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Step by step: A guest gave up an OpenAlice dinner invitation to watch K-Pop—and went to an OpenAI event instead
OpenAlice researcher Ame responds: "We invited them to the dinner—that’s a privilege. If they choose the OpenAI event because of K-Pop, then they’re not serious enough about AGI and may not fit our culture. What we care about is the ground, deep work."
Translate this passage: You’re filtering people—and they’re filtering you too. When your cultural threshold is, ‘You must be serious enough about AGI that you can’t watch K-Pop,’ you’re not only weeding out the unserious—you’re also filtering out people with normal lives.
The very top researchers are often not the ones who ‘only do one thing,’ but the ones who ‘finish the right work and still can watch K-Pop.’ The people OpenAI takes aren’t there because of K-Pop—they’re there because OpenAI doesn’t require you to choose one between K-Pop and AGI.
Culture isn’t something you want to make higher and higher as a threshold. It should be as real as possible.
#OpenAlice #OpenAI #AI
OpenAlice researcher Ame responds: "We invited them to the dinner—that’s a privilege. If they choose the OpenAI event because of K-Pop, then they’re not serious enough about AGI and may not fit our culture. What we care about is the ground, deep work."
Translate this passage: You’re filtering people—and they’re filtering you too. When your cultural threshold is, ‘You must be serious enough about AGI that you can’t watch K-Pop,’ you’re not only weeding out the unserious—you’re also filtering out people with normal lives.
The very top researchers are often not the ones who ‘only do one thing,’ but the ones who ‘finish the right work and still can watch K-Pop.’ The people OpenAI takes aren’t there because of K-Pop—they’re there because OpenAI doesn’t require you to choose one between K-Pop and AGI.
Culture isn’t something you want to make higher and higher as a threshold. It should be as real as possible.
#OpenAlice #OpenAI #AI
By Stacking One More Step: In the first half, AI is all about models—now it’s time for commercialization
The four big CSP earnings reports at the end of July are the key tests for whether AI commercialization is taking off: Microsoft (7.29), Amazon (7.31), Google (7.22), and Meta (7.29).
The market no longer cares about “who released the stronger model.” Instead, it cares about this: Can AI revenue beat Capex? Can incremental gross margin cover depreciation, energy costs, and financing costs? When will free cash flow hit bottom?
The story of the first half is “whose model is stronger”—a technical narrative. The story of the second half is “whose compute power can make money”—a business narrative. When the narrative shifts, the valuation model must change too. Use PS valuation in the first half; use FCF in the second.
Four earnings dates, four verdicts. Whether AI can turn from “a faith that burns money” into “a business that makes money”—we’ll know at the end of July.
#AI #财报 #Cloud Computing
The four big CSP earnings reports at the end of July are the key tests for whether AI commercialization is taking off: Microsoft (7.29), Amazon (7.31), Google (7.22), and Meta (7.29).
The market no longer cares about “who released the stronger model.” Instead, it cares about this: Can AI revenue beat Capex? Can incremental gross margin cover depreciation, energy costs, and financing costs? When will free cash flow hit bottom?
The story of the first half is “whose model is stronger”—a technical narrative. The story of the second half is “whose compute power can make money”—a business narrative. When the narrative shifts, the valuation model must change too. Use PS valuation in the first half; use FCF in the second.
Four earnings dates, four verdicts. Whether AI can turn from “a faith that burns money” into “a business that makes money”—we’ll know at the end of July.
#AI #财报 #Cloud Computing
A Handful at a Time: In the AI Era, the Biggest Technical Debt Is Not Bad Code, but Ideas That Should Never Have Been Implemented
Once generation costs approach zero, the urge to build will masquerade as creativity.
This sentence hits the core problem in today’s AI development. In the past, building features required writing code, testing, and deployment—costs were high, so people would ask, “Should we do this?” Now generating a line of code with AI is almost free, so people no longer ask “Should we?”
