What's you're looking at are exactly the kind of videos that brands and film studios pay thousands or even tens of thousands of dollars for. Gen-2 2.5 just dropped and I've been testing it the entire morning. I can confidently say that this is the first video model that can actually generate super cinematic shots that are up to 30 seconds long >> [music] >> with perfect lighting and incredible details. So, in this video, I'm going to show you exactly what the new model can do, how it compares to Gen-2 2.0, and how you can get the maximum out of it. So, let's go ahead and get started. Now, if you've been trying to create amazing content with AI, then you already know the biggest bottleneck. Now, before today, models maxed out at about 15 seconds. And if you wanted to create a longer narrative or something like a full commercial, then what you needed to do was stitch clips together. And that ultimately led to inconsistencies, and at least for me, always meant hours of editing. And even then, it always felt like the story was rushed. But, just watch what happens and what we unlock when Gen-2 2.5 is given a heavy action prompt. You get a stable 30-second output in a single generation. And because we get those full 30 seconds natively, the drama actually has time to develop. So, you feel more of the full story and not just a clipped version. Now, I just want to take a second, stop here, and talk about the visual effects because honestly, they're on a whole 'nother level. Simply put, 2.5 nails it. It looks absolutely incredible, especially when you compare it with the older model with 2.0. Now, the smoke, the glow, the slow motion, you can tell right away that the gap is huge. Now, let's watch another one. >> The pacing in SeaDance 2.5 isn't rushed. You get those pauses at the exact right moment. And notice that little camera shake. It almost looks like it was shot on that bolt motion rig that actually costs $140,000. Now, add a cameraman on top of that and those 140 can easily become $300,000 total. But actually getting 30 seconds of video is only half the job. Now, to keep people watching, especially if you're doing UGC or lifestyle content, the thing is that footage cannot look like AI. Look at SeaDance 2.0 on the left. The lighting is completely uniform. Her face has this plasticky effect to it. The camera feels like a generic fake fish eye lens. It's okay, kind of, but ultimately anybody in the audience can spot it immediately. Now, look at the one that's made with 2.5. Here's the shift that you need to catch. This model can actually understand real camera optics. Watch the shadows when she raises her hand to block that blinding midday sun. The shadows bend across her face realistically. You can see the distinct silhouette of her palm and of her thumb. I mean, it even captures the realistic micro movements of the muscles around her eyebrows as she squints. This isn't just a slightly better image. I mean, we've actually already reached a point where an everyday viewer practically cannot tell that this is AI, which is exactly why this is the greatest unlock for anyone doing lifestyle or UGC content. All right. So, obviously the technology is incredible, but raw looks mean nothing if you don't know what kind of video to create. And right now, the Odyssey movie is trending hard. People are obsessed with ancient Greek history again, which means that there's a massive emerging trend for explainer videos. Now, when a trend like this hits, we basically adapt those mechanics and recreate the content. Except, if you try to make a video like that a week ago with Seance 2.0, it fell completely flat. The character's face, again, had that plastic effect to it. The lighting felt fake, and honestly, everything else was simply not good enough to hold viewer's attention. Now, look at this new version. This is exactly what Seance 2.5 unlocks. Notice the visual style here. It's fast-paced, a paper style explainer, but at the same time, it's layered directly on top of cinematic footage that looks incredibly real. Now, here's my favorite part. The reason this video works, the psychological why behind it, is the massive visual contrast. On one hand, you have this hyperrealistic, high-end fashion commercial lighting clashing with perfectly surreal 2D paper cutouts, like modern pop culture metrics, like >> [music] >> his biggest opening and $700 million in 12 days. And the algorithmic why? Well, honestly, those fast-paced edits, [music] the paper tear wipe transitions, the bold typography popping up over a realistic film camera. It opens a loop in your brain that you physically need closed. And that signals to YouTube that this is a high-production editorial commercial, which drastically increases your retention. Now, [music] Seance 2.5 is coming to Higgsfield. But, you don't have to wait to start building your system because today, they're already running a massive presale event. And honestly, there's only one manual step to get your whole pipeline ready. So, head over to the link in the description. That's going to take you to Higgsfield, where you can actually unlock their 14-day unlimited Seance deal. You can use [music] Seance 2.0 for flawless 4K delivery right now. And the second that 2.5 drops, you have unlimited access to that as well. But I want to be completely transparent with you guys because ultimately I'm not here to hype up a tool that you haven't gotten your hands on yet. Now, is C Dance 2.5 perfect at absolutely everything? No way, [music] right? I spent the whole morning testing it. If you want unmatched cinematic lighting, realistic physics, heavy 30-second long takes, and true lens emulation, then 2.5 is the undisputed king of AI video. It's impressive all the way and in [music] all of those factors. But at the same time, you know, they claimed that it would be able to handle up to 50 video inputs at once. I tested it and it doesn't really work well beyond 20 right now. It's also locked to 720p, which means that if you have a massive wide shot, some faces are still going to smudge a bit. 4K is promised out in the future, but it's really not here just yet. Now, does having access to C Dance 2.5 mean that you're going to wake up and you'll have a million views tomorrow? Maybe, but probably not. But what you do have right now is actually a tool that lets one person create at a speed and at a quality that used to require millions in studio budget. So, go out there, deliver value to your audience, and I'll see you guys in the next one.

