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Code: SSIS-203 Title: Rara Anzai. Her Powers of Reason Fly Out the Window. Celibacy x add*ction to Aphrodisiacs. Abnormally Strong Climaxes Actress: Anzai Et Al.
87,408 просмотров • 9 часов назад •via X (Twitter)
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So many cover ups. So much lies. Here is a mini breakdown: 1. For whatever reason, she walked TO H. Fentenoy willingly. (Video below) 2. She never went INSIDE willingly; she was dragged inside. (Can see her flailing legs as she was forcefully yanked in) 3. The first photo below shows that it is absolutely impossible for someone to just fall out of that "fenlight" window. Reasons: A/ It is too damn HIGH UP. 4. When we discard the possibility of an accidental fall, all that is left is the possibility of her willfully jumping out. We know she was in an intoxicated state before her fall. Even if she did have the physical strength to hoist herself up in her natural state, it is extremely unlikely she could have done that in an intoxicated state. It does not add up. Police thoroughly refuted themselves merely by making the outrageously absurd claim that she "fell" out of that window. 5. We know (photos below) that Police cropped the publicised footage of whatever happened on the stairs. Conclusions: She was lured there and kidnapped. When somebody is kidnapped and found almost dead some hours later, it is the height of stupidity to even assume suicide. All of this points away from the possibility of a sex party gone wrong and towards a premeditated murder attempt. As the government and Maldives Police Service are doing their level best to cover up the real details of this case, this COULD stretch way beyond mere party boys. The questions we should find answers to are who and why. If this was a premeditated murder attempt for whatever reason, the possibility of another attempt is also high. It is even more likely (almost guaranteed) it will be passed off as death from injuries sustained in the fall if such a thing comes to pass. All in all, this is quite a chilling case, and it will forever be a dark stain on Dr Mohamed Muizzu's administration.
RaajjeLeaks
139,677 просмотров • 1 год назад
![FULL INTERVIEW HERE ON X: "After tabulating, calculating, and quantifying all of the various dimensions of the costs of the [pandemic] lockdown policies, we found that lockdowns are about 30 to 35 times more costly than what they could possibly have delivered in benefits… in terms of human life." This was Gigi Foster's (of Australians for Science and Freedom) stunning revelation to me in June of 2022, based on the results of her own research. I had been well aware that lockdown policies had signficant collateral damage associated with them. Indeed, this was the reason why common-sense public health policy for years had been to avoid them at all costs—until, that is, this was all thrown out the window in early 2020. But I hadn't been ready to accept a 35X differential. The bottom line, according to Gigi, who as a professor at the University of New South Wales is a behavioural economics expert and as such does these sorts of calculations routinely, was always: "How many people would you be willing to kill in order to save one from COVID? That is essentially the trade-off." From skipped cancer screenings to impeded speech development in toddlers to growing social inequality, when we add it all up, what price did we pay? Join me and Gigi to learn the truth in American Thought Leaders 🇺🇸 with @JanJekielek Episode #637, Gigi Foster: Did Our Pandemic Policies Kill More People Than They Saved?, and check out my channel on Box 📦 Club Leader 2.0—I'll post links in the thread below👇](https://image.24vids.com/tw-1744151403965145476/amplify_video_thumb/1744133745085493248/img/XaojbLVd-H6RwCHI.jpg)
![U-Net by hand ✍️ ~ 17 steps walkthrough below I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked! It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL. Now it is the backbone of diffusion models, which you see in almost all modern image generation models. I drew the network as a C so the matrix multiplication flows naturally down. Tilt your head to the right and it is a U again. 🤣 Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself. = 1. Given = An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use. = 2. Convolution 1 = Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map. = 3. Find the maxima = We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down. = 4. Max pool 1 = Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good. = 5. Convolution 2 = We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now. = 6. Find the maxima again = Same move as step 3, on the blue map. = 7. Max pool 2 = Eight columns become four. = 8. The bottleneck = Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept. = 9. Spread it out = We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them. = 10. Transposed convolution 1 = Let us fill those gaps by convolving over the spread-out grid. Four columns become eight. = 11. The first skip = We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away. = 12. Convolution with the skip = Let us convolve the upsampled features together with the copied ones. = 13. Spread it out again = Same as step 9, one level up. = 14. Transposed convolution 2 = Eight columns become sixteen, back to the width we started at. = 15. The second skip = The encoder's first feature map comes across, the one made before any pooling happened. = 16. Convolution and ReLU = We convolve, then cross out every negative and set it to zero. = 17. Output convolution = Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with. The outputs: R' = [3, 0, 7, 0, 7, 0, 17, 0, 3, 0, 9, 0, 2, 0, 6, 0] G' = [1, 20, 1, 10, 1, 12, 1, 19, 2, 5, 1, 11, 1, 3, 1, 7] B' = [4, 20, 8, 10, 8, 12, 18, 19, 5, 5, 10, 11, 3, 3, 7, 7] Congrats! You just calculated a U-Net by hand. 💾 Save this post!](https://image.24vids.com/tw-2081382845243691256/ext_tw_video_thumb/2081382802667249664/pu/img/n2gZcnKRYe9G781o.jpg)
