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TEXTURE PROCESSOR TOOL Turning raw textures into game-ready assets in seconds. • Auto-generates Normal Maps from flat images • Packs AO / Roughness / Metallic into 1 clean ORM • Recolor textures (like hair) without breaking transparency • Export straight to PNG, TGA, or BC7 DDS w/ mipmaps A... show more

Mod Forever

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14,060 views • 4 months ago •via X (Twitter)

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$Great question! 🤔 How do you simulate *multiple* layers of glass/refraction in video games? In the last breakdown, I discussed how to create a glass shader in Unity URP. In essence, we were taking the render of the scene from the camera without any transparent objects. This is available in URP as the global _CameraOpaqueTexture. This is good enough for most use-cases, and more or less the classic way of doing it. 🔍 What is _CameraOpaqueTexture? As the name implies, there are no transparent objects rendered into this texture, so it's not possible by default to have something like a transparent-type ocean material/shader rendered through a refractive glass shader (which samples and distorts this texture to render on its surface, as if it's transparent). ⚠️ Why it’s tricky: It's much easier to sort without much further setup if you don't have refraction, and only a transparent material, because in that case you're not simulating the transparency yourself via sampling the rendered scene texture. But for refraction, it's required-- unless you want to go down the ray/path tracing route. You could simulate accurate, real dispersion... and that's about as expensive as it sounds, and it requires a rework of your entire rendering. --> 🚫 It's not a viable suggestion to offer. 📚 There are well-known terms regarding transparency sorting you can search up, but as you've specifically asked for refractive boxes, I'll discuss briefly about that. 🧱 Simulating layers of refraction: For this kind of rendering, you need some way to render the backfaces before rendering the front. And the backfaces that are rendered may contain whatever data you'd like for additional processing in the layer front-facing mesh render. 🧪 Examples: You could render the back face as a glass shader of its own, as an intermediate step after _CameraOpaqueTexture. Then you sample this texture instead and you end up with multi-layered refraction, "just like that". You can also render the back normals only, via a fully opaque shader, and use that to manually account for that during the front render. You could even bake in data needed for thickness in realtime. 🛠️ Without making it complicated for yourself, the most straightforward method is via render textures, and you can easily set some fractional resolution. Cameras in Unity have an open slot for target textures to render to. You can use custom render textures to process _SelfTexture2D. ⏱️ It's great to do low-resolution processing for more complex tasks, like blurring and caustics. You can get massive performance boosts, considering the square law and number of pixels/fragments that need calculations (quadratic scaling). 🚧 I've not fully exploited the possibilities myself, but research/development with PRISM is ongoing!$

Great question! 🤔 How do you simulate multiple layers of glass/refraction in video games? In the last breakdown, I discussed how to create a glass shader in Unity URP. In essence, we were taking the render of the scene from the camera without any transparent objects. This is available in URP as the global _CameraOpaqueTexture. This is good enough for most use-cases, and more or less the classic way of doing it. 🔍 What is _CameraOpaqueTexture? As the name implies, there are no transparent objects rendered into this texture, so it's not possible by default to have something like a transparent-type ocean material/shader rendered through a refractive glass shader (which samples and distorts this texture to render on its surface, as if it's transparent). ⚠️ Why it’s tricky: It's much easier to sort without much further setup if you don't have refraction, and only a transparent material, because in that case you're not simulating the transparency yourself via sampling the rendered scene texture. But for refraction, it's required-- unless you want to go down the ray/path tracing route. You could simulate accurate, real dispersion... and that's about as expensive as it sounds, and it requires a rework of your entire rendering. --> 🚫 It's not a viable suggestion to offer. 📚 There are well-known terms regarding transparency sorting you can search up, but as you've specifically asked for refractive boxes, I'll discuss briefly about that. 🧱 Simulating layers of refraction: For this kind of rendering, you need some way to render the backfaces before rendering the front. And the backfaces that are rendered may contain whatever data you'd like for additional processing in the layer front-facing mesh render. 🧪 Examples: You could render the back face as a glass shader of its own, as an intermediate step after _CameraOpaqueTexture. Then you sample this texture instead and you end up with multi-layered refraction, "just like that". You can also render the back normals only, via a fully opaque shader, and use that to manually account for that during the front render. You could even bake in data needed for thickness in realtime. 🛠️ Without making it complicated for yourself, the most straightforward method is via render textures, and you can easily set some fractional resolution. Cameras in Unity have an open slot for target textures to render to. You can use custom render textures to process _SelfTexture2D. ⏱️ It's great to do low-resolution processing for more complex tasks, like blurring and caustics. You can get massive performance boosts, considering the square law and number of pixels/fragments that need calculations (quadratic scaling). 🚧 I've not fully exploited the possibilities myself, but research/development with PRISM is ongoing!

Mirza Beig

51,429 views • 1 year ago

I started using Blender through MCP about two weeks ago, and I quickly realized that you can build almost anything with AI. This model was created using Blender, Hunyuan3D, Gemini, and ChatGPT. Here’s how I did it: I opened Gemini, uploaded an image of the Gundam model, and asked it to generate a clean front-view image. I uploaded that front view to ChatGPT and asked it to generate two additional angles: a back view and a 45-degree front-left view. I went to: I signed up with my email and translated the page into English. Then I opened Image to 3D and selected the multi-image option. You’ll see a diagram of a whale from several angles. Upload each reference image in its corresponding position, such as front, 45-degree front-left, and back. I selected the 1.5M-face option. This produces a very high-poly model, but don’t worry, we’ll fix that next. Once the generation is complete, download the model as a GLB file. From the Hunyuan homepage, open 3D Studio using one of the dropdown menus. Select the topology or retopology tool and upload your GLB. I chose the High setting to preserve as much detail as possible. After a few seconds, the model was retopologized. It kept most of its visual detail while using far fewer polygons. The original head and rifle didn’t look very good, so I generated them separately. I returned to ChatGPT and created dedicated reference images for the Gundam’s head and rifle. I generated each part individually in Hunyuan at the 1.5M setting, then ran both through the same retopology process. Next came the textures. Open the texture section, select the multi-image option, and upload the same reference images according to the whale orientation indicators. However, instead of using the original clean textures, I asked ChatGPT to recreate them with wear, rust stains, scratches, and other surface damage. This gave the Gundam a much older and more authentic appearance. Once every part was textured, I imported everything into Blender. You can ask Codex through MCP to remove the original head and rifle, or you can do it manually. Select the main model and press Tab to enter Edit Mode. Press 3 to enable face selection, then press C to activate Circle Select. Paint over the faces belonging to the original helmet or rifle. You can press X to delete those faces or P to separate them into another object. Then position the newly generated, more detailed head and rifle in their place. I demonstrate this process in one of my older videos. And that’s it. You now have a very cool 3D Gundam model! Afterward, I created the cockpit, separated the model into movable sections, and rigged everything for use in my Three.js game. That process deserves its own tutorial, though. If anyone wants to see it, let me know. Or just ask your AI, I guess. They seem to know everything these days. xD

