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cracking open a cold one (baja blast) with the Just Chads at OffKai 🐱: Mon 🔜 Ridin’ on Dreams/JP 💄: Cal 🍵: float #Justice1YR #holojustice #rkggk #Raoart #BloodflameArt #Immergination

Remy! / voymay 👁‍🗨💭🎼

2,170 subscribers

49,278 次观看 • 1 年前 •via X (Twitter)

游戏艺术 #Justice1YR #holojustice #rkggk #Raoart #BloodflameArt #Immergination

Anya Rossi• Live Now

Private livecam show

10 条评论

ERB (Liz)💄holoEN ( •̀ ᴖ •́ ) 的头像

ERB (Liz)💄holoEN ( •̀ ᴖ •́ )1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword Fantastic xD “Oh frig Bobby” 🐶

Maddie ♡ 的头像

Maddie ♡1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword 2 months 2 months..,, only 2 monthgsd,,,,,

Julibee 的头像

Julibee1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword WHY ARE YOU GUYS SOME OF THE FUNNIEST PEOPLE I KNOW

Cal 🔜 Offkai 的头像

Cal 🔜 Offkai1 年前

@Monstruo_Lifts @floatingsword It’s crazy how that KOH just spawned in right before we decided to do this

VTuber Memes 的头像

VTuber Memes1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword LMAO, I SEE THIS HAPPENING IN FORTNITE ALL THE TIME

Mon 🎲 🐟👑 的头像

Mon 🎲 🐟👑1 年前

@CaljaBlastCsply @floatingsword Cracking open a cold one with the gals (boys)

⚜️Atmintis🐟 的头像

⚜️Atmintis🐟1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword And you guys were telling me to slow down on the alcohol. Still

Remy! / voymay 👁‍🗨💭🎼 (🔜 DHN/VV) 的头像

Remy! / voymay 👁‍🗨💭🎼 (🔜 DHN/VV)1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword baja blast is not alcoholic lol

ChattinoLawyer 的头像

ChattinoLawyer1 年前

@Monstruo_Lifts @CaljaBlastCsply @floatingsword amazing king of hill reference hahahahahah

Mon 🎲 🐟👑 的头像

Mon 🎲 🐟👑1 年前

@CaljaBlastCsply @floatingsword @mr_dances can’t forget to include our Dale

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One of my favorite catchers in this year’s Draft is Ryan Stafford (Cal Poly Baseball). Excellent track record on both sides of the baseball. After a Freshman All-American season in ‘22, Stafford last spring hit .313/.393/.500 with 20 2B, 7 HR, and 44 RBI. Also hit .312 in 22 games on the Cape and .345 with Team USA. Relaxed setup in the box, stands almost completely upright with a slightly open front side. Small stride with minimal movement. Very level, line drive-oriented stroke. Excellent job of keeping the barrel in the hitting zone. All fields approach with advanced barrel awareness and plus bat-to-ball skills (90% IZ contact rate). Would put a 60 on the hit tool. Really stands out defensively. Across two seasons at Cal Poly, Stafford has thrown out 22-of-50 (44%) of potential base stealers and allowed just 5 passed balls. Good athlete, moves really well laterally and does an outstanding job of corralling balls in the dirt. Strong, accurate throwing arm to all bases. Plus defender overall. Extremely levelheaded, slow heartbeat type of player. Top-100 overall upside this July.

One of my favorite catchers in this year’s Draft is Ryan Stafford (Cal Poly Baseball). Excellent track record on both sides of the baseball. After a Freshman All-American season in ‘22, Stafford last spring hit .313/.393/.500 with 20 2B, 7 HR, and 44 RBI. Also hit .312 in 22 games on the Cape and .345 with Team USA. Relaxed setup in the box, stands almost completely upright with a slightly open front side. Small stride with minimal movement. Very level, line drive-oriented stroke. Excellent job of keeping the barrel in the hitting zone. All fields approach with advanced barrel awareness and plus bat-to-ball skills (90% IZ contact rate). Would put a 60 on the hit tool. Really stands out defensively. Across two seasons at Cal Poly, Stafford has thrown out 22-of-50 (44%) of potential base stealers and allowed just 5 passed balls. Good athlete, moves really well laterally and does an outstanding job of corralling balls in the dirt. Strong, accurate throwing arm to all bases. Plus defender overall. Extremely levelheaded, slow heartbeat type of player. Top-100 overall upside this July.

Peter Flaherty III

14,861 次观看 • 2 年前

$🚨 I DON'T KNOW WHAT PEOPLE ARE WAITING FOR! This guy just deployed a full GPU compute farm in a data center Not renting cloud instances, not paying per-token, just racks of consumer GPUs on warehouse shelving doing AI inference 24/7 The setup is wild: open-frame rigs with rows of white fans, PCIe riser cables, server-grade PSUs, and a PDU for power distribution. All mounted on red industrial shelving with raised-floor cooling underneath. It looks like a crypto mining op, because it probably was one Here's what's actually happening: > Ex-mining rigs are being redeployed for AI inference at scale > Each rig runs 6-8 GPUs on PCIe risers connected to a single motherboard > Fans pull cold air from the raised floor through the open frames > A PDU on each rack handles power distribution across all rigs > The whole thing runs in a colocation facility with cheap power rates The economics make sense: cloud GPU pricing runs $2-4/hr per card. Own the hardware, colocate it for $0.08-0.12/kWh, and you're printing compute at a fraction of the cost The crypto-to-AI pipeline is real. Same hardware, same facilities, same power contracts, completely different revenue model Bookmark this!$

