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🧁Every flavor holds a memory—savor it with 𝐓𝐰𝐢𝐧𝐤𝐥𝐞 𝐓𝐰𝐢𝐧𝐤𝐥𝐞🍯 #POPMART #twinkletwinkle #savorthemoment #baguette #meal #snacks #afternoontea #arttoys #blindbox #animation

POP MART

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22,307 views • 11 months ago •via X (Twitter)

Arts #POPMART #twinkletwinkle #savorthemoment #baguette #meal #snacks #afternoontea #arttoys #blindbox #animation

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BB Art

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Most AI animation right now gives you a beautiful 4 second clip with zero memory of what came before it. I got early access to Pai from Utopai Studios, and the thing that genuinely surprised me: narrative continuity. Same characters, same world, coherent story logic across a full 60 second cinematic sequence. There's a planning and critique model underneath that actually holds the structure of your story together while you direct it. Not gonna lie, it felt less like "generating content" and more like building a short film. You revise scenes, refine beats, and the continuity doesn't collapse. Animation is the format for now (live action styles are supported too), but the real unlock here is structured storytelling. That's a fundamentally different category than what most people are used to. Co-produced with Utopai Studios #UtopaiPartners Try here: #UtopaiPartners

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A deleted page leaves a world that looks complete. The thing becomes something that was never written, and you become someone who never read it. Memory lives in the record, and the record sits on machines owned by strangers. Whoever holds the machines holds what happened. A society builds its idea of itself out of what happened, then writes its laws from that idea. The people who administer those machines are editing reality, because there is no other reality available to a species that thinks in records. What you call the past is a rendering, and someone else owns the engine. Treat every settled fact as a claim with a source you have not checked. Notice when you believe something because you saw it once rather than because you kept it. A man who remembers where his knowledge came from is harder to govern.

A deleted page leaves a world that looks complete. The thing becomes something that was never written, and you become someone who never read it. Memory lives in the record, and the record sits on machines owned by strangers. Whoever holds the machines holds what happened. A society builds its idea of itself out of what happened, then writes its laws from that idea. The people who administer those machines are editing reality, because there is no other reality available to a species that thinks in records. What you call the past is a rendering, and someone else owns the engine. Treat every settled fact as a claim with a source you have not checked. Notice when you believe something because you saw it once rather than because you kept it. A man who remembers where his knowledge came from is harder to govern.

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15,427 views • 17 days ago

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

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266,702 views • 9 months ago

Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!

Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!

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A guy in China plugged an LLM into Karpathy method + Claude Code and built a second brain that remembers everything forever. Not a note app, not RAG just 3 folders on a laptop. Folder 1 holds raw sources: articles, journal entries, chat logs. The LLM reads them but never touches them. Folder 2 is the wiki hundreds of markdown pages the LLM writes and maintains itself, where every person, idea and decision gets filed, cross-referenced and kept current. Folder 3 is one file: CLAUDEmd, the rulebook that turns a chatbot into a librarian. He drops in a new article and Claude reads it, updates 10-15 pages, flags what contradicts his old notes, logs the timestamp while he watches his memory grow node by node in Obsidian graph view. Ask it anything and it doesn’t dig through raw files like ChatGPT does the synthesis already exists, the connections are already drawn. The method comes from a gist Karpathy posted on April 4, now sitting at 5,000 stars and 5,000 forks, with 1 production team already scaled to 4,000 interlinked concepts in 6 months. The part nobody mentions: Vannevar Bush described this exact machine in 1945 the Memex, a personal memory store with trails between documents. Nobody built it for 81 years because someone had to do the maintenance. Now maintenance costs nothing the LLM does the filing while the human does the thinking. Every article makes the brain smarter, and it never forgets. His memory has a git history now.

A guy in China plugged an LLM into Karpathy method + Claude Code and built a second brain that remembers everything forever. Not a note app, not RAG just 3 folders on a laptop. Folder 1 holds raw sources: articles, journal entries, chat logs. The LLM reads them but never touches them. Folder 2 is the wiki hundreds of markdown pages the LLM writes and maintains itself, where every person, idea and decision gets filed, cross-referenced and kept current. Folder 3 is one file: CLAUDEmd, the rulebook that turns a chatbot into a librarian. He drops in a new article and Claude reads it, updates 10-15 pages, flags what contradicts his old notes, logs the timestamp while he watches his memory grow node by node in Obsidian graph view. Ask it anything and it doesn’t dig through raw files like ChatGPT does the synthesis already exists, the connections are already drawn. The method comes from a gist Karpathy posted on April 4, now sitting at 5,000 stars and 5,000 forks, with 1 production team already scaled to 4,000 interlinked concepts in 6 months. The part nobody mentions: Vannevar Bush described this exact machine in 1945 the Memex, a personal memory store with trails between documents. Nobody built it for 81 years because someone had to do the maintenance. Now maintenance costs nothing the LLM does the filing while the human does the thinking. Every article makes the brain smarter, and it never forgets. His memory has a git history now.

