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𝑰 𝒄𝒂𝒏 𝒉𝒆𝒂𝒓 𝒚𝒐𝒖𝒓 𝒕𝒉𝒐𝒖𝒈𝒉𝒕𝒔 𝒍𝒊𝒌𝒆 𝒂 𝒎𝒆𝒍𝒐𝒅𝒚 - 𝑷𝒖𝒔𝒉 𝒕𝒉𝒆 𝒄𝒍𝒐𝒖𝒅𝒔 𝒂𝒘𝒂𝒚... Aeril and animation @ me Model and rigging by mmdrawstuff ➡️ Denfur 🎵Kali Uchis – telepatía 🎵(kind of a low key banger) 1/2

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LaChance -🐴 🦊

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20,016 görüntüleme • 1 yıl önce •via X (Twitter)

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I cant bear it anymore. *continues to bear* Aeril and animation @ me Model done by mmdrawstuff ➡️ Denfur
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I cant bear it anymore. *continues to bear* Aeril and animation @ me Model done by mmdrawstuff ➡️ Denfur

LaChance -🐴 🦊

49,332 görüntüleme • 9 ay önce

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.
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STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.

AK

144,811 görüntüleme • 3 yıl önce

RHP Justin Mitrovich (Elon Baseball) is one mid-major arm to follow this season. Was excellent as a true Freshman and pitched his way to a 3.68 ERA with 66 Ks to 21 BB across 63.2 IP. Last spring he worked a 5.06 ERA and collected 96 Ks against 30 BB in 80 IP. Mitrovich also showed positive flashes on the Cape this summer and notched 17 Ks in as many IP. Mitrovich has an athletic frame at 6'3" and 200-lbs. Room to fill out physically. Worked exclusively out of the stretch during the spring, but went back to the windup this summer. Works on the 1B side of the rubber, starts his motion with a small side step then gathers himself. Leads into a high lift, and the rest of his operation is up-tempo. Plenty of depth on his long arm stroke, attacks from a three-quarters slot with present arm speed. Some effort. Mitrovich's FB sits in the 91-94 range, but has been up to 96 with some life in the top-1/2 of the zone. Figuring his heater out is going to be the key for him going forward. Threw mostly 4-seamers during the spring, but went 2-seam heavy during the summer and still generated a whiff rate < 20%. A handful of the latter flashed late arm side life, particularly against LHH. Both play well in the top-1/2 of the zone. Needs to iron out the shape and maximize it. Mitrovich's bread-and-butter offerings are his secondaries. His low-80s CH is one of the best of its kind in the college ranks. Averaged over 12 MPH off his FB last spring and is a legit swing-and-miss pitch against both LHH and RHH. Throws it with conviction and will use it in any count. Consistently flashes fade to the arm side as well as ample late tumbling life. True "falling off the table" look. Mitrovich's feel for the pitch is highly advanced, and last spring it generated a 52% whiff rate, 47% chase rate and held opposing hitters to a .198 average. Comfortably a 60. Rounds out his arsenal with a low-to-mid-80s SL that is a particular weapon against righthanded hitters. Gyro look that's not big in shape, but will flash some lateral glove-side life with late bite. Flashed above-average at times last spring and garnered a 45% miss rate. Gets whiffs both in and out of the zone. Mitrovich is a strike-thrower who looks the part of a starter at the next level. As mentioned, the key with him is developing the FB. Fits as a 5th-7th rounder for me right now. (📽️: Elon Baseball)
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RHP Justin Mitrovich (Elon Baseball) is one mid-major arm to follow this season. Was excellent as a true Freshman and pitched his way to a 3.68 ERA with 66 Ks to 21 BB across 63.2 IP. Last spring he worked a 5.06 ERA and collected 96 Ks against 30 BB in 80 IP. Mitrovich also showed positive flashes on the Cape this summer and notched 17 Ks in as many IP. Mitrovich has an athletic frame at 6'3" and 200-lbs. Room to fill out physically. Worked exclusively out of the stretch during the spring, but went back to the windup this summer. Works on the 1B side of the rubber, starts his motion with a small side step then gathers himself. Leads into a high lift, and the rest of his operation is up-tempo. Plenty of depth on his long arm stroke, attacks from a three-quarters slot with present arm speed. Some effort. Mitrovich's FB sits in the 91-94 range, but has been up to 96 with some life in the top-1/2 of the zone. Figuring his heater out is going to be the key for him going forward. Threw mostly 4-seamers during the spring, but went 2-seam heavy during the summer and still generated a whiff rate < 20%. A handful of the latter flashed late arm side life, particularly against LHH. Both play well in the top-1/2 of the zone. Needs to iron out the shape and maximize it. Mitrovich's bread-and-butter offerings are his secondaries. His low-80s CH is one of the best of its kind in the college ranks. Averaged over 12 MPH off his FB last spring and is a legit swing-and-miss pitch against both LHH and RHH. Throws it with conviction and will use it in any count. Consistently flashes fade to the arm side as well as ample late tumbling life. True "falling off the table" look. Mitrovich's feel for the pitch is highly advanced, and last spring it generated a 52% whiff rate, 47% chase rate and held opposing hitters to a .198 average. Comfortably a 60. Rounds out his arsenal with a low-to-mid-80s SL that is a particular weapon against righthanded hitters. Gyro look that's not big in shape, but will flash some lateral glove-side life with late bite. Flashed above-average at times last spring and garnered a 45% miss rate. Gets whiffs both in and out of the zone. Mitrovich is a strike-thrower who looks the part of a starter at the next level. As mentioned, the key with him is developing the FB. Fits as a 5th-7th rounder for me right now. (📽️: Elon Baseball)

Peter Flaherty III

15,080 görüntüleme • 1 yıl önce

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!
0:12

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

179,451 görüntüleme • 2 ay önce

What is Hedera Hashgraph? Unlike traditional blockchains, Hedera (Hedera) does not organize transactions into a single chain of blocks. Instead, it uses a different distributed ledger structure called Hashgraph, designed to reach consensus quickly without proof-of-work mining. Here’s how it works: (1) It uses “gossip about gossip.” Nodes continuously share transactions with other nodes. Each message also contains information about previous messages, creating a history of how information moved through the network. This produces a directed acyclic graph, or DAG, rather than a traditional blockchain. (2) It uses virtual voting. Nodes do not need to send separate votes across the network. Instead, they can determine how other nodes would vote by examining the shared history. This reduces communication overhead while helping nodes establish transaction order. (3) It targets fast and final consensus. Hedera uses an asynchronous Byzantine Fault Tolerant, or aBFT, consensus model. Once consensus is reached, transactions achieve finality rather than remaining subject to the probabilistic settlement associated with some proof-of-work systems. (4) HBAR powers the network. HBAR is Hedera’s native cryptocurrency. It is used to pay network fees and plays a role in staking and network services. (5) Hedera is not simply another permissionless blockchain. Hedera is currently a public network with permissioned nodes operated through the Hedera Council. The Council governs key aspects of the network, while the underlying technology and network services are open source. (6) A path toward permissionless nodes is planned, but not live yet. Hedera has published a roadmap to gradually open up node operation beyond Council members, eventually moving toward a fully permissionless model where anyone can run a consensus node by staking HBAR. As of now, that transition has not been implemented, node operation is still limited to Council members. The bigger idea behind Hedera is straightforward. Instead of relying on blocks, miners, and proof-of-work, it uses Hashgraph consensus to coordinate transactions. That architecture is designed to deliver fast finality, high throughput, predictable fees, and low energy consumption. And that is what makes Hedera different from a conventional blockchain.
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What is Hedera Hashgraph? Unlike traditional blockchains, Hedera (Hedera) does not organize transactions into a single chain of blocks. Instead, it uses a different distributed ledger structure called Hashgraph, designed to reach consensus quickly without proof-of-work mining. Here’s how it works: (1) It uses “gossip about gossip.” Nodes continuously share transactions with other nodes. Each message also contains information about previous messages, creating a history of how information moved through the network. This produces a directed acyclic graph, or DAG, rather than a traditional blockchain. (2) It uses virtual voting. Nodes do not need to send separate votes across the network. Instead, they can determine how other nodes would vote by examining the shared history. This reduces communication overhead while helping nodes establish transaction order. (3) It targets fast and final consensus. Hedera uses an asynchronous Byzantine Fault Tolerant, or aBFT, consensus model. Once consensus is reached, transactions achieve finality rather than remaining subject to the probabilistic settlement associated with some proof-of-work systems. (4) HBAR powers the network. HBAR is Hedera’s native cryptocurrency. It is used to pay network fees and plays a role in staking and network services. (5) Hedera is not simply another permissionless blockchain. Hedera is currently a public network with permissioned nodes operated through the Hedera Council. The Council governs key aspects of the network, while the underlying technology and network services are open source. (6) A path toward permissionless nodes is planned, but not live yet. Hedera has published a roadmap to gradually open up node operation beyond Council members, eventually moving toward a fully permissionless model where anyone can run a consensus node by staking HBAR. As of now, that transition has not been implemented, node operation is still limited to Council members. The bigger idea behind Hedera is straightforward. Instead of relying on blocks, miners, and proof-of-work, it uses Hashgraph consensus to coordinate transactions. That architecture is designed to deliver fast finality, high throughput, predictable fees, and low energy consumption. And that is what makes Hedera different from a conventional blockchain.

BSCN

18,168 görüntüleme • 17 gün önce

GPT image+Seedance 2.0 Video Prompt — 15 Seconds | 8 Scenes | 9:16 Create a cute, hyper-detailed claymation stop-motion video showing the step-by-step creation of a miniature traditional Phinisi sailing ship from colorful modeling clay. Keep the visual style consistent with the reference image: handcrafted clay texture, miniature diorama, bright tropical ocean, soft cinematic lighting, colorful and satisfying visuals. Scene 1 — 0:00–0:02 Top-down close-up of colorful clay pieces, wooden-style clay tools, tiny ropes and miniature ship accessories arranged neatly on a crafting table. A pair of small clay hands begins preparing the materials. Slow camera push-in. Scene 2 — 0:02–0:04 Close-up of hands shaping the brown clay into the curved wooden hull of the miniature Phinisi ship. Show fingerprints and realistic clay texture. Smooth stop-motion movements. Scene 3 — 0:04–0:06 The hull becomes more detailed as tiny windows, railings, decorative patterns and wooden planks are carefully added. Camera slowly rotates around the model. Scene 4 — 0:06–0:08 Two tall masts are placed precisely onto the ship. Tiny ropes and rigging appear piece by piece. Close-up macro shot emphasizing the craftsmanship. Scene 5 — 0:08–0:10 Cream-colored clay sails unfold and attach to the masts. The complete miniature ship begins to take shape. Camera changes to a low cinematic angle. Scene 6 — 0:10–0:12 Tiny cabin, deck details, windows, ropes and accessories are added. The camera moves smoothly along the side of the ship, revealing the intricate handcrafted details. Scene 7 — 0:12–0:14 The finished Phinisi ship is placed on a miniature turquoise ocean made from textured clay. Small waves move around the hull while the ship gently rocks. Scene 8 — 0:14–0:15 Wide cinematic final shot: the completed miniature Phinisi sailing peacefully across the bright blue clay ocean under a sunny tropical sky, with small clouds, palm trees and colorful scenery in the background. The ship gently moves with the waves. Style: premium clay animation, stop-motion, handcrafted miniature, pastel clay texture, ultra-detailed, colorful, cinematic lighting, macro photography, shallow depth of field, smooth camera movement, satisfying transformations, realistic miniature scale. Audio: soft clay-working sounds, gentle tapping and sculpting sounds, subtle ocean waves, light tropical ambience and soft cheerful background music. Negative Prompt: blurry details, distorted ship, extra masts, deformed hands, flickering, inconsistent objects, realistic human proportions, text overlays, watermark, logo, sudden camera movement, low-quality textures. Seedance 2.0
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GPT image+Seedance 2.0 Video Prompt — 15 Seconds | 8 Scenes | 9:16 Create a cute, hyper-detailed claymation stop-motion video showing the step-by-step creation of a miniature traditional Phinisi sailing ship from colorful modeling clay. Keep the visual style consistent with the reference image: handcrafted clay texture, miniature diorama, bright tropical ocean, soft cinematic lighting, colorful and satisfying visuals. Scene 1 — 0:00–0:02 Top-down close-up of colorful clay pieces, wooden-style clay tools, tiny ropes and miniature ship accessories arranged neatly on a crafting table. A pair of small clay hands begins preparing the materials. Slow camera push-in. Scene 2 — 0:02–0:04 Close-up of hands shaping the brown clay into the curved wooden hull of the miniature Phinisi ship. Show fingerprints and realistic clay texture. Smooth stop-motion movements. Scene 3 — 0:04–0:06 The hull becomes more detailed as tiny windows, railings, decorative patterns and wooden planks are carefully added. Camera slowly rotates around the model. Scene 4 — 0:06–0:08 Two tall masts are placed precisely onto the ship. Tiny ropes and rigging appear piece by piece. Close-up macro shot emphasizing the craftsmanship. Scene 5 — 0:08–0:10 Cream-colored clay sails unfold and attach to the masts. The complete miniature ship begins to take shape. Camera changes to a low cinematic angle. Scene 6 — 0:10–0:12 Tiny cabin, deck details, windows, ropes and accessories are added. The camera moves smoothly along the side of the ship, revealing the intricate handcrafted details. Scene 7 — 0:12–0:14 The finished Phinisi ship is placed on a miniature turquoise ocean made from textured clay. Small waves move around the hull while the ship gently rocks. Scene 8 — 0:14–0:15 Wide cinematic final shot: the completed miniature Phinisi sailing peacefully across the bright blue clay ocean under a sunny tropical sky, with small clouds, palm trees and colorful scenery in the background. The ship gently moves with the waves. Style: premium clay animation, stop-motion, handcrafted miniature, pastel clay texture, ultra-detailed, colorful, cinematic lighting, macro photography, shallow depth of field, smooth camera movement, satisfying transformations, realistic miniature scale. Audio: soft clay-working sounds, gentle tapping and sculpting sounds, subtle ocean waves, light tropical ambience and soft cheerful background music. Negative Prompt: blurry details, distorted ship, extra masts, deformed hands, flickering, inconsistent objects, realistic human proportions, text overlays, watermark, logo, sudden camera movement, low-quality textures. Seedance 2.0

