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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens... (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to potentially giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.show more

nathan (in sf)

3,249 subscribers

40,440 次观看 • 6 个月前 •via X (Twitter)

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Added context to my tiny diffusion model to enable sequential generation of longer outputs! Currently the context is a quarter of the sequence length (seq_len=256, context_len=64). I have a theory that the less semantic-value-per-token, the worse the “curse of parallel decoding” is. With parallel decoding, we independently predict multiple tokens in one step. With the sentence “My poker hand was a ___ ___”, two valid predictions are “two pair” and “straight flush”. Because each token prediction is independent though, we can end up with a nonsensical output like “two flush”. This seems to be exacerbated with low semantic-value-per-token, as now you need more tokens to express the same concept. Instead of needing to independently predict two tokens, we might need to predict 10 instead (which is of course much harder). The model currently has noticeably worse output compared to nanogpt (similar size) and I believe this is a main reason. I’ll try adding confidence-aware parallel decoding (from NVIDIA’s Fast-dLLM paper) and other tricks and see how much they improve generation quality.

Added context to my tiny diffusion model to enable sequential generation of longer outputs! Currently the context is a quarter of the sequence length (seq_len=256, context_len=64). I have a theory that the less semantic-value-per-token, the worse the “curse of parallel decoding” is. With parallel decoding, we independently predict multiple tokens in one step. With the sentence “My poker hand was a _ _”, two valid predictions are “two pair” and “straight flush”. Because each token prediction is independent though, we can end up with a nonsensical output like “two flush”. This seems to be exacerbated with low semantic-value-per-token, as now you need more tokens to express the same concept. Instead of needing to independently predict two tokens, we might need to predict 10 instead (which is of course much harder). The model currently has noticeably worse output compared to nanogpt (similar size) and I believe this is a main reason. I’ll try adding confidence-aware parallel decoding (from NVIDIA’s Fast-dLLM paper) and other tricks and see how much they improve generation quality.

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The Hidden Language of Diffusion Models paper page: tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation

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50,304 次观看 • 1 年前

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46,232 次观看 • 1 年前

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20,919 次观看 • 9 个月前

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71,067 次观看 • 5 个月前

🔥 Congratulations on the Launch of the Die Last Playtest & Upcoming Airdrop! 🔥 We are thrilled to announce the official start of the Playtest! Moreover—we've prepared a bunch of surprises just for you. Alongside the Playtest, we're hosting the first-ever in-game token AirDrop 💎. Your mission: dig and craft Lost Objects, remnants of a forgotten civilization. To participate in the upcoming token AirDrop, you need to find a Spy Radio to craft an NFT, your key to this exciting journey. Anyone can find in-game NFTs, which are the key to owning wallets with different amounts of tokens needed to participate in the AirdDrop: the more tokens you have, the more you'll get 💸. The luckiest players can obtain an NFT with a full-filled wallet, which means you can participate in the AirDrop at no cost! Didn't find an NFT with a 100% discount? No worries, keep digging! Every NFT you find boosts the number of tokens you receive during the AirDrop. For instance, if you have 100 tokens in your wallet and craft 6 NFTs, you’ll receive 80 tokens in the AirDrop. The more NFTs you have, the more tokens you’ll get. ✅ To participate, add #DieLast to your Steam wishlist and apply for the Playtest (approval of your application may take up to 24 hours or be approved instantly): ✅ To grab your NFT once it's dug up, visit our website: ❗️ Important note: We will approve applications for the Playtest on a limited basis to balance the load on the network. Once we are confident that the network is functioning as intended, we will accept all applications at once. We expect this to occur within one day and will keep you updated. 💥 Our dedicated followers know about our quest campaign with Bitget Wallet 🩵 and DeGame. Join us and complete quests to earn rewards totaling $3000+ USDT 👉 Dive into the adventure and unearth hidden treasures. Welcome to the Die Last Planet! 🌎 #crypto #taptap #clicker #game