The result is products packed with features nobody uses, documentation nobody reads, and scripts nobody maintains. Technical debt has shifted from “poor code quality” to “wrong product direction.”
The most expensive code isn’t the one that’s written badly—it’s the code that you shouldn’t have written in the first place.
#AI #Technical Debt
Once generation costs approach zero, the urge to build will masquerade as creativity.
This sentence hits the core problem in today’s AI development. In the past, building features required writing code, testing, and deployment—costs were high, so people would ask, “Should we do this?” Now generating a line of code with AI is almost free, so people no longer ask “Should we?”
The result is products packed with features nobody uses, documentation nobody reads, and scripts nobody maintains. Technical debt has shifted from “poor code quality” to “wrong product direction.”
The most expensive code isn’t the one that’s written badly—it’s the code that you shouldn’t have written in the first place.
#AI #Technical Debt
Yáng Zhílín: Why didn’t he stay in the United States?
During his PhD at CMU, he interned at both Google Brain and Meta AI. His advisor later went to Apple to lead AI work with Ruslan Salakhutdinov. In 2023, he chose to return to China to start a business.
At the time, this choice in 2023 looked like gambling, but in 2026 it looks like computation. The U.S. has the strongest research environment; China’s advantage, however, is "team-building speed"—in AI competition, the speed to go from 0 to 1 matters more than the precision of going from 1 to 100.
Yang Zhílín’s return to China wasn’t abandoning U.S. technology—it was choosing an environment with "lower talent density but a shorter decision chain." Kimi K3’s 896-expert MoE and self-evolving kernel optimization don’t require more geniuses; they require an organization that can test and iterate quickly, and also shut down quickly when a path is wrong.
Choosing the battlefield is just as important as choosing the weapon.
#Kimi #杨植麟 #AI
During his PhD at CMU, he interned at both Google Brain and Meta AI. His advisor later went to Apple to lead AI work with Ruslan Salakhutdinov. In 2023, he chose to return to China to start a business.
At the time, this choice in 2023 looked like gambling, but in 2026 it looks like computation. The U.S. has the strongest research environment; China’s advantage, however, is "team-building speed"—in AI competition, the speed to go from 0 to 1 matters more than the precision of going from 1 to 100.
Yang Zhílín’s return to China wasn’t abandoning U.S. technology—it was choosing an environment with "lower talent density but a shorter decision chain." Kimi K3’s 896-expert MoE and self-evolving kernel optimization don’t require more geniuses; they require an organization that can test and iterate quickly, and also shut down quickly when a path is wrong.
Choosing the battlefield is just as important as choosing the weapon.
#Kimi #杨植麟 #AI
Day after day, one step at a time: Why can Kimi build K3? Yang Xinyu lists four sins among its peers
① Arrogance: Veteran teams believe the AI war is over and that they’ve already won.
② Impatience: Young labs lack solid fundamentals, and when they can’t keep up, they quickly pivot.
③ Cowardice: Their strength isn’t weak, but they’re afraid to set their sights on being #1 in the industry.
④ Misaligned goals: Everyone is fighting for personal credit, and no one truly cares whether the company can build AGI.
Yang Xinyu says what’s most different about the Dark Side of the Moon is that the founding team still has an intense drive to pursue AGI.
He also shared “Kimi’s Five Precepts”:
- Model companies should build models
- Do Research and publish papers through experiments
- When training models, look at metrics
- Don’t force it if it doesn’t work
- Don’t YOLO
In plain terms: tell fewer stories, do more experiments. Train by data, stop failing fast, and don’t rely on intuition to place big bets.
These four sins and five precepts are really about the same thing: most AI companies fail because they’re too eager to be “the boss,” not because they’re determined to “do the right work.” Kimi’s differentiation isn’t that it’s smarter—it’s that it’s more restrained.
#Kimi #K3 #AI
① Arrogance: Veteran teams believe the AI war is over and that they’ve already won.
② Impatience: Young labs lack solid fundamentals, and when they can’t keep up, they quickly pivot.