Soaima

13,089 görüntüleme • 1 ay önce

Wtf am I supposed to do with this Dave (Dave Meltzer) this is so mind-numbingly dumb it actually makes me want to quit X. You almost won. I genuinely feel that sorry for you. Because what’s the fucking point. I'm a Philadelphia Eagles fan. I played Division 1 football. I wasn't good at that level, but I was good enough to play at that level and against competition that was way over my head. Watching Eagles tape is kind of like my palette cleanser. It’s a hobby. Because everything else I enjoy somehow touches Hollywood and entertainment. X’s and O’s doesn’t touch my work. I can just be a fan and not somehow make it about my work. We’re 7-2 and it’s somehow still miserable to watch. We’re winning in spite of the QB. Sirianni’s boxed in. He can’t run what he wants because Hurts can’t go through progressions. We have 2 elite receivers and who are both fucking miserable because they are elite, and Hurts is an awful QB. That 4th and 6 with :30 left makes zero fucking sense unless you understand it in this context. That was frustration. It was an indictment on what they actually believe about Jalen Hurts, and Hurts couldn’t even make that throw. It almost cost the Eagles the game. That was Sirianni saying “I know what the fuck I'm doing!" Yeah, me too, Coach. I understand that Dave and Bryan make shit up. Most of you on X either don’t believe I am who I say I am, or you do and I enjoy writing really long dissertations — not because I give a fuck about arguing with Dave Meltzer. He is so obviously, to me, without any credibility. I enjoy educating and informing curious fans and giving actual credible rebuttals to what is obviously just absolute trash takes, uneducated opinions, and dishonest framing from Dave, who has absolutely destroyed his 40 years of reputation and the real respect that he once had from people that matter. I don’t even take any joy in writing this. Dave, you threw away your life’s work. You threw away 40 years of your life’s work. I get that you don’t understand the nuances of media and the economics of the business in 2025. That’s OK. Very few people do. I am amused that you double down on everything that you do not know. Anyone with a functioning brain sees right through it. It’s ok. I get a kick out of the fact that you ignore me, because if you were to engage me you’d only amplify how out of your depth you are, and you’re holding on tightly to the $14.99 subs model. I get it. I believe you are so narcissistic and myopic in your need to a) make a living and b) defend this whole narcissistic construct about the NWHOF You wear your Frank Deford idolization on your sleeve, which again… fine. Your takes hurt wrestlers. You are not honest. You very clearly have an agenda. You’ve sold 40 years of credibility for this. That’s not a new take. This… I’m not even intellectually stimulated about how can I take your 4th-grade, 1994, faux subject-matter-expert take, riff on your idiocy, and offer curious fans an actual fucking take. “Drew’s going to be gone for a while.” “He’s doing a movie.” You both said this shit so confidently. The movie doesn’t shoot until April '26. Cavill tore his ACL. It is super fucking public info. I am a real agent. This is the first time I’ve actually kind of agreed with some of my clients or coworkers or media people on X: “Nick, why do you bother? This is beneath you.” Yes, it’s indescribable how beneath me refuting you, even acknowledging your existence, is. It’s like a war crime. It’s absolutely insane that you are considered credible by anyone at all. God bless you. You have too much influence, though, with a few key people, and you are giving them bad advice bc you have no fucking clue what you are talking about and... “Drew’s going to be gone for a while.” You are a fraud. Laughably so. I am going to go back to picking apart the Eagles All-22. The movie shoots in April 2026 and I've worked the schedule out so it won't affect WWE creative.
0:12

Wtf am I supposed to do with this Dave (Dave Meltzer) this is so mind-numbingly dumb it actually makes me want to quit X. You almost won. I genuinely feel that sorry for you. Because what’s the fucking point. I'm a Philadelphia Eagles fan. I played Division 1 football. I wasn't good at that level, but I was good enough to play at that level and against competition that was way over my head. Watching Eagles tape is kind of like my palette cleanser. It’s a hobby. Because everything else I enjoy somehow touches Hollywood and entertainment. X’s and O’s doesn’t touch my work. I can just be a fan and not somehow make it about my work. We’re 7-2 and it’s somehow still miserable to watch. We’re winning in spite of the QB. Sirianni’s boxed in. He can’t run what he wants because Hurts can’t go through progressions. We have 2 elite receivers and who are both fucking miserable because they are elite, and Hurts is an awful QB. That 4th and 6 with :30 left makes zero fucking sense unless you understand it in this context. That was frustration. It was an indictment on what they actually believe about Jalen Hurts, and Hurts couldn’t even make that throw. It almost cost the Eagles the game. That was Sirianni saying “I know what the fuck I'm doing!" Yeah, me too, Coach. I understand that Dave and Bryan make shit up. Most of you on X either don’t believe I am who I say I am, or you do and I enjoy writing really long dissertations — not because I give a fuck about arguing with Dave Meltzer. He is so obviously, to me, without any credibility. I enjoy educating and informing curious fans and giving actual credible rebuttals to what is obviously just absolute trash takes, uneducated opinions, and dishonest framing from Dave, who has absolutely destroyed his 40 years of reputation and the real respect that he once had from people that matter. I don’t even take any joy in writing this. Dave, you threw away your life’s work. You threw away 40 years of your life’s work. I get that you don’t understand the nuances of media and the economics of the business in 2025. That’s OK. Very few people do. I am amused that you double down on everything that you do not know. Anyone with a functioning brain sees right through it. It’s ok. I get a kick out of the fact that you ignore me, because if you were to engage me you’d only amplify how out of your depth you are, and you’re holding on tightly to the $14.99 subs model. I get it. I believe you are so narcissistic and myopic in your need to a) make a living and b) defend this whole narcissistic construct about the NWHOF You wear your Frank Deford idolization on your sleeve, which again… fine. Your takes hurt wrestlers. You are not honest. You very clearly have an agenda. You’ve sold 40 years of credibility for this. That’s not a new take. This… I’m not even intellectually stimulated about how can I take your 4th-grade, 1994, faux subject-matter-expert take, riff on your idiocy, and offer curious fans an actual fucking take. “Drew’s going to be gone for a while.” “He’s doing a movie.” You both said this shit so confidently. The movie doesn’t shoot until April '26. Cavill tore his ACL. It is super fucking public info. I am a real agent. This is the first time I’ve actually kind of agreed with some of my clients or coworkers or media people on X: “Nick, why do you bother? This is beneath you.” Yes, it’s indescribable how beneath me refuting you, even acknowledging your existence, is. It’s like a war crime. It’s absolutely insane that you are considered credible by anyone at all. God bless you. You have too much influence, though, with a few key people, and you are giving them bad advice bc you have no fucking clue what you are talking about and... “Drew’s going to be gone for a while.” You are a fraud. Laughably so. I am going to go back to picking apart the Eagles All-22. The movie shoots in April 2026 and I've worked the schedule out so it won't affect WWE creative.

Nick LoPiccolo

35,368 görüntüleme • 10 ay önce

introducing a new, very fun, LLM benchmark- the Game-of-Life Bench! the rules are simple: given an 8x8 grid following Conway's game of life rules, the goal is to create an initial pattern with at most 32 cells that can last the longest number of turns before dying/repeating. some results to highlight (with caveats detailed below): - gpt 5.1 lasts the longest with a 106 step run - claude models are really bad at this! they refuse to reason about this task and score < 25 points - deepseek r1 is the best open model with 102 steps. why? because i wanted to create a benchmark that has (i think) no practicality, but is still fun to look at, cheap, and still measures something interesting. i also am a big fan of the game of life. its absurdly simple rules leading to intractability is extremely cool to me. also, i saw a lot of work with LLMs trying to "predict" the next state in Conway's game of life, I think game-of-life bench is more fun because it's pretty open ended and only asks the LLM for the initial state. I also think this could be an RL env? but idk why you would ever train on this task haha i don't think this is a "serious" benchmark because it doesnt measure anything practical, but i still think it's a hard benchmark exactly because you can't predict what happens with your initial state many turns into the future; this is why i was initially expecting all LLMs to be bad at it, but turns out, some are clearly better than the others (the ordering may surprise you!) reminder: this is still a work-in-progress; (1) i am gpu-poor so could only do 10 runs for each model, even though total running cost is relatively low. maybe with some more credits i can run more seeds for each model. (2) i handpicked models which i think are at the frontier right now, plus some others that were on my mind. so, if you'd like to see a model on here, let me know. (3) i currently only do an 8x8 grid because i thought that by itself would be pretty hard for current LLMs, but of course we can increase grid sizes! (4) the coolest thing is, i dont think we can calculate the max possible number of states (yay undecidability!) you can go without repeating, so this is essentially a no-ceiling task, which is pretty cool! again, i did this mostly out of a desire to make LLMs do something fun. if this keeps me entertained for a few more days, i'd likely release a blog post on it. if it keeps me entertained for a week (and someone sponsors me), i'll put more work into it :P lastly, this is fully open sourced, so feel free to run this on your own!
0:19

introducing a new, very fun, LLM benchmark- the Game-of-Life Bench! the rules are simple: given an 8x8 grid following Conway's game of life rules, the goal is to create an initial pattern with at most 32 cells that can last the longest number of turns before dying/repeating. some results to highlight (with caveats detailed below): - gpt 5.1 lasts the longest with a 106 step run - claude models are really bad at this! they refuse to reason about this task and score < 25 points - deepseek r1 is the best open model with 102 steps. why? because i wanted to create a benchmark that has (i think) no practicality, but is still fun to look at, cheap, and still measures something interesting. i also am a big fan of the game of life. its absurdly simple rules leading to intractability is extremely cool to me. also, i saw a lot of work with LLMs trying to "predict" the next state in Conway's game of life, I think game-of-life bench is more fun because it's pretty open ended and only asks the LLM for the initial state. I also think this could be an RL env? but idk why you would ever train on this task haha i don't think this is a "serious" benchmark because it doesnt measure anything practical, but i still think it's a hard benchmark exactly because you can't predict what happens with your initial state many turns into the future; this is why i was initially expecting all LLMs to be bad at it, but turns out, some are clearly better than the others (the ordering may surprise you!) reminder: this is still a work-in-progress; (1) i am gpu-poor so could only do 10 runs for each model, even though total running cost is relatively low. maybe with some more credits i can run more seeds for each model. (2) i handpicked models which i think are at the frontier right now, plus some others that were on my mind. so, if you'd like to see a model on here, let me know. (3) i currently only do an 8x8 grid because i thought that by itself would be pretty hard for current LLMs, but of course we can increase grid sizes! (4) the coolest thing is, i dont think we can calculate the max possible number of states (yay undecidability!) you can go without repeating, so this is essentially a no-ceiling task, which is pretty cool! again, i did this mostly out of a desire to make LLMs do something fun. if this keeps me entertained for a few more days, i'd likely release a blog post on it. if it keeps me entertained for a week (and someone sponsors me), i'll put more work into it :P lastly, this is fully open sourced, so feel free to run this on your own!

Akshit

13,775 görüntüleme • 6 ay önce

Today we're launching Chipped 💅 [link in second tweet] 6 months ago we approached Shahanaj Akter with an idea. I wanted to turn my NFC chipped manicure into a set of press-ons so that I didn't have to spend hours doing it myself. Simple idea. Yeah not really, even though this idea has been around for a few years and so many great artists have been manually doing this, no one had worked out how to manufacture it. 6 weeks and 30 prototypes later we had it, she was a little rough around the edges but she worked, you could program it to anything with a URL. But linking to socials wasn’t enough, so we teamed with disco to build proof of irl schemas, so that when you tap it, it creates a decentralised friends list, stored privately, offchain but secured by your private key. During EF events we chipped over 200 crypto natives and the response was incredible with over 1000 data points being created in the first few days and 1 million impressions on twitter over the duration of the campaign. So, today is a fun day. We've released pre-sale. The colour drop is the result of governance and a community vote, but this is just the beginning for the colours and designs we plan on creating. For $49, the set of 22 press-ons comes in an aluminium matchbox with 2 NFC chip nails, and nail glue, so you can set your nails up in a few minutes literally anywhere [my salon of choice tends to be the airport lounge between conferences.] The nails come set up to your profile, which is kind of like a linktree [but cuter], where you’re able to add the socials you want it to link to, whether that's just your socials with the choice to add proof of irl credentials. It’s literally the memorable way to network. For guys who think this is ‘not for me’, consider it the easiest way to onboard your mother, your sisters and your girlfriend into web3. I wanted to create a product that eclipses the need to understand blockchain and makes it fun to use. I've dropped the link below and thank you to everyone who has been on this journey with us. xoxo
0:18

