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    BNB Price Soars to Near All-Time High as Technical Indicators Flash Strong Bullish Signals

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    Rebeca Moen
    Sep 12, 2025 10:59

    Binance Coin trades at $907.27, just shy of its 52-week high, with RSI at 65.57 and MACD showing bullish momentum as BNB breaks above key resistance levels.





    Quick Take

    • BNB currently trading at $907.27 (+0.74% in 24h)
    • Binance Coin RSI at 65.57 indicates healthy upward momentum without overbought conditions
    • BNB price sits just $0.01 below its 52-week high of $907.26, signaling potential breakout

    What’s Driving Binance Coin Price Today?

    While no significant news events have emerged in the past week to directly influence BNB price action, the token’s strong performance appears driven by technical momentum and broader market sentiment. The absence of negative catalysts has allowed Binance Coin to maintain its bullish trajectory, with the BNB/USDT pair demonstrating consistent strength across all major timeframes.

    The current price action suggests institutional and retail confidence remains high in Binance’s ecosystem, with trading volume reaching $187.2 million on Binance spot markets over the past 24 hours. This substantial volume supports the legitimacy of the current price movement and indicates genuine buying interest rather than low-liquidity pumps.

    BNB Technical Analysis: Strong Bullish Signals Emerge

    The Binance Coin technical analysis reveals overwhelmingly positive momentum across multiple indicators. BNB’s RSI reading of 65.57 positions the token in an ideal zone for continued upward movement, avoiding both oversold conditions that might suggest weakness and overbought levels that could trigger immediate corrections.

    Binance Coin’s MACD indicator shows particularly encouraging signals, with the MACD line at 17.72 well above the signal line at 15.20. The positive MACD histogram value of 2.52 confirms that bullish momentum is not only present but accelerating, suggesting the current uptrend has room to continue.

    The moving average structure further supports the bullish case for BNB price. Binance Coin trades above all key moving averages, with the SMA 7 at $886.11, SMA 20 at $866.95, and SMA 50 at $834.04 all providing stepped support levels. Most notably, BNB maintains a significant premium above the SMA 200 at $684.73, indicating the long-term trend remains firmly bullish.

    Binance Coin’s position relative to the Bollinger Bands presents both opportunity and caution. With a %B position of 1.04, BNB trades slightly above the upper band at $904.27, suggesting the token has reached a technically overbought condition in the short term. However, strong trends often see prices “walk the upper band,” making this positioning potentially sustainable if volume remains strong.

    Binance Coin Price Levels: Key Support and Resistance

    The immediate BNB resistance level sits at $909.69, representing both the 24-hour high and a crucial psychological barrier. A decisive break above this level could trigger significant momentum buying, potentially pushing Binance Coin toward new all-time highs.

    For traders watching Binance Coin support levels, the most immediate backstop appears at $829.59, coinciding with the lower Bollinger Band. This level represents approximately 8.6% downside from current prices and would likely attract significant buying interest given the strong technical picture.

    More substantial support for BNB lies at $730.01, representing the strong support level identified in our analysis. This level would constitute a deeper correction of approximately 19.5% but aligns with previous significant price action and could serve as an excellent entry point for long-term investors.

    The pivot point at $902.74 provides intraday traders with a key reference level, with BNB currently trading above this mark supporting the near-term bullish bias.

    Should You Buy BNB Now? Risk-Reward Analysis

    For aggressive traders, the current BNB price action presents an attractive momentum play with clearly defined risk parameters. Based on Binance spot market data, entry at current levels with a stop-loss below $890 offers a tight risk-reward setup, targeting the psychological $920-930 range for initial profit-taking.

    Conservative investors might consider waiting for a pullback toward the $870-880 range, where Binance Coin would find support from both the EMA 12 at $879.07 and the SMA 7 at $886.11. This approach would provide better entry prices while maintaining exposure to the strong underlying trend.

    Swing traders should monitor BNB’s ability to hold above the $900 level through the next trading session. A sustained break below this level might indicate short-term profit-taking is beginning, potentially offering better entry opportunities around the $880-885 support zone.

    The risk-reward profile heavily favors the upside given Binance Coin’s proximity to new highs and the absence of significant resistance levels above $910. However, traders should remain aware that the elevated Bollinger Band position suggests some near-term volatility is likely.

    Conclusion

    BNB price action over the next 24-48 hours will likely determine whether Binance Coin can establish new all-time highs or requires consolidation before the next leg higher. The technical picture strongly supports continued upward movement, with the RSI providing room for additional gains and the MACD confirming momentum remains positive.