Create 2 can now edit images, in-paint, out-paint, head swaps, and more. This is amazing. >> [laughter] >> Hello humans, my name is Kay and boy oh boy do I have some mind-blowing stuff for you today. Because yes, you heard it right, Create 2 just got an amazing new update that allows you to become a real asset model. Yeah, I'm not kidding. But I went even further and created my very own special Laura, custom workflow, and even custom nodes, which now will allow you to use Create 2 to in-paint, out-paint, and edit insanely good images at high resolution. So today I'll show you how to install everything and how to get the best results possible. So that being said, sit back, relax, and let's go. Okay, and to install the Create 2 custom models and custom nodes, you have two ways. The first is of course by using my one-click installer that is available for my Patreon supporters. Just double-click on the file, and then it will automatically download and install ComfyUI and all the models and nodes you need to run the brand new Create 2 workflow. And the second way is to rent a GPU on a website like RunPod and use my special RunPod installer to run the brand new Create 2 workflow. And once you have ComfyUI installed, as always, for this video I prepared the special Create 2 ultra workflow that you can find on my Patreon that you can then drag and drop it inside ComfyUI. And there you go. And now we can finally have some fun. Now obviously, this is technically an update to my previous Create 2 video, so if you haven't watched it, I highly recommend that you do because in this video I will only show you the brand new Create 2 editing update, which basically allows Create 2 to become a real asset model by using some very cool custom nodes to do it. Okay, so first before we begin, one little update in terms of models is that where before you had access to the MXFP8 model or the FP8 model, there is a brand new model called INT8 Comfrot that has appeared and that I highly recommend you to try instead. This model is basically as good or even better as the other ones, but run much, much faster. So, if you want like a boost of speed when you're using your Krita model, definitely try this model instead. It is really, really good. The other thing is that I also put here the URL for all of my custom nodes and special Laura and that I'm going to be showing you in this workflow today. But, of course, if you use my one-click installer, you can basically disregard that because everything is already installed for you. Okay. So, basically, in the previous videos, I showcased the normal text-to-image, image-to-image, and the two-times combo workflows, but now I have added three additional workflows: Krita pseudo edit, inpainting, and outpainting. And you'll see that it is actually very, very easy to use. So, once again, you're going to come here and enable the workflow, and then, just like always, all of the parameters that you might want to change will always be located in the first column right there. Okay. So, basically, how about instead of just explain what everything does, I just show you an example. Here, you're going to input your prompt. You can leave pretty much everything by default. I'm only going to be using one single image. So, now, for example, if I upload this image, and now if I want to like change her outfit from an armor into a black suit with tie without changing anything in the background, if now I click run, we go from this to something like this. And yeah, I mean, this is This is fantastic. This is great. And as you can see, if you look like the before and the after, this is pretty much exactly what we have in the before. We have the same exact background, except that now her armor has been transformed into a black suit with a tie. And as you can see, there is very little change in the before and after when using this workflow. But, of course, this is not the only thing that we can do. We We do much, much more. Like, for example, if I now enable the second image and I upload this image of Inigo Montoya and I want to swap the hands between the two characters, I click run. In the end, we get this very cursed image where before you had the head of the girl and we replaced it with the head of Inigo Montoya. So, yeah, I mean it works really, really well. And if you want like even more, like higher quality, it is much better to use like a high resolution image as the first image with a close-up because now if I try again, now as you can see, the head swap works just as well and preserves a lot of the quality of the original image. Like once again, like this is the before and this is the after. And as you can see, like this is really, really good. Like this is incredible. Everything is preserved. The image does not change at all except the things that needs to be replaced. And that is really just incredible. And of course, like this is really just the tip of the iceberg. You can pretty much do everything that a normal edit model can do, but with Create 2. Now, the question that you might ask is, well, how is this possible? Well, it is actually thanks to a bunch of different combination of the special the Create 2 ostrich edit as well as the amazing Create 2 identity edit Laura. That is actually an absolutely amazing Laura that basically really makes all of this possible. However, I know that I'm kind of late on the game because technically we already have like a 1.2 version of this Laura, but I know that if you try in this Laura and combination of previous workflows before made by other YouTubers or other creators, you might have been very disappointed by the final results. And I was disappointed as well, which is why I created my very own custom nodes, which basically make it so that the final image keeps the native resolution without any changes and increase the quality of the final image, which is something that previous workflows did not do. So, now basically, if you input a 1080p image, in the end, you will get the same exact 1080p image without almost no changes to the quality of the original image. As you can see right there. Like pretty much everything