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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...

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Nathan Barry

2,103 subscribers

89,040 görüntüleme • 9 ay önce •via X (Twitter)

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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.
0:13

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.

nathan (in sf)

40,440 görüntüleme • 6 ay önce

We took a 30B model and split it in two to write tokens in parallel instead of one at a time. Introducing Nemotron-Labs-TwoTower: a diffusion language model from NVIDIA Research adapted from Nemotron-3-Nano-30B-A3B. Here’s how it works: one half holds the context, the other writes the tokens, with both reusing the pretrained model instead of training a new one from scratch. We found it kept 98.7% of the original model’s quality at 2.42× faster generation.
0:13

We took a 30B model and split it in two to write tokens in parallel instead of one at a time. Introducing Nemotron-Labs-TwoTower: a diffusion language model from NVIDIA Research adapted from Nemotron-3-Nano-30B-A3B. Here’s how it works: one half holds the context, the other writes the tokens, with both reusing the pretrained model instead of training a new one from scratch. We found it kept 98.7% of the original model’s quality at 2.42× faster generation.

NVIDIA AI

758,294 görüntüleme • 21 gün önce

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
0:19

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

AK

41,746 görüntüleme • 3 yıl önce

as an intro to mechanistic interpretability, i decided to look into the formation of induction heads, which are circuits that allow LLMs to perform in-context learning by searching for previous occurrences of a sequence to predict the next token to form these circuits i trained attention-only transformers to repeat varying sequence lengths of random tokens. by randomizing the sequence length, i prevented the models from relying on rote memorization, forcing them to instead develop a generalizable circuit during this dive, i recorded some really cool findings and saw some interesting visual patterns emerging:
0:14

as an intro to mechanistic interpretability, i decided to look into the formation of induction heads, which are circuits that allow LLMs to perform in-context learning by searching for previous occurrences of a sequence to predict the next token to form these circuits i trained attention-only transformers to repeat varying sequence lengths of random tokens. by randomizing the sequence length, i prevented the models from relying on rote memorization, forcing them to instead develop a generalizable circuit during this dive, i recorded some really cool findings and saw some interesting visual patterns emerging:

Xander Chin

26,486 görüntüleme • 6 ay önce

Excited to be partnering with Token Sports Global as their official ambassador! This app is about to change the way we all play and enjoy cricket. 🏏 Follow @tokensportsgbl & use my code SIB for exclusive updates to be in with a chance of winning up to $5000 in tokens.
0:16

Excited to be partnering with Token Sports Global as their official ambassador! This app is about to change the way we all play and enjoy cricket. 🏏 Follow @tokensportsgbl & use my code SIB for exclusive updates to be in with a chance of winning up to $5000 in tokens.

BeefyBotham

23,225 görüntüleme • 1 yıl önce

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?
0:10

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?

Brian Roemmele

72,158 görüntüleme • 2 ay önce

Width and body turn are really important in the backswing. Many ams think they have a full turn only because the club gets to parallel but in reality, they barely get any turn. It’s a lot of “fake” turn. First swing is a demonstration of how it can look like the club gets to parallel but with small body turn. When I “unfold” my arms, the club is only at hip length. The second is the opposite. Some think I’m trying to keep a short backswing when in actuality my body is turning really full with width and no wrist cock. I can get to “parallel” really easily with some wrist cock.
0:14

Width and body turn are really important in the backswing. Many ams think they have a full turn only because the club gets to parallel but in reality, they barely get any turn. It’s a lot of “fake” turn. First swing is a demonstration of how it can look like the club gets to parallel but with small body turn. When I “unfold” my arms, the club is only at hip length. The second is the opposite. Some think I’m trying to keep a short backswing when in actuality my body is turning really full with width and no wrist cock. I can get to “parallel” really easily with some wrist cock.