    Traders should watch for a decisive break above $909.69 as the catalyst for the next significant move higher, while those seeking entry points might find opportunities on any pullback toward the $880-890 support zone. With no significant resistance until well above current levels, BNB appears positioned for continued strength in the near term.

    Image source: Shutterstock


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    How to day trade crypto using Google’s Gemini AI

    0

    Key takeaways:

    • Gemini AI serves as a powerful tool for researching day trade strategies but cannot be used to execute trades directly.

    • It summarizes fundamentals and compares assets to support daily trade decisions but still requires access to data sets.

    • Gemini AI helps manage trading discipline by turning watchlists, catalysts and post-mortems into structured loops that prevent traders from chasing noise.

    • Gemini Flash 2.5, the latest version, still lacks real-time data access, so pair it with data from tools like TradingView, Glassnode or Nansen.

    Day trading crypto moves fast, order books flip, narratives rotate, and liquidity pockets appear then vanish. Google’s Gemini AI can help you organize information, test ideas and automate routine analysis. It can function as a smart assistant that allows you to filter noise, structure market data and enable you to make insight-driven decisions.

    This article shows you a safe, compliance-minded workflow to research, simulate and automate parts of a day-trading stack using Gemini AI without handing it your keys or “letting the AI trade for you.”

    It is important to note that all prompts and examples were tested on Gemini Flash 2.5, which doesn’t stream real-time market data. That means you’ll need to cross-check AI-generated insight against live charts and reliable sources before acting on it. Crypto is volatile, so do your own research and trade responsibly.

    What is crypto day trading, and why is it brutal without AI?

    Day trading in crypto means opening and closing positions within the same day, often within hours or even minutes. Unlike swing traders who ride trends for days or long-term investors who hold for months, day traders thrive on short-term price moves. 

    Volatility is their playground, and crypto offers it in overdrive. That overdrive shows up in several ways unique to crypto markets:

    • 24/7 markets: There’s no closing bell. BTC can break out at 3 am.

    • Narrative-driven pumps: A token upgrade or social media post can flip sentiment instantly.

    • Liquidity pockets: Order books thin out, and slippage can wreck an unplanned entry.

    • Noise overload: Telegram, X, Discord, onchain alerts and macro news with hundreds of signals compete for attention.

    This is where AI tools like Google’s Gemini fit in. They don’t replace the trader but act as a co-pilot. They help by:

    • Summarizing order flow and sentiment

    • Filtering catalysts that actually move the price from background noise

    • Structuring data into sheets or dashboards so you see setups clearly

    • Helping you write, test and refine rules (instead of chasing FOMO).

    What Gemini can (and can’t) do for crypto day traders

    What it can do well

    • Reason over large context: Newer Gemini releases (e.g., Gemini 2.5 Pro) focus on long-context reasoning and strong coding ability, ideal for stitching market data and your notes into actionable summaries.

    • Live inside your tools: Gemini works across Google Workspace apps, including Docs and Sheets, where it can summarize data, clean it and generate charts, now even through in-cell AI functions in Sheets.

    • Developer-friendly: With Google AI Studio and the Gemini API, you can programmatically prompt models, analyze data sets and integrate outputs into your scripts or dashboards.

    What it shouldn’t do (directly)

    Hold crypto keys or auto-trade unsupervised. Keep Gemini focused on analysis, signal generation, backtesting and alerts. If you do connect to an exchange API, strictly gate permissions. 

    Did you know? Google’s Gemini can process up to 1 million tokens in a single prompt, meaning traders can feed entire research reports, news flows and charts into one query for faster insights.

    Select and set up your Gemini access and workspace

    1. Pick your Gemini access level

    • Google AI Studio + API key for developers building prompts and scripts.

    • Gemini in Workspace (Docs/Sheets) for no-code research and dashboards.

    • Google now bundles “Advanced” features under the Google AI Pro subscription for the Gemini app (bigger context windows, deeper research and brainstorming ideas with Gemini). If you need maximum context for multi-asset intraday notes, that can help.

    2. Create a trading notebook in Google Sheets

    Once you’ve chosen your Gemini access (Sheets, Docs or API for developers), the next step is to create a trading notebook, a structured space where AI helps you organize chaos into clarity.

    A simple Google Sheet with six tabs, as follows, can be a start:

    • Watchlist: Track the tokens you’re monitoring.

    • Catalysts: Note key events (upgrades, unlocks, macro reports).

    • Levels: Mark out support, resistance and liquidity pockets.