Today we're launching Chipped 💅 [link in second tweet] 6 months ago we approached Shahanaj Akter with an idea. I wanted to turn my NFC chipped manicure into a set of press-ons so that I didn't have to spend hours doing it myself. Simple idea. Yeah not really, even though this idea has been around for a few years and so many great artists have been manually doing this, no one had worked out how to manufacture it. 6 weeks and 30 prototypes later we had it, she was a little rough around the edges but she worked, you could program it to anything with a URL. But linking to socials wasn’t enough, so we teamed with disco to build proof of irl schemas, so that when you tap it, it creates a decentralised friends list, stored privately, offchain but secured by your private key. During EF events we chipped over 200 crypto natives and the response was incredible with over 1000 data points being created in the first few days and 1 million impressions on twitter over the duration of the campaign. So, today is a fun day. We've released pre-sale. The colour drop is the result of governance and a community vote, but this is just the beginning for the colours and designs we plan on creating. For $49, the set of 22 press-ons comes in an aluminium matchbox with 2 NFC chip nails, and nail glue, so you can set your nails up in a few minutes literally anywhere [my salon of choice tends to be the airport lounge between conferences.] The nails come set up to your profile, which is kind of like a linktree [but cuter], where you’re able to add the socials you want it to link to, whether that's just your socials with the choice to add proof of irl credentials. It’s literally the memorable way to network. For guys who think this is ‘not for me’, consider it the easiest way to onboard your mother, your sisters and your girlfriend into web3. I wanted to create a product that eclipses the need to understand blockchain and makes it fun to use. I've dropped the link below and thank you to everyone who has been on this journey with us. xoxo

winny

131,635 görüntüleme • 2 yıl önce

Not every hero wears a cape. Some just share their bread. Created with Seedance 2.0 + GPT Image 2 on OpenArt Prompt: TITLE The Piece of Bread REFERENCE Use the provided combined character sheet and storyboard board as the main visual reference. Follow the same woman design, stray cat design, bread, sidewalk, low wall, cloth shoulder bag, water bottle, warm sunset lighting, and emotional story beats. Keep the woman and cat visually consistent in every shot. Do not add extra characters. Do not change the core story. SUBJECTS Woman: A young woman in her early 20s with shoulder-length slightly messy dark brown hair loosely tied back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She appears hungry, humble, compassionate, and resilient. Her acting should remain subtle, natural, and emotional. Cat: A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, one ear with a tiny notch, a long curved tail, and expressive anime-style eyes. The cat feels timid, hungry, hopeful, innocent, and lovable. It begins cautious and hungry, then gradually becomes trusting and comforted. Bread: One small round bread bun. This is the central story object. The woman breaks it into two pieces and shares one half with the cat. ENVIRONMENT Quiet city sidewalk at sunset. Low concrete wall behind them. Soft blurred road and distant buildings in the background. A simple cloth shoulder bag and a small water bottle placed beside the woman. Warm golden-hour light with long shadows. Peaceful, lonely, emotional atmosphere. STYLE 2D Japanese anime short film with a hand-drawn aesthetic. Clean inked outlines, flat-to-soft cel shading, simplified shadow blocks, no CG or 3D rendering. Soft emotional storytelling. Warm golden-hour lighting with painterly anime backgrounds. Expressive anime eyes with subtle, believable facial acting. Gentle cinematic movement with a traditional anime animation feel. No 3D render. No CGI. No Pixar-style shading. No photorealism. No comedy. No chaos. No copyrighted characters. No text. No subtitles. No logos. No social media UI. No background music—only natural ambient sound effects. CAMERA 16:9 cinematic framing. Use close-ups and medium shots to emphasize emotion. End with one wide cinematic shot. Slow push-ins and gentle cuts. Shallow depth of field. Keep both characters clear and expressive. Avoid fast movement or exaggerated actions. TIMELINE 0:00–0:02 Extreme close-up. The woman slowly lifts a small bread bun toward her mouth. She is about to take a bite. Warm sunset light softly illuminates her face and hands. Her expression shows hunger, exhaustion, and quiet resilience. She pauses just before eating. SFX: quiet street ambience, soft breathing, gentle hand movement. --- 0:02–0:04 Medium shot from the woman's side. A small stray cat sits a few feet away on the sidewalk. The cat gazes at the bread with sad, hopeful eyes. It remains still, timid, and cautious. The woman notices the cat and slowly lowers the bread. SFX: soft cat meow, light breeze, distant city ambience. --- 0:04–0:06 Close-up of the woman's hands. She slowly breaks the bread into two pieces. Tiny crumbs fall gently. The moment feels like an important emotional decision. Her hands pause briefly after splitting the bread. SFX: soft bread tearing, tiny crumbs falling. --- 0:06–0:08 Medium side shot. The woman gently extends one half of the bread toward the cat. The cat looks at the bread, then into the woman's eyes. It is nervous but curious. The woman gives a soft, reassuring smile and keeps her hand perfectly still. SFX: gentle hand movement, cat sniffing, quiet breeze. --- 0:08–0:10 Low close shot near the cat. The cat slowly steps forward. It carefully takes the bread from the woman's hand. The woman remains calm and gentle. The cat begins eating, and its expression gradually softens into trust. SFX: tiny paw steps, soft bite, gentle chewing. --- 0:10–0:12 Medium shot. The woman sits comfortably on the sidewalk. The cat comes closer and sits beside her. She gently strokes the cat's head. The cat leans into her hand and relaxes. The moment feels warm, peaceful, and safe. SFX: soft fur brushing, content cat purring, distant street ambience. --- 0:12–0:14 Close emotional shot. The cat rests its head on the woman's lap. She looks down with a warm, slightly bittersweet smile. She still holds her own half of the bread in her other hand. Both appear comforted, no longer feeling completely alone. SFX: quiet breathing, soft breeze, distant city sounds. --- 0:14–0:15 Wide sunset shot from behind. The woman and the cat sit side by side facing the glowing sunset. Their long shadows stretch across the sidewalk. The cloth shoulder bag and water bottle rest nearby. The final frame feels peaceful, hopeful, and heartwarming. SFX: soft wind, distant street ambience, gentle satisfied cat purr. Storyboard Prompt: Create a single horizontal animation pre-production board for an original emotional 2D anime-style short film titled "The Piece of Bread." The output must be one image only and combine: 1. a character design sheet 2. a hand-drawn storyboard page. IMPORTANT Do not make the woman or cat resemble any reference screenshots or existing characters. Keep the same emotional story concept, but create completely original character designs, unique silhouettes, and distinct facial features. No copyrighted characters or close resemblance to any existing animated films or anime. STYLE Professional anime production board. Hand-drawn storyboard style with loose pencil sketch lines, light gray shading, red panel borders, blue motion arrows, and short handwritten production notes. Rendered with anime-inspired linework featuring clean inked outlines, expressive eyes, and simplified shading blocks. It should look like a real animation studio planning sheet, not a polished final illustration. LAYOUT Clean horizontal 16:9 board divided into two sections. SECTION A: CHARACTER SHEET Show both characters consistently. Woman A young woman in her early 20s with shoulder-length slightly messy dark brown hair tied loosely at the back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She should appear humble, exhausted, compassionate, and resilient. Show: front view side view 3/4 view expressions: hungry, thoughtful, soft smile, emotional pose holding a small bread bun pose offering bread Cat A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, slightly oversized anime-style eyes, one ear with a tiny notch, and a long curved tail. The cat should feel timid, hungry, hopeful, innocent, and lovable. Show: front view side view 3/4 view expressions: sad, shy, hopeful, happy, trusting sitting pose taking bread pose cuddling beside the woman Include tiny handwritten notes and a few small color swatches. SECTION B: STORYBOARD Create 8 cinematic storyboard panels arranged neatly in a grid. Keep character designs consistent throughout. Each panel should include simple handwritten shot notes and blue arrows indicating motion. STORY BEATS 1. Close-up of the woman about to take a bite from a small bread bun during sunset. 2. Medium shot of the hungry stray cat sitting nearby, staring at the bread with sad, hopeful eyes. 3. Close-up of the woman breaking the bread into two pieces as crumbs fall. 4. Medium shot of the woman offering one piece to the cat. 5. Close shot of the cat cautiously stepping forward and taking the bread. 6. Medium shot of the cat sitting beside the woman while she gently pets it. 7. Emotional close-up of the cat resting its head on the woman's lap. 8. Wide sunset shot from behind, showing the woman and cat sitting together with a cloth bag and water bottle beside them. ENVIRONMENT Quiet city sidewalk beside a low wall, soft urban background, cloth shoulder bag, small water bottle, warm golden sunset lighting, peaceful atmosphere, and an emotional mood. FINAL GOAL Create a dense, clean, production-ready anime pre-production board combining a character design sheet and storyboard in a single 16:9 horizontal image, conveying a heartfelt story of compassion and kindness through completely original character designs.
0:15

Not every hero wears a cape. Some just share their bread. Created with Seedance 2.0 + GPT Image 2 on OpenArt Prompt: TITLE The Piece of Bread REFERENCE Use the provided combined character sheet and storyboard board as the main visual reference. Follow the same woman design, stray cat design, bread, sidewalk, low wall, cloth shoulder bag, water bottle, warm sunset lighting, and emotional story beats. Keep the woman and cat visually consistent in every shot. Do not add extra characters. Do not change the core story. SUBJECTS Woman: A young woman in her early 20s with shoulder-length slightly messy dark brown hair loosely tied back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She appears hungry, humble, compassionate, and resilient. Her acting should remain subtle, natural, and emotional. Cat: A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, one ear with a tiny notch, a long curved tail, and expressive anime-style eyes. The cat feels timid, hungry, hopeful, innocent, and lovable. It begins cautious and hungry, then gradually becomes trusting and comforted. Bread: One small round bread bun. This is the central story object. The woman breaks it into two pieces and shares one half with the cat. ENVIRONMENT Quiet city sidewalk at sunset. Low concrete wall behind them. Soft blurred road and distant buildings in the background. A simple cloth shoulder bag and a small water bottle placed beside the woman. Warm golden-hour light with long shadows. Peaceful, lonely, emotional atmosphere. STYLE 2D Japanese anime short film with a hand-drawn aesthetic. Clean inked outlines, flat-to-soft cel shading, simplified shadow blocks, no CG or 3D rendering. Soft emotional storytelling. Warm golden-hour lighting with painterly anime backgrounds. Expressive anime eyes with subtle, believable facial acting. Gentle cinematic movement with a traditional anime animation feel. No 3D render. No CGI. No Pixar-style shading. No photorealism. No comedy. No chaos. No copyrighted characters. No text. No subtitles. No logos. No social media UI. No background music—only natural ambient sound effects. CAMERA 16:9 cinematic framing. Use close-ups and medium shots to emphasize emotion. End with one wide cinematic shot. Slow push-ins and gentle cuts. Shallow depth of field. Keep both characters clear and expressive. Avoid fast movement or exaggerated actions. TIMELINE 0:00–0:02 Extreme close-up. The woman slowly lifts a small bread bun toward her mouth. She is about to take a bite. Warm sunset light softly illuminates her face and hands. Her expression shows hunger, exhaustion, and quiet resilience. She pauses just before eating. SFX: quiet street ambience, soft breathing, gentle hand movement. --- 0:02–0:04 Medium shot from the woman's side. A small stray cat sits a few feet away on the sidewalk. The cat gazes at the bread with sad, hopeful eyes. It remains still, timid, and cautious. The woman notices the cat and slowly lowers the bread. SFX: soft cat meow, light breeze, distant city ambience. --- 0:04–0:06 Close-up of the woman's hands. She slowly breaks the bread into two pieces. Tiny crumbs fall gently. The moment feels like an important emotional decision. Her hands pause briefly after splitting the bread. SFX: soft bread tearing, tiny crumbs falling. --- 0:06–0:08 Medium side shot. The woman gently extends one half of the bread toward the cat. The cat looks at the bread, then into the woman's eyes. It is nervous but curious. The woman gives a soft, reassuring smile and keeps her hand perfectly still. SFX: gentle hand movement, cat sniffing, quiet breeze. --- 0:08–0:10 Low close shot near the cat. The cat slowly steps forward. It carefully takes the bread from the woman's hand. The woman remains calm and gentle. The cat begins eating, and its expression gradually softens into trust. SFX: tiny paw steps, soft bite, gentle chewing. --- 0:10–0:12 Medium shot. The woman sits comfortably on the sidewalk. The cat comes closer and sits beside her. She gently strokes the cat's head. The cat leans into her hand and relaxes. The moment feels warm, peaceful, and safe. SFX: soft fur brushing, content cat purring, distant street ambience. --- 0:12–0:14 Close emotional shot. The cat rests its head on the woman's lap. She looks down with a warm, slightly bittersweet smile. She still holds her own half of the bread in her other hand. Both appear comforted, no longer feeling completely alone. SFX: quiet breathing, soft breeze, distant city sounds. --- 0:14–0:15 Wide sunset shot from behind. The woman and the cat sit side by side facing the glowing sunset. Their long shadows stretch across the sidewalk. The cloth shoulder bag and water bottle rest nearby. The final frame feels peaceful, hopeful, and heartwarming. SFX: soft wind, distant street ambience, gentle satisfied cat purr. Storyboard Prompt: Create a single horizontal animation pre-production board for an original emotional 2D anime-style short film titled "The Piece of Bread." The output must be one image only and combine: 1. a character design sheet 2. a hand-drawn storyboard page. IMPORTANT Do not make the woman or cat resemble any reference screenshots or existing characters. Keep the same emotional story concept, but create completely original character designs, unique silhouettes, and distinct facial features. No copyrighted characters or close resemblance to any existing animated films or anime. STYLE Professional anime production board. Hand-drawn storyboard style with loose pencil sketch lines, light gray shading, red panel borders, blue motion arrows, and short handwritten production notes. Rendered with anime-inspired linework featuring clean inked outlines, expressive eyes, and simplified shading blocks. It should look like a real animation studio planning sheet, not a polished final illustration. LAYOUT Clean horizontal 16:9 board divided into two sections. SECTION A: CHARACTER SHEET Show both characters consistently. Woman A young woman in her early 20s with shoulder-length slightly messy dark brown hair tied loosely at the back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She should appear humble, exhausted, compassionate, and resilient. Show: front view side view 3/4 view expressions: hungry, thoughtful, soft smile, emotional pose holding a small bread bun pose offering bread Cat A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, slightly oversized anime-style eyes, one ear with a tiny notch, and a long curved tail. The cat should feel timid, hungry, hopeful, innocent, and lovable. Show: front view side view 3/4 view expressions: sad, shy, hopeful, happy, trusting sitting pose taking bread pose cuddling beside the woman Include tiny handwritten notes and a few small color swatches. SECTION B: STORYBOARD Create 8 cinematic storyboard panels arranged neatly in a grid. Keep character designs consistent throughout. Each panel should include simple handwritten shot notes and blue arrows indicating motion. STORY BEATS 1. Close-up of the woman about to take a bite from a small bread bun during sunset. 2. Medium shot of the hungry stray cat sitting nearby, staring at the bread with sad, hopeful eyes. 3. Close-up of the woman breaking the bread into two pieces as crumbs fall. 4. Medium shot of the woman offering one piece to the cat. 5. Close shot of the cat cautiously stepping forward and taking the bread. 6. Medium shot of the cat sitting beside the woman while she gently pets it. 7. Emotional close-up of the cat resting its head on the woman's lap. 8. Wide sunset shot from behind, showing the woman and cat sitting together with a cloth bag and water bottle beside them. ENVIRONMENT Quiet city sidewalk beside a low wall, soft urban background, cloth shoulder bag, small water bottle, warm golden sunset lighting, peaceful atmosphere, and an emotional mood. FINAL GOAL Create a dense, clean, production-ready anime pre-production board combining a character design sheet and storyboard in a single 16:9 horizontal image, conveying a heartfelt story of compassion and kindness through completely original character designs.

