell 28.6% from its June peak, and software gained. Situational Awareness’ unraveling is not proof that Leopold’s thesis is wrong.

Train Your AI like A New Employee with Claude Skills Learn what a Claude Skill is, how skills fire with commands and trigger words, and build your first one with a single prompt. 30+ client builds behind it. A couple of years ago, one of my colleagues tagged the CEO in a Trello card and said I relied too much on AI. Before researching Claude Skills for this article, I looked him up to see what he was doing, since he had left the company before I did. I saw that he now lists a Generative AI certification and had liked a post about Claude Fable 5. If your colleagues see you using Claude Skills, there’s a good chance they’ll ask questions like, “Can you trust this?”, “Are there any security risks?”, or “Is it safe?”, just like people did with OpenClaw. And three months later, whether on LinkedIn, in a Trello card, or in discussions, trust me, you will see the same people talking about AI and using it in their workflows. I have built more than 30 Claude skills for clients and worked with more people than I can count. Sometimes, more than I would have liked, but that is freelancing. Let me show you a message that one of my former clients sent to a friend I worked with and me: He was talking about the skills we built for him. A well-written skill makes the same AI system ten times more useful, and creating one takes nothing more than a folder and a Markdown file. In this guide, you’ll train your AI like a new employee using Claude Skills. By the end, you’ll be confident enough to build your own. What Is a Claude Skill? Plain English First A Claude Skill is a set of rules saved in a markdown file. And the file that has this skill is often called SKILL.md. Claude reads it before touching your task, so you stop repeating yourself. Here is one of my Claude Skills. This one audits my business. I feed it my newsletter stats, and it returns a ranked report of what is weak, what it costs me, and what to fix first. A consultant who read every number I ever produced. (Full build is here.) But where do Claude Skills run? Claude Skills run in Claude, Claude Cowork, and Claude Code. They also work in other agentic environments like Codex or OpenClaw. Let me show you where to find Claude. (In Claude, click on profile, settings, and skills to see yours.) You can browse more skills developed by Anthropic after clicking “Browse”. Toggle one on, and it is live in your next chat. How Do Claude Skills Work? Claude skills sit on disk as folders. Claude does not carry them around. Every SKILL.md opens with two metadata lines, a name and a description. When a chat starts, only those two lines get injected into Claude’s system prompt. That costs 30 to 100 tokens per skill, the price of one long sentence. The rest of the file stays on disk. When your task matches a description, Claude pulls the full file into context and follows it. Scripts and reference files in the folder load only if the job calls for them. Anthropic calls this progressive disclosure. This is why 30 installed skills cost you almost nothing until one fires. Calling Claude Skills Using Commands A Claude skill fires in two ways. The first is manual. You call it by name. In Claude Code, you type /skill-name, and it loads on the spot. It asked for my goal, and check this out, it even asked in my native language, Turkish. It connects to your Claude account and analyzes it automatically. Calling Skills using Trigger Words The second is the interesting one, trigger words. Claude compares your message against every description on the shelf, and when the words match, the folder opens on its own. Here, the same consultant skill was fired twice. First run, I typed the command. Second run, I typed a plain sentence, audited my business, and the trigger words caught it. How to create a Claude skill? Let me show you the fastest way. Just paste the prompt I’ll give you in a second. The link inside the prompt has the page inside the Claude Platform document, explaining how to prompt with Claude Fable 5. These are the newest models, and the team has changed how they should be prompted. Instead of learning everything from scratch and wasting tokens, let’s create a Claude Skill that learns directly from the official documentation the team has shared. Paste this prompt to Claude. I want to create a Skill from this system card: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5 The Skill should optimize my prompts based on this. With just one prompt, you started training your AI. You don’t have to learn how to prompt Claude Fable 5 in such depth. Let Claude learn it for you. Just skim the document, build a few projects with Fable 5, and before you know it, you’ll have learned how to prompt it effectively. By the time I finish writing here, the skill is ready. Now, I have read all of the document, and if you want to learn how to prompt with Claude Fable 5, you can read this one, where I explained. Let’s test our skill. I crafted a prompt using the old way. You are the world's best software architect, senior frontend engineer, UX designer, and product manager. Think step by step. Be extremely careful. Before writing any code, analyze everything in detail. Do not make assumptions. Consider every edge case. Create a comprehensive plan first, then explain your reasoning, then implement the code. I want to build a Bloomberg Terminal-like dashboard for stock investors. It should look modern, include watchlists, charts, news, financial statements, earnings, analyst ratings, insider trading, macroeconomic indicators, and AI summaries. Write production-ready code with best practices. Let’s call our skill with command and ask it to update this prompt for Claude Fable 5. Here is the optimized prompt. Here are the reasons for these optimizations. Pretty amazing, right? Also, the new method lets you create a skill by recording your screen. To discover this, read this one. Where to Find Ready-Made Claude Skills You do not have to write your first Claude skill. Thousands are ready to steal. Start inside Claude. (Head