    • Order flow: Capture onchain flows, funding rates or order book imbalance.

    • Plan: Write your playbook before the session begins.

    • Post-mortem: Log what worked, what failed and what to improve.

    Instead of staring at X or 10 chart tabs, you’re creating a repeatable loop: Watchlist → Catalysts → Levels → Plan → Order Flow → Post-Mortem → back to Watchlist. Gemini slots into each step as a reasoning partner.

    While you can manually create data sets, another way to run a trading loop is via data sets downloaded from analytics providers like Glassnode, TradingView or CryptoQuant. 

    Did you know? In a 2025 global survey of regulators, IOSCO reported that among broker-dealers, algorithmic trading (63%) was one of the most commonly observed AI use cases, alongside surveillance (53%), client communications (67%) and market analysis/trading insights (40%).

    Day trade using Gemini AI

    Example: Using Gemini AI to refine a watchlist

    Say your watchlist includes Bitcoin (BTC), Cardano (ADA) and Solana (SOL). Instead of scanning 50 tokens, you ask Gemini to highlight which ones had the biggest market swings or the highest percentage changes in the past 24 hours (pulled from your own data feed or an external data platform).

    A prompt might look like:
    “Summarize the top three coins by 24-hour price change from this data set. Rank them by potential risk of shorting.”

    Gemini will produce you context and a structured ranking that helps you focus your limited time on the most volatile assets based on the data set you provided.

    Example: Using Gemini AI for catalyst filtering

    Catalysts drive intraday moves, Consumer Price Index reports, US Federal Reserve minutes, token unlocks, tech upgrades or even airdrop rumors. But there’s more noise than signal. Instead of manually scrolling through X or Discord, paste in the headlines and ask Gemini AI.

    A prompt might look like:

    “Flag which of these news catalysts are most likely to impact ETH and SOL in the next 12 hours, based on past price reactions.”

    Example: Levels and liquidity mapping

    Support and resistance levels are the bread and butter of day trading. Gemini can’t stream live order books, but you can feed it recent OHLCV (open, high, low, close and volume) data or your own notes, then ask:

    “Identify the key price clusters where ETH was rejected multiple times this week and summarize as possible resistance.”

    Instead of eyeballing, you get a clean text summary: “ETH repeatedly rejected near $3,950-$40,000; prior support at $3,840 flipped resistance.”

    Example: Using Gemini AI for order flow sentiment

    If you’re tracking open interest, long/short ratios or whale wallet flows, Gemini AI can help make sense of it:

    “Summarize whether current BTC futures positioning looks more skewed to longs or shorts.”

    You still need the raw BTC data downloaded from your trading portals, but Gemini AI’s summary can help you avoid tunnel vision. Instead of staring at numbers, you can request an interpreted snapshot that tells you whether the crowd is leaning long, short or neutral.

    Example: Using Gemini AI for a daily trading plan

    The Plan tab is where Gemini helps enforce discipline. A prompt like:

    “Take today’s Watchlist, Catalysts and Levels tabs and draft three possible intraday scenarios with triggers and invalidations.”

    That might provide an output like:

    • Scenario A: Ether (ETH) breaks above $3,000 on high volume; long scalp with stop at $2,960.

    • Scenario B: BTC rejects $105,000 resistance again and fades into $100,000.

    • Scenario C: SOL reacts negatively to unlock event; short bounce into $170.

    Now you’ve got a structured plan instead of winging it.

    Example: Using Gemini AI for a post-mortem review

    After the session, you can paste your trades into Gemini AI and ask:

    “Analyze my last five trades and identify patterns in mistakes or strengths.”

    It might spot that you cut winners too early but let losers run, or that you always overtrade during high volatility. This turns mistakes into structured lessons.

    How can Gemini AI support risk management?

    Risk is the one variable every day trader must control because surviving bad trades matters more than catching perfect ones. Use Gemini AI for a discipline check:

    • Position sizing: Share your account size and maximum risk per trade, and Gemini AI can calculate safe position sizes under different leverage scenarios.

    • Scenario planning: Instead of mapping only bullish setups, prompt Gemini AI to also outline bearish and sideways cases so you’re never locked into one bias.

    • Risk-to-reward ratios: Paste your planned setups into Gemini and ask it to rank them by “r/r” ratio. This keeps your focus on the highest-quality trades.

    • Capital allocation: Ask Gemini to summarize your exposure across assets (e.g., too much ETH beta) so you can rebalance before it’s too late.

    Day trading crypto will always be a high-speed, high-risk game. What Gemini AI offers isn’t shortcuts, but the ability to process more information, stick to your rules and refine strategies faster than you could alone.