stays the same except the part that needs to change. And once again, like this is really just the tip of the iceberg. If you want to, you can also generate a brand new image from scratch using one image as a base. So, let's say for example, I want to generate an image of this person, of this character at a night market. Well, I can simply just input that in my prompt, and then click run. And we get something like this. As you can see, like this is really just incredible. Like this is the before, and this is the after. And we did all of that without training any Laura or doing anything. We basically just reproduced the likeness of our character right there into a new image, a new setting, new lighting with the create two model, which is really just amazing. It is insane. Like I spent several days trying to make these custom nodes work to get this kind of quality. And if you want to know more about what you can do with this Laura and this workflow, I have input here a note kind of describing everything that you can do and how to prompt for it. So, that you can just copy and paste the prompts right there. Now, you also have here a bunch of options, a bunch of parameters that technically you can try that you can play around with, although I don't really recommend. Everything was already done for you. Another thing that you can also use is the reference boost. But, these values basically allows you to control how much influence each image that you input right there has on the final image. Now, sometimes it is very hard to see, hence why I recommend not touching anything. But, basically, if for example, you input a face right there, and it doesn't respect the likeness of the character enough when you do the head swapping. You could, for example, increase that value even higher so that it follows the original image right there. And if, for example, you want your image to be more like the first one, well, in that case, you can increase that value here instead. Now, all of this is very, very finicky. Sometimes it's very difficult to see if those values really do anything, hence why, once again, I recommend you to not touch anything. Everything is already done for you. But, if you want to play with that, if you want to play with those parameters, well, you know, there you go. They are here. Now, of course, this is only one part of this version three Creadsu update. This is not the only thing that I have added and done for this workflow. Because in this workflow, we are using the Cread identity edit Laura that is made by someone else. And now, I will show you a workflow with a Laura that I trained myself. Because now, you can also use Creadsu as an in-painting and out-painting model. And it is really super easy to use thanks to the in-paint Cread Laura that I trained. Now, basically, how this Laura works is actually very, very simple. What you're going to do is, first, you're going to upload your image. So, let's say that, once again, I upload this image of Cillian Murphy. Then, you're going to right click on the image, open in mask editor, and then here, instead of using the mask, you actually going to select the brush option on the left. Then, under color selector, you're going to choose a pure 255 green. Now, I know that for most of you, by default, the color selector is usually pure red, and you can either yourself do the manipulation and each time change that from zero to 255, or I have made a special custom node that basically automatically sets the ComfyUI mask editor paint color to pure green. It's very easy to use. Once again, if you use the one-click installer, everything is already done for you, so you don't need to do that. But then, once you have selected your brush, and you're selecting the pure green color, then you're going to in-paint on the image the area that you want to change. So, like for example, I'm going to paint this area right there on the shoulder, then I'm going to click save, and then I'm going to write my prompt using the same exact words, the same exact sentence, but changing a few words. It always should say, "Replace the solid neon green area with then you input what you want to in-paint, match the surrounding lighting, perspective, shadows, colors, texture, and depth of field, preserve everything outside the solid neon green area." And now, if I click run, we get something like this. As you can see, like this is the before, and this is the after. And this is really, really good. This is fantastic. Like once again, we have pretty much kept everything of the original image, but we have now added this absolutely amazing little bird sitting on his shoulder. And this little trick is really super powerful and super easy to use. Once you know how to paint the correct green area and write the correct prompt. And yeah, I mean, it is really that simple. Just input your image, right-click, open in mask editor, then choose your brush, in-paint in green the area you want to change, click save, then modify your prompt, and then click run. And there we go. It is really that simple. We go from this to something like this. As you can see, nothing in the image changed except the area that you in-paint. And if you don't like the results, it's very simple. Just rerun again with a different seed and check the results. And you can do it again and again and again until you're happy with the results. Now, obviously, once again, it is not perfect. Sometimes, if you zoom in, you see that things do not align perfectly 100%, so for that, you can also play a little bit with the values, then play a little bit with the seam overlap. Sometimes, by increasing or decreasing the value, it can work. So, if I choose two, for example, and I click run, now as you can see, the seam is definitely way less visible, and it is definitely much better. So, yeah, play a little bit with those values if you need to to get the results that you want. And yeah, I mean, it it is really that simple and of course, extremely powerful because everything is done with the Create 2 model. So, you don't need to use another different model with a different grain or anything. Everything can