to Profile / Settings / Skills first). The Browse button you saw in settings lists skills built by Anthropic, one toggle and they work. Here are the skills for you to install. The bigger shelf is skills.sh. There are 800K+ skills available here. It is an open directory of community skills; each card shows install counts and a security audit. Find one you fancy, and let’s say this, caveman. It’s developed to cut your token usage by 80%, so before reading a post with a 10-item to-do list, download this, and do not hit the limit. How? The easiest way is this prompt (paste it into Claude Code): Hey Claude Download and install this skill: https://www.skills.sh/juliusbrussee/caveman/caveman Let’s see. To see the Skill you just downloaded, close Claude and reopen it. I once built a Wall Street terminal using multiple APIs to track my investments. Now I want to build something similar again, but this time I want it to feel closer to a Bloomberg Terminal, which costs around $32K per year. I also do not want to spend too many tokens, so I use the /caveman Skill for this build. /caveman I want to build a dashboard similar to the $32K Bloomberg Terminal. Use this project as the reference, including its architecture, code, and API keys: /Users/learnai/Desktop/LearnAIWithMe Projects/Wall Street Analyst I can’t stop building with AI, sorry. But we are in the era when people can build 99% of their paid subscription with Claude code, without being technical, and most of them don’t even know. I used to build wall street analyst using Claude, now I want to go one step further, but I don’t want to explain what I want from the beginning, and don’t want to spend a lot of tokens, that’s why I inherited my old project, used one prompt, and the caveman skill. By the way, the terminal is ready, even before I finished writing these sentences. If this were a Build-It article, I would explain what every component does: one brain that fetches live market prices, economic data, and news, one screen that paints it all like a Wall Street trading desk, and one AI analyst sitting inside who reads everything and tells you buy, hold, or sell. But this article is about Skills, so I’ll skip that part. As you can see, with a single prompt, you can build your own market terminal. What a fascinating time to be alive. What Not to Do When Creating a Skill Now I am going to give you eight mistakes that I see most often when advising clients to use their skills. Next Step Log your daily routine and define what you do repetitively. Next step is to turn them into skills, but I always suggest you stay in the loop because human touch is the most important and irreplaceable part, it was, and it always will be. I’ve built 22 skills in the last 6 months. And the list keeps growing day by day, because you can do anything using the skill. Example? Let me give you 5 of them that I’ve built with one sentence explanations. AI Second Brain: It turns your past conversations, projects, notes, and results into a living AI memory that learns from your work and suggests what to build, write, or do next. Job Hunter: It searches Indeed, Upwork, and LinkedIn for matching jobs, researches companies, writes cover letters, triggers by talking only, and sends the best opportunities to Slack. The Red Team: It stress-tests your ideas, launches, or business plans by attacking them from multiple angles to find critical weaknesses before they become expensive mistakes. The SEO Optimizer: It scores your article, fixes its SEO section by section without changing your voice, then scores it again to show the improvement. Reverse Engineer Pipeline Skill: It studies any successful product, breaks down how it works, and turns the research into a build prompt that Claude Code can use to recreate its core engine. As you can see, there are too many options available. So start with smaller steps, and start building. Don’t overthink, build it, and it will get better. Thanks for reading.

Claude Code After Generation 5 Models: The Master Guide A beginner-friendly guide to using Claude Code with better context, stronger models, and practical automation. Claude Code changed twice in the same month. First, the models became strong enough to stay inside a difficult problem for much longer. Then the tool around them learned how to keep that work moving without waiting for another prompt after every step. Opus 5 arrived on July 24 with stronger judgment, deeper root-cause analysis, better self-verification, and performance close to Fable 5 at roughly half its price. Sonnet 5 had already become a far more capable daily model. Fable 5 was built for work larger than one sitting. Claude Code also added completion goals, adjustable effort, verification Skills, one-million-token sessions, and dynamic workflows that can coordinate many agents through an executable plan. Then Anthropic revealed something more interesting than another benchmark. For Opus 5 and Fable 5, it removed more than 80% of Claude Code’s system prompt without a measurable loss on its coding evaluations. Many instructions that used to keep older models on track had become friction. The stronger models no longer needed every decision explained, every rule repeated, or every possible failure predicted in advance. That changes the way Claude Code should be used. The old instinct was to write one enormous prompt and control every move. The better approach now is to build a small working environment around Claude: give it the right project context, a clear target, useful tools, somewhere to record decisions, and a judge that can prove whether the work is finished. The prompt gets smaller. The system around it gets better. That is the real subject of this guide. Inside the full guide, you’ll get the complete Claude Code setup for the newest Claude models, how to choose between Sonnet 5, Opus 5, and Fable 5, use effort levels wisely, build project memory with CLAUDE.md, create Skills, connect MCPs and plugins safely, run subagents, loops, and graph workflows, and follow a practical start-to-finish system with commands, prompts, verification, and real examples you can use immediately.