🔥 Congratulations on the Launch of the Die Last Playtest & Upcoming Airdrop! 🔥 We are thrilled to announce the official start of the Playtest! Moreover—we've prepared a bunch of surprises just for you. Alongside the Playtest, we're hosting the first-ever in-game token AirDrop 💎. Your mission: dig and craft Lost Objects, remnants of a forgotten civilization. To participate in the upcoming token AirDrop, you need to find a Spy Radio to craft an NFT, your key to this exciting journey. Anyone can find in-game NFTs, which are the key to owning wallets with different amounts of tokens needed to participate in the AirdDrop: the more tokens you have, the more you'll get 💸. The luckiest players can obtain an NFT with a full-filled wallet, which means you can participate in the AirDrop at no cost! Didn't find an NFT with a 100% discount? No worries, keep digging! Every NFT you find boosts the number of tokens you receive during the AirDrop. For instance, if you have 100 tokens in your wallet and craft 6 NFTs, you’ll receive 80 tokens in the AirDrop. The more NFTs you have, the more tokens you’ll get. ✅ To participate, add #DieLast to your Steam wishlist and apply for the Playtest (approval of your application may take up to 24 hours or be approved instantly): ✅ To grab your NFT once it's dug up, visit our website: ❗️ Important note: We will approve applications for the Playtest on a limited basis to balance the load on the network. Once we are confident that the network is functioning as intended, we will accept all applications at once. We expect this to occur within one day and will keep you updated. 💥 Our dedicated followers know about our quest campaign with Bitget Wallet 🩵 and DeGame. Join us and complete quests to earn rewards totaling $3000+ USDT 👉 Dive into the adventure and unearth hidden treasures. Welcome to the Die Last Planet! 🌎 #crypto #taptap #clicker #game

Die Last

13,396 次观看 • 2 年前

Here’s my analysis on NotPixel Airdrop: - Mining phase: it was entertaining to paint on the canvas alongside with many of you! Personally, I had fun drawing 🧡 together with you, just for fun, no competition. For that I will rate it a ✅ - Earn Launchpool: personally, I think this is great that there’s such a tool, that allows holders effortlessly farm tokens. For me it’s a ✅ - Distribution: the major frustrations has been caused by the overload of the smart contract for Airdrop claim, where the biggest frustrations came from the fact that many were not able to claim their tokens for long (Including me) and watched the price go from $0,5 to $0,8 & to $0,2. Personally, I think that if Airdrop claim was done prior to the launch, it may have been better. For that, I will put a ❌ - Launch: NotPixel decided to launch its PX token only on DEX’es on TON. the price at launch actually was at $0,5-$0,8 however, it shortly went down to $0,2 , causing frustration to the community & making them question “who sold?!” & “why no CEX listings?” Here, it’s important to understand the way Sasha & his team decided that they want to build the project. As they mentioned on X “start from the basics, start from 0.” While many dislike this approach, I personally believe that this is how the launches must be happening. Launch on DEX -> generate traction & volume -> go list on CEX’es -> create more news -> grow more from new ppl joining For that I will put ✅ - Team allocation: while many have speculated that “team sold”. this is completely not true. In fact, team has locked their allocation & it can be verified on chain. Which is ✅ for me. - Future plans: NotPixel will have a big canvas on which you will be able to paint, but this time, with some NFT mechanics & PX token integration. For which it is ✅ to me. Burning: NotPixel plan with the new update also includes a mechanism for Burning, which means that PX token will be deflationary. Which is ✅ - ownership revoked: meaning that PX token supply cannot be increased. Nor any modifications could be made to the smart contract at this point. Which is a big ✅ in my opinion - Community: while many are still frustrated with the current token price on the market, many express desire for token appreciation & further growth. One of the interesting feedbacks I saw on the timeline was “NotPixel forced us all to hold”. Which shows that there are ppl who are not willing to get less than their target. Also many express that they want to see PX on CEX’es. For it to grow even more. ✅ - Holders: as with any Airdrop, many have sold their tokens on the market, the volume also came down a bit. However, what’s interesting is that while ~200k ppl sold, the token still fluctuates between the lowest point $0,13 to $0,26. Which shows that there’s a demand & ppl are accumulating more tokens. Currently PX has 205k holders. Which is a ✅ - BuyBacks: the NotPixel team recently announced the buybacks with the money raised from the NotPixel stickers sale. Which created more support for the price on the market. ✅ - Expectations vs reality: Many community members said that they were expecting price to be above $1 as minimum, many were predicting as high as $10. In reality, the price at launch turned out to be much lower than many expected. Leading to the frustration being expressed on the timeline. Which is understandable. ❌ Conclusion: - Mining phase: ✅ - Earn Launchpool: ✅ - Distribution: ❌ - Launch: ✅ - Team Allocation: ✅ - Future plans: ✅ - Burning: ✅ - Ownership revoked: ✅ - Community: ✅ - Holders: ✅ - BuyBacks: ✅ - Expectations vs reality: ❌ This is not bad execution, not many ppl will be able to pull this off in crypto. No one knows the future, but I chose to believe that Sasha & NotPixel team has a plan. Currently they are not listed on any CEX’es, but I think that Sasha & his team could easily get there. Let’s see what the future holds for us 🙌