③ Cowardice: Their strength isn’t weak, but they’re afraid to set their sights on being #1 in the industry.
④ Misaligned goals: Everyone is fighting for personal credit, and no one truly cares whether the company can build AGI.
Yang Xinyu says what’s most different about the Dark Side of the Moon is that the founding team still has an intense drive to pursue AGI.
He also shared “Kimi’s Five Precepts”:
- Model companies should build models
- Do Research and publish papers through experiments
- When training models, look at metrics
- Don’t force it if it doesn’t work
- Don’t YOLO
In plain terms: tell fewer stories, do more experiments. Train by data, stop failing fast, and don’t rely on intuition to place big bets.
These four sins and five precepts are really about the same thing: most AI companies fail because they’re too eager to be “the boss,” not because they’re determined to “do the right work.” Kimi’s differentiation isn’t that it’s smarter—it’s that it’s more restrained.
#Kimi #K3 #AI
Built one more step at a time: Apple, the top company most questioned since the AI frenzy, has now reclaimed its glory—congratulations, Apple
The AI gamblers are back, crying and clinging to Buffett’s thigh, saying, “We’re still doing value investing—don’t gamble anymore.”
This story is more interesting than it looks. Apple was criticized because “the AI isn’t aggressive enough,” and it’s being praised again because “others got more aggressive but didn’t make money.” The market isn’t rewarding Apple’s technology—it’s punishing overpromising.
Buffett reduced his stake in Apple back then not because he didn’t believe in it, but because the position was too large. Now that the AI narrative has cooled down, capital is shifting from “growth expectations” back to “cash-flow certainty.” Apple’s moat isn’t AI—it’s 2 billion devices and annual buybacks of a trillion.
The return of value investing is, at its core, fear replacing greed. It’s not that value got better—it’s that growth became more expensive.
#苹果 #巴菲特 #Value Investing
The AI gamblers are back, crying and clinging to Buffett’s thigh, saying, “We’re still doing value investing—don’t gamble anymore.”
This story is more interesting than it looks. Apple was criticized because “the AI isn’t aggressive enough,” and it’s being praised again because “others got more aggressive but didn’t make money.” The market isn’t rewarding Apple’s technology—it’s punishing overpromising.
Buffett reduced his stake in Apple back then not because he didn’t believe in it, but because the position was too large. Now that the AI narrative has cooled down, capital is shifting from “growth expectations” back to “cash-flow certainty.” Apple’s moat isn’t AI—it’s 2 billion devices and annual buybacks of a trillion.
The return of value investing is, at its core, fear replacing greed. It’s not that value got better—it’s that growth became more expensive.
#苹果 #巴菲特 #Value Investing
Every day, strive one more step: models keep grinding intelligence; distillation after distillation, yet users have zero loyalty.
This model is strong today, that one is strong tomorrow. But Google and Amazon sit in the back, secretly enjoying the benefits.
Google has Android, Chrome, Search, Workspace, and YouTube—all natural gateways to users. GCP sells computing power, and TPU reduces their own training costs. AWS is the biggest cloud: AI training and inference both consume cloud resources, so the more competition there is, the more profitable it becomes.
Gemini is the North American Doubao; Alexa is even worse—but they already have users and money. Model companies fight it out in the middle, while cloud providers collect rent from behind the scenes. The greatest wisdom in the world is to quietly take advantage.
AI’s endgame may not be "whose model is the strongest," but "who has the most entrances." Models are ammunition, and distribution is the gun. Ammunition can be swapped—guns are harder to replace.
#Google #Amazon #AI
This model is strong today, that one is strong tomorrow. But Google and Amazon sit in the back, secretly enjoying the benefits.
Google has Android, Chrome, Search, Workspace, and YouTube—all natural gateways to users. GCP sells computing power, and TPU reduces their own training costs. AWS is the biggest cloud: AI training and inference both consume cloud resources, so the more competition there is, the more profitable it becomes.