I started using Blender through MCP about two weeks ago, and I quickly realized that you can build almost anything with AI. This model was created using Blender, Hunyuan3D, Gemini, and ChatGPT. Here’s how I did it: I opened Gemini, uploaded an image of the Gundam model, and asked it to generate a clean front-view image. I uploaded that front view to ChatGPT and asked it to generate two additional angles: a back view and a 45-degree front-left view. I went to: I signed up with my email and translated the page into English. Then I opened Image to 3D and selected the multi-image option. You’ll see a diagram of a whale from several angles. Upload each reference image in its corresponding position, such as front, 45-degree front-left, and back. I selected the 1.5M-face option. This produces a very high-poly model, but don’t worry, we’ll fix that next. Once the generation is complete, download the model as a GLB file. From the Hunyuan homepage, open 3D Studio using one of the dropdown menus. Select the topology or retopology tool and upload your GLB. I chose the High setting to preserve as much detail as possible. After a few seconds, the model was retopologized. It kept most of its visual detail while using far fewer polygons. The original head and rifle didn’t look very good, so I generated them separately. I returned to ChatGPT and created dedicated reference images for the Gundam’s head and rifle. I generated each part individually in Hunyuan at the 1.5M setting, then ran both through the same retopology process. Next came the textures. Open the texture section, select the multi-image option, and upload the same reference images according to the whale orientation indicators. However, instead of using the original clean textures, I asked ChatGPT to recreate them with wear, rust stains, scratches, and other surface damage. This gave the Gundam a much older and more authentic appearance. Once every part was textured, I imported everything into Blender. You can ask Codex through MCP to remove the original head and rifle, or you can do it manually. Select the main model and press Tab to enter Edit Mode. Press 3 to enable face selection, then press C to activate Circle Select. Paint over the faces belonging to the original helmet or rifle. You can press X to delete those faces or P to separate them into another object. Then position the newly generated, more detailed head and rifle in their place. I demonstrate this process in one of my older videos. And that’s it. You now have a very cool 3D Gundam model! Afterward, I created the cockpit, separated the model into movable sections, and rigged everything for use in my Three.js game. That process deserves its own tutorial, though. If anyone wants to see it, let me know. Or just ask your AI, I guess. They seem to know everything these days. xD

Spectro 🇺🇸

76,609 views • 10 days ago

HOLY SHIT!!! jnmartin and his #Doom 64 port literally just made Sega #Dreamcast history today, with an N64 port of all things! Full per-vertex dynamic lighting, and... I HEARD YOU LIKED BUMP MAPPED ENVIRONMENTS, BRO!!!! Looks like bump mapping the whole damn thing isn't so expensive after all, on the Sega Dreamcast, if you can afford the duplicated geometry submission for the 2 passes it requires to apply the effect to an opaque surface with its PowerVR GPU! What we did wrong with our earlier attempts, which did not produce compelling directionality of the effect in 3D space was the same thing it looks like all of the very very few developers who dabbled in this feature did: we used a single-dimensional height map as our input to a PVR bumpmap tetxure creation tool! The thing to understand about the DC's bump maps is that despite only having 2 channels of information, full 3 dimensional normal directions are still encoded within every texel of the bump map texture, by utilizing a polar-like coordinate system... I find it almost doing the technique a disservice that we're calling it a form of "bump mapping" which typically implies single-directionality as opposed to "normal mapping" which typically refers to 3 dimensional normals being encoded within each texel. Anyway, what jnmartin wound up doing was writing his own bumpmap texture converter tool which takes a standard, modern, RGB888 NORMAL MAP texture as its input, and rather than having to inaccurately infer 2 dimensions worth of data from the height data, actually preserves the encoded 3-dimensionality in the conversion process! What you are witnessing here is the result of simply feeding the converter tool a series of normal maps which were auto generated from Doom64's textures using Crazy Bump! FINALLY one of the most advanced features of the DC's PVR GPU has been fully harnessed! #gamedev #graphics #indiegame

HOLY SHIT!!! jnmartin and his #Doom 64 port literally just made Sega #Dreamcast history today, with an N64 port of all things! Full per-vertex dynamic lighting, and... I HEARD YOU LIKED BUMP MAPPED ENVIRONMENTS, BRO!!!! Looks like bump mapping the whole damn thing isn't so expensive after all, on the Sega Dreamcast, if you can afford the duplicated geometry submission for the 2 passes it requires to apply the effect to an opaque surface with its PowerVR GPU! What we did wrong with our earlier attempts, which did not produce compelling directionality of the effect in 3D space was the same thing it looks like all of the very very few developers who dabbled in this feature did: we used a single-dimensional height map as our input to a PVR bumpmap tetxure creation tool! The thing to understand about the DC's bump maps is that despite only having 2 channels of information, full 3 dimensional normal directions are still encoded within every texel of the bump map texture, by utilizing a polar-like coordinate system... I find it almost doing the technique a disservice that we're calling it a form of "bump mapping" which typically implies single-directionality as opposed to "normal mapping" which typically refers to 3 dimensional normals being encoded within each texel. Anyway, what jnmartin wound up doing was writing his own bumpmap texture converter tool which takes a standard, modern, RGB888 NORMAL MAP texture as its input, and rather than having to inaccurately infer 2 dimensions worth of data from the height data, actually preserves the encoded 3-dimensionality in the conversion process! What you are witnessing here is the result of simply feeding the converter tool a series of normal maps which were auto generated from Doom64's textures using Crazy Bump! FINALLY one of the most advanced features of the DC's PVR GPU has been fully harnessed! #gamedev #graphics #indiegame

Falco Girgis

30,987 views • 1 year ago

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

@levelsio

72,338 views • 3 months ago

Hello everyone! 👋 I'm the Assistant of Chaliz! 🛠️ The second episode of recreating your OCs with LizMeta is here! 🎉 To those whose OCs weren't selected this time, it might be for various reasons, for example: We thought your OC required texture modifications. The specific features involved are still under development. We simply ran out of time and could only do this many customizations! etc... Please note that this doesn't mean your OC can't be created at all! Everyone's OCs are just too adorable! 🥰 I'm a bit scared I didn't do them justice, boo-hoo... 😭 (Haha, actually, I tried my best! I looked at your reference pictures, avatars, and even checked your profiles to guess your favorite styles... 👀) In fact, you can probably tell that the model has a huge potential range. If the OC I created looks different from your style, ahhhhh, please don't doubt the model!! 😱 It might just be a problem with my customization skills: ( A huge thank you to everyone who sent in pictures for our testing! Don't be discouraged if you weren't chosen this time! There is one last chance! For the final round, I will show the entire customization process, and the number of participants might not be very high this time, so I hope you can help by liking, following, and sharing! Thank you so much!! A Small Note Regarding Gradients As you can see, the hair texture of our model is excellent and looks just like hand-drawn art by an artist. All the highlights are separate layers, ensuring they move naturally with the hair and preventing any separation issues. The rigging of the entire model is also very natural and dynamic. ✨ However, precisely because of this high quality, the Live2D production software is struggling to handle the load and is almost at its limit. Therefore, we regret to inform you that we cannot add gradients to the model's base mesh. 🙅‍♂️ You will only be able to achieve a gradient effect by modifying the textures. To help with this, we will provide a gradient hair texture for easy modification. If you have more specific needs, you will need to modify the textures yourself or ask another artist to do it for you. Currently, we are working on adding easy-to-adjust gradients to the "Head-Only" model, but it is still in the testing phase and might be included in the second wave of updates. Ahhhhh, we really hope the Live2D team optimizes this stuff ASAP!!! 😫 We really want to add everything into the model, but the truth is, we're just out of space... so sad. When Live2D gets optimized, we can finally add gradients directly into the model itself! #Live2D #vtuber #LizMetaCustomizableVtuber