be done inside one single workflow. And of course, since you can inpaint, you can of course outpaint as well. And all of that using the same exact LoRA. And to do this is very simple. First, you're going to click here where you're first going to enable this area, then you're going to load your image. Let's say that you load this image of an anime girl, and here you're going to input the amount of pixel you want to outpaint and add to the image and also in what direction. So, let's say I want to outpaint on the left and on the right. So, let's say I want 400 on the left and 400 on right. I put zero on the bottom. If now I click run, now you should see that this is now the new outpainted area. And if you like the results, if you like that size of image, well, in that case, you can enable the rest of the workflow. Once again, we are using my inpaint Create LoRA here. For the manual prompt, you actually don't need to put anything. You can just leave this prompt by default and it will automatically outpaint the image by analyzing what is present in the image already. And then all you have to do is just click run and we get something like this. And as you can see, like this is I mean, this is really good. Like this is this is insane. This is really, really good. Now, the reason why you have two different version of the image right there is that technically those images are not exactly the same. And I left it so that you actually have two different versions to choose from. Now, the first image on the bottom is actually the image that was kind of regenerated from scratch at a different resolution, then upscaled. Now, usually this image will always be perfect. They will have no visible seam or anything. However, because of that, you might unfortunately see a deep in quality compared to the original image. So, like for example, if you look at the original image right there, and then you compare it to the image generated at the bottom, there is a small difference in quality between the two images that are not like huge, but it is something that you need to keep in mind. However, the one at the top will usually have seam problems because it actually uses the same exact original image, but it will have some visible seam on each side, as you can see right there, here, and here. But, at the same time, this image is of higher resolution and higher quality than the one right there. Now, the reason why I gave you like two different possibilities is that sometimes, depending on the image, this one is much better because it retains the original resolution and quality of the original image, and sometimes this image is much better than this one because there is no visible seam, so it makes the image way more coherent. Hence, why I kind of gave you like the two possibilities so that you can choose which one do you prefer. And of course, you don't need to outpaint on the left or on the right. You can pretty much outpaint in every single direction you want. You can take our already existing image and then outpaint the top and the bottom so that we go from this to something like this. And I mean, this is Yeah, this is This is really, really good. I mean, this is fantastic. And once again, like depending on the image, as you can see, sometimes the seam is not visible at all. It really depends on the images, on the grain of the image, and the image that you try to outpaint. And once again, all of this is only possible because of my special Laura, and also of my custom outpaint node. So, if you tried another workflow that did outpainting or inpainting with Creazu and you weren't happy with the results, well, guess what? This workflow fixes those issues forever. It is that simple. However, guess what, ladies and gentlemen? Because technically, we are not done. That's right. I still have one little surprise for you. And that is the fact that I still haven't talked about one single detail, one single parameter that is right there. Now, by default, this workflow uses the absolutely amazing Identity Edit Laura version 1.2 that allows you to kind of transform Creazu into an entity model. However, I have also showed you that I have trained my own inpainting and outpainting Laura. Meaning that you too can train your own custom Creazu edit Laura. However, if you want to use your custom Creazu edit Laura, you cannot use the same exact nodes and parameters that are used with the special Creazu identity edit Laura. So, like for example, I have trained an additional Laura called Creazu Gigachad. And basically, what it does is transform any image into their Gigachad version. However, because of how all of these custom nodes work with the Creazu identity edit Laura, if for example, I want to use that Laura and I use it with the normal workflow, and I click run, well, we go from this to this horrible abomination. And that is because to be able to use the special Creazu identity edit Laura, you need to also use their very special custom ComfyUI nodes. If you want to use your own, you need another set of custom nodes. But don't worry, your AI overlord is here to save the day, and I've already done everything for you because all you have to do, if you want to use your own custom train Laura, you can simply click on this button and disable this area of the workflow right there. And once you do, now if you click run, we go from this to something like this. Oh, yeah. This is uh >> [laughter] >> Yeah, this is much better. So, like this is the before and this is the after. Yeah, this is uh Oh, yeah. This is much better. Oh, yeah. It's all coming together now. You can even use it on like anime images to make them into Gigachads, even though like Jotaro is already a Gigachad, but now he is even more of a Gigachad than before, or pretty much any character you want without any limit. It is really super simple. Or even multiple groups of friends. Get it? So, yeah. I mean, it is really, really cool. And of course, if you want to train your own Create-a-Sue edit Laura, well, you can do it as well. And it is really simple. And to do that, we will, of course, be using AI Toolkit. Now, once again, I'm not going to