How to Build a Weekly Dashboard That Runs Your Week Here's how to build a weekly dashboard that carries the memory load for you, backed by a 1956 Harvard study and buildable in an afternoon (includes free prompt). Think back to last Friday. Did you close the week clean, or did you spend the weekend with that itch at the back of your skull, sure you forgot something but unable to name it? I lived in that state for years. This post is about the fix I wish I’d found sooner, a single spread I call a weekly dashboard that holds your week so your brain doesn’t have to. Access your FREE Solopreneur Success Hub - your subscribers-only comprehensive command center for building and scaling a successful one-person business. I created this all-in-one toolkit for building a profitable one-person business, something I wish existed when I first started, and it saves me 20+ hours a week. Now, it’s yours… FREE! For the better half of my life, I tried to remember everything I used to treat my memory like a filing cabinet. Yes! Deadlines, ideas, the name of the person I met last Tuesday. All of it went into my head, and I trusted my head to hand it back on demand. It didn’t. I’d wake at 2am remembering an invoice. I’d walk into a meeting and blank on the one number that mattered. I got good at looking calm while quietly panicking that something important had slipped through a crack I couldn’t see. The strange part is that I thought this was a discipline problem. Try harder, focus more, care enough and you’ll remember. That belief cost me years. The truth is simpler and kinder. My memory was doing exactly what human memory does. I was asking it to do a job it was never designed for. Humans have always been bad at this, and always tried to fix it We like to think productivity struggles are modern, a side effect of phones and endless tabs. But they aren’t. People have been outsourcing memory for as long as they’ve had something to write on. Tally marks on bone. Knotted cords. Ledgers, almanacs, the humble to-do list scratched on a scrap of paper. Every one of these tools exists for the same reason: The human mind is brilliant at thinking and terrible at storing. So we invent systems to hold information so our attention is free to actually use it. So if you’ve ever felt like you’re bad at staying organized, sit with this. You’re part of a very long line of people who hit the same wall and built something to lean on. The dashboard is just the latest version of that ancient move. George Miller and the magic number seven In 1956, a Harvard psychologist named George Miller published a paper with one of the best opening lines in science. He wrote that he had been persecuted by a number. That number was seven. Miller kept running into it everywhere he looked. His paper, “The Magical Number Seven, Plus or Minus Two,” argued that our working memory, the mental workspace we use to juggle things in the moment, has a hard ceiling. That ceiling sits at roughly seven items, give or take two. Call it five on a tired day, nine on a sharp one. Go past that limit and things fall apart. You drop items, mix them up, or freeze. Your workspace is simply full. What Miller’s experiment actually proved This next part is what makes the dashboard work. Miller tested people across three different tasks. - First, he read out lists of digits and asked people to repeat them back, stretching the lists longer and longer to find the breaking point. - Second, he had people match signals, like tones of different pitches, to labels they’d memorized. - Third, he showed groups of items and asked people to count them quickly at a glance. Different senses, different tasks, same ceiling. People handled about seven units before performance collapsed. Then he noticed something stranger. What counted as one unit was flexible. The word “seven” is one unit. The single digit 7 is one unit. Even a five letter word is one unit, not five. Miller’s insight was that memory is limited by the number of chunks, not the raw information. A chunk is the largest meaningful piece you can recognize as a single thing. A phone number is the classic example. Ten separate digits will overwhelm you. Group them, 876 then 1234, and suddenly you’re holding two or three chunks instead of ten. Same information, far less strain. That’s the hinge. If your mind can only hold about seven chunks, then the goal of any good planning system is to squeeze your entire week into seven chunks or fewer. That’s exactly what a dashboard does. Turning a psychology paper into a planning system So we take Miller’s ceiling and we build with it instead of against it. Your week has hundreds of moving parts. Tasks, appointments, habits, half-formed ideas, things you’re grateful for, things you’re dreading. You cannot hold all of that. Nobody can. A weekly dashboard forces you to crunch that mess down into a handful of meaningful chunks. The other stuff doesn’t vanish. You just decide in advance which chunks deserve a spot in your limited mental workspace. Do the hard sorting once, then coast on it all week. What a dashboard is, and why almost everyone loves them A dashboard is not a daily view, a weekly calendar, or a monthly grid. It’s a single spread, sometimes a single page, that shows you what