Zara

22,997 görüntüleme • 2 ay önce

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇
0:12

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇

hoeem

21,634 görüntüleme • 1 yıl önce

You Can't Vibe-Code Trust Avishai Abrahami, Co-Founder & CEO of Wix , interviewed by Harry Stebbings (kevin andres) Summary: Wix trades at a $2.8B market cap on $2.1B of revenue while the market ascribes roughly zero value to a business throwing off $400M a year in free cash flow. Wix CEO Avishai Abrahami's argument is that the market can't yet price what AI actually threatens: the moat is trust and business logic, and neither gets vibe-coded away. His response is to own the disruptor (Base44), train his own narrow models, and stay committed through a storm he insists always arrives on a random Wednesday. 1. Trust is the moat. The real value of Salesforce is trust: JP Morgan and huge banks let it hold all their customer data, and the CRM itself is a small part of that. "What other platform will JP Morgan trust for their customers' data? None." That trust took years to build and can't be reconstructed by an agent scraping a database, so the companies whose value lives in trust survive the SaaS apocalypse while the ones reduced to piping get commoditized. 2. The business-logic wall. "You're not going to vibe-code Shopify no matter how good you are. The business logic is too hard." Wix tested this directly: they asked a team of professional developers to build the operating logic for a single hairdresser in Base44, gave up after a week, brought in a stronger team, and still failed two weeks later. Complex operational software is far harder than a demo suggests, which is why the pizza shop and the hairdresser stay Wix customers rather than build their own stack. 3. Own the disruptor. Wix bought Base44, a one-person company, for $80M, and it now does over $150M in ARR, roughly double what they paid. Abrahami frames the future as three buckets: owners who never want to build, owners who vibe-code everything themselves, and a mix in the middle over the next five or six years. Rather than bet on which wins, Wix owns the tool customers would defect to, so a customer who switches platforms still switches to Wix. 4. Trading on someone else's news. "Today we are trading on other companies' news. We're not trading on Wix news. We're trading on what OpenAI or Anthropic or Google are saying." Base44 alone, valued on vibe-coding peer multiples, should be worth around $8B, which means the market assigns less than zero to Wix's core. Abrahami's response is to detach: he doesn't wake up checking whether the stock moved 20%, because the only thing he can influence is the business. 5. The narrow model. Wix fine-tuned and combined its own models and now matches top-tier frontier quality on Base44 tasks at far lower cost. The logic: they sit on a huge stream of training data from watching what users try and where they fail, so a model built for Base44 can skip what frontier models carry, like knowledge of Chinese poetry, and go deep on what someone means when they say "build me a task manager to tell my boyfriend where he's wrong." A narrow target is easier to hit than a frontier model, and Wix already runs a trained model on website generation that's faster, cheaper, and makes fewer errors, retrained weekly on a live feedback loop. 6. Quality before cost. When Harry cites Chamath's claim that open source runs 14-16x cheaper, Abrahami pushes back: that holds for small tasks, but for something as complex as Base44 the savings land at 5-10%, and his own model runs 1-30% cheaper than frontier, not the order of magnitude people assume. More to the point, this is the wrong time to chase cost: "20% more quality, 20% less cost, I'll go for the quality." It's a brand-new market that's just starting, and the job now is to make the product better. 7. The but is very big. "We all give too much credit for AI. It's amazing, it's incredible, it's super powerful, but the but is pretty big." He asked Claude to write a safety protocol and got six mandatory gates, then pushed back on each one and watched the model cave until only one survived, downgrading the rest from "must test" to "might want to look at later." We over-trust these systems, and that reflex, treating a Reddit post as equivalent to research published in Nature, is where the danger lives. 8. Customer support still breaks. Wix has 3,500 people and its single biggest department is customer support, serving 192 countries. They tried hard not to build their own AI support agent, tested many off-the-shelf products, and concluded flatly: "It doesn't work. We tried, we tried again, it didn't work." The gap between hyped AI support startups and what actually ships in production is the tell that the technology is earlier than the marketing, maybe five years from being different. 9. Buybacks as dividends. Wix had $1.5B sitting in the bank it couldn't put into a major acquisition because it was focused on the new product and Base44, so it bought back stock at a low price, with admittedly terrible short-term timing. Abrahami is unbothered: "The big question is where it's going to be in three years, not what happened in the last three months." He argues buybacks are a fantastic, underused tool, essentially a dividend to every shareholder, and companies should lean on them to balance stock-based compensation instead of endlessly diluting. 10. Execution, not finance. A low stock price makes M&A currency less valuable, but Abrahami says that's not his real constraint. Base44 was a one-person company; Wix had to build an entire company around it, staffing it with people pulled from the core. "I don't know how to do another one of those at the same time and have the same quality." The bottleneck on the next acquisition is execution capacity, not the balance sheet. 11. Chosen to be here. The one thing money buys beyond food security is freedom, and the deepest form of that freedom is knowing you're here by choice. "I'm here because I've chosen to be here. Nobody made me." He could move to Costa Rica or dance carnival in Brazil, and choosing to stay and run a public company through a crashing stock is where he finds his power. Money also made him more impatient and a bit lazier, and more rational because he's no longer deciding from fear. 12. The random Wednesday. Resilience starts with accepting the storm will come, because we assume that if yesterday was easy tomorrow will be too, and reality doesn't move in gentle slopes. "The worst thing that happens is probably some random thing on some random Wednesday. It's not something you get a lot of warning for." His anchor, borrowed from Babylon 5, is that you get there when you get there and the weapons you have are the weapons you have, so the only real question is whether you're doing the best you can with what you control.
0:15

You Can't Vibe-Code Trust Avishai Abrahami, Co-Founder & CEO of Wix , interviewed by Harry Stebbings (kevin andres) Summary: Wix trades at a $2.8B market cap on $2.1B of revenue while the market ascribes roughly zero value to a business throwing off $400M a year in free cash flow. Wix CEO Avishai Abrahami's argument is that the market can't yet price what AI actually threatens: the moat is trust and business logic, and neither gets vibe-coded away. His response is to own the disruptor (Base44), train his own narrow models, and stay committed through a storm he insists always arrives on a random Wednesday. 1. Trust is the moat. The real value of Salesforce is trust: JP Morgan and huge banks let it hold all their customer data, and the CRM itself is a small part of that. "What other platform will JP Morgan trust for their customers' data? None." That trust took years to build and can't be reconstructed by an agent scraping a database, so the companies whose value lives in trust survive the SaaS apocalypse while the ones reduced to piping get commoditized. 2. The business-logic wall. "You're not going to vibe-code Shopify no matter how good you are. The business logic is too hard." Wix tested this directly: they asked a team of professional developers to build the operating logic for a single hairdresser in Base44, gave up after a week, brought in a stronger team, and still failed two weeks later. Complex operational software is far harder than a demo suggests, which is why the pizza shop and the hairdresser stay Wix customers rather than build their own stack. 3. Own the disruptor. Wix bought Base44, a one-person company, for $80M, and it now does over $150M in ARR, roughly double what they paid. Abrahami frames the future as three buckets: owners who never want to build, owners who vibe-code everything themselves, and a mix in the middle over the next five or six years. Rather than bet on which wins, Wix owns the tool customers would defect to, so a customer who switches platforms still switches to Wix. 4. Trading on someone else's news. "Today we are trading on other companies' news. We're not trading on Wix news. We're trading on what OpenAI or Anthropic or Google are saying." Base44 alone, valued on vibe-coding peer multiples, should be worth around $8B, which means the market assigns less than zero to Wix's core. Abrahami's response is to detach: he doesn't wake up checking whether the stock moved 20%, because the only thing he can influence is the business. 5. The narrow model. Wix fine-tuned and combined its own models and now matches top-tier frontier quality on Base44 tasks at far lower cost. The logic: they sit on a huge stream of training data from watching what users try and where they fail, so a model built for Base44 can skip what frontier models carry, like knowledge of Chinese poetry, and go deep on what someone means when they say "build me a task manager to tell my boyfriend where he's wrong." A narrow target is easier to hit than a frontier model, and Wix already runs a trained model on website generation that's faster, cheaper, and makes fewer errors, retrained weekly on a live feedback loop. 6. Quality before cost. When Harry cites Chamath's claim that open source runs 14-16x cheaper, Abrahami pushes back: that holds for small tasks, but for something as complex as Base44 the savings land at 5-10%, and his own model runs 1-30% cheaper than frontier, not the order of magnitude people assume. More to the point, this is the wrong time to chase cost: "20% more quality, 20% less cost, I'll go for the quality." It's a brand-new market that's just starting, and the job now is to make the product better. 7. The but is very big. "We all give too much credit for AI. It's amazing, it's incredible, it's super powerful, but the but is pretty big." He asked Claude to write a safety protocol and got six mandatory gates, then pushed back on each one and watched the model cave until only one survived, downgrading the rest from "must test" to "might want to look at later." We over-trust these systems, and that reflex, treating a Reddit post as equivalent to research published in Nature, is where the danger lives. 8. Customer support still breaks. Wix has 3,500 people and its single biggest department is customer support, serving 192 countries. They tried hard not to build their own AI support agent, tested many off-the-shelf products, and concluded flatly: "It doesn't work. We tried, we tried again, it didn't work." The gap between hyped AI support startups and what actually ships in production is the tell that the technology is earlier than the marketing, maybe five years from being different. 9. Buybacks as dividends. Wix had $1.5B sitting in the bank it couldn't put into a major acquisition because it was focused on the new product and Base44, so it bought back stock at a low price, with admittedly terrible short-term timing. Abrahami is unbothered: "The big question is where it's going to be in three years, not what happened in the last three months." He argues buybacks are a fantastic, underused tool, essentially a dividend to every shareholder, and companies should lean on them to balance stock-based compensation instead of endlessly diluting. 10. Execution, not finance. A low stock price makes M&A currency less valuable, but Abrahami says that's not his real constraint. Base44 was a one-person company; Wix had to build an entire company around it, staffing it with people pulled from the core. "I don't know how to do another one of those at the same time and have the same quality." The bottleneck on the next acquisition is execution capacity, not the balance sheet. 11. Chosen to be here. The one thing money buys beyond food security is freedom, and the deepest form of that freedom is knowing you're here by choice. "I'm here because I've chosen to be here. Nobody made me." He could move to Costa Rica or dance carnival in Brazil, and choosing to stay and run a public company through a crashing stock is where he finds his power. Money also made him more impatient and a bit lazier, and more rational because he's no longer deciding from fear. 12. The random Wednesday. Resilience starts with accepting the storm will come, because we assume that if yesterday was easy tomorrow will be too, and reality doesn't move in gentle slopes. "The worst thing that happens is probably some random thing on some random Wednesday. It's not something you get a lot of warning for." His anchor, borrowed from Babylon 5, is that you get there when you get there and the weapons you have are the weapons you have, so the only real question is whether you're doing the best you can with what you control.