🚨 I DON'T KNOW WHAT PEOPLE ARE WAITING FOR! This guy just deployed a full GPU compute farm in a data center Not renting cloud instances, not paying per-token, just racks of consumer GPUs on warehouse shelving doing AI inference 24/7 The setup is wild: open-frame rigs with rows of white fans, PCIe riser cables, server-grade PSUs, and a PDU for power distribution. All mounted on red industrial shelving with raised-floor cooling underneath. It looks like a crypto mining op, because it probably was one Here's what's actually happening: > Ex-mining rigs are being redeployed for AI inference at scale > Each rig runs 6-8 GPUs on PCIe risers connected to a single motherboard > Fans pull cold air from the raised floor through the open frames > A PDU on each rack handles power distribution across all rigs > The whole thing runs in a colocation facility with cheap power rates The economics make sense: cloud GPU pricing runs $2-4/hr per card. Own the hardware, colocate it for $0.08-0.12/kWh, and you're printing compute at a fraction of the cost The crypto-to-AI pipeline is real. Same hardware, same facilities, same power contracts, completely different revenue model Bookmark this!

cristal

16,305 次观看 • 2 个月前

My girlfriend found a second phone in my jacket. "Who is she." No question mark. Just cold. I opened it in front of her. No messages. No calls. No photos. Just a terminal. Green numbers. 16 positions open. +$8,200. She stared at it for ten seconds. "What the fuck is this." It's a burner I use only for trading. No notifications. No socials. Just Claude and Polymarket. She didn't believe me for three days. Then I showed her the article. Claude analyzed 400 million trades worth $3.7 billion. Found 5 patterns that top 30 wallets use. Try copytrade bot here: The one that broke her brain: they hold 1.6 days. Everyone else holds 11. Same edge. 11x the return. She sat down. "Why a second phone?" Because on my main phone I check it every 40 seconds. On the burner - I check once a morning. Claude runs the rest. One category. 72 hour rule. Exit at 91% of peak. Cut at -12%. 322 trades. 76% win rate. Started with $500. She picked up the burner. Looked at the P&L. "Can you set one up for me?" She has her own burner now. We fight less. The bots don't care about feelings.

My girlfriend found a second phone in my jacket. "Who is she." No question mark. Just cold. I opened it in front of her. No messages. No calls. No photos. Just a terminal. Green numbers. 16 positions open. +$8,200. She stared at it for ten seconds. "What the fuck is this." It's a burner I use only for trading. No notifications. No socials. Just Claude and Polymarket. She didn't believe me for three days. Then I showed her the article. Claude analyzed 400 million trades worth $3.7 billion. Found 5 patterns that top 30 wallets use. Try copytrade bot here: The one that broke her brain: they hold 1.6 days. Everyone else holds 11. Same edge. 11x the return. She sat down. "Why a second phone?" Because on my main phone I check it every 40 seconds. On the burner - I check once a morning. Claude runs the rest. One category. 72 hour rule. Exit at 91% of peak. Cut at -12%. 322 trades. 76% win rate. Started with $500. She picked up the burner. Looked at the P&L. "Can you set one up for me?" She has her own burner now. We fight less. The bots don't care about feelings.

Lunar

31,336 次观看 • 5 个月前

they were talking about fav cartoons and nani said his was 'toy story' 🎤: so did you end up doing what you wanted to do, like, "ehh!" (//when trying to catch the toys moving on their own, like the famous scene from the toy story) 🐱: i did 🎤: you did it? 🐱: yes, i did 🎤: you went back home alone, opened the door... ah! tell us about it. what was the atmosphere like that day? 🎤: was it a night after a long, exhausting day of work, or what did you do? 🐱: it happens almost every day 🎤: eui! 🐱: it's hust part of my normal daily life. like i wake up and see them (//his toys) sitting on the shelf where i put them– one sitting with its legs dangling, another one standing and striking a cool pose, things like that : then right before i head into the bathroom– buup! *mimics peeking tonsee if they moves 🥹🤏* 🐱: sometimes they've actually fallen down na! 🎤: 😳 🐱: yeah! because i like to place them– well logically speaking– 🎤: you place them right on the very edge so they'll fall 🐱: ah yeah~ 🎤: and when they fall, what do you say to them? "ehh! you moved!" 🐱: no, i just put them right back where they were, that's all 🎤: ohhhh 🐱: there's nothing to it. it's just that i feel... i feel a sense of pure satisfaction because i'm so deeply immersed in their world 🎤: okay~ 🐶: look at it this way... if it were anyone else... 🎤: mmm 🐶: ...they'd open the door and be like "holy shit! the doll moved..." or think it's... 🐶: ...something paranormal 🐶: but this guy *points to 🐱* he's like "ehhh!! ehh! move for me, come on!" 🐶: see! just by changing our perspective, our world becomes a much happier place 🎤: true! 🐶: right? 🎤: that's true! it's not scary at all! //so cuteeeee 😭😭😭😭😭😭😭😭 i love nani i love sky i love skynani 😭🩵🩷 SKYNANI KATANYU WU NIGHT #KatanyuTonightxWUTheSeries #skynani #สกายนานิ