    This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

    Source link

    How to day trade crypto using Google’s Gemini AI

    0

    Key takeaways:

    • Gemini AI serves as a powerful tool for researching day trade strategies but cannot be used to execute trades directly.

    • It summarizes fundamentals and compares assets to support daily trade decisions but still requires access to data sets.

    • Gemini AI helps manage trading discipline by turning watchlists, catalysts and post-mortems into structured loops that prevent traders from chasing noise.

    • Gemini Flash 2.5, the latest version, still lacks real-time data access, so pair it with data from tools like TradingView, Glassnode or Nansen.

    Day trading crypto moves fast, order books flip, narratives rotate, and liquidity pockets appear then vanish. Google’s Gemini AI can help you organize information, test ideas and automate routine analysis. It can function as a smart assistant that allows you to filter noise, structure market data and enable you to make insight-driven decisions.

    This article shows you a safe, compliance-minded workflow to research, simulate and automate parts of a day-trading stack using Gemini AI without handing it your keys or “letting the AI trade for you.”

    It is important to note that all prompts and examples were tested on Gemini Flash 2.5, which doesn’t stream real-time market data. That means you’ll need to cross-check AI-generated insight against live charts and reliable sources before acting on it. Crypto is volatile, so do your own research and trade responsibly.

    What is crypto day trading, and why is it brutal without AI?

    Day trading in crypto means opening and closing positions within the same day, often within hours or even minutes. Unlike swing traders who ride trends for days or long-term investors who hold for months, day traders thrive on short-term price moves. 

    Volatility is their playground, and crypto offers it in overdrive. That overdrive shows up in several ways unique to crypto markets:

    • 24/7 markets: There’s no closing bell. BTC can break out at 3 am.

    • Narrative-driven pumps: A token upgrade or social media post can flip sentiment instantly.

    • Liquidity pockets: Order books thin out, and slippage can wreck an unplanned entry.

    • Noise overload: Telegram, X, Discord, onchain alerts and macro news with hundreds of signals compete for attention.

    This is where AI tools like Google’s Gemini fit in. They don’t replace the trader but act as a co-pilot. They help by:

    • Summarizing order flow and sentiment

    • Filtering catalysts that actually move the price from background noise

    • Structuring data into sheets or dashboards so you see setups clearly

    • Helping you write, test and refine rules (instead of chasing FOMO).

    What Gemini can (and can’t) do for crypto day traders

    What it can do well

    • Reason over large context: Newer Gemini releases (e.g., Gemini 2.5 Pro) focus on long-context reasoning and strong coding ability, ideal for stitching market data and your notes into actionable summaries.

    • Live inside your tools: Gemini works across Google Workspace apps, including Docs and Sheets, where it can summarize data, clean it and generate charts, now even through in-cell AI functions in Sheets.

    • Developer-friendly: With Google AI Studio and the Gemini API, you can programmatically prompt models, analyze data sets and integrate outputs into your scripts or dashboards.

    What it shouldn’t do (directly)

    Hold crypto keys or auto-trade unsupervised. Keep Gemini focused on analysis, signal generation, backtesting and alerts. If you do connect to an exchange API, strictly gate permissions. 

    Did you know? Google’s Gemini can process up to 1 million tokens in a single prompt, meaning traders can feed entire research reports, news flows and charts into one query for faster insights.

    Select and set up your Gemini access and workspace

    1. Pick your Gemini access level

    • Google AI Studio + API key for developers building prompts and scripts.

    • Gemini in Workspace (Docs/Sheets) for no-code research and dashboards.

    • Google now bundles “Advanced” features under the Google AI Pro subscription for the Gemini app (bigger context windows, deeper research and brainstorming ideas with Gemini). If you need maximum context for multi-asset intraday notes, that can help.

    2. Create a trading notebook in Google Sheets

    Once you’ve chosen your Gemini access (Sheets, Docs or API for developers), the next step is to create a trading notebook, a structured space where AI helps you organize chaos into clarity.

    A simple Google Sheet with six tabs, as follows, can be a start:

    • Watchlist: Track the tokens you’re monitoring.

    • Catalysts: Note key events (upgrades, unlocks, macro reports).

    • Levels: Mark out support, resistance and liquidity pockets.

    • Order flow: Capture onchain flows, funding rates or order book imbalance.

    • Plan: Write your playbook before the session begins.

    • Post-mortem: Log what worked, what failed and what to improve.