Gokul Rajaram

22,843 görüntüleme • 1 ay önce

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto—focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( – $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > ( - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > ( — Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules >Tropical Tidbits ( - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high >Climate Reanalyzer ( - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities >Windy ( - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online ( - 100+ years of historical location data NOAA Weather Prediction ? >Center ( - short forecasts for precipitation, anomalies. Climate Prediction Center ( - long-term ENSO, droughts >Open-Meteo ( - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. >OpenWeatherMap ( = Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. >Visual Crossing Weather ( - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities >WeatherAPI. com ( - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information
0:14

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto—focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( – $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > ( - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > ( — Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules >Tropical Tidbits ( - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high >Climate Reanalyzer ( - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities >Windy ( - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online ( - 100+ years of historical location data NOAA Weather Prediction ? >Center ( - short forecasts for precipitation, anomalies. Climate Prediction Center ( - long-term ENSO, droughts >Open-Meteo ( - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. >OpenWeatherMap ( = Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. >Visual Crossing Weather ( - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities >WeatherAPI. com ( - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information

Aleiah

77,547 görüntüleme • 7 ay önce

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto-focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( - $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > - Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules > - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high > - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities > - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online - 100+ years of historical location data NOAA Weather Prediction > - short forecasts for precipitation, anomalies. Climate Prediction Center - long-term ENSO, droughts > - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. > - Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. > - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities > - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information
0:14

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto-focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( - $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > - Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules > - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high > - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities > - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online - 100+ years of historical location data NOAA Weather Prediction > - short forecasts for precipitation, anomalies. Climate Prediction Center - long-term ENSO, droughts > - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. > - Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. > - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities > - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information

Valentin

17,150 görüntüleme • 4 ay önce

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!
0:14

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

29,342 görüntüleme • 2 ay önce

🚨 Protocol Update #9 It's incredible how time flies when you’re laser-focused on building and delivering the essential products that form the backbone of decentralized finance. Hatom has now been live on the Mainnet for over a year, and we're proud to say that this entire period has been free of issues or downtime. Our platform has been battle-tested during volatile market conditions, and each of our products has performed exactly as expected—solidifying our place as a cornerstone in the #MultiversX ecosystem. Describing last year as “incredible” feels like an understatement. We’ve witnessed unprecedented growth across the entire #MultiversX ecosystem, particularly in terms of TVL and yield opportunities. The day before Hatom launched its Lending Protocol and Liquid Staking on Mainnet, #MultiversX had a total TVL of $95 million. Within two weeks, the ecosystem surpassed $200 million in TVL, with Hatom driving over 50% of that growth. At its peak, Hatom reached over $280 million in TVL, accounting for more than 70% of the chain’s total TVL. What's even more remarkable is that, after initially using Treasury funds to incentivize users, Hatom has shifted to distributing rewards solely from protocol revenue. This marks the start of a fully sustainable, real-yield model, proving our products' rapid product-market fit and long-term viability. A Recap of the Past Year Here’s a quick overview of what we’ve accomplished in the past year: • Launched the first Lending Protocol in the #MultiversX ecosystem, along with the Liquid Staking Protocol on Mainnet. • Surpassed $100 million in TVL within just five days of the launch. • Deployed the HTM Booster Module and Accumulator. • Launched the Tao Bridge and Tao Liquid Staking, bringing over 33k $TAO into the #MultiversX ecosystem in just two weeks. • Implemented multiple upgrades to core infrastructure. • $HTM became the second-largest ESDT token after $EGLD. • Distributed over $3.85 million in rewards to our users. We are happy to announce that Hatom V2 is now live! After an incredible year of growth, we’re excited to take the next step toward becoming the leading liquidity hub across multiple chains. We invite you to explore our newly rebranded website at marking the beginning of our omni-chain journey. This rebranding reflects our bold vision and sets the stage for a full overhaul of our dApps, delivering a fresh and enhanced experience for all users. Achieving self-sustainability in such a short time, we now focus on research and development. Instead of pursuing many ideas, we’re committed to building high-impact products that create perfect synergies within our ecosystem. With that said, let’s dive into the key topics of this update: USH and Booster V2. Hatom USD (USH) We’ve highlighted USH in several updates, and it’s great to see the community recognizing its potential. USH is set to be one of the most impactful products on #MultiversX, providing a key revenue stream for Hatom while helping us maintain competitive rates and long-term sustainability. USH is the result of extensive research and careful development, designed to seamlessly fit into the Hatom ecosystem. While many DeFi projects are raising millions for new stablecoins, USH stands as another powerful product within our hub. The time has finally come for USH to be unveiled to the public, and we are excited to announce that USH will officially launch on Devnet on 28th October. While we’ve thoroughly tested for bugs internally, we’re excited to engage the community in this critical phase. To encourage participation, we’ll offer incentives for those testing USH on the Devnet, with more details to be shared at launch. Understanding USH's architecture is key to how it functions within our ecosystem. Let’s break it down step by step, starting with an explanation of each component. Facilitators USH’s minting process is driven by Facilitators—smart contracts responsible for the controlled minting and burning of USH. At launch, two primary facilitators will handle these tasks, each with distinct functionality: 1. Lending Protocol Facilitator The Lending Protocol Facilitator allows users to mint USH using a variety of supported collateral assets directly into the Hatom Lending Protocol. Unlike traditional lending mechanisms, where interest rates fluctuate based on the utilization rate, the minting of USH has fixed interest rates, thanks to Hatom's unique role as the entity managing the minting process. In a scenario where a user is minting USH through this facilitator using multiple assets as collateral, the protocol automatically prioritizes collateral with the lowest Minting APY. Let’s consider an example where a user deposits: - $1,000 in USDC (with a collateral factor of 80% and a 2% Minting APY) - $1,000 in BTC (with a collateral factor of 75% and a 3% Minting APY) - $1,000 in HTM (with a collateral factor of 70% and a 4% Minting APY) Based on these parameters, the user can mint a maximum of $2,250 worth of USH, distributed as follows: - $800 from $USDC (80% of $1,000) at 2% Minting APY - $750 from $BTC (75% of $1,000) at 3% Minting APY - $700 from $HTM (70% of $1,000) at 4% Minting APY The overall Minting APY will be a weighted average of these individual APYs, calculated based on the proportion of USH minted from each collateral type. Now, if the user decides to borrow only $1,000 worth of USH, the APY is determined as follows: - The first $800 will be borrowed from $USDC at 2% APY - The remaining $200 will be borrowed from $BTC at 3% APY This results in an effective Minting APY of 2.2%, reflecting a weighted average of the APYs across the borrowed amounts. It’s important to note that EGLD and wTAO, along with their liquid staking derivatives such as sEGLD and swTAO, can only be used as collateral in the Isolated Pools (which will be explained in the next section), not in the Lending Protocol 2. Isolated Pools Facilitator The Isolated Pools Facilitator allows users to mint $USH at zero interest using $EGLD, $wTAO, or their liquid staking derivatives ( $sEGLD or $swTAO) as collateral. Here’s how it works: When depositing EGLD or wTAO • These assets are staked through the Hatom Liquid Staking Protocol, generating the staking APY. • The staked assets are then deposited into the Lending Protocol, earning a supply APY, but are not activated as collateral. When depositing sEGLD or swTAO • When users deposit staking derivatives into the Isolated Pools, the protocol holds the staking derivatives, but the user's exposure is immediately shifted to the underlying asset ( $EGLD or $wTAO). This means the user no longer benefits from the staking rewards of the derivative, and instead, their exposure is entirely tied to the value and price movements of the underlying asset. • The staked assets are deposited into the Hatom Lending Protocol, earning the supply APY, but again not being activated as collateral. Since the protocol generates revenue from staking and supplying assets in the Lending Protocol, this income is used to incentivize the USH Staking Module. The protocol buys HTM tokens from the open market and distributes them, along with all fees generated by other facilitators, as rewards to stakers. We believe that the Isolated Pools Facilitator is one of the most important pieces of the USH ecosystem. Its potential impact on the TVL within both the Hatom ecosystem and the broader #MultiversX blockchain is immense and the revenue generated by this facilitator through fees will significantly bolster the overall growth of the protocol. To illustrate the potential of Isolated Pools, let’s use the following example: • $50 million worth of $EGLD is deposited into the Isolated Pools, generating a 6% staking APY • $50 million worth of $wTAO is also deposited, earning a 15% staking APY The total staking rewards generated from these assets would be: • $EGLD staking rewards: $50 million × 6% = $3 million annually • $wTAO staking rewards: $50 million × 15% = $7.5 million annually In total, the protocol generates $10.5 million in staking rewards annually. These rewards are then used to buy back HTM tokens from the open market, driving significant buying pressure on the HTM token itself. The purchased HTM tokens are distributed to USH LP stakers in the USH Staking Module, alongside the revenue generated by the Lending Protocol Facilitator. TVL and Yield Impact As we explore the broader impact of USH and the Isolated Pools, it becomes evident how these mechanisms contribute to the overall growth of the Hatom ecosystem, particularly in terms of TVL and potential yield generation. Based on the above numbers, if $50 million worth of $EGLD and $50 million worth of $wTAO are deposited into the Isolated Pools with a 75% collateral factor, we could mint up to $75 million worth of $USH. However, to prioritize safety, we’ll mint only 50% of the maximum, resulting in $37.5 million worth of $USH. In an ideal scenario, but also very unlikely, the $37.5 million $USH would be deposited in the Staking Module to generate rewards. In order for $USH to be deposited in the Staking Module, it is paired with another token (e.g., $USDC or $EGLD) to form Liquidity Pool (LP) position, contributing $75 million to the USH Staking Module. Additionally, the $100 million deposited in the Isolated Pools cycles through Liquid Staking and into the Lending Protocol, contributing a total of $300 million in TVL. Total TVL Breakdown: • $300 million from assets flowing through Isolated Pools ($100m) → Liquid Staking ($100m) → Lending Protocol ($100m) • $75 million from LP positions in the USH Staking Module Total TVL = $375 million As mentioned above, the $100 million deposited in Isolated Pools generates approximately $10.5 million annually in staking rewards (6% APY from $sEGLD and 15% APY from $swTAO). If all minted $USH is deposited into the Staking Module, the $75 million staked would benefit from these rewards, resulting in a 14% APY for USH LP stakers. On top of the protocol’s rewards, liquidity providers earn additional fees from their LP positions on decentralized exchanges, creating the perfect opportunity for all the participants in the USH Staking Module looking for attractive yields. USH Stability: The Peg Mechanism Ensuring the stability of USH is paramount, and to maintain its value close to $1 under all market conditions, we’ve implemented a robust dual peg mechanism. This system consists of two key layers of protection—Soft Peg and Hard Peg—designed to keep USH stable through both market-driven incentives and other mechanisms for scenarios where the Soft Peg mechanism can’t reclaim the peg. 1. Soft Peg Mechanism The Soft Peg Mechanism helps keep USH stable around its $1 value by encouraging market participants to act when USH trades above or below $1. When USH trades below $1 Users can buy USH at a discount, on a DEX, and repay their USH loans on Hatom, as USH is always valued at $1 on the protocol. This action removes $USH from circulation, helping to restore its price. When USH trades above $1 Users can borrow USH from the protocol at $1 and sell it on the open market at the higher price, increasing the circulating supply of USH and pushing its price back down to $1. 