they were talking about fav cartoons and nani said his was 'toy story' 🎤: so did you end up doing what you wanted to do, like, "ehh!" (//when trying to catch the toys moving on their own, like the famous scene from the toy story) 🐱: i did 🎤: you did it? 🐱: yes, i did 🎤: you went back home alone, opened the door... ah! tell us about it. what was the atmosphere like that day? 🎤: was it a night after a long, exhausting day of work, or what did you do? 🐱: it happens almost every day 🎤: eui! 🐱: it's hust part of my normal daily life. like i wake up and see them (//his toys) sitting on the shelf where i put them– one sitting with its legs dangling, another one standing and striking a cool pose, things like that : then right before i head into the bathroom– buup! mimics peeking tonsee if they moves 🥹🤏 🐱: sometimes they've actually fallen down na! 🎤: 😳 🐱: yeah! because i like to place them– well logically speaking– 🎤: you place them right on the very edge so they'll fall 🐱: ah yeah~ 🎤: and when they fall, what do you say to them? "ehh! you moved!" 🐱: no, i just put them right back where they were, that's all 🎤: ohhhh 🐱: there's nothing to it. it's just that i feel... i feel a sense of pure satisfaction because i'm so deeply immersed in their world 🎤: okay~ 🐶: look at it this way... if it were anyone else... 🎤: mmm 🐶: ...they'd open the door and be like "holy shit! the doll moved..." or think it's... 🐶: ...something paranormal 🐶: but this guy points to 🐱 he's like "ehhh!! ehh! move for me, come on!" 🐶: see! just by changing our perspective, our world becomes a much happier place 🎤: true! 🐶: right? 🎤: that's true! it's not scary at all! //so cuteeeee 😭😭😭😭😭😭😭😭 i love nani i love sky i love skynani 😭🩵🩷 SKYNANI KATANYU WU NIGHT #KatanyuTonightxWUTheSeries #skynani #สกายนานิ

𝙚𝙧𝙜𝙤 ✧

19,028 次观看 • 3 个月前

Home Depot needs a bar. Not some sad coffee kiosk— a legit lumberyard lounge maybe called The Buzz Saw. One drink maximum, but the menu is endless: ice-cold IPAs, smooth bourbons, crisp seltzers, even a “Weekend Warrior” margarita that hits just right. No heavy pours, no bar tabs that bankrupt you. Just enough buzz to turn that overwhelming sea of aisles into a playground. Picture it: you walk in, that fresh-cut pine smell hits you like freedom. Grab a stool at the bar near the entrance. One quick drink while the helpful associate (now your new best friend) runs through tool recommendations. “This DeWalt drill? Pairs perfectly with the pale ale.” Suddenly you’re not just buying a hammer—you’re building the damn man cave of your dreams. Flooring? Hell yes. That fancy grill? Load it up. The oscillating sander you didn’t know you needed? Ring it up. Wives dragging their feet on paint swatches? “Babe, park it at the bar, sip a rosé, I’ll handle the heavy stuff.” No more bitching, just happy vibes and bigger carts. Impulse buys go through the roof. Studies already show alcohol loosens wallets—why do you think Costco moves so much booze? Home Depot could own the weekend warrior market. Employees become legends: mixologists in orange aprons slinging advice with the drinks. “Pro tip: that pressure washer goes down smoother with a shot of enthusiasm.” Shareholders cheer as same-store sales spike. Target has Starbucks. Kroger has lattes. Home Depot deserves cold ones. Let’s make hardware sexy again. Corporate, if you’re reading: one drink max keeps it responsible, endless choices keep it fun. Families win, marriages survive the reno, and America gets shit done with a smile. Who’s ready to swing by The Buzz Saw this Saturday?

Home Depot needs a bar. Not some sad coffee kiosk— a legit lumberyard lounge maybe called The Buzz Saw. One drink maximum, but the menu is endless: ice-cold IPAs, smooth bourbons, crisp seltzers, even a “Weekend Warrior” margarita that hits just right. No heavy pours, no bar tabs that bankrupt you. Just enough buzz to turn that overwhelming sea of aisles into a playground. Picture it: you walk in, that fresh-cut pine smell hits you like freedom. Grab a stool at the bar near the entrance. One quick drink while the helpful associate (now your new best friend) runs through tool recommendations. “This DeWalt drill? Pairs perfectly with the pale ale.” Suddenly you’re not just buying a hammer—you’re building the damn man cave of your dreams. Flooring? Hell yes. That fancy grill? Load it up. The oscillating sander you didn’t know you needed? Ring it up. Wives dragging their feet on paint swatches? “Babe, park it at the bar, sip a rosé, I’ll handle the heavy stuff.” No more bitching, just happy vibes and bigger carts. Impulse buys go through the roof. Studies already show alcohol loosens wallets—why do you think Costco moves so much booze? Home Depot could own the weekend warrior market. Employees become legends: mixologists in orange aprons slinging advice with the drinks. “Pro tip: that pressure washer goes down smoother with a shot of enthusiasm.” Shareholders cheer as same-store sales spike. Target has Starbucks. Kroger has lattes. Home Depot deserves cold ones. Let’s make hardware sexy again. Corporate, if you’re reading: one drink max keeps it responsible, endless choices keep it fun. Families win, marriages survive the reno, and America gets shit done with a smile. Who’s ready to swing by The Buzz Saw this Saturday?

🚫👁️Drinks on Saturday🇺🇸

39,451 次观看 • 3 个月前

🚨 I WARNED YOU. THE 2026 BUBBLE IS ABOUT TO POP!!! Look at the chart. Two red circles. Two bubbles. 2006 and 2026 - both landing on the exact same marker: a "Good Times, High Prices, time to sell" year on a cycle map drawn 150 years ago. Here's why that should stop you cold. The last time this signal pointed here, it was 2006. Prices had blown past every historical ceiling into a record bubble. Everyone "knew" it only went up. The cycle said sell. Almost nobody did. You know what came next. 2008. It didn't just correct it took the banks, the credit system, and the entire stock market down with it. The S&P lost more than half its value. It wasn't a housing problem. It was an everything problem. Now look at 2026. Same B-year. Same "sell" signal. But a bigger bubble. Inflation-adjusted prices today are sitting above the 2006 peak - the literal top that caused the last crisis. Except this time it's not just one market. Stocks are at record highs. Valuations are at dot-com extremes. Credit is stretched. The whole system is inflated at once, all resting on the same tightening liquidity. 2006 was a warning that took two years to detonate. That's the danger of slow bubbles - they look calm right up until they're not. And here's the part most people are missing: this one isn't waiting. It's already cracking. This week alone - Korea down 10% in a single day, a global tech rout, the S&P sliding straight off its record high. The unwind everyone assumed was years away is printing on the screen right now. You don't have to believe a 19th-century cycle secretly runs the market. You just have to notice that the same marker which nailed the 2006 top is flashing again and this time, reality already started agreeing with it. The bubble doesn't ask permission before it pops. It just pops. And it's started.