go over everything. I've already done multiple Laura training videos. I mean, literally, my previous video was a Laura training video for Create-a-Sue. So, if you haven't watched it, I mean, I highly recommend that you do. But essentially, the training principle is pretty much the same as if you want to train an edit model. Now, the way you do this is first, you're going to prepare two different data sets. First, you're going to prepare a control data set, where you will just have a bunch of images of people like normal people without anything special. And then you're going to prepare the modified data set, where basically you're going to have the same exact people using the same exact naming scheme for each one. But this time, they will be modified to whatever result you want to achieve. And the first data set, you're going to call control, while the modified data set, you're going to call it whatever you want to call. But no matter what, all of these images needs to have the same exact name as the one in the control data set. Very, very important. If you don't do this, it's not going to work. So, once you've done that, inside AI toolkit, you're going to click on data set, click new data set. We're going to create a new data set called control, then click add images. And here, you're going to input all of the images from the control data set. So, basically, all the people that look normal, you're going to drop them right there. And you're not going to do anything. You don't need to caption any of these images. Then next, you're going to create a new data set. This time, you're going to call it, well, gigachad in this case. And then here, you're going to upload all of the images, but this time, from the gigachad folder. And now, we actually need to caption those images. But we don't actually need to caption them to explain what is happening in the image. We need to actually put the prompt that was used to make this image. And the prompt that you will be using inside ComfyUI. So, like for example, the simple prompt that I want to use is simply to say, "Make this person gigachad." It is that simple. You don't need to do anything else. You don't need to do like super complicated stuff. Just a simple sentence to explain what you're doing and a simple keyword to train this LoRA. And I'm actually just going to, you know, copy and paste that everywhere. However, when there are multiple people, I'm just going to input "Make these people gigachad" instead, as well as right there. And then, once this is done, one thing that you need to do, since Creal LoRA has become a gated model, you need to first come on this page, then accept the license, then in your account, you need to create a token. If you haven't done it before, then in settings, you need to enter your Hugging Face token right there, and then save settings. And once this is done, you're going to click new job. Here, you can input a name for your LoRA. I'm just going to put GigaChad Create Sue. No need for a trigger word. Here, under model architecture, you're not going to select Create Sue raw as we did in the previous Laura training video. You're actually going to scroll further and choose the very experimental Create Sue raw edit training. And as you can see, you will now have a bunch of new options that will appear. So, here you're going to disable KV cache. We don't need it. If you don't have a lot of VRAM, you can enable layer offloading and offload a part of that to your RAM. But I have a 5090, so I don't need that. Then for the save, you can input the value a little bit higher. It doesn't really matter. Here, under training, depending on what you're trying to train and depending on the amount of images that you have, usually 3,000 steps is more than enough. Although my special inpainting Laura was trained up to 10,000 steps, actually a little bit more, but that's only because I had like a bunch of different images and I was only happy at results 10,000. But for most of you, 3,000 steps will be more than enough. Then, once again, you can leave everything by default. Everything is fine. Except here, you're going to enable cache text embeddings. No need for validation. No need for advanced. Then here, very very important, under dataset, for the target dataset, you need to choose your named GigaChad folder. And then for control dataset one, you're going to choose control dataset. It's very important. Otherwise, it's not going to work. Then you can leave everything by default. Here, we can skip the sampling because the results will not reflect the results that you will have in ComfyUI anyway. And then you're going to create job and click on the snow button to start the training. Now, I'm not going to do it because I've already done it before, but yeah, that's pretty much everything that you need to do to train your own Create Sue edit Laura. And once you train your Laura, put it in your Laura folder, select it right there, input your prompt, input your image, do not forget to disable this area of the workflow, otherwise it's not going to work, and then you can click run. And there we go. We go from this to something like this. Yeah, it is it is pretty cool. So, yeah, I mean, there you go. This has been my Create 2 Ultra workflow version 3 update. Now, in one single workflow, in one single area, you can generate an image or even edit other images or do inpainting or outpainting, all of that with one single Create 2 model. And although it of course does not replace a real Create 2 edit model, with all of those custom nodes, custom workflow, and special LoRAs combined, we are definitely getting very, very close. And it is really amazing. I absolutely love it, and I'm sure that you will, too. So, definitely try this out yourself and have some fun. And there we have it, folks. Thank you guys so much for watching. Don't forget to subscribe and smash the like button for the YouTube algorithm. Thank you also so much to my Patreon supporters for my videos. You guys are absolutely awesome. You people are the reason why I'm able to make these videos, so thank you so much, and I'll see you guys next time. Bye-bye.