Here’s my analysis on NotPixel Airdrop: - Mining phase: it was entertaining to paint on the canvas alongside with many of you! Personally, I had fun drawing 🧡 together with you, just for fun, no competition. For that I will rate it a ✅ - Earn Launchpool: personally, I think this is great that there’s such a tool, that allows holders effortlessly farm tokens. For me it’s a ✅ - Distribution: the major frustrations has been caused by the overload of the smart contract for Airdrop claim, where the biggest frustrations came from the fact that many were not able to claim their tokens for long (Including me) and watched the price go from $0,5 to $0,8 & to $0,2. Personally, I think that if Airdrop claim was done prior to the launch, it may have been better. For that, I will put a ❌ - Launch: NotPixel decided to launch its PX token only on DEX’es on TON. the price at launch actually was at $0,5-$0,8 however, it shortly went down to $0,2 , causing frustration to the community & making them question “who sold?!” & “why no CEX listings?” Here, it’s important to understand the way Sasha & his team decided that they want to build the project. As they mentioned on X “start from the basics, start from 0.” While many dislike this approach, I personally believe that this is how the launches must be happening. Launch on DEX -> generate traction & volume -> go list on CEX’es -> create more news -> grow more from new ppl joining For that I will put ✅ - Team allocation: while many have speculated that “team sold”. this is completely not true. In fact, team has locked their allocation & it can be verified on chain. Which is ✅ for me. - Future plans: NotPixel will have a big canvas on which you will be able to paint, but this time, with some NFT mechanics & PX token integration. For which it is ✅ to me. Burning: NotPixel plan with the new update also includes a mechanism for Burning, which means that PX token will be deflationary. Which is ✅ - ownership revoked: meaning that PX token supply cannot be increased. Nor any modifications could be made to the smart contract at this point. Which is a big ✅ in my opinion - Community: while many are still frustrated with the current token price on the market, many express desire for token appreciation & further growth. One of the interesting feedbacks I saw on the timeline was “NotPixel forced us all to hold”. Which shows that there are ppl who are not willing to get less than their target. Also many express that they want to see PX on CEX’es. For it to grow even more. ✅ - Holders: as with any Airdrop, many have sold their tokens on the market, the volume also came down a bit. However, what’s interesting is that while ~200k ppl sold, the token still fluctuates between the lowest point $0,13 to $0,26. Which shows that there’s a demand & ppl are accumulating more tokens. Currently PX has 205k holders. Which is a ✅ - BuyBacks: the NotPixel team recently announced the buybacks with the money raised from the NotPixel stickers sale. Which created more support for the price on the market. ✅ - Expectations vs reality: Many community members said that they were expecting price to be above $1 as minimum, many were predicting as high as $10. In reality, the price at launch turned out to be much lower than many expected. Leading to the frustration being expressed on the timeline. Which is understandable. ❌ Conclusion: - Mining phase: ✅ - Earn Launchpool: ✅ - Distribution: ❌ - Launch: ✅ - Team Allocation: ✅ - Future plans: ✅ - Burning: ✅ - Ownership revoked: ✅ - Community: ✅ - Holders: ✅ - BuyBacks: ✅ - Expectations vs reality: ❌ This is not bad execution, not many ppl will be able to pull this off in crypto. No one knows the future, but I chose to believe that Sasha & NotPixel team has a plan. Currently they are not listed on any CEX’es, but I think that Sasha & his team could easily get there. Let’s see what the future holds for us 🙌