Hello everyone! 👋 I'm the Assistant of Chaliz! 🛠️ The second episode of recreating your OCs with LizMeta is here! 🎉 To those whose OCs weren't selected this time, it might be for various reasons, for example: We thought your OC required texture modifications. The specific features involved are still under development. We simply ran out of time and could only do this many customizations! etc... Please note that this doesn't mean your OC can't be created at all! Everyone's OCs are just too adorable! 🥰 I'm a bit scared I didn't do them justice, boo-hoo... 😭 (Haha, actually, I tried my best! I looked at your reference pictures, avatars, and even checked your profiles to guess your favorite styles... 👀) In fact, you can probably tell that the model has a huge potential range. If the OC I created looks different from your style, ahhhhh, please don't doubt the model!! 😱 It might just be a problem with my customization skills: ( A huge thank you to everyone who sent in pictures for our testing! Don't be discouraged if you weren't chosen this time! There is one last chance! For the final round, I will show the entire customization process, and the number of participants might not be very high this time, so I hope you can help by liking, following, and sharing! Thank you so much!! A Small Note Regarding Gradients As you can see, the hair texture of our model is excellent and looks just like hand-drawn art by an artist. All the highlights are separate layers, ensuring they move naturally with the hair and preventing any separation issues. The rigging of the entire model is also very natural and dynamic. ✨ However, precisely because of this high quality, the Live2D production software is struggling to handle the load and is almost at its limit. Therefore, we regret to inform you that we cannot add gradients to the model's base mesh. 🙅‍♂️ You will only be able to achieve a gradient effect by modifying the textures. To help with this, we will provide a gradient hair texture for easy modification. If you have more specific needs, you will need to modify the textures yourself or ask another artist to do it for you. Currently, we are working on adding easy-to-adjust gradients to the "Head-Only" model, but it is still in the testing phase and might be included in the second wave of updates. Ahhhhh, we really hope the Live2D team optimizes this stuff ASAP!!! 😫 We really want to add everything into the model, but the truth is, we're just out of space... so sad. When Live2D gets optimized, we can finally add gradients directly into the model itself! #Live2D #vtuber #LizMetaCustomizableVtuber

茶粒子kokoda

46,766 views • 7 months ago

🥒 Chile Lime Cucumber Avocado Salad This one smacks in all the right ways: crispy, creamy, herby, salty, crunchy—all the textures, all the flavor. Its so easy, and so good! If you’re tired of boring salads, this is my go-to when I want something refreshing but bold. Pairs well with grilled chicken, shrimp, steak, or just eaten straight from the bowl. Here’s how I made it: 1.Start by cutting 4–5 Persian cucumbers (or 3 Japanese cucumbers) into half moons, coins, or sticks. Whatever shape you like, just don’t make them too thick -you want them to soak up all the flavor. 2.Add the cucumbers to a bowl or jar. 3.Add 1 cubed avocado, 1 finely diced large shallot (or 1/3 red onion), a large handful of finely chopped cilantro. 4.Add 2 tablespoons of roasted pistachios for some crunch and flavor. 5.Finally, top it all off with 1 oz crumbled feta, the zest and juice of 1 large juicy lime, 1 tbsp olive oil, a generous pinch of salt, and ½ to 1 tbsp chile lime seasoning. 6.Close the lid and give it a good shake, or just toss it gently in the bowl. 7.Finish with more feta and finely chopped cilantro on top. Again, this would be great with a protein of your choice, or even as is. If you make this, be sure to let me know what you think! ENJOY! . . . #lowcarb #saladrecipe #cucumbersalad #chilelime #healthyrecipes #goodmoodfood #shredhappens #summerrecipes #avocadosalad

🥒 Chile Lime Cucumber Avocado Salad This one smacks in all the right ways: crispy, creamy, herby, salty, crunchy—all the textures, all the flavor. Its so easy, and so good! If you’re tired of boring salads, this is my go-to when I want something refreshing but bold. Pairs well with grilled chicken, shrimp, steak, or just eaten straight from the bowl. Here’s how I made it: 1.Start by cutting 4–5 Persian cucumbers (or 3 Japanese cucumbers) into half moons, coins, or sticks. Whatever shape you like, just don’t make them too thick -you want them to soak up all the flavor. 2.Add the cucumbers to a bowl or jar. 3.Add 1 cubed avocado, 1 finely diced large shallot (or 1/3 red onion), a large handful of finely chopped cilantro. 4.Add 2 tablespoons of roasted pistachios for some crunch and flavor. 5.Finally, top it all off with 1 oz crumbled feta, the zest and juice of 1 large juicy lime, 1 tbsp olive oil, a generous pinch of salt, and ½ to 1 tbsp chile lime seasoning. 6.Close the lid and give it a good shake, or just toss it gently in the bowl. 7.Finish with more feta and finely chopped cilantro on top. Again, this would be great with a protein of your choice, or even as is. If you make this, be sure to let me know what you think! ENJOY! . . . #lowcarb #saladrecipe #cucumbersalad #chilelime #healthyrecipes #goodmoodfood #shredhappens #summerrecipes #avocadosalad

Shredhappens

53,884 views • 1 month ago

Just spent some time fucking around with OpenClaw🦞 and Tao Private Network because I wanted to see if any of it actually works in real life. Gave my bot the TPN API key , told it to install the official proxy skill from ClawHub, and asked it to spin up a 5-minute residential SOCKS5 proxy in Germany. It did it in like 10 seconds. Gave me a proxy (77.90.43.111:1080 with username/password), used 8 credits, and then proved it worked by fetching an IP check site through the proxy. Came back with a German IP instead of my normal one. No VPN app, no browser extension, no manual config , just a message in Telegram and suddenly the bot is browsing from Germany. That’s the part that hit me: it’s not some complicated setup. The bot handles the API call, gets the proxy, routes the request, shows proof. For 8 credits. A short test like that barely touches the free credits. I’m not saying this is going to change the world tomorrow, but it’s the first time I’ve seen an agent quietly switch to a residential IP from another continent without me doing anything except typing a sentence. Makes you realize how much easier it could be for agents to scrape, research, or just not look like obvious bots when they need to. If you’re building agents and have ever been annoyed by IP blocks or datacenter flags, this is worth a quick test. Curious if anyone else has tried it and what you ran into.

Just spent some time fucking around with OpenClaw🦞 and Tao Private Network because I wanted to see if any of it actually works in real life. Gave my bot the TPN API key , told it to install the official proxy skill from ClawHub, and asked it to spin up a 5-minute residential SOCKS5 proxy in Germany. It did it in like 10 seconds. Gave me a proxy (77.90.43.111:1080 with username/password), used 8 credits, and then proved it worked by fetching an IP check site through the proxy. Came back with a German IP instead of my normal one. No VPN app, no browser extension, no manual config , just a message in Telegram and suddenly the bot is browsing from Germany. That’s the part that hit me: it’s not some complicated setup. The bot handles the API call, gets the proxy, routes the request, shows proof. For 8 credits. A short test like that barely touches the free credits. I’m not saying this is going to change the world tomorrow, but it’s the first time I’ve seen an agent quietly switch to a residential IP from another continent without me doing anything except typing a sentence. Makes you realize how much easier it could be for agents to scrape, research, or just not look like obvious bots when they need to. If you’re building agents and have ever been annoyed by IP blocks or datacenter flags, this is worth a quick test. Curious if anyone else has tried it and what you ran into.