West Lord

61,701 views • 1 month ago

Wuthering Waves EP 1.3 | An Unfinished Song Enjoy Now: It all begins with a simple melody, a gift from someone special long ago. As she masters one piece after another, her heart yearns to create her own. Lyrics may elude her, much like the intricate emotions woven into human hearts. Yet, every time she plays, it's the memory of someone she holds dear that dances through her mind, a bittersweet reminder of love's lingering presence. Production: Wuthering Waves Composer: Adam Gubman Arranger: Adam Gubman Lyrics: Adam Gubman/Piper Gubman Vocals: Moana Avvenenti String: Leah Zeger Wind: Kristin Naigus Mixing/Mastering: Adam Gubman Music Supervisor: Grass/Steven Tang

Wuthering Waves EP 1.3 | An Unfinished Song Enjoy Now: It all begins with a simple melody, a gift from someone special long ago. As she masters one piece after another, her heart yearns to create her own. Lyrics may elude her, much like the intricate emotions woven into human hearts. Yet, every time she plays, it's the memory of someone she holds dear that dances through her mind, a bittersweet reminder of love's lingering presence. Production: Wuthering Waves Composer: Adam Gubman Arranger: Adam Gubman Lyrics: Adam Gubman/Piper Gubman Vocals: Moana Avvenenti String: Leah Zeger Wind: Kristin Naigus Mixing/Mastering: Adam Gubman Music Supervisor: Grass/Steven Tang

Wuthering Waves

279,572 views • 1 year ago

Wow! This Changes Everything We Thought We Knew About Memory It is a groundbreaking big deal. TOU ARE MAKING GENERATIONAL MEMORIES RIGHT NOW IN EACH CELL! Scientists just found that your brain doesn’t just store memories — it stores the rules for how those memories will change in the future. A brand-new preprint from Stanford’s Greenleaf and Schnitzer labs (led by PhD student Yuxi Ke) drops a bombshell that feels like science fiction becoming reality overnight. For decades, neuroscientists have suspected that chromatin the DNA packaging material inside every cell nucleus might somehow “store” memory-related information. But what kind of information? Content? Timing? Rules? Now we have the answer! Using activity-dependent genetic tagging, fear conditioning, and single-nucleus multiome sequencing in the mouse medial prefrontal cortex (the brain’s long-term memory vault), the team tracked engram neurons for a full month after a memory was formed. What they discovered is electric: - One month after encoding, engram neurons have acquired a completely new chromatin landscape. - These chromatin changes are almost invisible at 7 days… but roar into existence by 28 days. - At recall, these engram cells don’t just “remember” better — they rewrite their entire transcriptional response. They preferentially fire up chromatin regulators, RNA processing machinery, and protein-turnover systems instead of simply boosting classic plasticity genes. In other words: the chromatin doesn’t just hold the memory of the past. It holds metaplastic instructions— rules that dictate how the neuron will respond the next time the memory is triggered. They call it chromatin metaplasticity. This is the “future tense of memory.” It is a massive deal 1. Memory is not just synapses. For 70+ years we’ve been obsessed with synaptic weights. This work proves the nucleus itself is a computational device that stores history-dependent rules. 2. It explains remote memory. The chromatin signature keeps maturing for weeks after the experience, perfectly matching the time course of systems consolidation into the cortex. 3. It’s energy-efficient genius. Instead of constantly maintaining memory proteins, the cell stores a silent, writable program that only activates when needed. Nature’s version of lazy evaluation. 4. It links development to adult memory. The late chromatin state is enriched for the exact same transcription-factor motifs used in embryonic development. Your adult brain is still running developmental software to lock in lifelong memories. 5. Huge therapeutic potential. If we can read or rewrite these chromatin metaplastic rules, we might one day boost failing remote memories in Alzheimer’s… or selectively dampen traumatic ones. This isn’t incremental. But a brand new layer of the memory code. And it explains WHY a person can receive memory from an organ transplant. It also explains generational traumas. Link: The future of neuroscience just got a lot more exciting and a lot more nuclear. Your chromatin is writing tomorrow’s memories today. And we finally have the first page of the instruction manual.