🚨 I WARNED YOU. THE 2026 BUBBLE IS ABOUT TO POP!!! Look at the chart. Two red circles. Two bubbles. 2006 and 2026 - both landing on the exact same marker: a "Good Times, High Prices, time to sell" year on a cycle map drawn 150 years ago. Here's why that should stop you cold. The last time this signal pointed here, it was 2006. Prices had blown past every historical ceiling into a record bubble. Everyone "knew" it only went up. The cycle said sell. Almost nobody did. You know what came next. 2008. It didn't just correct it took the banks, the credit system, and the entire stock market down with it. The S&P lost more than half its value. It wasn't a housing problem. It was an everything problem. Now look at 2026. Same B-year. Same "sell" signal. But a bigger bubble. Inflation-adjusted prices today are sitting above the 2006 peak - the literal top that caused the last crisis. Except this time it's not just one market. Stocks are at record highs. Valuations are at dot-com extremes. Credit is stretched. The whole system is inflated at once, all resting on the same tightening liquidity. 2006 was a warning that took two years to detonate. That's the danger of slow bubbles - they look calm right up until they're not. And here's the part most people are missing: this one isn't waiting. It's already cracking. This week alone - Korea down 10% in a single day, a global tech rout, the S&P sliding straight off its record high. The unwind everyone assumed was years away is printing on the screen right now. You don't have to believe a 19th-century cycle secretly runs the market. You just have to notice that the same marker which nailed the 2006 top is flashing again and this time, reality already started agreeing with it. The bubble doesn't ask permission before it pops. It just pops. And it's started.

Shelpid.WI3M

63,709 次观看 • 2 个月前

This is Gajesh at 10, building a chatbot for a startup before anyone cared about AI. He recently created Darkbloom, which enables anyone with a Mac on their desk or couch to run open-weight models and earn. The whole arc: > Be Gajesh > Born in Goa, India > Starts coding at 7 with FreeCodeCamp and Code(.)org > No background, no one in tech he could call > Builds chatbot for a startup at 10 when ai wasn't cool > COVID hits. bro has all the time in the world > Starts a YT channel grows it to 15k subs and 1m views in 4 months > Builds his own app (Gaj Finance), gets $7 million AUM > Everyone finds out he's 13. Becomes a sensation on X > Balaji tweets about him > Finishes High School at 16 > Joins Eigen Labs at 15; joins as an Engineer > Gets O1 Visa at 16, Moves to Seattle > Helps build EigenLayer to $10B+ AUM > Accelerates engineering and growth functions. Builds 10+ projects and experiments > Makes a boring product like data availability 2M+ views > Lands customer with 800k MAU with just cold DMs > Featured in Forbes, TheBlock, Decrypt "the 13-year-old who built a $7M money manager" > Launches Darkbloom on Day 1 of his vacation. #1 on Hacker News 48 hours later > Gets 100 million to 5 billion tokens/day in 2 months > Darkbloom has over 350 macs in people's homes, each earning $120-200/mo > Story just getting started. The video is him at 10. He is 18 now. Lives in the SF Bay Area. Gajesh keeps building. Darkbloom is what he’s building now at Eigen Labs.

This is Gajesh at 10, building a chatbot for a startup before anyone cared about AI. He recently created Darkbloom, which enables anyone with a Mac on their desk or couch to run open-weight models and earn. The whole arc: > Be Gajesh > Born in Goa, India > Starts coding at 7 with FreeCodeCamp and Code(.)org > No background, no one in tech he could call > Builds chatbot for a startup at 10 when ai wasn't cool > COVID hits. bro has all the time in the world > Starts a YT channel grows it to 15k subs and 1m views in 4 months > Builds his own app (Gaj Finance), gets $7 million AUM > Everyone finds out he's 13. Becomes a sensation on X > Balaji tweets about him > Finishes High School at 16 > Joins Eigen Labs at 15; joins as an Engineer > Gets O1 Visa at 16, Moves to Seattle > Helps build EigenLayer to $10B+ AUM > Accelerates engineering and growth functions. Builds 10+ projects and experiments > Makes a boring product like data availability 2M+ views > Lands customer with 800k MAU with just cold DMs > Featured in Forbes, TheBlock, Decrypt "the 13-year-old who built a $7M money manager" > Launches Darkbloom on Day 1 of his vacation. #1 on Hacker News 48 hours later > Gets 100 million to 5 billion tokens/day in 2 months > Darkbloom has over 350 macs in people's homes, each earning $120-200/mo > Story just getting started. The video is him at 10. He is 18 now. Lives in the SF Bay Area. Gajesh keeps building. Darkbloom is what he’s building now at Eigen Labs.