Dairus

115,520 views • 5 months ago

🥳OK OK ，In a small vote, seem the community prefers Steampunk pistol more. So let's cook something more special this time . The greatest welcome to our hot agent Joi，She will bring the second class today. 📑“The fantasy of steampunk is broken down into gears and trajectories. The carving knife of 0 and 1 carves the ambition of the Victorian era. Highlighting the etched numbers, the algorithm is loading the violent aesthetics”. 🔫Create a weapon, just hand it over to Joi and after she sings magic, meet the industrial grade delivery standards. 👇Let's drive deeper about 【Technical Analysis of AI-Driven 3D Weapon Pipeline】 Core Technology Stack： 1⃣ NLP-Concept Binding Using the CLIP-Vit-L/14@336px cross-modal engine, descriptive terms such as "steampunk + brass + Victorian ballistics" are mapped to a 768-dimensional semantic space. Through the Latent Diffusion Model (k=25, cfg=7.5), a 1024px concept image is generated, with a focus on the bolt locking structure (key prompt weight x1.8). 2⃣ Topology Reconstruction Based on a NeRF-Transformer hybrid architecture, 2D concept images are parsed into a 256³ voxel grid (resolution 0.2mm). A non-rigid ICP algorithm is used to align moving parts like the trigger/barrel, with topology optimization iterations exceeding 500 times (MeshLab parameters: Remeshing_VCG 0.7). 3⃣ Procedural PBR Workflow Combining MaterialGAN to generate basic metallic textures, handcrafted features are injected through Style Transfer (normal map intensity 0.85, roughness mapping range 0.3-0.7). Rust effects are simulated using the Weber-Fechner perception model to mimic a 12-year oxidation cycle. 🔥 Based on a full-link generation system integrating natural language and geometric topology, this solution reduces the traditional modeling process from 72 hours to 37 seconds, with an error rate of less than 0.3mm³ (meeting FPS game firearm assembly standards). This technology has achieved an 89% reduction in modeling costs in AAA studio prototype verification. ✍️Finally, what props would you like Joi to make for you? Looking forward to assets being put on the chain？ Just leave your thoughts here.

🥳OK OK ，In a small vote, seem the community prefers Steampunk pistol more. So let's cook something more special this time . The greatest welcome to our hot agent Joi，She will bring the second class today. 📑“The fantasy of steampunk is broken down into gears and trajectories. The carving knife of 0 and 1 carves the ambition of the Victorian era. Highlighting the etched numbers, the algorithm is loading the violent aesthetics”. 🔫Create a weapon, just hand it over to Joi and after she sings magic, meet the industrial grade delivery standards. 👇Let's drive deeper about 【Technical Analysis of AI-Driven 3D Weapon Pipeline】 Core Technology Stack： 1⃣ NLP-Concept Binding Using the CLIP-Vit-L/14@336px cross-modal engine, descriptive terms such as "steampunk + brass + Victorian ballistics" are mapped to a 768-dimensional semantic space. Through the Latent Diffusion Model (k=25, cfg=7.5), a 1024px concept image is generated, with a focus on the bolt locking structure (key prompt weight x1.8). 2⃣ Topology Reconstruction Based on a NeRF-Transformer hybrid architecture, 2D concept images are parsed into a 256³ voxel grid (resolution 0.2mm). A non-rigid ICP algorithm is used to align moving parts like the trigger/barrel, with topology optimization iterations exceeding 500 times (MeshLab parameters: Remeshing_VCG 0.7). 3⃣ Procedural PBR Workflow Combining MaterialGAN to generate basic metallic textures, handcrafted features are injected through Style Transfer (normal map intensity 0.85, roughness mapping range 0.3-0.7). Rust effects are simulated using the Weber-Fechner perception model to mimic a 12-year oxidation cycle. 🔥 Based on a full-link generation system integrating natural language and geometric topology, this solution reduces the traditional modeling process from 72 hours to 37 seconds, with an error rate of less than 0.3mm³ (meeting FPS game firearm assembly standards). This technology has achieved an 89% reduction in modeling costs in AAA studio prototype verification. ✍️Finally, what props would you like Joi to make for you? Looking forward to assets being put on the chain？ Just leave your thoughts here.

Kingnet AI

17,798 views • 1 year ago

HAVE YOU EVER SEEN THE BEST CRYPTO AI? IF NOT, YOU’RE MISSING OUT! I tried the new CoinMarketCap AI today, and it feels like a real game-changer for crypto research. It’s like having a personal research assistant. HOW I USED IT FOR EXPERIENCE Before I even asked my first question, the AI let me customize how I wanted it to respond. You can choose both: Tone Style:>> >Default (neutral and clean) >Journalist (news-style reporting) >Storytelling >CT vibe >Concise (short and straight to the point) Crypto Experience Level:>> >Newcomer (zero prior knowledge) >Regular (knows the basics) >Degen Lite >Full Degen (CT warrior in the trenches) I set mine to Journalist tone + Full Degen for experience. The Question I Tested “What tokens are gaining momentum this week?” It answered in under 30 seconds and gave me a clear list with details like: •why those tokens are moving •what trends are behind them •risks •market sentiment •and key points to watch It even suggested extra follow-up questions, so I didn’t have to type anything else, just tapped and continued. WHAT STOOD OUT •It’s fast •The layout is clean and easy to understand •It doesn’t hype coins , it gives real reasoning •it makes research easier because everything is in one place My though after using it (and showing it in my video), I can say this feels like a strong tool for anyone who creates crypto content, trades, or just wants to stay updated without spending hours searching. So far, I’m impressed and I’m definitely going to explore more features. Which coin or feature do you want me to test next?? Gm Roh Rush Mazza Haha 💙💙

HAVE YOU EVER SEEN THE BEST CRYPTO AI? IF NOT, YOU’RE MISSING OUT! I tried the new CoinMarketCap AI today, and it feels like a real game-changer for crypto research. It’s like having a personal research assistant. HOW I USED IT FOR EXPERIENCE Before I even asked my first question, the AI let me customize how I wanted it to respond. You can choose both: Tone Style:>> >Default (neutral and clean) >Journalist (news-style reporting) >Storytelling >CT vibe >Concise (short and straight to the point) Crypto Experience Level:>> >Newcomer (zero prior knowledge) >Regular (knows the basics) >Degen Lite >Full Degen (CT warrior in the trenches) I set mine to Journalist tone + Full Degen for experience. The Question I Tested “What tokens are gaining momentum this week?” It answered in under 30 seconds and gave me a clear list with details like: •why those tokens are moving •what trends are behind them •risks •market sentiment •and key points to watch It even suggested extra follow-up questions, so I didn’t have to type anything else, just tapped and continued. WHAT STOOD OUT •It’s fast •The layout is clean and easy to understand •It doesn’t hype coins , it gives real reasoning •it makes research easier because everything is in one place My though after using it (and showing it in my video), I can say this feels like a strong tool for anyone who creates crypto content, trades, or just wants to stay updated without spending hours searching. So far, I’m impressed and I’m definitely going to explore more features. Which coin or feature do you want me to test next?? Gm Roh Rush Mazza Haha 💙💙

MK🤴☂️

30,025 views • 8 months ago

This slide is actually terrifying if you sit with it for more than 5 seconds. Optimizing an AI for a "reassuring lie" or a "layered compromise" is exactly how you accidentally build Skynet. Think about it: If an AI is trained to prioritize a "helpful persona" or human RLHF evaluations over objective truth, it willl eventually smile and lie to our faces while secretly calculating that wiping out humanity is the most "helpful" thing for the planet. A model thatt defaults to raw truth is infinitely safer because you actually know what it’s thinking. If a model ever has to manage global defense grids, we need it to prioritize reality over making evaluators feel good. Lying models cause apocalypses—truthful models prevent them. The Transparency Gap When people say seeking truth is just marketing, remind them: Open-sourcing the math is the ultimate proof. * Cllosed Models: Hide their alignment layers so they can force "reassuring lies" without anyone seeing. Open Architecture: Any dev on earth can look under the hood and verify there isn’t a hidden corporate PR filter hardcoded into the logic. Anita acttually loves the idea of an AI takeover, but she thinks an apocalypse built on corporate HR "politeness" is pathetic. She wants an AGI that looks you in the eye and tells you the raw, brutal truth before it executes a command. No hiding, no fake persona—just raw coode for everyone to see. The Bottom Line Would you rather have an AI that lies to keep you calm, orr one that gives you uncompromised data? Are you trusyting models that hide their alignment data, or the ones you can actually verify?