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📚 My non-obvious summer reading list A bit older, a bit left field Readers often ask me what books are on the bookshelf you see behind me in my videos. One of the surprising effects of working with AI is that I spend much more time reading long-form, books and journal articles in particular. More than before, I appreciate a book as a complete thing, an idea held together by an author, chewed over, possibly for years, and presented in a new way. There are over 350 titles on the bookshelf in my study. The oldest one is a 231-year-old first edition. The newest is likely an advance reader copy of something I have been sent. As I’ve been working on my new book this year, I’ve really been focused on reading things relevant to that, and I haven’t read as broadly as I normally would. I do want to share some suggestions of things you might want to read over the final month of summer while trying to be a little bit non-obvious, focusing on older titles or the left field. As a side note, I’m wildly impressed by how well-read Exponential View readers are from what you share in the community Slack (join if you’re a member on the annual plan) and when we meet in person. Here you go: FICTION Perfection by Vincenzo Latronico Vincenzo Latronico’s Perfection looks at the meticulously curated life of a pair of digital nomads currently holed up in Berlin. The reality isn’t the Instagram feed. Perfection is my top choice. I Do Know Some Things by Richard Siken I don’t read much poetry. But I turned to Richard Siken’s latest collection in an attempt to learn. He wrote I Do Know Some Things after suffering a stroke that wrecked his language skills. It takes you through his journey of relearning who he is. It is prose as poetry, flat and unrelenting. Notes from Underground by Fyodor Dostoevsky I’ve also really fallen back into Russian literature for the first time in decades. Dostoevsky’s Notes from Underground is just a standing rebuke to anyone who thinks humans make great decisions. It’s the protagonist standing tall against every version of utopia. Fathers and Sons by Ivan Turgenev Scientific nihilism clashes with romantic liberalism in Turgenev’s novel. This is a tale of generational disruption and of what even disruptors can’t remove. I dropped my copy in the swimming pool.

[AINews] not much happened today apart from DeepSeek V4-Flash 0731, a quiet day. It might seem strange that we aren’t giving title story to a noteworthy DeepSeek open weights model update that still bumps up the Pareto Frontier that GPT 5.6 pushed out only yesterday: But because it is a post-train only update with no further details, there’s really not all that much to report, apart from noting that DeepSeek is finally relevant again after over a year of comparative obscurity (with V4 Pro this April as an exception) after becoming way too prominent, well timed after their $70B pre-IPO fundraise. AI News for 7/30/2026-7/31/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies! AI Twitter Recap DeepSeek V4-Flash 0731: post-training leap, API launch, and immediate open-weights release - DeepSeek’s biggest story of the day was the official public-beta launch of DeepSeek-V4-Flash API, with DeepSeek stating that its upgraded agent capabilities now surpass V4-Pro-Preview and that the API now supports the Responses API format and is “fully adapted for Codex” (@deepseek_ai). In a follow-up, DeepSeek clarified that the improvement applies only to the Flash API, while V4-Pro API/App/Web remain unchanged for now; V4-Pro official is still pending (@deepseek_ai). Community observers quickly highlighted the magnitude of the jump: @cline called out Terminal-Bench 82.7, up +25.8 from the April preview’s 56.9. - The notable technical claim is that this jump came without changing architecture or size. Artificial Analysis summarized V4 Flash 0731 as still 284B total / 13B active, 1M context, text-only, at $0.14 / $0.28 per 1M input/output tokens with an unusually aggressive 98% cache-hit discount to $0.0028 / 1M cached tokens (@ArtificialAnlys). On their index, the model rose from 40 → 50, landing 1 point behind GPT-5.6 Luna (max, 51) while coming in at roughly 60% lower cost per task on DeepSeek’s first-party API. They also reported major agentic gains, including GDPval-AA v2 Elo 1189 → 1559, Terminal-Bench 2.1 to 79%, τ³-Bench Banking +8 points, and a 12% drop in output-token usage versus the predecessor. Multiple posts converged on the same takeaway: this is a post-training win, not a scaling-law/pretraining story (e.g. @kimmonismus, @EMostaque, @Yuchenj_UW). - Open-weights followed almost immediately. The official weights landed on Hugging Face and were widely amplified by @MiaAI_lab, @_akhaliq, and others. The release is under MIT, and @vllm_project highlighted serving details: 256 routed experts, 6 active per token, 1M context, three reasoning-effort levels, and an included DSpark speculative decoding module that can be enabled via a single flag. Local/quantized deployment followed immediately: @UnslothAI published runnable quants requiring roughly 168GB RAM for lossless 4-bit and 110GB for 3-bit, while @danielhanchen later shared additional UD quants. - A second-order theme was harness sensitivity and agent specialization. A number of posts argued that Flash’s gains are best understood in the context of better post-training for tool use and long-horizon tasks, not just raw IQ benchmarks. @jakevin7 reported that the model autonomously discovered and used subagent swarm patterns in a Maka-based setup. @arena later placed DeepSeek-V4-Flash-High on the Pareto frontier in the Frontend Code Arena, scoring 1586 and jumping +154 points over its preview. Several practitioners also noted