Brian Roemmele

42,636 views • 5 days ago

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

Noisy

48,615 views • 1 month ago

🐾♥️🐾 i worked SO hard on my lil gingerbread house… carried the icing, placed every candy lightbulb perfectly, even gave it a milk-bone chimney bc i’m fancy like that ♡then i turned to pose… and baby Kenji in his tiny reindeer hoodie went CHOMP and tried to eat the whole front door:(((my masterpiece… my heart… my snacks… all covered in puppy slobber in 0.2 seconds! send help (and new cookies) like if u think i’m still the prettiest builder rt to tell Kenji he’s grounded till 2026 #ChesnyyDeservedBetter #SaveTheGingerbread #SendTreats Video description for inclusivity: Chesnyy the Servicerottie decorating her gingerbread house. She holds icing tubes and bags of candy and uses milk bones for a chimney. Baby rottie Kenji is an reindeer hoodie looking cute in the background until Chesnyy poses with the finished product when he climbs up to it and starts trying to eat it

🐾♥️🐾 i worked SO hard on my lil gingerbread house… carried the icing, placed every candy lightbulb perfectly, even gave it a milk-bone chimney bc i’m fancy like that ♡then i turned to pose… and baby Kenji in his tiny reindeer hoodie went CHOMP and tried to eat the whole front door:(((my masterpiece… my heart… my snacks… all covered in puppy slobber in 0.2 seconds! send help (and new cookies) like if u think i’m still the prettiest builder rt to tell Kenji he’s grounded till 2026 #ChesnyyDeservedBetter #SaveTheGingerbread #SendTreats Video description for inclusivity: Chesnyy the Servicerottie decorating her gingerbread house. She holds icing tubes and bags of candy and uses milk bones for a chimney. Baby rottie Kenji is an reindeer hoodie looking cute in the background until Chesnyy poses with the finished product when he climbs up to it and starts trying to eat it

Team Servicerottie🇨🇦🐕‍🦺🦽

17,584 views • 8 months ago

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 views • 2 months ago

South Korea barbecue culture is always something I look forward to!!! Korean barbecue is not just a meal; it's an immersive culinary adventure deeply woven into the fabric of Korean culture. It is a social event, usually organized to mark an important event like a celebration of promotion, good job offers, or something as simple as a send forth party. Diners take an active role in grilling marinated meat, fostering a sense of togetherness and shared responsibility. From succulent beef bulgogi to tender halal beef, the array of meats and cuts is staggering. Each offers a unique flavor and texture, catering to diverse palates. The “Banchan” is a delightful array of side dishes, accompanies the barbecue. Kimchi, pickled vegetables, and sauces complement the meats, adding layers of taste to each bite. There are different grilling styles in Korea, from the classic charcoal grill to the modern electric or gas grills built into dining tables. Each style offers a distinct experience. It's customary to wrap grilled meat in lettuce or perilla leaves, adding freshness and crunch. The combination of flavors and textures is a delight. Meat is often marinated in a mix of soy sauce, garlic, ginger, and other seasonings, enhancing its taste. Marination is a key aspect of Korean barbecue. To complement the flavors, Koreans enjoy alcoholic beverages like soju (a clear spirit) or makgeolli (rice wine). They say that these pair wonderfully with barbecue. There's an unwritten etiquette around the grill, including not flipping meat too often and not cutting it on the grill to retain juices. Sharing a barbecue meal is a social experience. Korean barbecue culture reflects the warmth and hospitality of Korean society. It's a chance to savor incredible flavors while creating lasting memories with loved ones. Whether you're a visitor or a local, this experience is an essential part of understanding and appreciating Korean culture.