![how i write viral articles on X i've written a little over 43 articles. by my count, they've generated roughly 20–30 million views directly and indirectly. give this to your agent. here's my 7-step process for finding ideas, researching, writing, and designing titles + thumbnails people want to click and save. [the full workflow, tools, and prompts are in the article below.] 1. find the intersection of attention and experience start with: → what people are currently talking about → something you've personally built, tested, read deeply, or figured out that gives you a timely topic and a reason people should listen. i use Perplexity and Grok Bot to find questions, research papers, and existing explanations. then i read the sources and develop my own argument. research prompt: “what questions remain unanswered about [topic]? show me the best sources, conflicting evidence, and gaps i can explore using my experience in [area].” 2. design the title, thumbnail, and outline together i do this before polishing the article. → the title sells the outcome → the thumbnail makes the value visible → the content delivers the substance if i can't communicate the value in one line and one visual, the idea needs more work. the title should explain what the reader can achieve or make a bold claim the article fully supports. “How to Build X” gives readers an outcome they can pursue. “I Built This” asks them to care about your project. i generally use the broader framing to reach more people, then bring my specific experience into the piece. how-tos, blueprints, resources, and guides make the value easy to understand. a title can be a single-line question or summary. either way, make the payoff clear. you cannot LARP through the title. 3. normie-max the packaging make the title and thumbnail broadly understandable, then bring the depth as people read. give people a familiar outcome before introducing the technical details. i call the visual side the cocomelon effect: → brighter colors → no more than three dominant elements → consistent typography the goal is instant recognition. a diagram can contain several steps while remaining one dominant visual element. 4. make the thumbnail deliver value before the click i use Pinterest for inspiration. useful searches: → technical editorial diagram → serif poster typography → minimal workflow infographic pick references for specific properties: typography, color, and layout. then build the thumbnail around the article: → use a large diagram that visually summarizes the idea → keep annotations to around four or five words → use GPT or another image-generation tool for a clean workflow diagram → choose a background with enough contrast to keep everything readable → make the subject feel detailed and approachable thumbnail prompt: “turn this article into one clear visual. show [workflow] with short labels. use these references for typography and color. keep the composition simple, readable at thumbnail size, and in [aspect ratio].” when revising, name the exact change: → keep the diagram → shrink the title → fix the connector → preserve the aspect ratio protect what already works. the thumbnail should make someone think, “this is useful enough to save.” the title gives them the conviction to click and learn how to do it. 5. give the payoff early i use Typeless and Whisperflow to talk through my ideas quickly, then organize the material into a clear argument. the first five to seven lines should explain: → what the reader gets → why it matters → what experience supports it put a strong tldr near the top. someone who doesn't have time to read everything should still leave with something useful. then bring the authenticity through your actual experience: → what you tried → what happened → what you changed → what you learned give readers the prompts, steps, screenshots, and expected results they need to follow along. each paragraph should help explain or deliver the promise. 6. give readers something they can reuse for technical articles, i like a public GitHub repo containing: → the full article → the prompts → the diagrams → the relevant project files it gives the work a searchable home outside X. people can download it, inspect it, and give it to their own agents without depending on those agents being able to retrieve an X article. 7. check the promise before publishing ask: → where would a beginner get stuck? → which claims need evidence? → is the thumbnail readable at its intended size? → does the article deliver what the title promises? giving value upfront shows readers their time is respected. the deeper explanation gives them a reason to keep reading.](https://image.24vids.com/tw-2096899110578328054/amplify_video_thumb/2096898978973663232/img/_Sz1mwP_Bj-EUpzg.jpg)