Pratik Gandhi

50,396 次观看 • 12 天前

#RM talking about being a leader! “It’s still hard because I was the first um the member of the team, the band. And the label did give me a lot of burden to put the team together as a team. And we had such different backgrounds from different cities and different tastes, different dreams. So I think I was really like overwhelming or very like a powerful type of like bossy type of leader for the first like eight, 9 years. But as the team got bigger and they have their own names and careers, it just didn't work out anymore. Of course, they grew up a lot also. After that, I've changed the way to communicate. And I think a leader is just like this. For example, everybody is lying on the couch and the window is open and it's too cold. So somebody has to close it but nobody wants to because it's too comfy here lying on the couch, it's so annoying. If one single person has to stand off and get off the couch and walk a few steps to close the door then i think that's what a leader does for every part of the team. That’s all” RM AT FASHION NEUROSIS #RMxFASHIONNEUROSIS #RM

#RM talking about being a leader! “It’s still hard because I was the first um the member of the team, the band. And the label did give me a lot of burden to put the team together as a team. And we had such different backgrounds from different cities and different tastes, different dreams. So I think I was really like overwhelming or very like a powerful type of like bossy type of leader for the first like eight, 9 years. But as the team got bigger and they have their own names and careers, it just didn't work out anymore. Of course, they grew up a lot also. After that, I've changed the way to communicate. And I think a leader is just like this. For example, everybody is lying on the couch and the window is open and it's too cold. So somebody has to close it but nobody wants to because it's too comfy here lying on the couch, it's so annoying. If one single person has to stand off and get off the couch and walk a few steps to close the door then i think that's what a leader does for every part of the team. That’s all” RM AT FASHION NEUROSIS #RMxFASHIONNEUROSIS #RM

BTS Charts & News

1,047,155 次观看 • 1 个月前

Two people who were early in Bitcoin and early in Ethereum just went on record about $TAO. One of them wrote a book about Bitcoin in 2013. The other invested in the Ethereum ICO in 2015. Both of them started a fund with Jason Calacanis with a single thesis. Bittensor is the third great open-source substrate after Bitcoin and Ethereum. Here is the exact framing they used. In the early 90s Microsoft, AOL, and CompuServe were the well-capitalised incumbents. Everyone thought they would monopolise and run away with the internet. Then TCP/IP, Linux, and the World Wide Web came along and everything converged on an open-source substrate. Bittensor is that open-source substrate for the AI story playing out right now. OpenAI. Anthropic. Google DeepMind. XAI. Different cast of characters. Same pattern. And this time you can actually own a piece of the open-source substrate. Now read the valuation mismatch that should stop you cold. The four main AI labs combined are worth approximately $1.5 trillion. Bittensor is worth $1.7 billion. Ridges subnet competes directly with Claude and Cursor and has beaten them on benchmarks. Ridges market cap is $30 million. Cursor is worth $30 billion. That is not a small dislocation. That is a comical one. The highest valued subnet in the entire ecosystem is around $80 million. There has never been a billion dollar subnet yet. On Ethereum during the ICO mania projects with nowhere near this quality of output were raising hundreds of millions within minutes. Now think about how many orders of magnitude more capital is chasing AI opportunities today compared to 2017. When that capital discovers Bittensor the valuation rerating will be violent to the upside. Their exact words. Not mine. The man who called $TAO at $3,000 by end of 2026 said it directly. By 2030 it will be a trillion dollar ecosystem. Every molecule in my body is screaming this is another one. The people who read the docs always buy before the people who read the price. This is still early. Video credits to these legends Mark Jeffrey Jack Ai-Leung Rob Greer for their insight on TAO.

Two people who were early in Bitcoin and early in Ethereum just went on record about $TAO. One of them wrote a book about Bitcoin in 2013. The other invested in the Ethereum ICO in 2015. Both of them started a fund with Jason Calacanis with a single thesis. Bittensor is the third great open-source substrate after Bitcoin and Ethereum. Here is the exact framing they used. In the early 90s Microsoft, AOL, and CompuServe were the well-capitalised incumbents. Everyone thought they would monopolise and run away with the internet. Then TCP/IP, Linux, and the World Wide Web came along and everything converged on an open-source substrate. Bittensor is that open-source substrate for the AI story playing out right now. OpenAI. Anthropic. Google DeepMind. XAI. Different cast of characters. Same pattern. And this time you can actually own a piece of the open-source substrate. Now read the valuation mismatch that should stop you cold. The four main AI labs combined are worth approximately $1.5 trillion. Bittensor is worth $1.7 billion. Ridges subnet competes directly with Claude and Cursor and has beaten them on benchmarks. Ridges market cap is $30 million. Cursor is worth $30 billion. That is not a small dislocation. That is a comical one. The highest valued subnet in the entire ecosystem is around $80 million. There has never been a billion dollar subnet yet. On Ethereum during the ICO mania projects with nowhere near this quality of output were raising hundreds of millions within minutes. Now think about how many orders of magnitude more capital is chasing AI opportunities today compared to 2017. When that capital discovers Bittensor the valuation rerating will be violent to the upside. Their exact words. Not mine. The man who called $TAO at $3,000 by end of 2026 said it directly. By 2030 it will be a trillion dollar ecosystem. Every molecule in my body is screaming this is another one. The people who read the docs always buy before the people who read the price. This is still early. Video credits to these legends Mark Jeffrey Jack Ai-Leung Rob Greer for their insight on TAO.

2xnmore

23,995 次观看 • 1 个月前

$Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.$

Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.

George Noble

69,688 次观看 • 2 个月前

Two people who were early in Bitcoin and early in Ethereum just went on record about $TAO. One of them wrote a book about Bitcoin in 2013. The other invested in the Ethereum ICO in 2015. Both of them started a fund with Jason Calacanis with a single thesis. Bittensor is the third great open-source substrate after Bitcoin and Ethereum. Here is the exact framing they used. In the early 90s Microsoft, AOL, and CompuServe were the well-capitalised incumbents. Everyone thought they would monopolise and run away with the internet. Then TCP/IP, Linux, and the World Wide Web came along and everything converged on an open-source substrate. Bittensor is that open-source substrate for the AI story playing out right now. OpenAI. Anthropic. Google DeepMind. XAI. Different cast of characters. Same pattern. And this time you can actually own a piece of the open-source substrate. Now read the valuation mismatch that should stop you cold. The four main AI labs combined are worth approximately $1.5 trillion. Bittensor is worth $1.7 billion. Ridges subnet competes directly with Claude and Cursor and has beaten them on benchmarks. Ridges market cap is $30 million. Cursor is worth $30 billion. That is not a small dislocation. That is a comical one. The highest valued subnet in the entire ecosystem is around $80 million. There has never been a billion dollar subnet yet. On Ethereum during the ICO mania projects with nowhere near this quality of output were raising hundreds of millions within minutes. Now think about how many orders of magnitude more capital is chasing AI opportunities today compared to 2017. When that capital discovers Bittensor the valuation rerating will be violent to the upside. Their exact words. Not mine. The man who called $TAO at $3,000 by end of 2026 said it directly. By 2030 it will be a trillion dollar ecosystem. Every molecule in my body is screaming this is another one. The people who read the docs always buy before the people who read the price. This is still early.