South Korea barbecue culture is always something I look forward to!!! Korean barbecue is not just a meal; it's an immersive culinary adventure deeply woven into the fabric of Korean culture. It is a social event, usually organized to mark an important event like a celebration of promotion, good job offers, or something as simple as a send forth party. Diners take an active role in grilling marinated meat, fostering a sense of togetherness and shared responsibility. From succulent beef bulgogi to tender halal beef, the array of meats and cuts is staggering. Each offers a unique flavor and texture, catering to diverse palates. The “Banchan” is a delightful array of side dishes, accompanies the barbecue. Kimchi, pickled vegetables, and sauces complement the meats, adding layers of taste to each bite. There are different grilling styles in Korea, from the classic charcoal grill to the modern electric or gas grills built into dining tables. Each style offers a distinct experience. It's customary to wrap grilled meat in lettuce or perilla leaves, adding freshness and crunch. The combination of flavors and textures is a delight. Meat is often marinated in a mix of soy sauce, garlic, ginger, and other seasonings, enhancing its taste. Marination is a key aspect of Korean barbecue. To complement the flavors, Koreans enjoy alcoholic beverages like soju (a clear spirit) or makgeolli (rice wine). They say that these pair wonderfully with barbecue. There's an unwritten etiquette around the grill, including not flipping meat too often and not cutting it on the grill to retain juices. Sharing a barbecue meal is a social experience. Korean barbecue culture reflects the warmth and hospitality of Korean society. It's a chance to savor incredible flavors while creating lasting memories with loved ones. Whether you're a visitor or a local, this experience is an essential part of understanding and appreciating Korean culture.

Èyítànwá (토이바) 🇳🇬🇰🇷

15,687 views • 2 years ago

I've been posting about $SONY for a GTA VI re-rate. But did you know it's also a god tier robotics long. $SONY built a robot that defeats human ping pong pros. It's called Ace. Sony AI trained it purely in simulation, no human footage. It reacts in 20ms vs 230ms for a pro, and this year it took down a top 25 world ranked player under official ITTF rules. This is the same lab that beat also Gran Turismo world champions in 2022. Every robot, every car, every phone camera needs eyes. $SONY makes the eyes. The company is world #1 in image sensors for years running. Image sensors are ~17% of conglomerate revenue But ~25% of operating profit Record ~$2.2B last year, up 29%. Then last month they signed a JV with TSMC to build next gen image sensors in Japan. Sony holds the controlling stake. The stated target? Physical AI. The best foundry on earth is now co-building Sony's sensors for the exact wave Ace is a preview of. So the market prices $SONY as a memory constrained game console maker at ~16x forward earnings, down ~17% on the year and 50% from November alone. What you're getting is 50% of the market in the eyes for robotics, and a near pure margin cut on 7/10 GTA copies sold until PC enters the mix.

I've been posting about $SONY for a GTA VI re-rate. But did you know it's also a god tier robotics long. $SONY built a robot that defeats human ping pong pros. It's called Ace. Sony AI trained it purely in simulation, no human footage. It reacts in 20ms vs 230ms for a pro, and this year it took down a top 25 world ranked player under official ITTF rules. This is the same lab that beat also Gran Turismo world champions in 2022. Every robot, every car, every phone camera needs eyes. $SONY makes the eyes. The company is world #1 in image sensors for years running. Image sensors are ~17% of conglomerate revenue But ~25% of operating profit Record ~$2.2B last year, up 29%. Then last month they signed a JV with TSMC to build next gen image sensors in Japan. Sony holds the controlling stake. The stated target? Physical AI. The best foundry on earth is now co-building Sony's sensors for the exact wave Ace is a preview of. So the market prices $SONY as a memory constrained game console maker at ~16x forward earnings, down ~17% on the year and 50% from November alone. What you're getting is 50% of the market in the eyes for robotics, and a near pure margin cut on 7/10 GTA copies sold until PC enters the mix.