Michael S. Kim

577,447 görüntüleme • 1 yıl önce

🚨BRAD GARLINGHOUSE CONFIRMS #XRP WILL REACH THE TOP SPOT!! ONCE XRP SURPASSES BTC AND CLAIMS THE #1 POSITION, IT'S EXPECTED TO REACH A MINIMUM OF $29.37 PER COIN!! ‼️ THE LEADING DEFI TOKENS ON THE XRP LEDGER ARE POISED FOR A MASSIVE RISE!! IMAGINE A MARKET CAP OF $1.65 TRILLION! THE CTF TOKEN, WITH ONLY $20 BILLION IN MARKET VALUE, COULD SOAR FROM $0.80 TO $748.50 PER COIN!! CTF TOKEN HAS A CIRCULATING SUPPLY OF JUST 119 MILLION!! HOW CAN A SUPPLY SHORTAGE NOT SPUR A SURGE!!! THE CTF TOKEN IS DESTINED FOR A HUGE UPSWING!!! Trade CTF token here: Trade CTF token on MEXC: Official Website:
0:19

🚨BRAD GARLINGHOUSE CONFIRMS #XRP WILL REACH THE TOP SPOT!! ONCE XRP SURPASSES BTC AND CLAIMS THE #1 POSITION, IT'S EXPECTED TO REACH A MINIMUM OF $29.37 PER COIN!! ‼️ THE LEADING DEFI TOKENS ON THE XRP LEDGER ARE POISED FOR A MASSIVE RISE!! IMAGINE A MARKET CAP OF $1.65 TRILLION! THE CTF TOKEN, WITH ONLY $20 BILLION IN MARKET VALUE, COULD SOAR FROM $0.80 TO $748.50 PER COIN!! CTF TOKEN HAS A CIRCULATING SUPPLY OF JUST 119 MILLION!! HOW CAN A SUPPLY SHORTAGE NOT SPUR A SURGE!!! THE CTF TOKEN IS DESTINED FOR A HUGE UPSWING!!! Trade CTF token here: Trade CTF token on MEXC: Official Website:

JackTheRippler ©️

156,114 görüntüleme • 1 yıl önce

Vintage Camera Experts 🎥 We appreciate all the camera operator experts and enthusiast that have been reaching out. Ideally, we would love a robust army of imagers helping us gather as much imagery as possible - from different angles, distances, and altitudes with a variety of equipment. Two of the primary challenges for our camera operators right now are digital interference and the extreme speed and movement of the objects. For the former, there seems to be something about the phenomenon that interferes with digital camera technology. Because of this we are looking to also add a vintage camera suite. There’s lots of evidence that suggests analog imagery of the phenomenon produces better results in some circumstances. In regards to the speed and movement challenge, please see the below video clip from episode two. This is what we are dealing with. Note the speed and movement of the Tic Tacs at 100% vs. 15% . If you feel you’re up to the challenge and have something of value to add, please continue to reach out and offer solutions. 🙏🇺🇸
0:10

Vintage Camera Experts 🎥 We appreciate all the camera operator experts and enthusiast that have been reaching out. Ideally, we would love a robust army of imagers helping us gather as much imagery as possible - from different angles, distances, and altitudes with a variety of equipment. Two of the primary challenges for our camera operators right now are digital interference and the extreme speed and movement of the objects. For the former, there seems to be something about the phenomenon that interferes with digital camera technology. Because of this we are looking to also add a vintage camera suite. There’s lots of evidence that suggests analog imagery of the phenomenon produces better results in some circumstances. In regards to the speed and movement challenge, please see the below video clip from episode two. This is what we are dealing with. Note the speed and movement of the Tic Tacs at 100% vs. 15% . If you feel you’re up to the challenge and have something of value to add, please continue to reach out and offer solutions. 🙏🇺🇸

jakebarber

76,502 görüntüleme • 1 yıl önce

PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start
0:19

PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start

Robin Delta

15,729 görüntüleme • 1 ay önce

alright we are not announcing this publicly on AssetDash until tomorrow but $UFD is a perfect case study so here we go many of you are using Whale Watch to discover new coins daily (thank you) and we wanted to release a feature that allows YOU to sell the AssetDash audience on your favorite memes now - anyone on AssetDash can upgrade a token to Pro and add two key aspects to the token page: 1. The Bull Case - 3 to 4 bullet points on why you are bullish on the meme plus additional context 2. The Meme Gallery - a gallery of pictures of the meme TLDR: this is a great way to work for your bags! Upgrade your favorite token here ->
0:15

alright we are not announcing this publicly on AssetDash until tomorrow but $UFD is a perfect case study so here we go many of you are using Whale Watch to discover new coins daily (thank you) and we wanted to release a feature that allows YOU to sell the AssetDash audience on your favorite memes now - anyone on AssetDash can upgrade a token to Pro and add two key aspects to the token page: 1. The Bull Case - 3 to 4 bullet points on why you are bullish on the meme plus additional context 2. The Meme Gallery - a gallery of pictures of the meme TLDR: this is a great way to work for your bags! Upgrade your favorite token here ->