2. Hard Peg Mechanism (Redemption Mode) In cases where the Soft Peg alone cannot restore USH to $1 and its price drops significantly below the peg, the Hard Peg Mechanism is triggered through Redemption Mode. This mechanism allows any market participant to step in and help restore the peg by repaying USH loans for other borrowers, seizing their collateral at the full $1 value. It's important to note that Redemption Mode is only activated in the Isolated Pools and does not impact users minting USH through the Lending Protocol. Here’s how Redemption Mode works: When USH trades below $1 and the Redemption Mode is activated, redeemers can buy USH at the lower market price (e.g., $0.95), and use it to repay borrowers' debts at the full $1 value within the protocol. The redeemer receives collateral in the form of liquid staked tokens(such as $sEGLD or $swTAO) equivalent to the USH they repaid at its full $1 value, profiting from the difference between the discounted purchase price and the redemption value. The borrower being redeemed also benefits by receiving a redemption bonus, which allows them to keep a portion of their collateral after part of it is seized after loan was repaid. This system ensures that borrowers are not penalized during redemption, creating a balanced mechanism where both the redeemer and the borrower have something to gain. Redemption Mode differs from Liquidation in several ways: Redemption is triggered by USH falling below $1 and involves repaying borrower accounts to restore the peg. Both the redeemer and the borrower benefit, with the redeemer profiting from the price difference, and the borrower receiving a bonus from their collateral. Liquidation occurs when a borrower’s collateral falls below a certain threshold, making them risky. During liquidation, a portion of the borrower’s loan is repaid, and the collateral is seized, while also incurring a liquidation penalty. Redemption Mode uses a data structure known as a Red-Black Tree to efficiently monitor and rank all borrower positions within the protocol smart contract itself. This structure dynamically tracks borrowers based on their Borrow Limit Used, which is the percentage of collateral they have utilized relative to their borrowing capacity. The system prioritizes borrowers with the highest Borrow Limit Used, meaning those who have borrowed the most relative to their collateral are considered first for redemption. USH Airdrop Regarding the USH Airdrop, we would like to inform you that snapshots will end once USH is deployed on the Public Mainnet. The airdrop will be concluded shortly after, once all liquidity pools are stable and we determine the optimal moment to distribute the rewards to the community. USH Staking Module & Booster V2 The USH Staking Module will play a critical role in maintaining deep liquidity for USH while offering users high-yield opportunities. By staking USH LP tokens, such as USH/USDC and USH/EGLD, users can earn rewards generated by USH facilitators. This approach strengthens USH’s liquidity pools, making them robust enough to handle significant trades without destabilizing its price, thus reinforcing USH’s peg and overall stability. Beyond creating robust liquidity, the USH Staking Module serves as the key utility module within the USH ecosystem, designed to provide users with an opportunity to earn high yields on their USH holdings in a sustainable and organic way. All rewards distributed through the module are generated by various products across the Hatom ecosystem, ensuring long-term sustainability. For users seeking a more stable yield, the USH/USDC LP provides lower risk and steady returns. Those looking to leverage their EGLD holdings can opt for the USH/EGLD LP, which can be staked in the USH Staking Module. A key advantage of staking in the USH Staking Module is that rewards are based on the full value of the LP, not just the USH portion, maximizing your yield potential. As we continue to grow, we’ll be adding more LPs, providing users with even greater flexibility and options for staking their USH in the module. While our current focus is on LP tokens, we’re also exploring the possibility of allowing direct USH staking in the future, expanding the staking opportunities across the ecosystem. The Integration of Booster V2 with the Staking Module Booster V2 will be available for testing with the USH Devnet release, and with its introduction, we’ve strengthened the relationship between the HTM token and USH. Our ecosystem now features two independent boosters: one for the Lending Protocol and one for the USH Staking Module, each operating with the goal of maximizing yields for users. Key Improvements in Booster V2 Booster V2 brings several enhancements that elevate the functionality and user experience: Support for Multiple Token Types: Users will be able to deposit Pool Tokens, Farm Tokens, Dual Farm Tokens, or Staked HTM Tokens (via xExchange). Only the HTM portion will be considered for boosting. Unlimited Staking: The cap on HTM deposits will be removed, allowing users to stake without limits. This will foster a competitive environment where the more HTM you stake, the higher your potential APY. Integrated xExchange Management: Users will be able to manage their xExchange positions directly from the Booster dashboard. This will include creating pools, farming, dual farming, and staking HTM tokens, all from one convenient dashboard. Energy Management Integration: Booster V2 will allow users to manage their xExchange Energy directly from the dashboard, providing an additional way to boost rewards even further. Seamless Migration: Users will be able to migrate HTM between the Lending Protocol Booster and the USH Staking Module Booster without any cooldown periods, making it easier to optimize strategies across both modules. How the Yields Work Booster V2 will introduce a more structured and competitive approach to yield distribution across both the Lending Protocol and the Staking Module. HTM Booster in the Lending Protocol Base APY (First Batch): This is available to all users who stake a specific percentage of HTM relative to their collateral value. Any user can achieve this Base APY by staking the required amount of HTM. Boosted APY (Second Batch): After achieving the base level, users can boost their returns further by staking additional HTM, competing for the second batch of rewards. The more HTM staked beyond the base threshold, the higher the potential yield. USH Staking Module Yields Staking APY: Users who deposit USH-related LP tokens without boosting through the HTM Booster will still receive a Staking APY. This ensures that even passive participants which are not looking to stake their HTM in the Booster can take advantage of the USH Ecosystem to generate yields. Booster APY: Similar to the system in the Lending Protocol, users can stake HTM to unlock a Base APY. Beyond this threshold, any additional HTM staked will increase their APY in a competitive manner, allowing users to maximize their returns based on the amount of HTM they commit to boosting their positions. Rollout Plan for USH USH will be deployed in a phased rollout to ensure smooth implementation: Public Devnet: Open for testing, with incentives for participants to explore and stress-test the platform. Private Mainnet: A limited launch with partners to mint USH, bootstrap USH liquidity and generate initial protocol revenue. Public Mainnet: A full-scale launch, enabling all users to mint, stake, and trade USH. We know DeFi can be complex, which is why we’re committed to providing the tools and resources needed to navigate our ecosystem. With the USH Public Devnet launch, we’ll release updated documentation offering clear guidance on Hatom’s products. Developer documentation is also in the works, and we’re exploring the idea of a Hatom Academy for educational resources. Plus, we’ll soon roll out content focused on USH, helping users fully tap into its potential within Hatom and the MultiversX ecosystem. What’s Next? Hatom Pulse As Hatom grows, our focus remains on pushing DeFi boundaries while expanding across multiple ecosystems. Although this update doesn’t include a full roadmap—that will come later—our priority is clear: expanding Hatom across chains. To stand out in the competitive DeFi landscape, we’re committed to developing standout products. With that in mind, we’re excited to give you an exclusive preview of one of our most innovative products in development: Hatom Pulse. Over-collateralized non-custodial lending protocols, liquid staking, and over-collateralized stablecoins already exist on #Ethereum. What sets us apart is the synergy between these components within a unified ecosystem. By integrating these pillars, we tackle capital inefficiencies, allowing one protocol to enhance strategies that benefit the others, maximizing returns across the board. For example, when USH is minted, it means that EGLD is deposited, liquid-staked, and supplied in the lending protocol—all three protocols working in harmony. Hatom Pulse will elevate this synergy to another level, solving key issues faced by Aave, Compound Labs , and other leading protocols. We believe this innovation will be pivotal as we work to gain market share while expanding cross-chain. Our proof of concept will be deployed and battle-tested on #MultiversX, but the real growth will come when we scale this to markets that are thousands of times larger. This will be a turning point for Hatom. So, what is Hatom Pulse? On Hatom, like on Aave and other leading lending protocols, the largest assets used as collateral are often not borrowed, leading to substantial revenue loss for the protocol. This also results in very low income on the supply side, as borrowing fees depend on utilization rates, which only increase when borrowing activity rises. Generally, lending protocols are used to provide assets for borrowing stablecoins or for leveraging liquid staking strategies. This inefficiency locks up billions of dollars in dormant assets, and users earn very low supply rates on their collateral, which doesn’t help offset their loan interest. Hatom Pulse is designed to address these inefficiencies by leveraging the synergy between our existing products. It creates sophisticated vaults that activate dormant assets, unlocking advanced yield opportunities through a delta-neutral strategy. By utilizing assets like $EGLD, $sEGLD, $wTAO, and $swTAO, Hatom Pulse enables users to engage in delta-neutral strategies, where we long and short these assets on (CEXs), earning funding rates and staking rewards while keeping their assets intact. (The exact strategy, along with all the details, will be shared once USH is fully established). Initially, these vaults will operate on CEXs, where liquidity is highest, and will be managed through custodians like Copper.co to mitigate counterparty risks. Later, we plan to extend this to DEXs where all operations will be governed by smart contracts, ensuring full decentralization. serves as a strong proof of concept for us in this regard. However, our strategy will differ, as our focus will be on protecting the unit value, rather than the dollar value. Although Hatom Pulse is still in its research phase, early estimates suggest that this product alone could generate over 18% annual returns on $EGLD and more than 35% on $wTAO, with what we believe to be minimal risk. It’s important to note that these figures reflect current metrics based on internal calculations and may slightly differ upon product launch. But imagine reaching this on #Ethereum, while allowing users to borrow using their assets—this could be a disruptive protocol. We believe Hatom Pulse has the potential to become a cornerstone product as we transition into an omni-chain future. In a competitive DeFi landscape, it could give us a significant edge by offering something truly groundbreaking, capable of competing with well-established protocols across various chains. This strategy represents immense untapped potential. Hatom Pulse is being developed for risk-averse users who seek higher returns without excessive risk. By addressing inefficiencies in current DeFi strategies, we aim to offer a secure, robust option for yield generation that could rival established protocols. It's been an intense year for our team, and we sincerely thank the community for their patience, trust, and unwavering support as we've worked hard to build and deliver these groundbreaking products. As Hatom's omni-chain expansion nears, we remain focused on improving our existing products and researching new innovations to stay ahead in this competitive market. Our goal is to build a comprehensive DeFi ecosystem, accessible across all blockchains. With USH approaching its Mainnet release, we're proud of how our products have reshaped the DeFi landscape on MultiversX. By filling key gaps in the on-chain economy, we've created opportunities for users to generate yield, unlock the potential of decentralized finance, and provide strong utility for EGLD. In just over a year, we’ve built a strong ecosystem, but this is only the beginning. We’re ready to go even further, developing better products and unlocking new opportunities for our users. We’ll share more about our expansion plans in a dedicated post, staying focused on what matters most. Rest assured, what’s coming will be truly impressive for Hatom and our growing community!
0:16