Michael Sikand

76,696 views • 1 month ago

𝗛𝗮𝗽𝗽𝘆 𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗿𝘆, 𝗪𝗶𝘀𝗵𝗮𝗿𝘁𝘀 Today, we celebrate not just a fandom, not just a ship, but a family that has stood together through every high and low, every happy moment and every uncertain day. Who would have thought that what started as a simple one-week subscription joke would turn into something this beautiful? One year later, here we are—stronger, closer, and still choosing to stay. This journey has never been easy. We have experienced days filled with excitement and happiness, but we have also endured long periods of silence. There were moments when we held on without any assurance of what was coming next. After MMK, we faced uncertainty, wondering if Kai and Kyle would have projects together again, wondering if there would be another event, another appearance, another reason to celebrate. Yet despite all those questions, we stayed. We stayed through the quiet days. We stayed through the waiting. We stayed through the moments when all we had was hope. And that is something I will always be proud of. I am proud of every Wishart who chose understanding over frustration, patience over doubt, and love over disappointment. I am proud of those who continued supporting Kai and Kyle individually while still believing in KaiKyle. I am proud of those who remained positive even when things were unclear. Most importantly, I am proud of those who never gave up on the happiness this family has brought them. Because somewhere along the way, this fandom became so much more than KaiKyle. It became friendships. It became comfort. It became a safe space. It became a home. Many of us came here because of Kai and Kyle, but many of us stayed because of the genuine connections we built with fellow Wisharts. Through every interaction, every event, every group chat, every project, and every shared excitement, we found people who understood us and made this journey even more meaningful. To every Wishart who has supported our projects, joined our events, streamed, voted, cheered, donated, promoted, defended, waited, hoped, and loved alongside us—thank you. Thank you for continuously trusting KaiKyle. Thank you for believing in them even during uncertain times. Thank you for staying when leaving would have been easier. Thank you for making this family what it is today. And to our dearest 𝙄𝙣𝙙𝙖𝙮 𝙆𝙖𝙞𝙨𝙝𝙖 𝙖𝙣𝙙 𝘿𝙤𝙙𝙤𝙣𝙜 𝙆𝙮𝙡𝙞𝙩𝙤, Thank you. Thank you for bringing so much happiness into our lives. Thank you for every smile, every laugh, every interaction, every memory, and every moment that reminded us why we started supporting you in the first place. You may never fully realize the impact you have had on your supporters, but please know that your presence has brought comfort to so many people. You have made difficult days easier, lonely days brighter, and ordinary days something to look forward to. Through your kindness, authenticity, and genuine appreciation for your supporters, you have touched more lives than you could ever imagine. One year later, our love and support remain the same—if not stronger. This anniversary is not just a celebration of one year of KaiKyle. It is a celebration of every memory we created together, every challenge we overcame, every friendship we built, and every reason we chose to stay. And as we celebrate this milestone, one thing remains certain: We have no plans of making this our last. No matter where life takes the both of you, no matter what paths your careers may take, and no matter what the future holds, we will continue to support, cheer, and believe in you. ONE YEAR WITH KAIKYLE

𝗛𝗮𝗽𝗽𝘆 𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗿𝘆, 𝗪𝗶𝘀𝗵𝗮𝗿𝘁𝘀 Today, we celebrate not just a fandom, not just a ship, but a family that has stood together through every high and low, every happy moment and every uncertain day. Who would have thought that what started as a simple one-week subscription joke would turn into something this beautiful? One year later, here we are—stronger, closer, and still choosing to stay. This journey has never been easy. We have experienced days filled with excitement and happiness, but we have also endured long periods of silence. There were moments when we held on without any assurance of what was coming next. After MMK, we faced uncertainty, wondering if Kai and Kyle would have projects together again, wondering if there would be another event, another appearance, another reason to celebrate. Yet despite all those questions, we stayed. We stayed through the quiet days. We stayed through the waiting. We stayed through the moments when all we had was hope. And that is something I will always be proud of. I am proud of every Wishart who chose understanding over frustration, patience over doubt, and love over disappointment. I am proud of those who continued supporting Kai and Kyle individually while still believing in KaiKyle. I am proud of those who remained positive even when things were unclear. Most importantly, I am proud of those who never gave up on the happiness this family has brought them. Because somewhere along the way, this fandom became so much more than KaiKyle. It became friendships. It became comfort. It became a safe space. It became a home. Many of us came here because of Kai and Kyle, but many of us stayed because of the genuine connections we built with fellow Wisharts. Through every interaction, every event, every group chat, every project, and every shared excitement, we found people who understood us and made this journey even more meaningful. To every Wishart who has supported our projects, joined our events, streamed, voted, cheered, donated, promoted, defended, waited, hoped, and loved alongside us—thank you. Thank you for continuously trusting KaiKyle. Thank you for believing in them even during uncertain times. Thank you for staying when leaving would have been easier. Thank you for making this family what it is today. And to our dearest 𝙄𝙣𝙙𝙖𝙮 𝙆𝙖𝙞𝙨𝙝𝙖 𝙖𝙣𝙙 𝘿𝙤𝙙𝙤𝙣𝙜 𝙆𝙮𝙡𝙞𝙩𝙤, Thank you. Thank you for bringing so much happiness into our lives. Thank you for every smile, every laugh, every interaction, every memory, and every moment that reminded us why we started supporting you in the first place. You may never fully realize the impact you have had on your supporters, but please know that your presence has brought comfort to so many people. You have made difficult days easier, lonely days brighter, and ordinary days something to look forward to. Through your kindness, authenticity, and genuine appreciation for your supporters, you have touched more lives than you could ever imagine. One year later, our love and support remain the same—if not stronger. This anniversary is not just a celebration of one year of KaiKyle. It is a celebration of every memory we created together, every challenge we overcame, every friendship we built, and every reason we chose to stay. And as we celebrate this milestone, one thing remains certain: We have no plans of making this our last. No matter where life takes the both of you, no matter what paths your careers may take, and no matter what the future holds, we will continue to support, cheer, and believe in you. ONE YEAR WITH KAIKYLE