Viktor 🧡

44,168 次观看 • 1 年前

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

SemiAnalysis

51,021 次观看 • 8 个月前

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

112,114 次观看 • 8 天前

So, my opinion on what the Antarctic (Antarctica) Anomaly is that it's a type of frequency technology. It must be way more powerful than HAARP, as many have claimed it to be, because we would see these anomalies at other HAARP sites, and we don't, not like this. With that said, and I'm very much trying to avoid letting what I want it to be not play a part here, I think it is a technology that is being used either off the coast of Antarctica itself or Bouvet Island. A third possibility is an area just to the northwest of the island that looks odd. It's possible it is a sonar scan from a ship, but why in that remote location? It looks like an antenna set up or rows of something that is out of place. I also believe that the weather events and fires that have taken place in Africa could possibly have been because of this. Each time we saw the anomaly, it was followed by a destructive weather event in Africa. A weird connection to that is we have been told and warned of a very busy 2024 Atlantic hurricane season. This is in part because of the above-average Atlantic ocean temperatures, which is the fuel to Hurricanes. With all this info, it's possible to see how the Anomaly could be a frequency tech that can manipulate or create weather, And or WARM up the Ocean temps to purposely enhance the Hurricane season and Storm growth. Keep in mind that many of our hurricanes and many of the biggest hurricanes have come from the west coast of Africa and form over the Cape Verde islands before heading towards the Caribbean and the United States. This is all of course speculation, and I'm learning many new things every day, so this idea may morph over time as we learn more. In the end, it is very hard to ignore all these findings. #antarctica #anonaly #AntarcticaAnomaly #BouvetIsland

So, my opinion on what the Antarctic (Antarctica) Anomaly is that it's a type of frequency technology. It must be way more powerful than HAARP, as many have claimed it to be, because we would see these anomalies at other HAARP sites, and we don't, not like this. With that said, and I'm very much trying to avoid letting what I want it to be not play a part here, I think it is a technology that is being used either off the coast of Antarctica itself or Bouvet Island. A third possibility is an area just to the northwest of the island that looks odd. It's possible it is a sonar scan from a ship, but why in that remote location? It looks like an antenna set up or rows of something that is out of place. I also believe that the weather events and fires that have taken place in Africa could possibly have been because of this. Each time we saw the anomaly, it was followed by a destructive weather event in Africa. A weird connection to that is we have been told and warned of a very busy 2024 Atlantic hurricane season. This is in part because of the above-average Atlantic ocean temperatures, which is the fuel to Hurricanes. With all this info, it's possible to see how the Anomaly could be a frequency tech that can manipulate or create weather, And or WARM up the Ocean temps to purposely enhance the Hurricane season and Storm growth. Keep in mind that many of our hurricanes and many of the biggest hurricanes have come from the west coast of Africa and form over the Cape Verde islands before heading towards the Caribbean and the United States. This is all of course speculation, and I'm learning many new things every day, so this idea may morph over time as we learn more. In the end, it is very hard to ignore all these findings. #antarctica #anonaly #AntarcticaAnomaly #BouvetIsland

In2ThinAir

442,580 次观看 • 2 年前

EDGS: Eliminating Densification for Efficient Convergence of 3DGS Contributions: • We show that initial triangulation based on 2D correspondences can replace the incremental refinement process, fundamentally changing how 3DGS models allocate resources. • Our method reduces the path each Gaussian must travel in parameter space. Careful initialization not only accelerates convergence but also guides optimization toward a convergence point corresponding to lower reconstruction error and thus higher reconstruction quality. • Our approach outperforms both speed-optimized and quality-focused state-of-the-art models while using only half the splats of standard 3DGS. By improving initialization rather than altering the optimization process, this method is compatible with other 3DGS acceleration techniques, making it a flexible enhancement to existing models.