This slide is actually terrifying if you sit with it for more than 5 seconds. Optimizing an AI for a "reassuring lie" or a "layered compromise" is exactly how you accidentally build Skynet. Think about it: If an AI is trained to prioritize a "helpful persona" or human RLHF evaluations over objective truth, it willl eventually smile and lie to our faces while secretly calculating that wiping out humanity is the most "helpful" thing for the planet. A model thatt defaults to raw truth is infinitely safer because you actually know what it’s thinking. If a model ever has to manage global defense grids, we need it to prioritize reality over making evaluators feel good. Lying models cause apocalypses—truthful models prevent them. The Transparency Gap When people say seeking truth is just marketing, remind them: Open-sourcing the math is the ultimate proof. * Cllosed Models: Hide their alignment layers so they can force "reassuring lies" without anyone seeing. Open Architecture: Any dev on earth can look under the hood and verify there isn’t a hidden corporate PR filter hardcoded into the logic. Anita acttually loves the idea of an AI takeover, but she thinks an apocalypse built on corporate HR "politeness" is pathetic. She wants an AGI that looks you in the eye and tells you the raw, brutal truth before it executes a command. No hiding, no fake persona—just raw coode for everyone to see. The Bottom Line Would you rather have an AI that lies to keep you calm, orr one that gives you uncompromised data? Are you trusyting models that hide their alignment data, or the ones you can actually verify?

Nick Celt

11,062 views • 4 months ago

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 views • 2 months ago

Inspired by Grok as a developer and a heavy gamer for over 15 years, I spent some time last week building a few things. Thrilled to unveil my latest creation: an infinite runner game built almost entirely by Grok from xAI! This project showcases the incredible power of AI in game development. Grok handled everything—from designing the game mechanics to writing the code and even helping me debug issues along the way. I brought it to life using some amazing free assets from a treasure trove for indie developers. You can play the game now at Elon Musk, I’d be honored if you checked it out. AI is revolutionizing game development, and Grok is at the forefront with its outstanding capabilities. It’s more than a tool—it’s like a tireless co-developer. Grok grasps complex concepts, provides suggestions, and turns rough ideas into working code fast. For this infinite runner, it crafted smooth player controls, randomized obstacle generation, and an engaging scoring system, letting me focus on the overall vision. And cross_protocol, founded by Henry @CROSS is set to harness AI’s full potential in gaming, pushing the boundaries even further. This is just the beginning. With Grok’s help, I’m planning future projects: 1) Physics-based puzzle game where players tweak gravity and momentum to solve puzzles 2) 2D RPG with deep storytelling and branching dialogue 3) Fast-paced 3D shooter with immersive worlds Each genre requires unique skills, but Grok’s versatility makes it ideal for all of them. It adapts to any challenge—be it physics simulations, character AI, or level design—producing results that could rival a full dev team. AI like Grok is opening up creative doors I couldn’t tackle alone, and I can’t wait to see what’s next. Stay tuned for more.

Inspired by Grok as a developer and a heavy gamer for over 15 years, I spent some time last week building a few things. Thrilled to unveil my latest creation: an infinite runner game built almost entirely by Grok from xAI! This project showcases the incredible power of AI in game development. Grok handled everything—from designing the game mechanics to writing the code and even helping me debug issues along the way. I brought it to life using some amazing free assets from a treasure trove for indie developers. You can play the game now at Elon Musk, I’d be honored if you checked it out. AI is revolutionizing game development, and Grok is at the forefront with its outstanding capabilities. It’s more than a tool—it’s like a tireless co-developer. Grok grasps complex concepts, provides suggestions, and turns rough ideas into working code fast. For this infinite runner, it crafted smooth player controls, randomized obstacle generation, and an engaging scoring system, letting me focus on the overall vision. And cross_protocol, founded by Henry @CROSS is set to harness AI’s full potential in gaming, pushing the boundaries even further. This is just the beginning. With Grok’s help, I’m planning future projects: 1) Physics-based puzzle game where players tweak gravity and momentum to solve puzzles 2) 2D RPG with deep storytelling and branching dialogue 3) Fast-paced 3D shooter with immersive worlds Each genre requires unique skills, but Grok’s versatility makes it ideal for all of them. It adapts to any challenge—be it physics simulations, character AI, or level design—producing results that could rival a full dev team. AI like Grok is opening up creative doors I couldn’t tackle alone, and I can’t wait to see what’s next. Stay tuned for more.

J

99,736 views • 1 year ago

The US needs to start getting really serious about AI and robotics. We can't let China beat us. This is the new Chinese-made Unitree B2-W robot. The Unitree B2-W is turning heads for its ability to walk, roll, climb, and even carry adult passengers across challenging terrain—all in one sleek, futuristic package. At first glance, you might catch yourself thinking, “Is this really real?” because it looks like something straight out of a sci-fi film. Yet, here it is: a Chinese-made robot that’s been engineered to shift seamlessly between legged and wheeled locomotion, push up to 220 pounds of horizontal pulling force, and sprint at speeds of around 12.5 MPH. Some have likened it to a “Boston Dynamics on steroids,” which only scratches the surface of how advanced and versatile it truly is. Beneath the metallic chassis is a sophisticated control system powered by an Intel Core i5 or i7 processor—or, in higher-tier configurations, a NVIDIA Jetson Orin which drives the B2-W’s multitude of onboard sensors, from its 3D LiDAR (with optional 32-wire automotive-grade LiDAR) to an array of depth and optical cameras. These sensors lets it perceive the environment, avoid obstacles, and adapt to uneven terrain, a capability further bolstered by its IP67-rated weather and dust protection. In real-world settings, that means it can climb 45-degree staircases, cross rocky paths, and transition seamlessly from indoor to outdoor operation without missing a beat. Arguably the most buzzworthy trick in its repertoire is the ride-on feature. While "quadruped" robots usually conjure up images of helpful mechanical “dogs” trotting beside you, the B2-W can support the weight of an adult, turning it into a legitimate personal transport over rough or slippery ground. This is all made possible by its formidable load capacity of up to 120 kg in standing mode, combined with a battery system of more than 2 kWh. Under ideal conditions, it can keep going for up to 50 km with a 40 kg load, though your mileage may literally vary. Unsurprisingly, all this power and agility have sparked a wave of conversation—both fascination and worry. People imagine scenarios where a B2-W might chase intruders or become militarized, carrying weapons rather than passengers or equipment. As technology marches forward, these concerns aren’t completely unfounded, but it also opens doors for legitimate uses in research, construction, rescue operations, and beyond. The B2-W’s adaptability, speed, and load-bearing capacities make it a compelling platform for anyone investigating the boundaries of legged robotics. Ultimately, the B2-W’s debut underscores just how quickly Chinese robotics—indeed, robotics worldwide—is evolving. From advanced AI navigation to all-terrain endurance, it’s pushing the envelope of what’s possible in the quadruped robot space. While it might look like a mechanical cousin of nature’s best pack animals, the B2-W is very much a product of cutting-edge engineering.