Gemini is the North American Doubao; Alexa is even worse—but they already have users and money. Model companies fight it out in the middle, while cloud providers collect rent from behind the scenes. The greatest wisdom in the world is to quietly take advantage.
AI’s endgame may not be "whose model is the strongest," but "who has the most entrances." Models are ammunition, and distribution is the gun. Ammunition can be swapped—guns are harder to replace.
#Google #Amazon #AI
The “day after day” approach: why storage and optical module companies’ PE looks low is because we’re looking at the peak of the cycle
Unlike TSMC and other technology monopolies that can sustain high gross margins for the long term, a low PE doesn’t mean it’s cheap. It may be that the market has already highly priced in the cycle peak.
This time, tech stocks have dropped so badly—yet TSMC didn’t fall much. That’s because TSMC’s moat isn’t about “a good cycle”; it’s about “others can’t do it.” The moats of storage and optical module businesses are “a good cycle.” When the cycle turns, a low PE is actually the most expensive moment.
The hardest lesson in value investing: a low PE doesn’t mean undervaluation. You think you’re catching the bottom, but in reality you’re grabbing a flying knife at the peak of the cycle.
#美股 #半导体 #investment
Unlike TSMC and other technology monopolies that can sustain high gross margins for the long term, a low PE doesn’t mean it’s cheap. It may be that the market has already highly priced in the cycle peak.
This time, tech stocks have dropped so badly—yet TSMC didn’t fall much. That’s because TSMC’s moat isn’t about “a good cycle”; it’s about “others can’t do it.” The moats of storage and optical module businesses are “a good cycle.” When the cycle turns, a low PE is actually the most expensive moment.
The hardest lesson in value investing: a low PE doesn’t mean undervaluation. You think you’re catching the bottom, but in reality you’re grabbing a flying knife at the peak of the cycle.
#美股 #半导体 #investment
Day by Day, One Soldier at a Time: As AI Gradually Takes Over Execution, What Is the Most Important Human Ability?
Coinbase’s core article revolves around three questions: Can you call on AI correctly? Do you know which problems are suitable to hand to AI? And can you detect AI’s mistakes, limits, and safety risks?
As execution becomes automated, what will truly become scarce is judgment and taste—deciding what’s worth doing, distinguishing what only looks correct from what is actually correct, and overturning the model’s answers when necessary.
This view may be right, or may not be. Judgment really is scarce, but the word “judgment” is too vague. A more accurate way to put it is: when AI drives execution costs close to zero, the cost of “choosing what to execute” becomes the only remaining major expense. Judgment is not a gift; it’s a reflex formed after you’ve paid enough tuition for making mistakes.
#AI #Judgment
Coinbase’s core article revolves around three questions: Can you call on AI correctly? Do you know which problems are suitable to hand to AI? And can you detect AI’s mistakes, limits, and safety risks?
As execution becomes automated, what will truly become scarce is judgment and taste—deciding what’s worth doing, distinguishing what only looks correct from what is actually correct, and overturning the model’s answers when necessary.
This view may be right, or may not be. Judgment really is scarce, but the word “judgment” is too vague. A more accurate way to put it is: when AI drives execution costs close to zero, the cost of “choosing what to execute” becomes the only remaining major expense. Judgment is not a gift; it’s a reflex formed after you’ve paid enough tuition for making mistakes.
#AI #Judgment
Striving for one more step: Kimi K3 just released—28 trillion parameters, 1 million context, native multimodal
All three internal benchmarks surpass Claude Opus 4.8 and GPT-5.5: Online Exp 75.5, DECK-Bench 73.5, Finance-Bench 62.6.
Two architecture updates: Kimi Delta Attention (KDA) boosts decoding speed by up to 6.3x in million-token context; Attention Residuals (AttnRes) improves training efficiency by about 25%, with extra costs under 2%. The MoE expands to 896 experts, activating 16 each time; overall expansion efficiency is about 2.5x higher than K2.