EDGS: Eliminating Densification for Efficient Convergence of 3DGS Contributions: • We show that initial triangulation based on 2D correspondences can replace the incremental refinement process, fundamentally changing how 3DGS models allocate resources. • Our method reduces the path each Gaussian must travel in parameter space. Careful initialization not only accelerates convergence but also guides optimization toward a convergence point corresponding to lower reconstruction error and thus higher reconstruction quality. • Our approach outperforms both speed-optimized and quality-focused state-of-the-art models while using only half the splats of standard 3DGS. By improving initialization rather than altering the optimization process, this method is compatible with other 3DGS acceleration techniques, making it a flexible enhancement to existing models.

MrNeRF

124,131 次观看 • 1 年前

“I DON’T NEED NO MAN”-Black American women This mentality of not needing a man That has been said and installed In black girls/womens minds for decades now has plagued the community With single homes and boys that have to go to the army to learn discipline Do black women have a lot of good options in the black community dating wise? NO Because so many black men have been plagued with street nigga culture Which leads them to prison or death And so many can’t keep a decent job And provide well enough to get respect I LOVE ANGEL REESE BUT WHEN SHE SAYS THIS I FEEL LIKE IT’S MORE OF LIKE A REPETITIOUS QUOTE FROM SISTERS THAT GOES BACK FOR DECADES BECAUSE SO MANY SISTERS HAD TO DO IT BY THEMSELVES THAT THEY GOT USED TOO IT BUT THAT DON’T MEAN THAT WE ARE PRODUCING THE BEST NEVER GENERATION IF THEY ARE ALL COMING FROM BROKEN HOMES WHERE THE FATHERS ARE SEEN AS AFTER THOUGHTS INSTEAD OF MEN THAT ARE NEEDED IN THE HOME JUST AS MUCH AS THE MOTHERS TO PROVIDE THE KIDS WITH ADEQUATE DISCIPLINE AND OTHER SKILLS ESPECIALLY THE YOUNG MEN

“I DON’T NEED NO MAN”-Black American women This mentality of not needing a man That has been said and installed In black girls/womens minds for decades now has plagued the community With single homes and boys that have to go to the army to learn discipline Do black women have a lot of good options in the black community dating wise? NO Because so many black men have been plagued with street nigga culture Which leads them to prison or death And so many can’t keep a decent job And provide well enough to get respect I LOVE ANGEL REESE BUT WHEN SHE SAYS THIS I FEEL LIKE IT’S MORE OF LIKE A REPETITIOUS QUOTE FROM SISTERS THAT GOES BACK FOR DECADES BECAUSE SO MANY SISTERS HAD TO DO IT BY THEMSELVES THAT THEY GOT USED TOO IT BUT THAT DON’T MEAN THAT WE ARE PRODUCING THE BEST NEVER GENERATION IF THEY ARE ALL COMING FROM BROKEN HOMES WHERE THE FATHERS ARE SEEN AS AFTER THOUGHTS INSTEAD OF MEN THAT ARE NEEDED IN THE HOME JUST AS MUCH AS THE MOTHERS TO PROVIDE THE KIDS WITH ADEQUATE DISCIPLINE AND OTHER SKILLS ESPECIALLY THE YOUNG MEN

MASTER STUDENT🤲🏾

58,668 次观看 • 3 个月前

There’s a turf war in San Francisco. Fighting for territory? Chihuahuas and labs. These are the two most common dog breeds found in the city, according to detailed data obtained by the Chronicle from San Francisco Animal Care and Control on every dog registered since 2020, a total of 50,000 pups. But depending on which neighborhood you are in, you are much more likely to see one or the other. There are also a few pockets of the city where other dogs dominate. While there’s more than enough data to give us a strong sense of the city’s dog scene, the dataset doesn’t represent every canine in San Francisco. Animal Care and Control estimated in 2018 that the dog population was between 120,000 and 150,000. The department said that not all dogs are licensed because people might not be aware of the law requiring it or don’t want to pay the fee, and there are only 11 officers who patrol the city to look for unlicensed dogs. With this bounty of doggo information, we have anointed this “Dog data week,” in which we will explore a different aspect of San Francisco’s four-pawed population each day. Today, we start with San Francisco’s great breed divide. In addition to names, colors, and breeds, the data also includes the ZIP code of every registered dog.