a stretch of time holds at a glance. Think of a car dashboard. You don’t read a manual to drive, you glance down and instantly know your speed, your fuel, your warnings. That’s why people love them. A weekly dashboard relieves you of the burden of rebuilding your week from memory every morning. You open one page and the shape of your week is right there, already sorted, already prioritized. Deciding what to do next stops being a fresh decision and becomes a glance. The crunch is what does the work, reducing everything to the few things you actually need to see. The different types of dashboard widgets Most dashboards are built from four families of widgets. Pick from these like ingredients. Planning and priorities This is the core. A priorities block holds your two or three most important outcomes for the week, the ones tied to real goals, not “take out the trash.” Keep it to three. A due this week list catches the smaller running tasks, where the laundry and the trash live. A schedule at a glance maps appointments and deadlines across the days so you know what’s happening and when. Data and habit tracking A habit tracker is binary. Did you do it or not? Mark a clear symbol for a miss, never leave it blank, because a blank box is ambiguous later. Data inputs work differently. Instead of yes or no, you record a value, hours slept, hours worked, steps, mood. I track my focus work hours every week because I run my own business, and those two numbers tell me fast whether I’m heading toward burnout or balance. Reflection These widgets hold values and feelings, not hard data. - A gratitude log gives you one line a day on what you’re thankful for. - A captain’s log is more factual, a quick record of what you did and how it felt. Both keep you grounded. Keep them short, a line or two, since this is dashboard scale, not journal scale. Notes and capture A notes block is a scratch pad for anything that lands on your desk mid week, a phone number, a stray idea. A next week block is a landing spot for things that don’t belong to this week, so when you build your next dashboard you’re pulling from real captured input instead of thin air. The rule that ties it together: each widget is one chunk. Choose around five, cap it at seven. If the page makes you wince when you look at it, you’ve added too much. Trust that feeling. How to build a dashboard on paper Start analog. It’s faster to think with a pen and there’s nothing to configure. - Pick a spread. Open your notebook or planner to two facing pages, or one page if you prefer tight. - Choose your chunks. From the four families above, pick five widgets, six at most. For me that’s schedule at a glance, priorities, due this week, a sleep and work tracker, and a notes block. - Draw the frames first. Block out a box for each widget before you write anything inside. This stops the page from turning into a wall of text. - Size by importance. Give priorities and schedule the most room. Trackers and notes can be small. - Fill it the night before or each morning. Spend five minutes loading the week in, then let the page carry it. That’s the whole method. Look at it daily, adjust weekly. The paper version is the best place to learn what you actually use before you build anything fancier. How to build a dashboard using a Claude live artifact Once you know your five or six widgets, you can turn the same layout into an interactive version using a Claude live artifact. An artifact is a working mini app Claude builds and renders right beside the chat, so you get clickable checkboxes and editable fields instead of static ink. Here’s the flow I use: - Describe your exact widgets. Tell Claude the five or six blocks you settled on with paper. Precision here matters, so name them: “a priorities box with three slots, a Monday to Sunday schedule row, a due this week checklist, a sleep and work hours tracker, and a notes area.” - Ask for a single-page HTML dashboard artifact. Say something like: “Build me a weekly dashboard as an interactive HTML artifact. One screen, no scrolling, clean and minimal.” Claude renders it live so you can see it immediately. - Set the constraint. Tell it to respect the magic number seven, no more than seven widgets, so it doesn’t over build. - Iterate by talking. Ask for checkable boxes, a color for missed habits, bigger priority text, whatever’s off. Each message updates the live artifact in place. - Use it or export it. Keep it open as your week’s home screen, or ask Claude for the code to host it yourself. Steps Choose “Artifacts” Select “Create Cowork artifact” And enter your prompt. Your ready-to-use prompt Here’s a single prompt you can paste straight into Claude to build the week-ahead version. It packs exactly seven widgets, edit accordingly based on your own situation and needs. Build me a weekly dashboard for the week ahead as an interactive, single-page HTML artifact. One screen, no scrolling, clean and minimal. Respect the magic number seven and include exactly these seven widgets, no more: 1. Calendar for the week ahead: a Monday to Sunday view showing my appointments and time-bound events. 2. Email planning box: a space where I paste this week's key emails, and you pull out what needs action and turn it into planned tasks for the week ahead. 