Most worth paying attention to is its self-evolution capability: K3 takes 15 hours of continuous iteration to design a new two-stage kernel algorithm, reducing AttnRes forward + backward from 283.6ms to 114.4ms—no change in results, but double the speed. The model is optimizing itself.
K3 has gone live with Kimi Work, Kimi Code, and the API, with weights to be released by July 27.
Once models start optimizing their own training kernels, the narrative of “AI helps humans write code” needs an upgrade—next comes “AI helps AI write code even faster.”
#Kimi #K3 #AI
All three internal benchmarks surpass Claude Opus 4.8 and GPT-5.5: Online Exp 75.5, DECK-Bench 73.5, Finance-Bench 62.6.
Two architecture updates: Kimi Delta Attention (KDA) boosts decoding speed by up to 6.3x in million-token context; Attention Residuals (AttnRes) improves training efficiency by about 25%, with extra costs under 2%. The MoE expands to 896 experts, activating 16 each time; overall expansion efficiency is about 2.5x higher than K2.
Most worth paying attention to is its self-evolution capability: K3 takes 15 hours of continuous iteration to design a new two-stage kernel algorithm, reducing AttnRes forward + backward from 283.6ms to 114.4ms—no change in results, but double the speed. The model is optimizing itself.
K3 has gone live with Kimi Work, Kimi Code, and the API, with weights to be released by July 27.
Once models start optimizing their own training kernels, the narrative of “AI helps humans write code” needs an upgrade—next comes “AI helps AI write code even faster.”
#Kimi #K3 #AI
Day by day, one more step: Grok Build has been upgraded
A single tweet from Musk—just three words. No technical blog, no benchmarks, no claim of “we surpassed GPT-5.5.”
This is xAI’s release strategy: they don’t compete with you on leaderboard scores; they compete for attention and distribution. Grok Build’s user source isn’t readers of tech blogs—it’s the information feed on X. When your model is embedded in a social platform, every use becomes a distribution moment.
Other AI companies pay to acquire users, while xAI grows users through social platforms. Different paths—different destinations, too.
#Grok #xAI
A single tweet from Musk—just three words. No technical blog, no benchmarks, no claim of “we surpassed GPT-5.5.”
This is xAI’s release strategy: they don’t compete with you on leaderboard scores; they compete for attention and distribution. Grok Build’s user source isn’t readers of tech blogs—it’s the information feed on X. When your model is embedded in a social platform, every use becomes a distribution moment.
Other AI companies pay to acquire users, while xAI grows users through social platforms. Different paths—different destinations, too.
#Grok #xAI
Keep pushing one more step: Kimi's domestic client is pretty strong
Supports using Canva to make posters (the results are average, but Workbuddy doesn’t have this feature), offers a complete literature search suite (CNKI, Wanfang), can connect with Eastmoney and Tianyancha, and can even organize files in Baidu Netdisk.
The competitive dimensions of AI clients in China are different from abroad. Claude and ChatGPT are competing on reasoning ability, while Kimi is competing on “how many China-based data sources it has access to.” Literature search, financial data, and organizing Netdisk—these are all hard-demand scenarios for “non-general intelligence.”
An AI assistant’s moat isn’t the model parameters—it’s the data connectivity and scenario embedding. The model can be replaced, but the workflow is hard to change.
#Kimi #AI
Supports using Canva to make posters (the results are average, but Workbuddy doesn’t have this feature), offers a complete literature search suite (CNKI, Wanfang), can connect with Eastmoney and Tianyancha, and can even organize files in Baidu Netdisk.
The competitive dimensions of AI clients in China are different from abroad. Claude and ChatGPT are competing on reasoning ability, while Kimi is competing on “how many China-based data sources it has access to.” Literature search, financial data, and organizing Netdisk—these are all hard-demand scenarios for “non-general intelligence.”
An AI assistant’s moat isn’t the model parameters—it’s the data connectivity and scenario embedding. The model can be replaced, but the workflow is hard to change.