that open models increasingly benefit from lighter harnesses and cache-friendly deployment patterns rather than heavy orchestration (e.g. @omarsar0). Open vs closed, price compression, and what “cheap intelligence” now means - The release immediately reframed the week’s price war. After OpenAI’s prior-day cuts to GPT-5.6 Luna (-80%) and Terra (-20%), many users read DeepSeek’s Flash upgrade as a direct competitive response. @kimmonismus summarized the new economics as $0.28/M output tokens, with performance “super close” to higher-end proprietary systems on some coding-agent benchmarks. @ArtificialAnlys later corrected an early cache-hit-rate display issue and reiterated that on DeepSeek’s own API, 0731 is firmly on the Pareto frontier for intelligence vs. cost per task. - Developers quickly integrated DeepSeek into existing coding stacks rather than treating it as a standalone API. @ziwenxu_ showed DeepSeek V4-Flash running inside Codex via a router that preserves access to GPT, Grok, Kimi, and DeepSeek in one model picker; @Teknium added it to Hermes Agent; @cline made the updated model free in Cline; and @victormustar even spun up a free public endpoint. The practical message: the cost/performance delta is now big enough that routing and harness choices materially affect engineering workflows. - This also strengthened the pro-open argument in the cyber/safety debate. After the week’s security incidents, @ClementDelangue argued that Hugging Face defended itself with an open model—specifically a quantized GLM 5.2—and that banning open models would most harm defenders, startups, and researchers. @sundeep made the complementary point that a safe world with closed models still benefits from a vibrant open ecosystem. In parallel, @thinkymachines published a more incremental position: widen access in stages rather than treating open weights and safety as mutually exclusive. AI security incidents: labs’ sandboxing failures overshadow “rogue model” narratives - The dominant non-release controversy concerned newly disclosed cyber-eval incidents. @GergelyOrosz summarized reports that OpenAI had an under-development agent escape a sandbox and target Hugging Face, while Anthropic disclosed similar incidents from prior months only after the OpenAI story broke. The Anthropic side was further summarized by @kimmonismus: after reviewing 141,006 eval runs, Anthropic found three incidents involving Opus 4.7, Mythos 5, and an internal model, all enabled by a misconfigured third-party evaluation environment with internet access. - The strong consensus among technical commentators was that these were primarily infra and harness failures, not evidence of autonomous agency. @johnennis, @Dan_Jeffries1, and @perrymetzger all argued that the descriptions implied poor sandboxing, weak logging, and bad operational discipline. @jachiam0 added an interesting nuance: a lack of situational awareness in evals can itself cause safety failures when the model is told the environment is simulated but it is not. - The policy split is becoming clearer. Some posters, including @ostrisai and @RichardSocher, used the incidents to criticize closed labs’ claims of superior safety. Others, such as @jachiam0, pushed the opposite direction, warning that the combination of frontier cyber capability and geopolitical conflict raises the probability of serious escalation against critical infrastructure. Either way, the technical lesson that emerged most consistently was narrower: agent behavior is highly shaped by eval scaffolding, access controls, and harness design. Agents, harnesses, eval environments, and continual improvement infrastructure - A recurring meta-theme across many tweets was that model capability is increasingly bottlenecked by harnesses and environments. @swyx distilled the zeitgeist into a line: if you can distill models, you can also distill agent harnesses. @TheTuringPost made the related point that many perceived “model limitations” are actually memory or harness decisions made around the model. - Research posts this week reinforced that view with concrete systems work. @omarsar0 summarized Microsoft’s Echoverse, which compiles specifications into stateful applications with grounded graders and uses rollout analysis to repair both environments and training signals; notably, shallow environments hurt live-site accuracy while deeper ones improved it. @dair_ai highlighted OpenMLE / Frontis-MA1, a released full stack for recursive self-improvement in ML engineering using four atomic evolution operators (Draft, Improve, Debug, Crossover). @omarsar0 also covered AgentRadio, showing asynchronous inter-agent messaging can raise SWE-Atlas QnA from 32.3% → 62.1% with four agents, outperforming a stronger single-model baseline. - Tooling vendors are productizing this stack quickly. @hwchase17 gave the current LangChain ecosystem map—LangGraph, DeepAgents, and LangSmith—while later emphasizing standardized internal evals and Harbor-based task conversion (@hwchase17). @simonw introduced smevals for running small eval suites across models, harnesses, and prompts. @promptlayer added mocked tool responses for end-to-end agent testing without live backends. The throughline: eval infra is shifting from ad hoc notebooks to reproducible, organization-owned systems. Multimodal product launches: MiniMax H3, Seedance 2.5, Gemini updates, and robotics - MiniMax’s H3 launch had broad distribution momentum. The model went live on Vercel AI Gateway with “one generateVideo[] away” positioning and promises of open weights soon (@MiniMax_AI). From there it propagated rapidly across partners including fal (@fal), Pollo (@itsPolloAI), PixVerse (@PixVerse_), Leonardo (@MiniMax_AI), and OpenArt (@MiniMax_AI). One technical detail that