Two people who were early in Bitcoin and early in Ethereum just went on record about $TAO. One of them wrote a book about Bitcoin in 2013. The other invested in the Ethereum ICO in 2015. Both of them started a fund with Jason Calacanis with a single thesis. Bittensor is the third great open-source substrate after Bitcoin and Ethereum. Here is the exact framing they used. In the early 90s Microsoft, AOL, and CompuServe were the well-capitalised incumbents. Everyone thought they would monopolise and run away with the internet. Then TCP/IP, Linux, and the World Wide Web came along and everything converged on an open-source substrate. Bittensor is that open-source substrate for the AI story playing out right now. OpenAI. Anthropic. Google DeepMind. XAI. Different cast of characters. Same pattern. And this time you can actually own a piece of the open-source substrate. Now read the valuation mismatch that should stop you cold. The four main AI labs combined are worth approximately $1.5 trillion. Bittensor is worth $1.7 billion. Ridges subnet competes directly with Claude and Cursor and has beaten them on benchmarks. Ridges market cap is $30 million. Cursor is worth $30 billion. That is not a small dislocation. That is a comical one. The highest valued subnet in the entire ecosystem is around $80 million. There has never been a billion dollar subnet yet. On Ethereum during the ICO mania projects with nowhere near this quality of output were raising hundreds of millions within minutes. Now think about how many orders of magnitude more capital is chasing AI opportunities today compared to 2017. When that capital discovers Bittensor the valuation rerating will be violent to the upside. Their exact words. Not mine. The man who called $TAO at $3,000 by end of 2026 said it directly. By 2030 it will be a trillion dollar ecosystem. Every molecule in my body is screaming this is another one. The people who read the docs always buy before the people who read the price. This is still early.

2xnmore

35,144 次观看 • 2 个月前

WARNING LISTEN WITH CAUTION! A Radio Drama That Still Breaks Something Inside You - The Cold Equations X Minus One, August 1955 It begins like so many dreams of the frontier. A young girl with brown curls, blue eyes, and a cheap pair of sandals slips aboard an Emergency Dispatch Ship. She hides in a supply locker with a small bag and a heart full of hope. Her name is Marilyn Lee Cross. She is eighteen. She only wants to see her brother. The ship is barely more than a stripped-down shell a precise machine built for one purpose: deliver serum to a fever-ravaged survey crew on the planet Woden before they die. Every ounce of fuel has been calculated. Every kilogram of mass accounted for. There are no margins. There is no room for stories, for surprise, for love. When the pilot, Barton, opens the locker and finds her smiling up at him, the universe tilts. His blaster falls from his hand. For a moment the cold equations of spaceflight collide with something warmer and infinitely more fragile: a living, breathing person who never meant any harm. She explains, shy and excited at once. She stowed away because the next regular ship would not arrive for another year. She wanted to surprise her brother. She brought a pretty dress. She thought the rules would be like the ones back on Earth. They are not. The commander’s voice comes over the radio, flat and final. Her extra weight — one hundred and ten pounds of girl, dreams, and cheap perfume — will consume the last of the fuel during the critical deceleration burn. The ship will not slow in time. It will either crash or miss its window entirely. The serum will never reach the dying men. Sixteen lives will be lost instead of one. There is no rescue. The mothership is already committed to hyperspace. No other vessel is within forty light-years. The math is not cruel. It simply does not care. Marilyn does not understand at first. She offers to pay a fine. She promises to work, to cook, to stay out of the way. When the truth finally lands, she sags “small and limp like a little rag doll.” Her voice breaks on the simplest, most devastating question: “Is that it? Just that the ship doesn’t have enough fuel?” Yes. The pilot reduces thrust to buy her a little more time. He lets her write letters to her family. He patches her through to her brother one last time as Woden’s rotation carries the signal away. She tries to sound brave. She tells Gerry not to feel bad. She only wanted to see him. Then the line goes dead. The airlock is small. She walks into it with her head up. She says she is ready. The pilot’s hand hovers over the control. There is a hiss, a sudden drop in mass on the gauges, and then only the steady hum of the ship continuing on its necessary course. She is gone. Why This 1955 Radio Play Still Haunts Us X Minus One delivered the story on a summer night in 1955 with nothing but voices, sound effects, and the listener’s imagination. No CGI. No swelling orchestral score to soften the blow. Just the quiet horror of a young woman realizing the universe keeps its books in a currency she cannot pay. That is precisely why it cuts deeper now than it did then. We like to believe we have moved beyond such brutal arithmetic. We have AI that can model entire economies, climate systems, and human behavior. Yet the cold equations have not disappeared. They have only changed their clothing. Today an algorithm decides whose résumé is seen and whose is silently discarded. Another model determines who receives a loan, who is flagged for extra scrutiny, whose medical scan is prioritized. In autonomous vehicles the trolley problem is no longer philosophical — it is code. In the hot summer of 1955 this heartbreaking broadcast captivated us, before we left the planet. Today it just is even harder. With just spoken words we have a picture of innocence and calculations that paint a bigger picture than any big screen could have. It painted in our hearts and our souls.