🚨 Protocol Update #9 It's incredible how time flies when you’re laser-focused on building and delivering the essential products that form the backbone of decentralized finance. Hatom has now been live on the Mainnet for over a year, and we're proud to say that this entire period has been free of issues or downtime. Our platform has been battle-tested during volatile market conditions, and each of our products has performed exactly as expected—solidifying our place as a cornerstone in the #MultiversX ecosystem. Describing last year as “incredible” feels like an understatement. We’ve witnessed unprecedented growth across the entire #MultiversX ecosystem, particularly in terms of TVL and yield opportunities. The day before Hatom launched its Lending Protocol and Liquid Staking on Mainnet, #MultiversX had a total TVL of $95 million. Within two weeks, the ecosystem surpassed $200 million in TVL, with Hatom driving over 50% of that growth. At its peak, Hatom reached over $280 million in TVL, accounting for more than 70% of the chain’s total TVL. What's even more remarkable is that, after initially using Treasury funds to incentivize users, Hatom has shifted to distributing rewards solely from protocol revenue. This marks the start of a fully sustainable, real-yield model, proving our products' rapid product-market fit and long-term viability. A Recap of the Past Year Here’s a quick overview of what we’ve accomplished in the past year: • Launched the first Lending Protocol in the #MultiversX ecosystem, along with the Liquid Staking Protocol on Mainnet. • Surpassed $100 million in TVL within just five days of the launch. • Deployed the HTM Booster Module and Accumulator. • Launched the Tao Bridge and Tao Liquid Staking, bringing over 33k $TAO into the #MultiversX ecosystem in just two weeks. • Implemented multiple upgrades to core infrastructure. • $HTM became the second-largest ESDT token after $EGLD. • Distributed over $3.85 million in rewards to our users. We are happy to announce that Hatom V2 is now live! After an incredible year of growth, we’re excited to take the next step toward becoming the leading liquidity hub across multiple chains. We invite you to explore our newly rebranded website at marking the beginning of our omni-chain journey. This rebranding reflects our bold vision and sets the stage for a full overhaul of our dApps, delivering a fresh and enhanced experience for all users. Achieving self-sustainability in such a short time, we now focus on research and development. Instead of pursuing many ideas, we’re committed to building high-impact products that create perfect synergies within our ecosystem. With that said, let’s dive into the key topics of this update: USH and Booster V2. Hatom USD (USH) We’ve highlighted USH in several updates, and it’s great to see the community recognizing its potential. USH is set to be one of the most impactful products on #MultiversX, providing a key revenue stream for Hatom while helping us maintain competitive rates and long-term sustainability. USH is the result of extensive research and careful development, designed to seamlessly fit into the Hatom ecosystem. While many DeFi projects are raising millions for new stablecoins, USH stands as another powerful product within our hub. The time has finally come for USH to be unveiled to the public, and we are excited to announce that USH will officially launch on Devnet on 28th October. While we’ve thoroughly tested for bugs internally, we’re excited to engage the community in this critical phase. To encourage participation, we’ll offer incentives for those testing USH on the Devnet, with more details to be shared at launch. Understanding USH's architecture is key to how it functions within our ecosystem. Let’s break it down step by step, starting with an explanation of each component. Facilitators USH’s minting process is driven by Facilitators—smart contracts responsible for the controlled minting and burning of USH. At launch, two primary facilitators will handle these tasks, each with distinct functionality: 1. Lending Protocol Facilitator The Lending Protocol Facilitator allows users to mint USH using a variety of supported collateral assets directly into the Hatom Lending Protocol. Unlike traditional lending mechanisms, where interest rates fluctuate based on the utilization rate, the minting of USH has fixed interest rates, thanks to Hatom's unique role as the entity managing the minting process. In a scenario where a user is minting USH through this facilitator using multiple assets as collateral, the protocol automatically prioritizes collateral with the lowest Minting APY. Let’s consider an example where a user deposits: - $1,000 in USDC (with a collateral factor of 80% and a 2% Minting APY) - $1,000 in BTC (with a collateral factor of 75% and a 3% Minting APY) - $1,000 in HTM (with a collateral factor of 70% and a 4% Minting APY) Based on these parameters, the user can mint a maximum of $2,250 worth of USH, distributed as follows: - $800 from $USDC (80% of $1,000) at 2% Minting APY - $750 from $BTC (75% of $1,000) at 3% Minting APY - $700 from $HTM (70% of $1,000) at 4% Minting APY The overall Minting APY will be a weighted average of these individual APYs, calculated based on the proportion of USH minted from each collateral type. Now, if the user decides to borrow only $1,000 worth of USH, the APY is determined as follows: - The first $800 will be borrowed from $USDC at 2% APY - The remaining $200 will be borrowed from $BTC at 3% APY This results in an effective Minting APY of 2.2%, reflecting a weighted average of the APYs across the borrowed amounts. It’s important to note that EGLD and wTAO, along with their liquid staking derivatives such as sEGLD and swTAO, can only be used as collateral in the Isolated Pools (which will be explained in the next section), not in the Lending Protocol 2. Isolated Pools Facilitator The Isolated Pools Facilitator allows users to mint $USH at zero interest using $EGLD, $wTAO, or their liquid staking derivatives ( $sEGLD or $swTAO) as collateral. Here’s how it works: When depositing EGLD or wTAO • These assets are staked through the Hatom Liquid Staking Protocol, generating the staking APY. • The staked assets are then deposited into the Lending Protocol, earning a supply APY, but are not activated as collateral. When depositing sEGLD or swTAO • When users deposit staking derivatives into the Isolated Pools, the protocol holds the staking derivatives, but the user's exposure is immediately shifted to the underlying asset ( $EGLD or $wTAO). This means the user no longer benefits from the staking rewards of the derivative, and instead, their exposure is entirely tied to the value and price movements of the underlying asset. • The staked assets are deposited into the Hatom Lending Protocol, earning the supply APY, but again not being activated as collateral. Since the protocol generates revenue from staking and supplying assets in the Lending Protocol, this income is used to incentivize the USH Staking Module. The protocol buys HTM tokens from the open market and distributes them, along with all fees generated by other facilitators, as rewards to stakers. We believe that the Isolated Pools Facilitator is one of the most important pieces of the USH ecosystem. Its potential impact on the TVL within both the Hatom ecosystem and the broader #MultiversX blockchain is immense and the revenue generated by this facilitator through fees will significantly bolster the overall growth of the protocol. To illustrate the potential of Isolated Pools, let’s use the following example: • $50 million worth of $EGLD is deposited into the Isolated Pools, generating a 6% staking APY • $50 million worth of $wTAO is also deposited, earning a 15% staking APY The total staking rewards generated from these assets would be: • $EGLD staking rewards: $50 million × 6% = $3 million annually • $wTAO staking rewards: $50 million × 15% = $7.5 million annually In total, the protocol generates $10.5 million in staking rewards annually. These rewards are then used to buy back HTM tokens from the open market, driving significant buying pressure on the HTM token itself. The purchased HTM tokens are distributed to USH LP stakers in the USH Staking Module, alongside the revenue generated by the Lending Protocol Facilitator. TVL and Yield Impact As we explore the broader impact of USH and the Isolated Pools, it becomes evident how these mechanisms contribute to the overall growth of the Hatom ecosystem, particularly in terms of TVL and potential yield generation. Based on the above numbers, if $50 million worth of $EGLD and $50 million worth of $wTAO are deposited into the Isolated Pools with a 75% collateral factor, we could mint up to $75 million worth of $USH. However, to prioritize safety, we’ll mint only 50% of the maximum, resulting in $37.5 million worth of $USH. In an ideal scenario, but also very unlikely, the $37.5 million $USH would be deposited in the Staking Module to generate rewards. In order for $USH to be deposited in the Staking Module, it is paired with another token (e.g., $USDC or $EGLD) to form Liquidity Pool (LP) position, contributing $75 million to the USH Staking Module. Additionally, the $100 million deposited in the Isolated Pools cycles through Liquid Staking and into the Lending Protocol, contributing a total of $300 million in TVL. Total TVL Breakdown: • $300 million from assets flowing through Isolated Pools ($100m) → Liquid Staking ($100m) → Lending Protocol ($100m) • $75 million from LP positions in the USH Staking Module Total TVL = $375 million As mentioned above, the $100 million deposited in Isolated Pools generates approximately $10.5 million annually in staking rewards (6% APY from $sEGLD and 15% APY from $swTAO). If all minted $USH is deposited into the Staking Module, the $75 million staked would benefit from these rewards, resulting in a 14% APY for USH LP stakers. On top of the protocol’s rewards, liquidity providers earn additional fees from their LP positions on decentralized exchanges, creating the perfect opportunity for all the participants in the USH Staking Module looking for attractive yields. USH Stability: The Peg Mechanism Ensuring the stability of USH is paramount, and to maintain its value close to $1 under all market conditions, we’ve implemented a robust dual peg mechanism. This system consists of two key layers of protection—Soft Peg and Hard Peg—designed to keep USH stable through both market-driven incentives and other mechanisms for scenarios where the Soft Peg mechanism can’t reclaim the peg. 1. Soft Peg Mechanism The Soft Peg Mechanism helps keep USH stable around its $1 value by encouraging market participants to act when USH trades above or below $1. When USH trades below $1 Users can buy USH at a discount, on a DEX, and repay their USH loans on Hatom, as USH is always valued at $1 on the protocol. This action removes $USH from circulation, helping to restore its price. When USH trades above $1 Users can borrow USH from the protocol at $1 and sell it on the open market at the higher price, increasing the circulating supply of USH and pushing its price back down to $1. 2. Hard Peg Mechanism (Redemption Mode) In cases where the Soft Peg alone cannot restore USH to $1 and its price drops significantly below the peg, the Hard Peg Mechanism is triggered through Redemption Mode. This mechanism allows any market participant to step in and help restore the peg by repaying USH loans for other borrowers, seizing their collateral at the full $1 value. It's important to note that Redemption Mode is only activated in the Isolated Pools and does not impact users minting USH through the Lending Protocol. Here’s how Redemption Mode works: When USH trades below $1 and the Redemption Mode is activated, redeemers can buy USH at the lower market price (e.g., $0.95), and use it to repay borrowers' debts at the full $1 value within the protocol. The redeemer receives collateral in the form of liquid staked tokens(such as $sEGLD or $swTAO) equivalent to the USH they repaid at its full $1 value, profiting from the difference between the discounted purchase price and the redemption value. The borrower being redeemed also benefits by receiving a redemption bonus, which allows them to keep a portion of their collateral after part of it is seized after loan was repaid. This system ensures that borrowers are not penalized during redemption, creating a balanced mechanism where both the redeemer and the borrower have something to gain. Redemption Mode differs from Liquidation in several ways: Redemption is triggered by USH falling below $1 and involves repaying borrower accounts to restore the peg. Both the redeemer and the borrower benefit, with the redeemer profiting from the price difference, and the borrower receiving a bonus from their collateral. Liquidation occurs when a borrower’s collateral falls below a certain threshold, making them risky. During liquidation, a portion of the borrower’s loan is repaid, and the collateral is seized, while also incurring a liquidation penalty. Redemption Mode uses a data structure known as a Red-Black Tree to efficiently monitor and rank all borrower positions within the protocol smart contract itself. This structure dynamically tracks borrowers based on their Borrow Limit Used, which is the percentage of collateral they have utilized relative to their borrowing capacity. The system prioritizes borrowers with the highest Borrow Limit Used, meaning those who have borrowed the most relative to their collateral are considered first for redemption. USH Airdrop Regarding the USH Airdrop, we would like to inform you that snapshots will end once USH is deployed on the Public Mainnet. The airdrop will be concluded shortly after, once all liquidity pools are stable and we determine the optimal moment to distribute the rewards to the community. USH Staking Module & Booster V2 The USH Staking Module will play a critical role in maintaining deep liquidity for USH while offering users high-yield opportunities. By staking USH LP tokens, such as USH/USDC and USH/EGLD, users can earn rewards generated by USH facilitators. This approach strengthens USH’s liquidity pools, making them robust enough to handle significant trades without destabilizing its price, thus reinforcing USH’s peg and overall stability. Beyond creating robust liquidity, the USH Staking Module serves as the key utility module within the USH ecosystem, designed to provide users with an opportunity to earn high yields on their USH holdings in a sustainable and organic way. All rewards distributed through the module are generated by various products across the Hatom ecosystem, ensuring long-term sustainability. For users seeking a more stable yield, the USH/USDC LP provides lower risk and steady returns. Those looking to leverage their EGLD holdings can opt for the USH/EGLD LP, which can be staked in the USH Staking Module. A key advantage of staking in the USH Staking Module is that rewards are based on the full value of the LP, not just the USH portion, maximizing your yield potential. As we continue to grow, we’ll be adding more LPs, providing users with even greater flexibility and options for staking their USH in the module. While our current focus is on LP tokens, we’re also exploring the possibility of allowing direct USH staking in the future, expanding the staking opportunities across the ecosystem. The Integration of Booster V2 with the Staking Module Booster V2 will be available for testing with the USH Devnet release, and with its introduction, we’ve strengthened the relationship between the HTM token and USH. Our ecosystem now features two independent boosters: one for the Lending Protocol and one for the USH Staking Module, each operating with the goal of maximizing yields for users. Key Improvements in Booster V2 Booster V2 brings several enhancements that elevate the functionality and user experience: Support for Multiple Token Types: Users will be able to deposit Pool Tokens, Farm Tokens, Dual Farm Tokens, or Staked HTM Tokens (via xExchange). Only the HTM portion will be considered for boosting. Unlimited Staking: The cap on HTM deposits will be removed, allowing users to stake without limits. This will foster a competitive environment where the more HTM you stake, the higher your potential APY. Integrated xExchange Management: Users will be able to manage their xExchange positions directly from the Booster dashboard. This will include creating pools, farming, dual farming, and staking HTM tokens, all from one convenient dashboard. Energy Management Integration: Booster V2 will allow users to manage their xExchange Energy directly from the dashboard, providing an additional way to boost rewards even further. Seamless Migration: Users will be able to migrate HTM between the Lending Protocol Booster and the USH Staking Module Booster without any cooldown periods, making it easier to optimize strategies across both modules. How the Yields Work Booster V2 will introduce a more structured and competitive approach to yield distribution across both the Lending Protocol and the Staking Module. HTM Booster in the Lending Protocol Base APY (First Batch): This is available to all users who stake a specific percentage of HTM relative to their collateral value. Any user can achieve this Base APY by staking the required amount of HTM. Boosted APY (Second Batch): After achieving the base level, users can boost their returns further by staking additional HTM, competing for the second batch of rewards. The more HTM staked beyond the base threshold, the higher the potential yield. USH Staking Module Yields Staking APY: Users who deposit USH-related LP tokens without boosting through the HTM Booster will still receive a Staking APY. This ensures that even passive participants which are not looking to stake their HTM in the Booster can take advantage of the USH Ecosystem to generate yields. Booster APY: Similar to the system in the Lending Protocol, users can stake HTM to unlock a Base APY. Beyond this threshold, any additional HTM staked will increase their APY in a competitive manner, allowing users to maximize their returns based on the amount of HTM they commit to boosting their positions. Rollout Plan for USH USH will be deployed in a phased rollout to ensure smooth implementation: Public Devnet: Open for testing, with incentives for participants to explore and stress-test the platform. Private Mainnet: A limited launch with partners to mint USH, bootstrap USH liquidity and generate initial protocol revenue. Public Mainnet: A full-scale launch, enabling all users to mint, stake, and trade USH. We know DeFi can be complex, which is why we’re committed to providing the tools and resources needed to navigate our ecosystem. With the USH Public Devnet launch, we’ll release updated documentation offering clear guidance on Hatom’s products. Developer documentation is also in the works, and we’re exploring the idea of a Hatom Academy for educational resources. Plus, we’ll soon roll out content focused on USH, helping users fully tap into its potential within Hatom and the MultiversX ecosystem. What’s Next? Hatom Pulse As Hatom grows, our focus remains on pushing DeFi boundaries while expanding across multiple ecosystems. Although this update doesn’t include a full roadmap—that will come later—our priority is clear: expanding Hatom across chains. To stand out in the competitive DeFi landscape, we’re committed to developing standout products. With that in mind, we’re excited to give you an exclusive preview of one of our most innovative products in development: Hatom Pulse. Over-collateralized non-custodial lending protocols, liquid staking, and over-collateralized stablecoins already exist on #Ethereum. What sets us apart is the synergy between these components within a unified ecosystem. By integrating these pillars, we tackle capital inefficiencies, allowing one protocol to enhance strategies that benefit the others, maximizing returns across the board. For example, when USH is minted, it means that EGLD is deposited, liquid-staked, and supplied in the lending protocol—all three protocols working in harmony. Hatom Pulse will elevate this synergy to another level, solving key issues faced by Aave, Compound Labs , and other leading protocols. We believe this innovation will be pivotal as we work to gain market share while expanding cross-chain. Our proof of concept will be deployed and battle-tested on #MultiversX, but the real growth will come when we scale this to markets that are thousands of times larger. This will be a turning point for Hatom. So, what is Hatom Pulse? On Hatom, like on Aave and other leading lending protocols, the largest assets used as collateral are often not borrowed, leading to substantial revenue loss for the protocol. This also results in very low income on the supply side, as borrowing fees depend on utilization rates, which only increase when borrowing activity rises. Generally, lending protocols are used to provide assets for borrowing stablecoins or for leveraging liquid staking strategies. This inefficiency locks up billions of dollars in dormant assets, and users earn very low supply rates on their collateral, which doesn’t help offset their loan interest. Hatom Pulse is designed to address these inefficiencies by leveraging the synergy between our existing products. It creates sophisticated vaults that activate dormant assets, unlocking advanced yield opportunities through a delta-neutral strategy. By utilizing assets like $EGLD, $sEGLD, $wTAO, and $swTAO, Hatom Pulse enables users to engage in delta-neutral strategies, where we long and short these assets on (CEXs), earning funding rates and staking rewards while keeping their assets intact. (The exact strategy, along with all the details, will be shared once USH is fully established). Initially, these vaults will operate on CEXs, where liquidity is highest, and will be managed through custodians like Copper.co to mitigate counterparty risks. Later, we plan to extend this to DEXs where all operations will be governed by smart contracts, ensuring full decentralization. serves as a strong proof of concept for us in this regard. However, our strategy will differ, as our focus will be on protecting the unit value, rather than the dollar value. Although Hatom Pulse is still in its research phase, early estimates suggest that this product alone could generate over 18% annual returns on $EGLD and more than 35% on $wTAO, with what we believe to be minimal risk. It’s important to note that these figures reflect current metrics based on internal calculations and may slightly differ upon product launch. But imagine reaching this on #Ethereum, while allowing users to borrow using their assets—this could be a disruptive protocol. We believe Hatom Pulse has the potential to become a cornerstone product as we transition into an omni-chain future. In a competitive DeFi landscape, it could give us a significant edge by offering something truly groundbreaking, capable of competing with well-established protocols across various chains. This strategy represents immense untapped potential. Hatom Pulse is being developed for risk-averse users who seek higher returns without excessive risk. By addressing inefficiencies in current DeFi strategies, we aim to offer a secure, robust option for yield generation that could rival established protocols. It's been an intense year for our team, and we sincerely thank the community for their patience, trust, and unwavering support as we've worked hard to build and deliver these groundbreaking products. As Hatom's omni-chain expansion nears, we remain focused on improving our existing products and researching new innovations to stay ahead in this competitive market. Our goal is to build a comprehensive DeFi ecosystem, accessible across all blockchains. With USH approaching its Mainnet release, we're proud of how our products have reshaped the DeFi landscape on MultiversX. By filling key gaps in the on-chain economy, we've created opportunities for users to generate yield, unlock the potential of decentralized finance, and provide strong utility for EGLD. In just over a year, we’ve built a strong ecosystem, but this is only the beginning. We’re ready to go even further, developing better products and unlocking new opportunities for our users. We’ll share more about our expansion plans in a dedicated post, staying focused on what matters most. Rest assured, what’s coming will be truly impressive for Hatom and our growing community!