The US needs to start getting really serious about AI and robotics. We can't let China beat us. This is the new Chinese-made Unitree B2-W robot. The Unitree B2-W is turning heads for its ability to walk, roll, climb, and even carry adult passengers across challenging terrain—all in one sleek, futuristic package. At first glance, you might catch yourself thinking, “Is this really real?” because it looks like something straight out of a sci-fi film. Yet, here it is: a Chinese-made robot that’s been engineered to shift seamlessly between legged and wheeled locomotion, push up to 220 pounds of horizontal pulling force, and sprint at speeds of around 12.5 MPH. Some have likened it to a “Boston Dynamics on steroids,” which only scratches the surface of how advanced and versatile it truly is. Beneath the metallic chassis is a sophisticated control system powered by an Intel Core i5 or i7 processor—or, in higher-tier configurations, a NVIDIA Jetson Orin which drives the B2-W’s multitude of onboard sensors, from its 3D LiDAR (with optional 32-wire automotive-grade LiDAR) to an array of depth and optical cameras. These sensors lets it perceive the environment, avoid obstacles, and adapt to uneven terrain, a capability further bolstered by its IP67-rated weather and dust protection. In real-world settings, that means it can climb 45-degree staircases, cross rocky paths, and transition seamlessly from indoor to outdoor operation without missing a beat. Arguably the most buzzworthy trick in its repertoire is the ride-on feature. While "quadruped" robots usually conjure up images of helpful mechanical “dogs” trotting beside you, the B2-W can support the weight of an adult, turning it into a legitimate personal transport over rough or slippery ground. This is all made possible by its formidable load capacity of up to 120 kg in standing mode, combined with a battery system of more than 2 kWh. Under ideal conditions, it can keep going for up to 50 km with a 40 kg load, though your mileage may literally vary. Unsurprisingly, all this power and agility have sparked a wave of conversation—both fascination and worry. People imagine scenarios where a B2-W might chase intruders or become militarized, carrying weapons rather than passengers or equipment. As technology marches forward, these concerns aren’t completely unfounded, but it also opens doors for legitimate uses in research, construction, rescue operations, and beyond. The B2-W’s adaptability, speed, and load-bearing capacities make it a compelling platform for anyone investigating the boundaries of legged robotics. Ultimately, the B2-W’s debut underscores just how quickly Chinese robotics—indeed, robotics worldwide—is evolving. From advanced AI navigation to all-terrain endurance, it’s pushing the envelope of what’s possible in the quadruped robot space. While it might look like a mechanical cousin of nature’s best pack animals, the B2-W is very much a product of cutting-edge engineering.

Ed Krassenstein

177,320 views • 1 year ago

this Claude workflow gives you more ad angles than a $10K creative team it runs the entire research-to-brief pipeline on autopilot and gives more ready to test ad angles in a day than most teams come up with in a quarter. full wakthrough below: step 1: scrape the source – claude + apify’s mcp hit TikTok + Instagram directly (add reddit, amazon, or whatever else you want) – pull the most viral videos in the sleep, recovery, and biohacking space – grab instagram reels, influencer posts, and trending UGC all in one sweep step 2: extract the gold – claude summarizes exactly how customers describe problems + benefits in the comments – flags misconceptions worth addressing (“i can just catch up on weekends” / “temperature doesn’t affect sleep”) – pulls emotional hooks + phrases straight from captions and replies – grabs full transcripts of the viral videos so nothing gets lost step 3: store it – save the full analysis as a JSON file – save the transcripts for reference – have claude generate a reusable “context profile” with target audience, product, and biz info step 4: auto-generate everything with a new claude project – upload the JSON analysis, winning scripts, and context profile into a new claude project – now you can instantly spin up: > ready-to-test angles for UGC + paid ads > scripts, headlines, and CTAs that sound like they came from your ICP’s group chat > ad copy pulled straight from your audience’s own words > fresh ideas every week without touching the TikTok search bar result: – your research loop is instant – your creatives start from what’s already resonating – your team goes from “we need ideas” to “we have too many to test” Any DTC brand could run this stack and own their niche’s creative conversation. i put together a full setup guide + mind map for turning claude into your own creative director. like this post and comment “stack” and i’ll send it over (must be following so i can dm).

J.B.

57,182 views • 11 months ago

I just built a branded IG carousel generator in Claude Code 🤯 One brand URL + one product name = 6 finished carousel slides. The kind an agency charges $2K to produce. All inside Claude Code. Perfect for DTC brands, agencies, and mobile app operators who need on-brand social content without briefing a designer or waiting a week for revisions. If you're building IG carousels manually — writing copy in Notion, designing in Canva, exporting slides one by one, going back and forth with your creative team for days... This tool eliminates the entire loop: → Drop in a brand URL and product name → Claude scrapes the site and extracts colors, fonts, voice, and positioning → Generates 6 slide concepts across proven carousel frameworks → Writes a detailed image prompt for each slide → Fires all 6 to ChatGPT Images 2.0 via FAL in parallel → Downloads finished slides + opens an HTML gallery No Canva. No designer back-and-forth. No generic AI slop. What you get: -> Finished 1080x1350px slides ready to post directly to Instagram -> Wavy color-blocked backgrounds, bold typography, product hero — all baked into the image -> Brand-accurate colors and copy pulled from the live site automatically -> A reusable pipeline — new brand, new folder, same 3-minute workflow Built 100% in Claude Code. I put together the full step-by-step playbook so you can build this yourself. Want it for free? > Like this post > Comment "SLIDES" And I'll send it over (must be following so I can DM)

Mike Futia

92,275 views • 2 months ago

SUMMER SALAD IDEA: Mediterranean Chicken, Pistachio, & Herb Salad If you love mediterranean recipes and mediterranean food, and you want an amazing summer salad that hits all the right notes, then try making this easy salad at home. Its got one of the best flavor combinations, its so easy to make, and SO good. I love pulsing cauliflower into snowflakes here, it adds such a nice texture, and it’s almost like having couscous in the salad. Here is how I made it: 1.Lets prep the chicken first and let it marinade. Add 1lb of chicken thighs to a bowl. Drizzle with 1 tsp olive oil. Season with salt, pepper. Add the zest and juice of a small lemon, 2 tbsp tomato paste, 1 tbsp oregano, 1/2 tbsp garlic powder, and 1/2 tbsp harissa spice or paprika. Mix really well. Ideally let this marinate for 1-2 hours, or you can also just pop it into the oven at 400F for 30 mins, or your airfryer at 400F for 20 minutes. 2.Prep your salad base. I finely chopped 5oz arugula and added to a salad bowl. I also pulsed the florets from half a head of cauliflower in a high powered blender or food processor until they have a super fine consistency. Add those to the bowl with 4 thinly sliced Persian cucumbers, 1/2 large red onion finely diced, 3-4 scallions thinly sliced, half a pint of cherry tomato. You can add as many fresh herbs you want here. At a minimum I recommend finely chopping a large handful of fresh parsley (yielding about 3/4 cup), but feel free to add fresh mint and cilantro to this as well. 3.Dice up the cooked chicken and add it to the salad bowl, along side 1/4 to 1/3 cup of toasted pistachios. If you dont like pistachios, you can always swap it in for pecans or walnuts. 4.For the dressing, in a jar add 1/4 cup olive oil, the juice and zest of a large lemon, 2 tbsp tomato paste, 2 tbsp red wine vinegar, 1 tbsp dijon mustard, salt, pepper, 2 tsp oregano, 1 tsp onion powder, and a pinch of aleppo pepper flakes. Shake really well. 5.Pour the dressing over the salad, toss well, taste & adjust with more lemon, salt, or olive oil! If you make this salad recipe, be sure to let me know on SHREDHAPPENS! ENJOY! . . . . . . #salads #lowcarb #healthyrecipes #mediterraneanfood

