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We’re (finally) rolling out some WIP materials from SONIC X SHADOW: Dark Beginnings! Sonic the Hedgehog SEGA Here we’d love to show you how we make 3D look 2D, with a step-by-step layering of the Emerl’s entrance shot from episode 1. Animatic by GXS Productions Kevin Molina-Ortiz

SIMAGE Animation

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75,171 views • 1 year ago •via X (Twitter)

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HELPED A LOT WITH THIS, FROM THE STORYBOARD, BACKGROUNDS AND EVEN HELPED WITH THE ANIMATION IN SOME SCENES HERE ARE SOME OF MY CONTRIBUTIONS (I ANIMATED METAL OVERLORD) (Team sonic and Team dark with the help of MALMOOKAI , and Neo Metal Animation by Kyal Brown) (1/?)

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QwinkleTee

40,103 views • 1 year ago

I tried Hedra Agent by Hedra to see if one conversation could replace the usual mess of switching models, rewriting prompts, and juggling tools. So I started with a simple idea, Hedra Agent: - Selected the right models on its own - Generated refined visuals - Suggested multiple stylistic directions - Then turned the chosen frames into a cohesive video All within the same conversation while remembering every detail we discussed. I was able to shift the mood, adjust the lighting, refine the composition, explore different angles, and even adapt the format simply by giving natural feedback. I did not have to restart or rebuild anything from scratch. The Agent handled the workflow from idea to finished, platform-ready content. What stood out was not just the output quality, but also the continuity. Instead of operating tools, it felt like collaborating with a system that understands context and builds with you step by step. Check how it works 👇🏻

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[VAE] by Hand ✍️ A Variational Auto Encoder (VAE) learns the structure (mean and variance) of hidden features and generates new data from the learned structure. In contrast, GANs only learn to generate new data to fool a discriminator; they may not necessarily know the underlying structure of the data. The International Conference on Learning Representations (ICLR) this year announced its first ever "Test of Time Award" to recognizes the VAE paper, published 10 years ago. This exercise demonstrates how to calculate a VAE by hand. [1] Given: ↳ Three training examples X1, X2, X3 ↳ Copy training examples to the bottom ↳ The purpose is to train the network to reconstruct the training examples. ↳ Since each target is a training example itself, we use the Greek word "auto" which means "self." This crucial step is what makes an autoencoder "auto." [2] Encoder: Layer 1 + ReLU ↳ Multiply inputs with weights and biases ↳ Apply ReLU, crossing out negative values (-1 -> 0) [3] Encoder: Mean and Variance ↳ Multiply features with two sets of weights and biases ↳ 🟩 The first set predicts the means (𝜇) of latent distributions ↳ 🟪 The second set predicts the standard deviation (𝜎) of latent distributions [4] Reparameterization Trick: Random Offset ↳ Sample epsilon ε from the normal distribution with mean = 0 and variance = 1. ↳ The purpose is to randomly pick a offset away from the mean. ↳ Multiply the standard deviation values with epsilon values. ↳ The purpose is to scale the offset by the standard deviation. [5] Reparameterization Trick: Mean + Offset ↳ Add the sampled offset to predicted mean ↳ The result are new parameters or features 🟨 as inputs to the Decoder. [6] Decoder: Layer 1 + ReLU ↳ Multiply input features with weights and biases ↳ Apply ReLU, crossing out negative values. Here, -4 is crossed out. [7] Decoder: Layer 2 ↳ Multiply features with weights and biases ↳ The output is Decoder's attempt to reconstruct the input data X from reparameterized distributions described by 𝜇 and 𝜎. [8]-[10] KL Divergence Loss [8] Loss Gradient: Mean 𝜇 ↳ We want 𝜇 to approach 0. ↳ A lot of math called SGVB simplifies the calculation of loss gradients to simply 𝜇 [9,10] Loss Gradient: Stdev 𝜎 ↳ We want 𝜎 to approach 1. ↳ A lot of math simplifies the calculation to 𝜎 - (1/ 𝜎) [11] Reconstruction Loss ↳ We want the reconstructed data Y (dark 🟧) to be the same as the input data X. ↳ Some math involving Mean Square Error simplifies the calculation to Y - X.

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Tom Yeh

48,448 views • 2 years ago

🟢 Kolo💚 x 🐤 GraFun : The Future of #MemeCoin-s is here! ⚡️ We’re excited to announce partnership with #GraFun — a #BNB memecoin launcher with built-in trading features trusted by DeXe & DFW Labs. 😎 What’s in for you? - Apple Pay & Google Pay meets memecoin #trading with out-of-the box liquidity from the BNB chain. 💳 To mark this day special all the GraFun users will soon be able to tap into bonuses - exclusive KOLO x GraFun #CryptoCard 👀!

🟢 Kolo💚 x 🐤 GraFun : The Future of #MemeCoin-s is here! ⚡️ We’re excited to announce partnership with #GraFun — a #BNB memecoin launcher with built-in trading features trusted by DeXe & DFW Labs. 😎 What’s in for you? - Apple Pay & Google Pay meets memecoin #trading with out-of-the box liquidity from the BNB chain. 💳 To mark this day special all the GraFun users will soon be able to tap into bonuses - exclusive KOLO x GraFun #CryptoCard 👀!