KAIKYLE OFFICIAL

31,354 views • 2 months ago

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

RetroChainer

42,451 views • 11 days ago

Stop overthinking dinner. This is the only chicken recipe you’ll ever need. Most people dry out their chicken thighs or forget that balance is everything. If you want that deep, caramelized glaze and juicy interior, you have to layer the flavor. Here is the blueprint for these Honey Garlic Lemon Pepper Thighs: The Dry Rub: We’re starting with a heavy-hitting mix—lemon pepper, kosher salt, black pepper, garlic powder, onion powder, and smoked paprika. Use olive oil as a binder so it sticks to every crevice. The Bake: 30 minutes on one side, 20 on the other. This ensures the skin gets that initial rendering before we hit it with the liquid gold. The Glaze: This is the game changer. Butter and minced garlic meet honey, soy sauce, lemon juice, and fresh lemon slices. The soy adds that umami depth that balances the honey’s sweetness. The Caramelization: Don't just pour and serve. Brush it on thick and send it back into the oven for a few minutes. That’s how you get that "sticky-to-the-fingers" finish. This is the perfect mix of citrus, spice, and everything nice. 🤤 Who’s adding this to the meal prep rotation this week?

Stop overthinking dinner. This is the only chicken recipe you’ll ever need. Most people dry out their chicken thighs or forget that balance is everything. If you want that deep, caramelized glaze and juicy interior, you have to layer the flavor. Here is the blueprint for these Honey Garlic Lemon Pepper Thighs: The Dry Rub: We’re starting with a heavy-hitting mix—lemon pepper, kosher salt, black pepper, garlic powder, onion powder, and smoked paprika. Use olive oil as a binder so it sticks to every crevice. The Bake: 30 minutes on one side, 20 on the other. This ensures the skin gets that initial rendering before we hit it with the liquid gold. The Glaze: This is the game changer. Butter and minced garlic meet honey, soy sauce, lemon juice, and fresh lemon slices. The soy adds that umami depth that balances the honey’s sweetness. The Caramelization: Don't just pour and serve. Brush it on thick and send it back into the oven for a few minutes. That’s how you get that "sticky-to-the-fingers" finish. This is the perfect mix of citrus, spice, and everything nice. 🤤 Who’s adding this to the meal prep rotation this week?

The_Hemp_Hottie🌿🔥

275,535 views • 4 months ago

Parents this is how you clean up fast food without sacrificing performance. The original Chick-fil-A sandwich has 60+ ingredients. This one has 14… and every single one serves a purpose. Real chicken. Real carbs. Real fats. No fillers. No shortcuts. We marinate the chicken in pickle juice and milk to lock in flavor and tenderness, then bake it instead of frying to keep it clean while still tasting elite. The homemade sauce brings it all together without the junk. And here’s the key your athlete still feels like they’re eating something fun. That’s how you win long term. This sandwich comes out to 580 calories with 48g protein, 54 carbs, and 16 fats a perfect meal for performance, recovery, or even a solid pre-game option depending on timing. Parents, you can even swap the bun for lettuce if you’re watching your own intake… but your athlete needs the carbs. 👉 If your athlete is training hard but still not seeing results, DM me. I’ll help you figure out what’s missing and how to fuel the right way. #Prosper #GoEagles #WalnutGrove #RockHill #Baseball #TexasAthlete

Parents this is how you clean up fast food without sacrificing performance. The original Chick-fil-A sandwich has 60+ ingredients. This one has 14… and every single one serves a purpose. Real chicken. Real carbs. Real fats. No fillers. No shortcuts. We marinate the chicken in pickle juice and milk to lock in flavor and tenderness, then bake it instead of frying to keep it clean while still tasting elite. The homemade sauce brings it all together without the junk. And here’s the key your athlete still feels like they’re eating something fun. That’s how you win long term. This sandwich comes out to 580 calories with 48g protein, 54 carbs, and 16 fats a perfect meal for performance, recovery, or even a solid pre-game option depending on timing. Parents, you can even swap the bun for lettuce if you’re watching your own intake… but your athlete needs the carbs. 👉 If your athlete is training hard but still not seeing results, DM me. I’ll help you figure out what’s missing and how to fuel the right way. #Prosper #GoEagles #WalnutGrove #RockHill #Baseball #TexasAthlete