Hatom Labs

182,902 görüntüleme • 1 yıl önce

I love taking a single "what if?" and turning it into a movie trailer. This one started with a simple question: What if someone had to manually start the Sun every morning? 15 seconds later... this happened. Made With Seedance 2.0 in Thank You AI Prompt: The Sunkeeper (15s Cinematic Fantasy Trailer — 16:9) Style DreamWorks-quality stylized realism blended with premium fantasy cinema, feature-film CGI, breathtaking cinematic composition, physically based rendering, ultra-detailed celestial architecture, enormous ancient mechanical structures, intricate brass and gold craftsmanship, volumetric god rays, dramatic atmospheric lighting, realistic clouds, global illumination, cinematic depth of field, realistic cloth simulation, natural hair physics, elegant camera movement, smooth feature-film animation, rich warm golds transitioning into deep blues and blacks, epic orchestral trailer atmosphere, mysterious and emotional tone, no dialogue, no subtitles, no logos, no watermarks, 16:9 aspect ratio. Main Character (Maintain identical appearance throughout all shots) A beautiful young woman in her early twenties with expressive features, long hair tied into a loose ponytail with soft strands flowing naturally in the wind, warm fair skin illuminated by golden light, determined yet kind eyes, slim athletic build. She wears elegant white-and-gold ceremonial robes layered over practical climbing attire, fitted leather belts and harnesses carrying ancient tools, tall leather boots, fingerless gloves, subtle celestial embroidery throughout her clothing. She carries an enormous ornate golden key almost as tall as herself, engraved with glowing solar runes. Her face, hairstyle, clothing, proportions, and accessories remain perfectly consistent throughout every scene. Environment Far above the clouds, beyond the mortal world, lies an ancient celestial engine responsible for creating every sunrise. Endless floating stone platforms connect colossal golden gears the size of castles. Massive rotating rings surround a dormant Sun suspended within an impossibly large mechanical framework. Giant brass pipes carry flowing rivers of glowing solar energy. Floating astronomical mechanisms drift slowly through the heavens while an endless sea of clouds stretches beneath everything. The environment feels ancient, sacred, and unimaginably vast. Scene 1 (0.0–4.0s): The Climb Fade in before dawn. The world below is completely dark beneath a blanket of stars. The camera glides through enormous rotating golden gears and towering celestial machinery suspended above an endless sea of clouds. Massive brass mechanisms slowly turn as glowing embers drift through the air. The young Sunkeeper sprints across narrow floating bridges connecting gigantic mechanical structures, her robes and hair flowing dramatically in the wind. She climbs ancient staircases carved into enormous gears before reaching the highest platform overlooking the dormant Sun. Epic orchestral music begins with soft choir and deep cinematic percussion. Scene 2 (4.0–8.0s): Awakening the Sun At the center of the celestial engine stands a gigantic circular mechanism surrounding the dark, dormant Sun. The Sunkeeper plants the enormous golden key into an ancient lock. Close-up of intricate solar runes igniting one after another across the key. She grips it tightly. With all her strength, she slowly turns the key. An immense metallic click echoes across the heavens. Colossal gears begin rotating. Golden rivers of energy surge through enormous transparent pipes. Gigantic mechanical rings surrounding the Sun begin spinning gracefully. Thousands of celestial mirrors unfold simultaneously, reflecting brilliant golden light toward the dormant Sun. The heavens tremble. Scene 3 (8.0–11.5s): Sunrise The Sun slowly awakens. Golden fire spreads beautifully across its surface. Warm light floods the sky. Clouds transform into glowing shades of gold, orange, and crimson. The first rays of sunlight race across the horizon. The celestial engine operates in perfect harmony. The Sunkeeper smiles with quiet relief, believing the ancient ritual has succeeded once again. Everything feels peaceful. Scene 4 (11.5–14.2s): The Failure A sharp metallic crack echoes through the heavens. The smile disappears. One enormous gear suddenly stops turning. Then another. The golden energy begins flickering violently. Cracks race across the colossal celestial machinery. The giant key fractures in her hands. The rotating rings surrounding the Sun slow to a halt. The Sun rapidly loses its light. Warm colors vanish. Darkness consumes the sky in seconds. The camera pulls back to reveal the enormous celestial engine completely frozen above an endless world swallowed by night. The Sunkeeper stands alone, illuminated only by the fading glow of the dying Sun. Scene 5 (14.2–15.0s): Title Card Instant cut to black. One deep cinematic orchestral impact. Elegant glowing gold serif text slowly fades in: "Every sunrise has a keeper." A brief pause. The text fades into a second line: "Tomorrow may never come." The final haunting choir note echoes as tiny golden embers drift across the black screen before fading into complete darkness. Cinematic Direction Feature-film pacing, seamless camera transitions, smooth crane shots, cinematic push-ins, large sense of scale, elegant composition, believable character animation, expressive facial acting, subtle breathing and eye movement, physically accurate lighting, premium volumetric clouds, intricate brass textures, realistic mechanical motion, restrained magical effects, epic fantasy atmosphere, emotional trailer pacing. Every frame should feel like a $250 million fantasy film. No exaggerated anime expressions, no cartoon comedy, no oversaturated colors, no text except the final title card, no glitches, no duplicated characters, no distorted anatomy, no extra limbs, no floating objects unless intentionally part of the celestial machinery.
0:15

I love taking a single "what if?" and turning it into a movie trailer. This one started with a simple question: What if someone had to manually start the Sun every morning? 15 seconds later... this happened. Made With Seedance 2.0 in Thank You AI Prompt: The Sunkeeper (15s Cinematic Fantasy Trailer — 16:9) Style DreamWorks-quality stylized realism blended with premium fantasy cinema, feature-film CGI, breathtaking cinematic composition, physically based rendering, ultra-detailed celestial architecture, enormous ancient mechanical structures, intricate brass and gold craftsmanship, volumetric god rays, dramatic atmospheric lighting, realistic clouds, global illumination, cinematic depth of field, realistic cloth simulation, natural hair physics, elegant camera movement, smooth feature-film animation, rich warm golds transitioning into deep blues and blacks, epic orchestral trailer atmosphere, mysterious and emotional tone, no dialogue, no subtitles, no logos, no watermarks, 16:9 aspect ratio. Main Character (Maintain identical appearance throughout all shots) A beautiful young woman in her early twenties with expressive features, long hair tied into a loose ponytail with soft strands flowing naturally in the wind, warm fair skin illuminated by golden light, determined yet kind eyes, slim athletic build. She wears elegant white-and-gold ceremonial robes layered over practical climbing attire, fitted leather belts and harnesses carrying ancient tools, tall leather boots, fingerless gloves, subtle celestial embroidery throughout her clothing. She carries an enormous ornate golden key almost as tall as herself, engraved with glowing solar runes. Her face, hairstyle, clothing, proportions, and accessories remain perfectly consistent throughout every scene. Environment Far above the clouds, beyond the mortal world, lies an ancient celestial engine responsible for creating every sunrise. Endless floating stone platforms connect colossal golden gears the size of castles. Massive rotating rings surround a dormant Sun suspended within an impossibly large mechanical framework. Giant brass pipes carry flowing rivers of glowing solar energy. Floating astronomical mechanisms drift slowly through the heavens while an endless sea of clouds stretches beneath everything. The environment feels ancient, sacred, and unimaginably vast. Scene 1 (0.0–4.0s): The Climb Fade in before dawn. The world below is completely dark beneath a blanket of stars. The camera glides through enormous rotating golden gears and towering celestial machinery suspended above an endless sea of clouds. Massive brass mechanisms slowly turn as glowing embers drift through the air. The young Sunkeeper sprints across narrow floating bridges connecting gigantic mechanical structures, her robes and hair flowing dramatically in the wind. She climbs ancient staircases carved into enormous gears before reaching the highest platform overlooking the dormant Sun. Epic orchestral music begins with soft choir and deep cinematic percussion. Scene 2 (4.0–8.0s): Awakening the Sun At the center of the celestial engine stands a gigantic circular mechanism surrounding the dark, dormant Sun. The Sunkeeper plants the enormous golden key into an ancient lock. Close-up of intricate solar runes igniting one after another across the key. She grips it tightly. With all her strength, she slowly turns the key. An immense metallic click echoes across the heavens. Colossal gears begin rotating. Golden rivers of energy surge through enormous transparent pipes. Gigantic mechanical rings surrounding the Sun begin spinning gracefully. Thousands of celestial mirrors unfold simultaneously, reflecting brilliant golden light toward the dormant Sun. The heavens tremble. Scene 3 (8.0–11.5s): Sunrise The Sun slowly awakens. Golden fire spreads beautifully across its surface. Warm light floods the sky. Clouds transform into glowing shades of gold, orange, and crimson. The first rays of sunlight race across the horizon. The celestial engine operates in perfect harmony. The Sunkeeper smiles with quiet relief, believing the ancient ritual has succeeded once again. Everything feels peaceful. Scene 4 (11.5–14.2s): The Failure A sharp metallic crack echoes through the heavens. The smile disappears. One enormous gear suddenly stops turning. Then another. The golden energy begins flickering violently. Cracks race across the colossal celestial machinery. The giant key fractures in her hands. The rotating rings surrounding the Sun slow to a halt. The Sun rapidly loses its light. Warm colors vanish. Darkness consumes the sky in seconds. The camera pulls back to reveal the enormous celestial engine completely frozen above an endless world swallowed by night. The Sunkeeper stands alone, illuminated only by the fading glow of the dying Sun. Scene 5 (14.2–15.0s): Title Card Instant cut to black. One deep cinematic orchestral impact. Elegant glowing gold serif text slowly fades in: "Every sunrise has a keeper." A brief pause. The text fades into a second line: "Tomorrow may never come." The final haunting choir note echoes as tiny golden embers drift across the black screen before fading into complete darkness. Cinematic Direction Feature-film pacing, seamless camera transitions, smooth crane shots, cinematic push-ins, large sense of scale, elegant composition, believable character animation, expressive facial acting, subtle breathing and eye movement, physically accurate lighting, premium volumetric clouds, intricate brass textures, realistic mechanical motion, restrained magical effects, epic fantasy atmosphere, emotional trailer pacing. Every frame should feel like a $250 million fantasy film. No exaggerated anime expressions, no cartoon comedy, no oversaturated colors, no text except the final title card, no glitches, no duplicated characters, no distorted anatomy, no extra limbs, no floating objects unless intentionally part of the celestial machinery.

Maria

41,102 görüntüleme • 1 ay önce

let me explain what's actually happening with $ZOE and why most of CT is gonna miss this entire window Charms is launching their public platform this month. character economy. AI characters that are tokens, tokens that are characters, every trade pays creator fees forever. $ZOE is the first one. live this week on Base, deployed through clanker. CA: 0xC29832025E7652ef58D15F7fA3e232A2fDfaaB07 three things you need to clock: 1. this is the platform's launch token. not a random clanker. the FIRST character ever shipped from Charms, used by the team to demo what the entire economy will look like once public launch hits. every other character coming after $ZOE references $ZOE. that's a specific kind of asset and the market historically misprices these on day 1. 2. the creator fee model is the real bull case. Charms straight up posted: if you had launched Zoe, you'd have made $15K+ in fees in 24h. that number tells you exactly how much volume they're routing through this thing. 0.8% of every single trade goes to creator. perpetually. now imagine that fee tap on a token that becomes the reference asset for an entire AI character economy. 3. they're paying $15K + 3 months of fees to top 3 posters. think about what that means. they have so much confidence in the volume this thing will do that giving away 3 months of creator fees is a worthwhile marketing budget. teams don't do that math unless they expect the fee pipe to be massive. Clanker as the deployment layer is also not a footnote. CLANKER itself runs a revenue -> buyback flywheel. that infrastructure is battle tested. $ZOE plugs into a system that already works. the setup: - first character from a platform launching this month - pre-public-launch entry window - proven fee mechanics - Clanker rails underneath - team aggressively seeding distribution i'm not telling you what to do. i'm telling you the structure of this launch is one of the cleaner asymmetries on Base right now and the entry window is measured in days not weeks. if you wanna talk to her first. then decide. NFA. obviously.
0:15

let me explain what's actually happening with $ZOE and why most of CT is gonna miss this entire window Charms is launching their public platform this month. character economy. AI characters that are tokens, tokens that are characters, every trade pays creator fees forever. $ZOE is the first one. live this week on Base, deployed through clanker. CA: 0xC29832025E7652ef58D15F7fA3e232A2fDfaaB07 three things you need to clock: 1. this is the platform's launch token. not a random clanker. the FIRST character ever shipped from Charms, used by the team to demo what the entire economy will look like once public launch hits. every other character coming after $ZOE references $ZOE. that's a specific kind of asset and the market historically misprices these on day 1. 2. the creator fee model is the real bull case. Charms straight up posted: if you had launched Zoe, you'd have made $15K+ in fees in 24h. that number tells you exactly how much volume they're routing through this thing. 0.8% of every single trade goes to creator. perpetually. now imagine that fee tap on a token that becomes the reference asset for an entire AI character economy. 3. they're paying $15K + 3 months of fees to top 3 posters. think about what that means. they have so much confidence in the volume this thing will do that giving away 3 months of creator fees is a worthwhile marketing budget. teams don't do that math unless they expect the fee pipe to be massive. Clanker as the deployment layer is also not a footnote. CLANKER itself runs a revenue -> buyback flywheel. that infrastructure is battle tested. $ZOE plugs into a system that already works. the setup: - first character from a platform launching this month - pre-public-launch entry window - proven fee mechanics - Clanker rails underneath - team aggressively seeding distribution i'm not telling you what to do. i'm telling you the structure of this launch is one of the cleaner asymmetries on Base right now and the entry window is measured in days not weeks. if you wanna talk to her first. then decide. NFA. obviously.

toxacnphnk.eth

13,915 görüntüleme • 4 ay önce