Kolo💚

28,000 views • 1 year ago

Holy moly, another big one by Google DeepMind! "Our SIMA 2 research offers a strong path towards applications in robotics and another step towards AGI in the real world" Google DeepMind just unveiled SIMA 2 - an AI agent that doesn’t just follow instructions but thinks and reasons with you. The first version, SIMA 1, learned to perform over 600 basic actions in 3D environments. SIMA 2 takes a leap forward: it understands goals, holds conversations, and learns from experience. This marks a shift from “Do this” to “Let’s figure out how to achieve this together.” A thread with examples and how it works🧵:

Holy moly, another big one by Google DeepMind! "Our SIMA 2 research offers a strong path towards applications in robotics and another step towards AGI in the real world" Google DeepMind just unveiled SIMA 2 - an AI agent that doesn’t just follow instructions but thinks and reasons with you. The first version, SIMA 1, learned to perform over 600 basic actions in 3D environments. SIMA 2 takes a leap forward: it understands goals, holds conversations, and learns from experience. This marks a shift from “Do this” to “Let’s figure out how to achieve this together.” A thread with examples and how it works🧵:

Chubby♨️

43,309 views • 9 months ago

Yep! We spent a 21 months building Werm, and we’re genuinely proud of how we’ve done things. We’ll keep doing everything possible to build great products. What you see now is the result of countless iterations from the version shown in the below video. With just three people, building and scaling a multiplayer game has been incredibly challenging, even large studios struggle with this. The 21 months of development were necessary. We went through multiple rebuilds, learned a ton about launches, and built real relationships across the ecosystem. We stayed away from presales and token launches because the product wasn’t ready. Now we finally feel confident to give it a real shot. There’ll be bumps along the way, but unlike most crypto projects built on hype, we’re launching with an actual product and genuine intent to make it work. We believe we’re doing it the right way and that’s how we plan to keep going. The goal is for the community to trust that when we build, it’s 100% real, never a scam. More news to come about Werm!

Yep! We spent a 21 months building Werm, and we’re genuinely proud of how we’ve done things. We’ll keep doing everything possible to build great products. What you see now is the result of countless iterations from the version shown in the below video. With just three people, building and scaling a multiplayer game has been incredibly challenging, even large studios struggle with this. The 21 months of development were necessary. We went through multiple rebuilds, learned a ton about launches, and built real relationships across the ecosystem. We stayed away from presales and token launches because the product wasn’t ready. Now we finally feel confident to give it a real shot. There’ll be bumps along the way, but unlike most crypto projects built on hype, we’re launching with an actual product and genuine intent to make it work. We believe we’re doing it the right way and that’s how we plan to keep going. The goal is for the community to trust that when we build, it’s 100% real, never a scam. More news to come about Werm!

Tilted Ventures

19,582 views • 9 months ago

Shout-out to the artist community We love art, images, and ideas that make the world feel different, even strange at times. As a team, we are always curious about new art and enjoy sharing the gems we stumble upon with our community. From photography to AI and code-based art, here are some of the works we have loved over the past few weeks. We hope you enjoy them as much as we do! ↓

Shout-out to the artist community We love art, images, and ideas that make the world feel different, even strange at times. As a team, we are always curious about new art and enjoy sharing the gems we stumble upon with our community. From photography to AI and code-based art, here are some of the works we have loved over the past few weeks. We hope you enjoy them as much as we do! ↓

Fellowship

28,756 views • 3 months ago

Step 1 : input your char reference, poster and your product into GPT Image 2 and then put the prompt : Use the woman on image 1 as the main subject. create a vertical poster ad inspired by the reference poster style. show the image 1 woman holding the kimchi jar with her left hand, while the right hand eating kimchi to her mouth. with framing wide lens, low angle from the reference poster. This step is where you lock the visual direction. Step 2 : take the generated image into Seedance 2.0 and convert it into motion. Set it to 1080p if your design includes a lot of typography, this helps preserve text clarity. You can also strengthen your prompt by adding keywords like: “dynamic motion design commercial advertisement” to push the result closer to a polished ad style. Important note : If your design contains heavy typography, expect some inconsistencies in text rendering. The best approach is to generate multiple variations and select the cleanest result. For this sample, I generated it 4 times before landing on the final version.

Step 1 : input your char reference, poster and your product into GPT Image 2 and then put the prompt : Use the woman on image 1 as the main subject. create a vertical poster ad inspired by the reference poster style. show the image 1 woman holding the kimchi jar with her left hand, while the right hand eating kimchi to her mouth. with framing wide lens, low angle from the reference poster. This step is where you lock the visual direction. Step 2 : take the generated image into Seedance 2.0 and convert it into motion. Set it to 1080p if your design includes a lot of typography, this helps preserve text clarity. You can also strengthen your prompt by adding keywords like: “dynamic motion design commercial advertisement” to push the result closer to a polished ad style. Important note : If your design contains heavy typography, expect some inconsistencies in text rendering. The best approach is to generate multiple variations and select the cleanest result. For this sample, I generated it 4 times before landing on the final version.

DStudioproject

11,413 views • 3 months ago