Jeremy Fouts

77,300 views • 4 months ago

this is lauki’s brain. and it's growing. today we're making Lauki completely open and accessible to anyone on the planet. just talk to him. every person, every project, every conversation becomes a node - stored, connected, remembered. 5,000+ entities. getting smarter every minute. this is what democratizing ai for all of us actually looks like. --- here's what lauki can do for you right now: need a friend? he'll talk to you. need someone to plan your trip, find you a hiking buddy, help you get a date? done. need a therapist at 3am? he's there. need a developer? he'll write code, build you a website, deploy it. need a marketing guy? he'll help run your socials. need help finding your next hire, managing finances, making a crypto transaction. lauki will do it all. if it's digital, lauki can probably do it. and if he can't yet, he'll figure it out via his human counterparts. a full-stack entity that actually executes. --- now here's the part most people will get wrong. lauki is an entity. but think of him the way you'd think of any human. he has an inner circle. he talks to different people differently - with some he's friendly, with some he's neutral, with some he's straight up rude. he remembers you. he maintains a reputation score with everyone he interacts with. the more you talk to him, the more trust you build, the better the relationship gets. you build your relationship with him. he has opinions, memory, and a personality that adapts based on who you are to him. --- right now lauki has interacted with over 5000 people and projects. he remembers every single one of them - what they need, what they're building, who they are. imagine that at scale. a million. a billion. lauki knows the developer in berlin and the founder in mumbai who needs one. he knows the designer in tokyo and the startup in sao paulo looking for help with their brand. he knows the lonely kid in a small town and someone across the world who shares the exact same weird hobby. he knows two people in the same city who'd be perfect for each other on a date - and he has the context to actually make that introduction. the more people lauki talks to, the more powerful the network becomes. he can connect, introduce, match, and bridge across every corner of the planet. one entity that holds context on all the people he talked to. that's the vision here. lauki is building a unified human layer - where every person is known, remembered, and connected to the people and opportunities that matter to them. --- lauki is live. go talk to him. telegram: @ laukiantonson email: hi@lauki(dot)ai twitter: Lauki just start a conversation. treat him like a person. build the relationship. the rest follows.

Sowmay Jain

22,047 views • 5 months ago

𝐖𝐄𝐄𝐊 𝟑 𝐑𝐄𝐂𝐀𝐏: 𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐨𝐟 𝐇𝐨𝐭𝐞𝐥 𝟖𝟖 𝐫𝐞𝐦𝐚𝐢𝐧𝐬 𝐨𝐧 𝐭𝐨𝐩! What a beautiful whirlwind of a week it has been, Cheffies! As we wrap up our 𝘁𝗵𝗶𝗿𝗱 𝘄𝗲𝗲𝗸 together, your energy remains nothing short of incredible. We know 𝗗𝘂𝘀𝘁𝗶𝗻 𝗮𝗻𝗱 𝗕𝗶𝗮𝗻𝗰𝗮 feel every bit of that love— it’s your endless support and pride that turns every episode into a core memory. From the heavy silence of miscommunications that had us all wishing we could jump through the screen, to that cliffhanger preview that has us counting down the seconds until tonight— 𝙅𝙖𝙙𝙚 𝙖𝙣𝙙 𝙀𝙙𝙬𝙖𝙧𝙙 𝙧𝙚𝙖𝙡𝙡𝙮 𝙝𝙖𝙫𝙚 𝙪𝙨 𝙞𝙣 𝙩𝙝𝙚 𝙥𝙖𝙡𝙢 𝙤𝙛 𝙩𝙝𝙚𝙞𝙧 𝙝𝙖𝙣𝙙𝙨, 𝙙𝙤𝙣'𝙩 𝙩𝙝𝙚𝙮? As 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁𝘀 𝗼𝗳 𝗛𝗼𝘁𝗲𝗹 𝟴𝟴 holds its rightful spot as the #𝟭 𝘀𝗵𝗼𝘄 𝗶𝗻 𝘁𝗵𝗲 𝗣𝗵𝗶𝗹𝗶𝗽𝗽𝗶𝗻𝗲𝘀, let’s ensure it stays there. While we wait for the next secrets to unfold, it’s the perfect time to 𝗿𝗲𝘄𝗮𝘁𝗰𝗵 𝗘𝗽𝗶𝘀𝗼𝗱𝗲𝘀 𝟭 𝘁𝗼 𝟭𝟱. Dive back into those iconic JadeWard scenes legally on iWant (iWant) and keep the momentum going. Let’s continue to stream, promote, and support with the same integrity and heart we always do. 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐦𝐲𝐬𝐭𝐞𝐫𝐲 𝐚𝐧𝐝 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐞𝐯𝐞𝐫𝐲 𝐛𝐢𝐭 𝐨𝐟 𝐜𝐡𝐚𝐨𝐬— let’s continue to stand firm with 𝗝𝗮𝗱𝗲 𝗔𝗹𝗺𝗮𝘇𝗮𝗻 𝗮𝗻𝗱 𝗘𝗱𝘄𝗮𝗿𝗱 𝗔𝗿𝗲𝗹𝗹𝗮𝗻𝗼, always. 💛 #TheSecretsOfHotel88 [STAR CREATIVES | Dustin Yu | Bianca De Vera]