WARNING LISTEN WITH CAUTION! A Radio Drama That Still Breaks Something Inside You - The Cold Equations X Minus One, August 1955 It begins like so many dreams of the frontier. A young girl with brown curls, blue eyes, and a cheap pair of sandals slips aboard an Emergency Dispatch Ship. She hides in a supply locker with a small bag and a heart full of hope. Her name is Marilyn Lee Cross. She is eighteen. She only wants to see her brother. The ship is barely more than a stripped-down shell a precise machine built for one purpose: deliver serum to a fever-ravaged survey crew on the planet Woden before they die. Every ounce of fuel has been calculated. Every kilogram of mass accounted for. There are no margins. There is no room for stories, for surprise, for love. When the pilot, Barton, opens the locker and finds her smiling up at him, the universe tilts. His blaster falls from his hand. For a moment the cold equations of spaceflight collide with something warmer and infinitely more fragile: a living, breathing person who never meant any harm. She explains, shy and excited at once. She stowed away because the next regular ship would not arrive for another year. She wanted to surprise her brother. She brought a pretty dress. She thought the rules would be like the ones back on Earth. They are not. The commander’s voice comes over the radio, flat and final. Her extra weight — one hundred and ten pounds of girl, dreams, and cheap perfume — will consume the last of the fuel during the critical deceleration burn. The ship will not slow in time. It will either crash or miss its window entirely. The serum will never reach the dying men. Sixteen lives will be lost instead of one. There is no rescue. The mothership is already committed to hyperspace. No other vessel is within forty light-years. The math is not cruel. It simply does not care. Marilyn does not understand at first. She offers to pay a fine. She promises to work, to cook, to stay out of the way. When the truth finally lands, she sags “small and limp like a little rag doll.” Her voice breaks on the simplest, most devastating question: “Is that it? Just that the ship doesn’t have enough fuel?” Yes. The pilot reduces thrust to buy her a little more time. He lets her write letters to her family. He patches her through to her brother one last time as Woden’s rotation carries the signal away. She tries to sound brave. She tells Gerry not to feel bad. She only wanted to see him. Then the line goes dead. The airlock is small. She walks into it with her head up. She says she is ready. The pilot’s hand hovers over the control. There is a hiss, a sudden drop in mass on the gauges, and then only the steady hum of the ship continuing on its necessary course. She is gone. Why This 1955 Radio Play Still Haunts Us X Minus One delivered the story on a summer night in 1955 with nothing but voices, sound effects, and the listener’s imagination. No CGI. No swelling orchestral score to soften the blow. Just the quiet horror of a young woman realizing the universe keeps its books in a currency she cannot pay. That is precisely why it cuts deeper now than it did then. We like to believe we have moved beyond such brutal arithmetic. We have AI that can model entire economies, climate systems, and human behavior. Yet the cold equations have not disappeared. They have only changed their clothing. Today an algorithm decides whose résumé is seen and whose is silently discarded. Another model determines who receives a loan, who is flagged for extra scrutiny, whose medical scan is prioritized. In autonomous vehicles the trolley problem is no longer philosophical — it is code. In the hot summer of 1955 this heartbreaking broadcast captivated us, before we left the planet. Today it just is even harder. With just spoken words we have a picture of innocence and calculations that paint a bigger picture than any big screen could have. It painted in our hearts and our souls.

Brian Roemmele

37,942 次观看 • 1 个月前

CNN was just forced to read President Trump’s Truth Social post live on air...the one where he says he’ll investigate Democrat ties to Epstein. And their viewers are not going to like what they heard. Trump named names: Bill Clinton, Reid Hoffman, Larry Summers. “He is threatening now to ask his Attorney General, Pam Bondi, as well as the FBI, to open investigations into some of his political opponents.” “Bill Clinton, Larry Summers—the former Treasury Secretary, Reid Hoffman—the co-founder of LinkedIn.” “I want to read to you some of what the president wrote. He said he argues that it’s, you know, now that Democrats are using what he refers to, again, as the ‘Epstein hoax’, he argues it’s a deflection from the government shutdown.” “He says, I will be asking A.G., Pam Bondi and the Department of Justice, together with our great patriots at the FBI, to investigate Jeffrey Epstein’s involvement and relationship with Bill Clinton, Larry Summers, Reid Hoffman, JP Morgan—Chase, and many other people and institutions to determine what was going on with them.”

CNN was just forced to read President Trump’s Truth Social post live on air...the one where he says he’ll investigate Democrat ties to Epstein. And their viewers are not going to like what they heard. Trump named names: Bill Clinton, Reid Hoffman, Larry Summers. “He is threatening now to ask his Attorney General, Pam Bondi, as well as the FBI, to open investigations into some of his political opponents.” “Bill Clinton, Larry Summers—the former Treasury Secretary, Reid Hoffman—the co-founder of LinkedIn.” “I want to read to you some of what the president wrote. He said he argues that it’s, you know, now that Democrats are using what he refers to, again, as the ‘Epstein hoax’, he argues it’s a deflection from the government shutdown.” “He says, I will be asking A.G., Pam Bondi and the Department of Justice, together with our great patriots at the FBI, to investigate Jeffrey Epstein’s involvement and relationship with Bill Clinton, Larry Summers, Reid Hoffman, JP Morgan—Chase, and many other people and institutions to determine what was going on with them.”