3. To-do for the week ahead: a checklist with clickable checkboxes. 4. Substack Note tracker: one row per day to log whether I posted a Note and its topic. 5. Focus work hour tracker: log deep-work hours per day with a simple weekly total. 6. Notes area: a free-form scratch pad for stray ideas and quick capture. 7. Gratitude log: one line per day for what I'm thankful for. Make all checkboxes clickable, keep everything on one screen with a calm, minimal style with orange, white and grey colour theme, and make the layout easy to edit. After you build it, I'll ask for tweaks like bigger priority text or a color to mark missed days. Tweak it by talking. Ask for bigger headers, a color for missed days, or a running total under the focus tracker, and Claude updates the artifact in place. The advantage over paper is recall and reuse. You describe the structure once and regenerate a fresh blank dashboard every week in seconds. Here’s mine: Every time you open Claude, you re-explain your business from scratch. Your niche. Your voice. Your offers. Ten minutes lost before it writes a single useful word. Solopreneur OS for Claude fixes that. One setup interview. Then 16 skills covering positioning, content, sales pages, launches, weekly reviews that all read your profile before every output. Validate → Build → Sell → Review. One system, one voice, one business. Final thoughts You were never going to win by remembering harder. Your working memory tops out around seven chunks, and no amount of willpower moves that ceiling. A weekly dashboard works because it stops fighting your brain and starts feeding it exactly what it can hold. Build one this week. Grab a notebook, pick five widgets, draw the boxes. Then, if you like it, rebuild it as a Claude artifact you can regenerate everyday. Either way, you’ll walk into Monday with your week already mapped, and you’ll close Friday without that nagging feeling that something got left behind. If you want more systems like this, ones built for solopreneurs who’d rather work with their brain than against it, that’s exactly what I write about at Solopreneur Code. You’re doing everything. But nothing is moving? You are doing everything. But nothing is moving. That is not a motivation problem. Most solopreneurs are learning from everywhere and getting nowhere. Too much information. No clear system connecting effort to results. You have everything it takes. You just do not have a clear system yet. That is what paid subscribers get. Every system, playbook, prompt, and template. All inside the Premium Vault. All for $79/year. That’s $6.58/month. Upgrade now and unlock the Premium Vault worth thousands of dollars. The Premium Vault holds the secret behind posts like this one, including the tools and resources I use to build the one-person business I love. Thanks for reading! Ready for the next step? Let’s crack the growth equation and build a thriving one-person business on your terms! Anfernee

The $10K Etsy niche nobody is running with Claude yet One niche. Three assets that feed each other. Built with Claude to a quality people actually pay for, without the spam that gets you banned Most “make money with AI” guides teach you the same move: flood a platform with junk, cash in for six weeks, then watch the account get banned and the income vanish overnight. This is the opposite, and it is built to still be standing in a year. One niche. Three genuine assets that feed each other. Each earns on its own, and together they build the one thing you actually own: an audience no algorithm can take back. Here is the part those guides bury: the product is not the asset. The audience is. A single Etsy sale is a transaction. An email list that trusts you is a business, one nobody can deplatform. And the ground is wide open right now: ▫️ Etsy just posted its first buyer growth in two years, 86.6M active buyers ▫️ Pinterest sends 41% of all of Etsy’s social traffic, and 75% of weekly Pinners arrive ready to buy (versus 28% on Facebook) ▫️ Email still returns about $36 for every $1, on a list nobody can starve The window is the story. Custom digital products used to need a designer and days of work. Claude collapses that to minutes, which means the edge belongs to whoever builds genuine quality at volume before the category fills up. Right now, it is close to empty. One example niche runs the whole way through: travel. Keep it, or swap it at the end with one prompt. The system is the point. What’s inside the full playbook: ▫️ The custom-product engine that builds a digital product people pay a premium for, with the margin math ▫️ The Etsy launch kit that ranks without keyword-stuffing ▫️ The 4-prompt content engine built for the AI-search era, where depth is the only moat left ▫️ The owned-audience funnel where you show up as yourself, over astroturfing your way to a ban ▫️ The one-brand structure that turns three hustles into one business ▫️ The niche-swap prompt that rebuilds all of it for any market One subscription unlocks every system Plus 3 fresh systems every week. One product line from this covers the subscription many times over. 🧭 The Claude Digital Business System Get the full playbook below 👇 Try premium free for 7 days. Or get 50% off this week only.