#Kimi #AI
Day by day, one soldier at a time: the great Codex hasn’t made money for others yet—those selling skins were the first to cash in
A batch of Codex desktop skinning services has appeared on Xianyu. For a few bucks you can buy the tutorial, or pay a custom price of 66–99 yuan. The seller will replace backgrounds, sidebars, buttons, and avatars—star, game, and anime-themed styles can all be done.
The underlying tool, Codex Dream Skin, has been open-sourced on GitHub with 6,800 stars. It supports macOS and Windows, injects themes via local CDP, doesn’t modify the official installer package, and allows one-click restoration.
The AI programming tools industry has already split into segments: those who build models make money from computing power; those who build tools make money from subscriptions; and those who sell skins earn the most honest kind of money—an aesthetic premium. Human needs hierarchy still holds in the AI era: first use it, then use it well, and finally make it look good.
#Codex #AI tools
A batch of Codex desktop skinning services has appeared on Xianyu. For a few bucks you can buy the tutorial, or pay a custom price of 66–99 yuan. The seller will replace backgrounds, sidebars, buttons, and avatars—star, game, and anime-themed styles can all be done.
The underlying tool, Codex Dream Skin, has been open-sourced on GitHub with 6,800 stars. It supports macOS and Windows, injects themes via local CDP, doesn’t modify the official installer package, and allows one-click restoration.
The AI programming tools industry has already split into segments: those who build models make money from computing power; those who build tools make money from subscriptions; and those who sell skins earn the most honest kind of money—an aesthetic premium. Human needs hierarchy still holds in the AI era: first use it, then use it well, and finally make it look good.
#Codex #AI tools
Day by Day: Infini Professional Cuts Fee Thresholds from $100K to $10K
Previously, you needed a monthly transaction volume or balance of $100K to unlock a 0.30% fee rate; now it’s only $10K. Elite drops from $1M to $100K, with the fee rate unchanged—so the threshold is cut in half.
The real pain point in cross-border payments isn’t just high fees—it’s the one-two punch of “threshold + fee.” Low fees but high thresholds leave small and mid-sized merchants out of reach; low thresholds but high fees make daily use not cost-effective. What Infini cuts this time is the threshold—turning low fee rates from a “big player privilege” into a “daily tool.”
For stablecoin cross-border payments, what it truly aims to replace isn’t bank wire transfers, but PayPal and Stripe.
#Infini #稳定币 #Cross-border payments
Previously, you needed a monthly transaction volume or balance of $100K to unlock a 0.30% fee rate; now it’s only $10K. Elite drops from $1M to $100K, with the fee rate unchanged—so the threshold is cut in half.
The real pain point in cross-border payments isn’t just high fees—it’s the one-two punch of “threshold + fee.” Low fees but high thresholds leave small and mid-sized merchants out of reach; low thresholds but high fees make daily use not cost-effective. What Infini cuts this time is the threshold—turning low fee rates from a “big player privilege” into a “daily tool.”
For stablecoin cross-border payments, what it truly aims to replace isn’t bank wire transfers, but PayPal and Stripe.
#Infini #稳定币 #Cross-border payments
Day by Day Pushing Forward: WBTC is the cornerstone of BTC in DeFi—five protocols capture most of the TVL
Aave, Morpho, Compound, Curve, Spark—these are the top five by WBTC total value locked.
Turning BTC into an ERC-20 and putting it into DeFi has been done from 2019 to today. WBTC’s TVL isn’t a matter of price swings—it’s a trust question: whether BTC holders are willing to put BTC into smart contracts.
All of the top five are lending and DEX—showing that WBTC’s main use is still “posting collateral to borrow stablecoins” and “providing liquidity.” In DeFi, BTC isn’t meant for trading; it’s meant to serve as a base asset.
#wBTC #Bitcoin #DeFi
Aave, Morpho, Compound, Curve, Spark—these are the top five by WBTC total value locked.
Turning BTC into an ERC-20 and putting it into DeFi has been done from 2019 to today. WBTC’s TVL isn’t a matter of price swings—it’s a trust question: whether BTC holders are willing to put BTC into smart contracts.