Matias | Moby 🐳

71,873 görüntüleme • 1 yıl önce

My dual RTX PRO 6000 setup is currently training a Draft model for Qwen 3.6 27B! 🔥 I'm taking the paper DeepSeek dropped on 6/26 and going for a super ambitious application to the 27B scale. Thanks to my homelab, I was able to dive straight in — I read the paper and immediately started experimenting. The amount I've learned has been insane: - How memory bandwidth bottlenecks speed and clever ways to hack around it - Methods to train the draft model and boost its accuracy - Mechanisms to reference tokens all the way back to the previous one to skyrocket draft acceptance rates - The impact of Attention vs. GateDeltaNet on speculative decoding performance and how to handle those differences - The unique approaches and trade-offs of MTP, Dflash, JetSpec, and DSpark I could go on forever, but just from speculative decoding alone I've learned so much. The 27B architecture feels way more DSpark-native than JetSpec, so once draft training finishes, I'm going all-in with DSpark! My goal is to beat existing speculative decoding speeds outright — no task-specific shortcuts or cheating, pure general improvement. If you're into this kind of research, I'd love to hear your thoughts, impressions, and any suggestions — please reply! 🚀
0:17

My dual RTX PRO 6000 setup is currently training a Draft model for Qwen 3.6 27B! 🔥 I'm taking the paper DeepSeek dropped on 6/26 and going for a super ambitious application to the 27B scale. Thanks to my homelab, I was able to dive straight in — I read the paper and immediately started experimenting. The amount I've learned has been insane: - How memory bandwidth bottlenecks speed and clever ways to hack around it - Methods to train the draft model and boost its accuracy - Mechanisms to reference tokens all the way back to the previous one to skyrocket draft acceptance rates - The impact of Attention vs. GateDeltaNet on speculative decoding performance and how to handle those differences - The unique approaches and trade-offs of MTP, Dflash, JetSpec, and DSpark I could go on forever, but just from speculative decoding alone I've learned so much. The 27B architecture feels way more DSpark-native than JetSpec, so once draft training finishes, I'm going all-in with DSpark! My goal is to beat existing speculative decoding speeds outright — no task-specific shortcuts or cheating, pure general improvement. If you're into this kind of research, I'd love to hear your thoughts, impressions, and any suggestions — please reply! 🚀

Hikari∣LocalLLM⚡

56,314 görüntüleme • 25 gün önce

🚨WE ARE BUYING BACK! We’re thrilled to share a major milestone that reflects our confidence in the project and our deep commitment to our community: 🔁 Since June 19, a total of 300.000 GAG Tokens have been successfully bought back! This is a strong signal of our dedication to the GAG Token ecosystem and our long-term value creation strategy. But this is just the beginning. 🚀 MONTHLY GAG TOKEN BUYBACK BEGINS! After months of careful planning, we’re proud to launch a powerful new initiative to further empower our community: Adgager is officially starting a monthly GAG Token BUYBACK program! 🔍 What does this mean? 📈 Constant demand 💎 Positive impact on token value 🤝 A clear show of loyalty to our community This is more than a financial strategy It’s a reflection of our vision, our dedication, and our respect for you. We’ve been building GAG Token together since day one. Now, we’re taking it to the next level with a sustainable and transparent approach. ✨ Stronger together. Building the future side by side. Stay tuned This is just the beginning! 🚀 #GAGToken $GAG #BuyBack
0:13

🚨WE ARE BUYING BACK! We’re thrilled to share a major milestone that reflects our confidence in the project and our deep commitment to our community: 🔁 Since June 19, a total of 300.000 GAG Tokens have been successfully bought back! This is a strong signal of our dedication to the GAG Token ecosystem and our long-term value creation strategy. But this is just the beginning. 🚀 MONTHLY GAG TOKEN BUYBACK BEGINS! After months of careful planning, we’re proud to launch a powerful new initiative to further empower our community: Adgager is officially starting a monthly GAG Token BUYBACK program! 🔍 What does this mean? 📈 Constant demand 💎 Positive impact on token value 🤝 A clear show of loyalty to our community This is more than a financial strategy It’s a reflection of our vision, our dedication, and our respect for you. We’ve been building GAG Token together since day one. Now, we’re taking it to the next level with a sustainable and transparent approach. ✨ Stronger together. Building the future side by side. Stay tuned This is just the beginning! 🚀 #GAGToken $GAG #BuyBack