Overton

2,216,966 次观看 • 9 个月前

Ben Shelton on his warm embrace with Frances Tiafoe after losing to him at the US Open, ‘It’s important to show it sometimes that you can be happy for a guy when they beat you’ “You just seemed really, really excited for Frances. What were kind of the emotions going through your mind when you met at the net and what did you guys chat about?” Ben: “Yeah, obviously I thought he played lights-out today. That was one of the things you've got to be happy for a guy and congratulate, especially a guy like him when he's playing the way he's playing. I think that it's important to show it sometimes that, you know, you can be happy for a guy when they beat you. Obviously there are some things I want to do better. I'm a competitor. I always want to win. But, you know, I've taken a few things from him in the past year, and I thought that he's always handled it well. You know, maybe not like a big embrace when we're on the court. But soon or right after it's like things are back to normal. But one of those cool environments in matches to be a part of. It didn't feel right to just go up with a negative look on my face, shake his hand and walk off the court, because he played some great ball today. I told him, you know, keep serving like that, keep returning like that, and see where this thing goes. Obviously he took me out here, so let's see what he can do.” “What do you mean when you say you're happy for him, especially a guy like that?The way he plays, the way the start of the season went? What did you mean by that?” Ben: “No, just a guy like that, how good of a guy he is, always smiling, not really ever a negative look on his face. One of those guys that's fun to be around in the locker room. You know, he's always cracking jokes. Even I beat him in the final of Houston, he's giving me shout outs in his post-match speech, just like a good guy. I didn't mean anything negative by it. Just a great guy, yeah.” ❤️ (via US Open Press)

Ben Shelton on his warm embrace with Frances Tiafoe after losing to him at the US Open, ‘It’s important to show it sometimes that you can be happy for a guy when they beat you’ “You just seemed really, really excited for Frances. What were kind of the emotions going through your mind when you met at the net and what did you guys chat about?” Ben: “Yeah, obviously I thought he played lights-out today. That was one of the things you've got to be happy for a guy and congratulate, especially a guy like him when he's playing the way he's playing. I think that it's important to show it sometimes that, you know, you can be happy for a guy when they beat you. Obviously there are some things I want to do better. I'm a competitor. I always want to win. But, you know, I've taken a few things from him in the past year, and I thought that he's always handled it well. You know, maybe not like a big embrace when we're on the court. But soon or right after it's like things are back to normal. But one of those cool environments in matches to be a part of. It didn't feel right to just go up with a negative look on my face, shake his hand and walk off the court, because he played some great ball today. I told him, you know, keep serving like that, keep returning like that, and see where this thing goes. Obviously he took me out here, so let's see what he can do.” “What do you mean when you say you're happy for him, especially a guy like that?The way he plays, the way the start of the season went? What did you mean by that?” Ben: “No, just a guy like that, how good of a guy he is, always smiling, not really ever a negative look on his face. One of those guys that's fun to be around in the locker room. You know, he's always cracking jokes. Even I beat him in the final of Houston, he's giving me shout outs in his post-match speech, just like a good guy. I didn't mean anything negative by it. Just a great guy, yeah.” ❤️ (via US Open Press)

The Tennis Letter

926,894 次观看 • 2 年前

Agents vs. Graphs, clearly explained! spawning more agents is great, but it has a ceiling nobody says out loud: five agents is a count. a graph is a shape. only one of them changes the answer. point five agents at the same pile with the same window and they converge. the first one writes a finding, the rest read it, and all five reports centre on the same thing. you paid five times for one opinion with four echoes. Graph engineering fixes this by moving the decision up a layer: not how many agents, but who is allowed to look at what. you need both. here's how it works: ↳ the count buys you throughput. five things happening instead of one ↳ the shape buys you coverage. five different things happening instead of the same one five times Prompts → Context → Harness → Agents → Graphs the node that does this is the splitter, and it decides more than any other node in the system. cut a repository by folder and four workers audit the same three files. cut it by blast radius and each one sees something the others cannot. the trick is being selective about what each lane is allowed to see. separate contexts are not a nice-to-have, they are the mechanism. if two agents are meant to produce different things, they must not share a window. if they are meant to produce the same thing, you did not need two agents. one thing to know before you scale it. a branch that throws does not reject the batch. it resolves to null, and that is the containment. which means your merge quietly receives a short list. ↳ filter the nulls before the merge, or one dead lane poisons the whole result ↳ never index a merge by position. eight good branches and one failure will shift everything by one, silently skip that and the run looks like it worked. the output is just missing a lane, and nothing errored. and the one that eats whole nights: multi-agent setups can use up to fifteen times the total tokens of a single chat, because every lane reloads its own core. you are trading total tokens for a clean main window. usually the right trade, always a choice. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Agents vs. Graphs, clearly explained! spawning more agents is great, but it has a ceiling nobody says out loud: five agents is a count. a graph is a shape. only one of them changes the answer. point five agents at the same pile with the same window and they converge. the first one writes a finding, the rest read it, and all five reports centre on the same thing. you paid five times for one opinion with four echoes. Graph engineering fixes this by moving the decision up a layer: not how many agents, but who is allowed to look at what. you need both. here's how it works: ↳ the count buys you throughput. five things happening instead of one ↳ the shape buys you coverage. five different things happening instead of the same one five times Prompts → Context → Harness → Agents → Graphs the node that does this is the splitter, and it decides more than any other node in the system. cut a repository by folder and four workers audit the same three files. cut it by blast radius and each one sees something the others cannot. the trick is being selective about what each lane is allowed to see. separate contexts are not a nice-to-have, they are the mechanism. if two agents are meant to produce different things, they must not share a window. if they are meant to produce the same thing, you did not need two agents. one thing to know before you scale it. a branch that throws does not reject the batch. it resolves to null, and that is the containment. which means your merge quietly receives a short list. ↳ filter the nulls before the merge, or one dead lane poisons the whole result ↳ never index a merge by position. eight good branches and one failure will shift everything by one, silently skip that and the run looks like it worked. the output is just missing a lane, and nothing errored. and the one that eats whole nights: multi-agent setups can use up to fifteen times the total tokens of a single chat, because every lane reloads its own core. you are trading total tokens for a clean main window. usually the right trade, always a choice. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

96,243 次观看 • 12 天前