All of the top five are lending and DEX—showing that WBTC’s main use is still “posting collateral to borrow stablecoins” and “providing liquidity.” In DeFi, BTC isn’t meant for trading; it’s meant to serve as a base asset.
#wBTC #Bitcoin #DeFi
Daily Work a Soldier One Step at a Time: On July 15, the stock lifecycle on Wall Street was end-to-end moved onto the blockchain for the first time
DTCC—the U.S. central clearinghouse for all stocks, with 130 years of history—completed blockchain live trading for the first time: SPY, QQQ, U.S. Treasuries, and Microsoft stock. More than 50 institutions participated, including BlackRock, Goldman Sachs, and JPMorgan. Official commercial use begins in October.
On the same day, Cantor Fitzgerald (the top U.S. IPO underwriter in 2025) and Securitize (the largest tokenization platform, with $4 billion in assets) launched an on-chain IPO framework—each token is equivalent to real shares, with the same legal status. Securitize itself listed on the NYSE on July 2, and on the same day tokenized the shares onto Avalanche and Solana—the world’s first.
More importantly, JPMorgan used tokenized QQQ as derivative margin collateral.
If you break the stock lifecycle into parts: issuance → trading → settlement → collateral. Cantor × Securitize moved issuance on-chain, DTCC moved trading and settlement on-chain, and JPMorgan moved collateral on-chain. For the first time, all four links were run end-to-end on the blockchain.
The settlement cycle shortened from T+2 to seconds. This isn’t an efficiency upgrade—it’s an infrastructure replacement.
#DTCC #代币化 #RWA
DTCC—the U.S. central clearinghouse for all stocks, with 130 years of history—completed blockchain live trading for the first time: SPY, QQQ, U.S. Treasuries, and Microsoft stock. More than 50 institutions participated, including BlackRock, Goldman Sachs, and JPMorgan. Official commercial use begins in October.
On the same day, Cantor Fitzgerald (the top U.S. IPO underwriter in 2025) and Securitize (the largest tokenization platform, with $4 billion in assets) launched an on-chain IPO framework—each token is equivalent to real shares, with the same legal status. Securitize itself listed on the NYSE on July 2, and on the same day tokenized the shares onto Avalanche and Solana—the world’s first.
More importantly, JPMorgan used tokenized QQQ as derivative margin collateral.
If you break the stock lifecycle into parts: issuance → trading → settlement → collateral. Cantor × Securitize moved issuance on-chain, DTCC moved trading and settlement on-chain, and JPMorgan moved collateral on-chain. For the first time, all four links were run end-to-end on the blockchain.
The settlement cycle shortened from T+2 to seconds. This isn’t an efficiency upgrade—it’s an infrastructure replacement.
#DTCC #代币化 #RWA
Day by Day: Tether’s USDT on Optimism surpasses a market value of $200 million
Stablecoin transfers on-chain aren’t news—where stablecoins are gaining momentum is news. Optimism isn’t the largest USDT chain, but a $200 million market cap shows that L2 is shifting from “DeFi-only” to “stablecoin infrastructure.”
USDT on Tron is a payment tool; on Ethereum it’s DeFi collateral; on Solana it’s meme fuel. On Optimism, what is it? At least for now, it appears to be a byproduct of sequencer revenue—when the chain is cheap, stablecoins naturally come.
#USDT #Tether #Optimism
Stablecoin transfers on-chain aren’t news—where stablecoins are gaining momentum is news. Optimism isn’t the largest USDT chain, but a $200 million market cap shows that L2 is shifting from “DeFi-only” to “stablecoin infrastructure.”
USDT on Tron is a payment tool; on Ethereum it’s DeFi collateral; on Solana it’s meme fuel. On Optimism, what is it? At least for now, it appears to be a byproduct of sequencer revenue—when the chain is cheap, stablecoins naturally come.
