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90 DEGREE TURN WALKTHROUGH~ PART 1: Separation 🧵 I'm splitting these into two three parts; Separation, X Angle and Y Angle. To more clearly explain, i'll probably make a video for part 2 + 3 so it might take longer 🙏 (1/4) Model artist: @/cyannida VTuber: @/iijensii #Live2D

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27,037 views • 2 years ago •via X (Twitter)

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I've been going down the WarpFusion rabbit-hole. Outside of Kaiber, which makes a completely different style of video, I think WF is the best video-to-video model out there. But it appears complicated on the surface. So I'm learning it so I can turn around and teach what I learn. For this one, I used the same snapping video that I used in the past. This took about 2-hours and $20 to make. LOL. But I'll get all these various settings dialed in and then make a YouTube video about how to use it. I'm nowhere near Mrboofy level yet though. ;)

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🚨BROKEN PASS PART 2🚨 GK->CB->CB———————>ST->Goal✅ 1) From goal kick pass to CB 2) Pass it to your other CB 3) Do an R1 Square pass (RB X on Xbox) with full power first time and aim the pass in behind the opponents CBs. 4) Go and score✅ Drop a ❤️ and follow for more!🤝

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AI video is moving from one-off experiments to repeatable production workflows. Dreamina Seedance 2.5 is coming soon to the Dreamina platform. For creative and marketing teams, the upcoming model will bring: 1. Up to 50 multimodal references Combine images, videos, audio, and other creative directions in one generation. 2. Up to 30 seconds of continuous video Create longer scenes with more room for product stories and brand narratives. 3. White-model and green-screen references Improve control over movement, composition, and visual direction. 4. Multilingual creation Adapt content for more global markets. 5. Precise local editing Refine specific parts without recreating the entire video. The result: a more flexible path from creative brief to finished content. 👉 #dreamina #dreaminapartner #seedance #dreaminaseedance25

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47,946 views • 3 years ago

Month 1: $0 Month 2: $0 Month 3: $205 Month 4: 50,532 This is what actually happens when you stop overthinking and start a faceless YouTube channel the right way. Most people quit in the first 60 days because they see zero results and assume it doesn’t work. But the ones who push through the first two months with consistency usually see their first real payout in month 3. Here’s the simple system that makes this possible: Step 1: Pick one proven niche (history, facts, motivation, or luxury) and stick to it. Step 2: Use AI to generate 30 video ideas in one sitting so you never run out of content. Step 3: Outsource or use AI to create the videos (script + voice + editing) so you can upload consistently without burning out. Step 4: Upload 3–5 videos per week and stay consistent for 90 days without checking analytics every day. The first two months are almost always quiet. The third month is where most people either quit or finally get paid. If you’re willing to be patient for 90 days, this model still works extremely well in 2026. Watch the full breakdown in the video.

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Yoona 主人公 | Live2D | COMMS OPEN

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MLP in PyTorch by hand ✍️ ~ 7 steps walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearning

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Tom Yeh

13,318 views • 2 months ago