There’s a turf war in San Francisco. Fighting for territory? Chihuahuas and labs. These are the two most common dog breeds found in the city, according to detailed data obtained by the Chronicle from San Francisco Animal Care and Control on every dog registered since 2020, a total of 50,000 pups. But depending on which neighborhood you are in, you are much more likely to see one or the other. There are also a few pockets of the city where other dogs dominate. While there’s more than enough data to give us a strong sense of the city’s dog scene, the dataset doesn’t represent every canine in San Francisco. Animal Care and Control estimated in 2018 that the dog population was between 120,000 and 150,000. The department said that not all dogs are licensed because people might not be aware of the law requiring it or don’t want to pay the fee, and there are only 11 officers who patrol the city to look for unlicensed dogs. With this bounty of doggo information, we have anointed this “Dog data week,” in which we will explore a different aspect of San Francisco’s four-pawed population each day. Today, we start with San Francisco’s great breed divide. In addition to names, colors, and breeds, the data also includes the ZIP code of every registered dog.

San Francisco Chronicle

26,409 次观看 • 1 个月前

I decided to share part of a prompt you can use to research any protocol in seconds using INFINIT Intelligence by INFINIT. As an example, I used Silo Labs, where I currently farm most of my stablecoin yields. 🔖 Bookmark this + read until the end for a bonus. Just replace [PROTOCOL NAME] with any protocol you want to research. Prompt: "Conduct thorough research on [PROTOCOL NAME] and answer the following questions: - What is the project building? - What problem does it solve, and for whom? - What makes it different from others? - What blockchain is it built on? - Is there a token? What’s its purpose? - How does the protocol work technically? - How does it make money or sustain itself? - What’s the staking model, emissions, burns, and treasury? - Who are the founders, and is the team public and credible? - Who funded it? Who holds most tokens? - Are users, TVL, and volume growing? - How strong is the ecosystem around it? - Who are the main competitors, and how does it compare? - Is the protocol audited? Any past hacks? - What are the best ways to use the protocol (strategies)? - What’s coming next? Key milestones or launches? - What are the biggest risks? - How strong is the community and social traction?" Bonus: Quote this post with a reason why you like INFINIT, and I’ll DM you the full version of the prompt within the next 24 hours.

I decided to share part of a prompt you can use to research any protocol in seconds using INFINIT Intelligence by INFINIT. As an example, I used Silo Labs, where I currently farm most of my stablecoin yields. 🔖 Bookmark this + read until the end for a bonus. Just replace [PROTOCOL NAME] with any protocol you want to research. Prompt: "Conduct thorough research on [PROTOCOL NAME] and answer the following questions: - What is the project building? - What problem does it solve, and for whom? - What makes it different from others? - What blockchain is it built on? - Is there a token? What’s its purpose? - How does the protocol work technically? - How does it make money or sustain itself? - What’s the staking model, emissions, burns, and treasury? - Who are the founders, and is the team public and credible? - Who funded it? Who holds most tokens? - Are users, TVL, and volume growing? - How strong is the ecosystem around it? - Who are the main competitors, and how does it compare? - Is the protocol audited? Any past hacks? - What are the best ways to use the protocol (strategies)? - What’s coming next? Key milestones or launches? - What are the biggest risks? - How strong is the community and social traction?" Bonus: Quote this post with a reason why you like INFINIT, and I’ll DM you the full version of the prompt within the next 24 hours.

Keno

15,876 次观看 • 1 年前