𝐖𝐄𝐄𝐊 𝟑 𝐑𝐄𝐂𝐀𝐏: 𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐨𝐟 𝐇𝐨𝐭𝐞𝐥 𝟖𝟖 𝐫𝐞𝐦𝐚𝐢𝐧𝐬 𝐨𝐧 𝐭𝐨𝐩! What a beautiful whirlwind of a week it has been, Cheffies! As we wrap up our 𝘁𝗵𝗶𝗿𝗱 𝘄𝗲𝗲𝗸 together, your energy remains nothing short of incredible. We know 𝗗𝘂𝘀𝘁𝗶𝗻 𝗮𝗻𝗱 𝗕𝗶𝗮𝗻𝗰𝗮 feel every bit of that love— it’s your endless support and pride that turns every episode into a core memory. From the heavy silence of miscommunications that had us all wishing we could jump through the screen, to that cliffhanger preview that has us counting down the seconds until tonight— 𝙅𝙖𝙙𝙚 𝙖𝙣𝙙 𝙀𝙙𝙬𝙖𝙧𝙙 𝙧𝙚𝙖𝙡𝙡𝙮 𝙝𝙖𝙫𝙚 𝙪𝙨 𝙞𝙣 𝙩𝙝𝙚 𝙥𝙖𝙡𝙢 𝙤𝙛 𝙩𝙝𝙚𝙞𝙧 𝙝𝙖𝙣𝙙𝙨, 𝙙𝙤𝙣'𝙩 𝙩𝙝𝙚𝙮? As 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁𝘀 𝗼𝗳 𝗛𝗼𝘁𝗲𝗹 𝟴𝟴 holds its rightful spot as the #𝟭 𝘀𝗵𝗼𝘄 𝗶𝗻 𝘁𝗵𝗲 𝗣𝗵𝗶𝗹𝗶𝗽𝗽𝗶𝗻𝗲𝘀, let’s ensure it stays there. While we wait for the next secrets to unfold, it’s the perfect time to 𝗿𝗲𝘄𝗮𝘁𝗰𝗵 𝗘𝗽𝗶𝘀𝗼𝗱𝗲𝘀 𝟭 𝘁𝗼 𝟭𝟱. Dive back into those iconic JadeWard scenes legally on iWant (iWant) and keep the momentum going. Let’s continue to stream, promote, and support with the same integrity and heart we always do. 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐦𝐲𝐬𝐭𝐞𝐫𝐲 𝐚𝐧𝐝 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐞𝐯𝐞𝐫𝐲 𝐛𝐢𝐭 𝐨𝐟 𝐜𝐡𝐚𝐨𝐬— let’s continue to stand firm with 𝗝𝗮𝗱𝗲 𝗔𝗹𝗺𝗮𝘇𝗮𝗻 𝗮𝗻𝗱 𝗘𝗱𝘄𝗮𝗿𝗱 𝗔𝗿𝗲𝗹𝗹𝗮𝗻𝗼, always. 💛 #TheSecretsOfHotel88 [STAR CREATIVES | Dustin Yu | Bianca De Vera]

TEAM DUSTBIA OFFICIAL

12,959 views • 5 months ago