GAG Token

26,999 görüntüleme • 11 ay önce

A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.
0:17

A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

ALCHEMIST AI 🔮

48,721 görüntüleme • 1 yıl önce

🔥 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
0:16

🔥 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 görüntüleme • 2 yıl önce

The one thing we absolutely know with certainty about the Richat Structure is that there was an incredible amount of human activity here from the dawn of toolmaking and tool use itself. The Acheulean Hand Axe is the second tool that humans ever made and this technology spread throughout Africa, Europe and Asia while no definitive evidence exists that this technology ever made it to the Americas. I think this is evidence that this technology was shared and disseminated intentionally, that neighboring humans didn’t come up with this independently on their own but this was a legacy, an institution, a skill and an industry that was shared and taught. It is perhaps the earliest evidence we have of widespread cultural diffusion throughout Africa, Europe and Asia. And yes, it’s right there at the Richat Structure which leads me to question the role that the Richat played to humans half a million years ago. It seems like that’s where we have to start with the Richat.
0:13

The one thing we absolutely know with certainty about the Richat Structure is that there was an incredible amount of human activity here from the dawn of toolmaking and tool use itself. The Acheulean Hand Axe is the second tool that humans ever made and this technology spread throughout Africa, Europe and Asia while no definitive evidence exists that this technology ever made it to the Americas. I think this is evidence that this technology was shared and disseminated intentionally, that neighboring humans didn’t come up with this independently on their own but this was a legacy, an institution, a skill and an industry that was shared and taught. It is perhaps the earliest evidence we have of widespread cultural diffusion throughout Africa, Europe and Asia. And yes, it’s right there at the Richat Structure which leads me to question the role that the Richat played to humans half a million years ago. It seems like that’s where we have to start with the Richat.

Archaic Lens

35,584 görüntüleme • 8 ay önce

XDC Network is Now Omnichain with LayerZero’s OFT Standard We’re excited to share that the XDC token is now live as an Omnichain Fungible Token (OFT) via LayerZero , enabling seamless movement of assets across multiple blockchains without the need for wrapping or intermediaries. Here’s how it works: tokens are locked or burned on the source chain, a message is securely sent via LayerZero, and the same number of tokens are minted or unlocked on the destination chain. The result? A single, unified token supply across all supported networks. This integration opens up powerful new possibilities for developers and enterprises to build interoperable applications with greater trust, faster execution, and simplified cross-chain communication. We invite builders in the Real World Asset (RWA), DeFi, tokenization, and payment infrastructure to leverage XDC’s interoperable architecture to scale globally and build cross-chain solutions. #XDCNetwork #LayerZero #BlockchainInnovation #CrossChain #Omnichain #Web3 #Tokenization #RWA #DeFi #Interoperability
0:10

XDC Network is Now Omnichain with LayerZero’s OFT Standard We’re excited to share that the XDC token is now live as an Omnichain Fungible Token (OFT) via LayerZero , enabling seamless movement of assets across multiple blockchains without the need for wrapping or intermediaries. Here’s how it works: tokens are locked or burned on the source chain, a message is securely sent via LayerZero, and the same number of tokens are minted or unlocked on the destination chain. The result? A single, unified token supply across all supported networks. This integration opens up powerful new possibilities for developers and enterprises to build interoperable applications with greater trust, faster execution, and simplified cross-chain communication. We invite builders in the Real World Asset (RWA), DeFi, tokenization, and payment infrastructure to leverage XDC’s interoperable architecture to scale globally and build cross-chain solutions. #XDCNetwork #LayerZero #BlockchainInnovation #CrossChain #Omnichain #Web3 #Tokenization #RWA #DeFi #Interoperability

XDC Network

197,583 görüntüleme • 1 yıl önce

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
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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 görüntüleme • 2 yıl önce