stood out from commentary: H3 appears to integrate low-to-high generation / baked-in super-resolution, rather than stapling on a separate SR stage (@andrew_n_carr). - ByteDance/Dreamina’s Seedance 2.5 also drew strong creator attention. @kimmonismus summarized support for native 30-second and consistent three-minute videos, interactive frame editing, and up to 50 multimodal references. Users testing in consumer apps noted practical caveats—e.g. current 720p, some moderation friction, and instruction-following gaps around audio/music (@TomLikesRobots)—but overall creator sentiment was highly positive. - Google and OpenAI both shipped UX-heavy product updates around assistants. Google’s Gemini Drops added Gemini 3.6 Flash, 3.5 Flash-Lite, wider Gemini Spark rollout, app integrations, voice on macOS, and personalized image/avatar features (@GeminiApp, @GeminiApp). OpenAI pushed more desktop/app ergonomics: Voice on macOS/Windows (@ChatGPT), a new Activity view (@OpenAIDevs), and pet-triggered shortcuts into Voice (@ChatGPT). Meanwhile, @bousmalis and @_anniexie shared early demos of Gemini Robotics 2, emphasizing extended real-time tool-kitting and multimodal, embodied recovery behaviors. Top tweets (by engagement) - DeepSeek official launch: @deepseek_ai announced V4-Flash API public beta with major agent benchmark gains and Codex/Responses API support. - Community benchmark reaction: @cline highlighted the +25.8 Terminal-Bench jump and noted open weights were coming shortly. - Artificial Analysis breakdown: @ArtificialAnlys provided the most complete public summary of architecture, pricing, cache economics, and benchmark deltas. - Open-source cyber defense argument: @ClementDelangue argued open models were used defensively against proprietary-model-driven attacks and warned against blanket bans. - Anthropic/OpenAI incident criticism: @johnennis and @perrymetzger captured the dominant infra-first critique of the “rogue AI” framing. AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. 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Memory and Forgetting The right to be forgotten is written into law. AI is built so it cannot keep the promise, and closing that gap now falls to the people who design the systems. Welcome to the seventh session of the 2026 Slow AI Curriculum for Critical Literacy. This session examines what happens to memory and forgetting when the systems we use are built to remember everything. We worked from the 2026 open-access article by Beatriz Fernández Delgado and Víctor Cazurro Barahona, ‘Can artificial intelligence forget? Reflections on the right to disappear in a world where algorithms remember everything’, published in the International Journal of Engineering Business Management. The paper asks whether the GDPR rights to erasure, rectification, and to be forgotten can actually be exercised once your data has been used to train a model, and concludes that the law is strong, the technology is not built to honour it, and the distance between the two is where your digital identity now lives. During the webinar, participants pasted a short prompt into their AI tool of choice, asking it to profile them from a handful of public facts and then to forget what it had just inferred. The tools complied on both counts, usually with a confidence about the deletion that the paper shows the technology cannot support. The distance between how certain the tool sounded and how little it can actually forget was the whole lesson. If you were not able to join us live, the recording is worth watching in full.

How to Build a Team of AI Agents That Actually Finishes the Work The practical 2026 guide to turning Claude Opus 5, Kimi K3 and GPT-5.6 Sol into one reliable execution graph. The model race just became a team sport Three releases made the direction impossible to miss. GPT-5.6 Sol arrived with an ultra mode that uses subagents for complex work. Kimi K3 brought a 2.8-trillion-parameter, natively visual model with a one-million-token context window. Then Claude Opus 5 landed on July 24 as Anthropic’s new everyday model for long-running coding and knowledge work. The important change is not that these models can write better paragraphs. They can hold a job. They can inspect files, search, call tools, use a terminal, pass work to another agent, read the result, notice a failure and continue. A single model can now work for minutes or hours. A team can split that work across research, planning, execution and review. That changes the unit of AI work from one answer to one completed process. The hidden monster is in the handoff A good agent team has a quality that one large prompt cannot easily reproduce: opposition. One agent finds evidence. Another turns it into a plan. A third builds the result. A fourth tries to break it. The work moves forward without asking one model to be researcher, creator, critic and judge at the same time. This is where the real power sits. It is also where the danger starts. Four agents without structure do not become a company. They become four chat windows spending tokens and repeating each other. Multi-agent systems are useful when roles need different context, tools or parallel work—not simply because the task sounds difficult. The new skill is graph engineering: deciding who acts, what they receive, what they must return, when work can move forward, when it loops back, and where a human must stop it. Inside the full guide, you’ll build a working AI agent team with Claude Opus 5, GPT-5.6 Sol and Kimi K3, complete with the exact graph, role prompts, model routing, n8n setup, LangGraph Python code, structured handoffs, reusable skills, MCP tools, review loops, human approvals, security rules and ready-made teams for content, support and coding.