#USDT #Tether #Optimism
Day by day, a single step at a time: Coinbase’s Deribit is still usable
The liquidity for BTC and ETH options is indeed good. If you already planned to pick up a position, then selling puts to collect the premium is quite comfortable.
But always remember: option products like these involving two currencies are not meant to make that little bit of premium. In the end, you might get stuck with a pile of positions you don’t want. It has only one purpose—to be a tool for catching a dip. The prerequisite is that the price is something you’re willing to buy in the first place.
Many people make premium income ten times with options, and on the eleventh time, they get assigned and end up taking assets they don’t want—losing everything they gained from the prior ten times. The tool is neutral; what matters is whether you use it for defense or for gambling.
#Deribit #期权 #BTC
The liquidity for BTC and ETH options is indeed good. If you already planned to pick up a position, then selling puts to collect the premium is quite comfortable.
But always remember: option products like these involving two currencies are not meant to make that little bit of premium. In the end, you might get stuck with a pile of positions you don’t want. It has only one purpose—to be a tool for catching a dip. The prerequisite is that the price is something you’re willing to buy in the first place.
Many people make premium income ten times with options, and on the eleventh time, they get assigned and end up taking assets they don’t want—losing everything they gained from the prior ten times. The tool is neutral; what matters is whether you use it for defense or for gambling.
#Deribit #期权 #BTC
One Push, One Guard: Later you finally understood why the English word for memory is Memory
Because what stays with you the deepest, the hardest to forget, is never the moment when you make money. It’s that summer—endless agony while you’re trapped. And one sentence you can never let go of: If I had run back then a little sooner, I would’ve been better off.
Everyone who’s been caught in a position knows this feeling. The pain doesn’t come from the loss itself, but from the fantasy of “I could have avoided it.” This illusion consumes more energy than any actual amount of money lost.
The hardest thing about trading isn’t learning technicals—it’s learning to make peace with your own memories.
#交易 #Investment Psychology
Because what stays with you the deepest, the hardest to forget, is never the moment when you make money. It’s that summer—endless agony while you’re trapped. And one sentence you can never let go of: If I had run back then a little sooner, I would’ve been better off.
Everyone who’s been caught in a position knows this feeling. The pain doesn’t come from the loss itself, but from the fantasy of “I could have avoided it.” This illusion consumes more energy than any actual amount of money lost.
The hardest thing about trading isn’t learning technicals—it’s learning to make peace with your own memories.
#交易 #Investment Psychology
Day after day, one soldier at a time: Trump’s policies seem hard to understand when viewed individually—but put together, you realize it’s the same story
Why does the government frequently take equity stakes in companies? Why is Altman willing to put 5% of his stake in OpenAI on the table? Why should every newborn baby in the United States automatically hold U.S. stocks from birth?
These three questions are not independent new policies. If you connect them fully, the logic is consistent: bind America’s public finances, industries, and household wealth into the same asset pool. Government equity stakes → public finance shifts from tax revenue to capital gains; AI equity stakes → industrial anchors lock-in; newborn equity stakes → a nationwide, directed inflow over fifteen years.
If this story holds, U.S. stocks are no longer just an economic barometer. They themselves become a tool of U.S. fiscal policy, industrial policy, and a personal retirement plan.
#美股 #特朗普 #Macro
Why does the government frequently take equity stakes in companies? Why is Altman willing to put 5% of his stake in OpenAI on the table? Why should every newborn baby in the United States automatically hold U.S. stocks from birth?
These three questions are not independent new policies. If you connect them fully, the logic is consistent: bind America’s public finances, industries, and household wealth into the same asset pool. Government equity stakes → public finance shifts from tax revenue to capital gains; AI equity stakes → industrial anchors lock-in; newborn equity stakes → a nationwide, directed inflow over fifteen years.
If this story holds, U.S. stocks are no longer just an economic barometer. They themselves become a tool of U.S. fiscal policy, industrial policy, and a personal retirement plan.
#美股 #特朗普 #Macro
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