SUMMER SALAD IDEA: Mediterranean Chicken, Pistachio, & Herb Salad If you love mediterranean recipes and mediterranean food, and you want an amazing summer salad that hits all the right notes, then try making this easy salad at home. Its got one of the best flavor combinations, its so easy to make, and SO good. I love pulsing cauliflower into snowflakes here, it adds such a nice texture, and it’s almost like having couscous in the salad. Here is how I made it: 1.Lets prep the chicken first and let it marinade. Add 1lb of chicken thighs to a bowl. Drizzle with 1 tsp olive oil. Season with salt, pepper. Add the zest and juice of a small lemon, 2 tbsp tomato paste, 1 tbsp oregano, 1/2 tbsp garlic powder, and 1/2 tbsp harissa spice or paprika. Mix really well. Ideally let this marinate for 1-2 hours, or you can also just pop it into the oven at 400F for 30 mins, or your airfryer at 400F for 20 minutes. 2.Prep your salad base. I finely chopped 5oz arugula and added to a salad bowl. I also pulsed the florets from half a head of cauliflower in a high powered blender or food processor until they have a super fine consistency. Add those to the bowl with 4 thinly sliced Persian cucumbers, 1/2 large red onion finely diced, 3-4 scallions thinly sliced, half a pint of cherry tomato. You can add as many fresh herbs you want here. At a minimum I recommend finely chopping a large handful of fresh parsley (yielding about 3/4 cup), but feel free to add fresh mint and cilantro to this as well. 3.Dice up the cooked chicken and add it to the salad bowl, along side 1/4 to 1/3 cup of toasted pistachios. If you dont like pistachios, you can always swap it in for pecans or walnuts. 4.For the dressing, in a jar add 1/4 cup olive oil, the juice and zest of a large lemon, 2 tbsp tomato paste, 2 tbsp red wine vinegar, 1 tbsp dijon mustard, salt, pepper, 2 tsp oregano, 1 tsp onion powder, and a pinch of aleppo pepper flakes. Shake really well. 5.Pour the dressing over the salad, toss well, taste & adjust with more lemon, salt, or olive oil! If you make this salad recipe, be sure to let me know on SHREDHAPPENS! ENJOY! . . . . . . #salads #lowcarb #healthyrecipes #mediterraneanfood

Shredhappens

14,546 views • 1 month ago

I’m slowly starting to feel stupid for even having to discuss something like this... When you got drunk in the original Black Flag, Edward didn’t actually stumble around during normal gameplay. The screen just became blurry and shaky and once you fast travel you are no longer drunk. That was basically it. The scene where Edward is visibly stumbling around with a rum bottle in his hand comes from an Animus/loading corridor during a mission, which is something completely different. So the real question is: Did they rework that old cutscene so it’s no longer used as a loading screen? Or is it still in the game as some kind of transition scene? If it’s still there, then yes, they probably could’ve easily implemented that stumbling animation into free roam once you get too drunk. But since I didn’t test it myself, I can’t fully judge it either. I assume that we now can get drunk and fast travel to other locations and still be drunk and THAT is where this cutscene comes from. That’s why, if people want to complain about this stuff, at least do it with actual FACTS. Because I genuinely don’t think the video the AC account uploaded is even the same scene everyone is talking about.

I’m slowly starting to feel stupid for even having to discuss something like this... When you got drunk in the original Black Flag, Edward didn’t actually stumble around during normal gameplay. The screen just became blurry and shaky and once you fast travel you are no longer drunk. That was basically it. The scene where Edward is visibly stumbling around with a rum bottle in his hand comes from an Animus/loading corridor during a mission, which is something completely different. So the real question is: Did they rework that old cutscene so it’s no longer used as a loading screen? Or is it still in the game as some kind of transition scene? If it’s still there, then yes, they probably could’ve easily implemented that stumbling animation into free roam once you get too drunk. But since I didn’t test it myself, I can’t fully judge it either. I assume that we now can get drunk and fast travel to other locations and still be drunk and THAT is where this cutscene comes from. That’s why, if people want to complain about this stuff, at least do it with actual FACTS. Because I genuinely don’t think the video the AC account uploaded is even the same scene everyone is talking about.

MisterAlpha444

100,205 views • 2 months ago

Thread on why $Goop is the next Runner: What is Goop Coin about? Dont know if you have heard of it but there is a game called ARC RAIDERS going insanely viral rn. And GOOP is the biggest meme/term in the game. Essentially when you kill smb and take their gear and get good loot its called GOOP. Streamers like TFUE even came back to play this game and makes content for it everyday and it just keeps getting bigger and bigger. People compare the current status of the game to PRIME FORTNITE. Watch the videos below from Ninja etc talking about the game. And here is the thesis for Goop, imagine you were in January 2018 and memecoins existed. And now imagine you were able to invest into the attention fortnite got in the form of a memecoin. Once in a lifetime opportunity. Thats the base thesis behind GOOP, ARC Raiders will be THE game of 2026 dominating all charts and just going harder and harder. Me and my friends plan is to go and stream us going into the game, shilling the coin to players INGAME and clipfarm all day long to go viral for it. Streamsniping streamers, kidnapping players so that they go buy the coin and join the Community and stuff like that😂 Regarding the Community the plan is to run up a discord server to become one of THE BIGGEST Arc Raider Communities ever. Back on MW2 i ran up the second biggest server in germany with 1k+ Members in a short time, we had sponsors and shit so ik how to make it work. We will onboard ARC Raider players to crypto by actually bringing VALUE to them. 1. Obv we will create the best enviroment to find teammates and trade ingame items with other people. 2. Using the Creator Fees we will host multiple giveaways and loot drops to funnel people into the Server. 3. Using the Creator Fees we will pay for Whop like campaigns for onboarding clips. The terms are not 100% set yet, but think of like 10 x 100$ for the best onboarding clips + 500$ bonus if you manage to onboard a streamer or get the most viral clip with one. ALL Creator Fees $GOOP makes will be used for this to create a sort of Fly Wheel. This is what Creator fees should be fucking used for. In a matter of a small time GOOP will be the most hyped and the biggest ARC Raiders Server and onboard hundreds of people onchain. Nobody has done anything like this yet and i want $GOOP to become a innovation in both onboarding and what bagworking really means for a coin again. 5ME6tBrsv8zNnUngiTe1EBULf1xY8vdC8KjhmoUhpump

Thread on why $Goop is the next Runner: What is Goop Coin about? Dont know if you have heard of it but there is a game called ARC RAIDERS going insanely viral rn. And GOOP is the biggest meme/term in the game. Essentially when you kill smb and take their gear and get good loot its called GOOP. Streamers like TFUE even came back to play this game and makes content for it everyday and it just keeps getting bigger and bigger. People compare the current status of the game to PRIME FORTNITE. Watch the videos below from Ninja etc talking about the game. And here is the thesis for Goop, imagine you were in January 2018 and memecoins existed. And now imagine you were able to invest into the attention fortnite got in the form of a memecoin. Once in a lifetime opportunity. Thats the base thesis behind GOOP, ARC Raiders will be THE game of 2026 dominating all charts and just going harder and harder. Me and my friends plan is to go and stream us going into the game, shilling the coin to players INGAME and clipfarm all day long to go viral for it. Streamsniping streamers, kidnapping players so that they go buy the coin and join the Community and stuff like that😂 Regarding the Community the plan is to run up a discord server to become one of THE BIGGEST Arc Raider Communities ever. Back on MW2 i ran up the second biggest server in germany with 1k+ Members in a short time, we had sponsors and shit so ik how to make it work. We will onboard ARC Raider players to crypto by actually bringing VALUE to them. 1. Obv we will create the best enviroment to find teammates and trade ingame items with other people. 2. Using the Creator Fees we will host multiple giveaways and loot drops to funnel people into the Server. 3. Using the Creator Fees we will pay for Whop like campaigns for onboarding clips. The terms are not 100% set yet, but think of like 10 x 100$ for the best onboarding clips + 500$ bonus if you manage to onboard a streamer or get the most viral clip with one. ALL Creator Fees $GOOP makes will be used for this to create a sort of Fly Wheel. This is what Creator fees should be fucking used for. In a matter of a small time GOOP will be the most hyped and the biggest ARC Raiders Server and onboard hundreds of people onchain. Nobody has done anything like this yet and i want $GOOP to become a innovation in both onboarding and what bagworking really means for a coin again. 5ME6tBrsv8zNnUngiTe1EBULf1xY8vdC8KjhmoUhpump

monarch🦇

25,279 views • 6 months ago

no money for grok or midjourney? this tool is for you. there's a FREE tool created by an anon dev. open-source. runs locally. 117k stars on github. it generates: > images & video > 3d models > audio > 20+ models here's how to set it up in under 5 minutes: 1️⃣download ComfyUI Desktop go to and grab the desktop app for your system. windows 10+, mac (apple silicon), or linux. it installs like any normal app, it sets up python and every dependency for you in the background. no terminal, no config files. 2️⃣open it first launch, it spins up its own environment automatically. you just wait a few seconds and you're in. you'll land on a node canvas, that's the whole interface. 3️⃣load a starter workflow top menu → Workflow → Browse Templates → Image Generation. click it. this drops a ready-made setup onto your canvas so you don't build anything from scratch. 4️⃣grab a model comfyui ships empty on purpose, the model is the brain, and you pick it. in the template, the "Load Checkpoint" node has a Download button when no model is installed. click it. it pulls one in for you (a few GB, this is the only real wait). 5️⃣install ComfyUI Manager this is the one add-on you don't skip. it lets you install models, custom nodes, and updates with a click instead of the command line. grab it from github (link in comments). it's the difference between fighting comfyui and flying in it. one honest note: an NVIDIA gpu makes this fast, apple silicon works great too, and a weak machine still runs it just slower. that's the whole setup. you now own an image, video, and 3D studio that costs you nothing per month. save this. and the next time grok or midjourney asks for your card. you won't need it. disclaimer: comfyui itself is 100% free. so are the local models (sdxl, flux, wan 2.2, ltx-2). some premium models like seedance are pay-per-use api models, only if you want top-tier quality. the free local ones cover most of what you need. (github link in the comments) follow and turn on post notification for daily AI contents.

no money for grok or midjourney? this tool is for you. there's a FREE tool created by an anon dev. open-source. runs locally. 117k stars on github. it generates: > images & video > 3d models > audio > 20+ models here's how to set it up in under 5 minutes: 1️⃣download ComfyUI Desktop go to and grab the desktop app for your system. windows 10+, mac (apple silicon), or linux. it installs like any normal app, it sets up python and every dependency for you in the background. no terminal, no config files. 2️⃣open it first launch, it spins up its own environment automatically. you just wait a few seconds and you're in. you'll land on a node canvas, that's the whole interface. 3️⃣load a starter workflow top menu → Workflow → Browse Templates → Image Generation. click it. this drops a ready-made setup onto your canvas so you don't build anything from scratch. 4️⃣grab a model comfyui ships empty on purpose, the model is the brain, and you pick it. in the template, the "Load Checkpoint" node has a Download button when no model is installed. click it. it pulls one in for you (a few GB, this is the only real wait). 5️⃣install ComfyUI Manager this is the one add-on you don't skip. it lets you install models, custom nodes, and updates with a click instead of the command line. grab it from github (link in comments). it's the difference between fighting comfyui and flying in it. one honest note: an NVIDIA gpu makes this fast, apple silicon works great too, and a weak machine still runs it just slower. that's the whole setup. you now own an image, video, and 3D studio that costs you nothing per month. save this. and the next time grok or midjourney asks for your card. you won't need it. disclaimer: comfyui itself is 100% free. so are the local models (sdxl, flux, wan 2.2, ltx-2). some premium models like seedance are pay-per-use api models, only if you want top-tier quality. the free local ones cover most of what you need. (github link in the comments) follow and turn on post notification for daily AI contents.

m0h

14,542 views • 1 month ago

$🧑‍🏫 How to make a glass/refraction shader: 🍷 Refraction will ultimately have the effect that whatever is behind your mesh should appear distorted by the surface of the mesh itself. We're not going for external caustics projection, just modelling glass-like, distorting "transparency". 🌆 In Unity, you can sample the *global* _CameraOpaqueTexture (make sure it's enabled in your URP asset settings), which is what your scene looks like rendered without any transparent objects. In Shader Graph, you can simply use the Scene Colour node. 🔢 The UVs required for this texture are the normalized screen coordinates, so if we offset/warp/distort these coordinates and sample the texture, we ultimately produce a distorted image. We can offset the UVs by some normal map, as well as a refraction vector based on the direction from the camera -> the vertex/fragment (flip viewDir, which is otherwise vertex/fragment -> camera) and normals of the object. 📸 Input the (reversed) world space view direction and normal into HLSL refract. **Convert the refraction direction vector to tangent space before adding it to the screen UV.** Use the result to sample _CameraOpaqueTexture. refract(-worldViewDirection, worldNormal, eta); eta -> refraction ratio (from_IOR / to_IOR), > for air, 1.0 / indexOfRefraction (IOR). IOR of water = 1.33, glass = 1.54... 💡 You can also do naive "looks about right" hacks: fresnel -> normal from grayscale, which can be used for distortion. Or distort it any other way (without even specifically using refract at all), really... 🧠 Thus, even if your object is rendered as a transparent type (and vanilla Unity URP will require that it is), it is fully 'opaque' (max alpha), but it renders on its surface what is behind it, using the screen UV. If you distort those UVs by the camera view and normals of the surface it will be rendered on, it then appears like refractive glass on that surface. > Transparent render queue, but alpha = 1.0.$

🧑‍🏫 How to make a glass/refraction shader: 🍷 Refraction will ultimately have the effect that whatever is behind your mesh should appear distorted by the surface of the mesh itself. We're not going for external caustics projection, just modelling glass-like, distorting "transparency". 🌆 In Unity, you can sample the global _CameraOpaqueTexture (make sure it's enabled in your URP asset settings), which is what your scene looks like rendered without any transparent objects. In Shader Graph, you can simply use the Scene Colour node. 🔢 The UVs required for this texture are the normalized screen coordinates, so if we offset/warp/distort these coordinates and sample the texture, we ultimately produce a distorted image. We can offset the UVs by some normal map, as well as a refraction vector based on the direction from the camera -> the vertex/fragment (flip viewDir, which is otherwise vertex/fragment -> camera) and normals of the object. 📸 Input the (reversed) world space view direction and normal into HLSL refract. Convert the refraction direction vector to tangent space before adding it to the screen UV. Use the result to sample _CameraOpaqueTexture. refract(-worldViewDirection, worldNormal, eta); eta -> refraction ratio (from_IOR / to_IOR), > for air, 1.0 / indexOfRefraction (IOR). IOR of water = 1.33, glass = 1.54... 💡 You can also do naive "looks about right" hacks: fresnel -> normal from grayscale, which can be used for distortion. Or distort it any other way (without even specifically using refract at all), really... 🧠 Thus, even if your object is rendered as a transparent type (and vanilla Unity URP will require that it is), it is fully 'opaque' (max alpha), but it renders on its surface what is behind it, using the screen UV. If you distort those UVs by the camera view and normals of the surface it will be rendered on, it then appears like refractive glass on that surface. > Transparent render queue, but alpha = 1.0.

Mirza Beig

125,253 views • 1 year ago