    Instead of staring at X or 10 chart tabs, you’re creating a repeatable loop: Watchlist → Catalysts → Levels → Plan → Order Flow → Post-Mortem → back to Watchlist. Gemini slots into each step as a reasoning partner.

    While you can manually create data sets, another way to run a trading loop is via data sets downloaded from analytics providers like Glassnode, TradingView or CryptoQuant. 

    Did you know? In a 2025 global survey of regulators, IOSCO reported that among broker-dealers, algorithmic trading (63%) was one of the most commonly observed AI use cases, alongside surveillance (53%), client communications (67%) and market analysis/trading insights (40%).

    Day trade using Gemini AI

    Example: Using Gemini AI to refine a watchlist

    Say your watchlist includes Bitcoin (BTC), Cardano (ADA) and Solana (SOL). Instead of scanning 50 tokens, you ask Gemini to highlight which ones had the biggest market swings or the highest percentage changes in the past 24 hours (pulled from your own data feed or an external data platform).

    A prompt might look like:
    “Summarize the top three coins by 24-hour price change from this data set. Rank them by potential risk of shorting.”

    Gemini will produce you context and a structured ranking that helps you focus your limited time on the most volatile assets based on the data set you provided.

    Example: Using Gemini AI for catalyst filtering

    Catalysts drive intraday moves, Consumer Price Index reports, US Federal Reserve minutes, token unlocks, tech upgrades or even airdrop rumors. But there’s more noise than signal. Instead of manually scrolling through X or Discord, paste in the headlines and ask Gemini AI.

    A prompt might look like:

    “Flag which of these news catalysts are most likely to impact ETH and SOL in the next 12 hours, based on past price reactions.”

    Example: Levels and liquidity mapping

    Support and resistance levels are the bread and butter of day trading. Gemini can’t stream live order books, but you can feed it recent OHLCV (open, high, low, close and volume) data or your own notes, then ask:

    “Identify the key price clusters where ETH was rejected multiple times this week and summarize as possible resistance.”

    Instead of eyeballing, you get a clean text summary: “ETH repeatedly rejected near $3,950-$40,000; prior support at $3,840 flipped resistance.”

    Example: Using Gemini AI for order flow sentiment

    If you’re tracking open interest, long/short ratios or whale wallet flows, Gemini AI can help make sense of it:

    “Summarize whether current BTC futures positioning looks more skewed to longs or shorts.”

    You still need the raw BTC data downloaded from your trading portals, but Gemini AI’s summary can help you avoid tunnel vision. Instead of staring at numbers, you can request an interpreted snapshot that tells you whether the crowd is leaning long, short or neutral.

    Example: Using Gemini AI for a daily trading plan

    The Plan tab is where Gemini helps enforce discipline. A prompt like:

    “Take today’s Watchlist, Catalysts and Levels tabs and draft three possible intraday scenarios with triggers and invalidations.”

    That might provide an output like:

    • Scenario A: Ether (ETH) breaks above $3,000 on high volume; long scalp with stop at $2,960.

    • Scenario B: BTC rejects $105,000 resistance again and fades into $100,000.

    • Scenario C: SOL reacts negatively to unlock event; short bounce into $170.

    Now you’ve got a structured plan instead of winging it.

    Example: Using Gemini AI for a post-mortem review

    After the session, you can paste your trades into Gemini AI and ask:

    “Analyze my last five trades and identify patterns in mistakes or strengths.”

    It might spot that you cut winners too early but let losers run, or that you always overtrade during high volatility. This turns mistakes into structured lessons.

    How can Gemini AI support risk management?

    Risk is the one variable every day trader must control because surviving bad trades matters more than catching perfect ones. Use Gemini AI for a discipline check:

    • Position sizing: Share your account size and maximum risk per trade, and Gemini AI can calculate safe position sizes under different leverage scenarios.

    • Scenario planning: Instead of mapping only bullish setups, prompt Gemini AI to also outline bearish and sideways cases so you’re never locked into one bias.

    • Risk-to-reward ratios: Paste your planned setups into Gemini and ask it to rank them by “r/r” ratio. This keeps your focus on the highest-quality trades.

    • Capital allocation: Ask Gemini to summarize your exposure across assets (e.g., too much ETH beta) so you can rebalance before it’s too late.

    Day trading crypto will always be a high-speed, high-risk game. What Gemini AI offers isn’t shortcuts, but the ability to process more information, stick to your rules and refine strategies faster than you could alone.

    This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

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    An Experiment Incubated at Harvard to Resolve the Global Debt Crisis (Part 2 of 7)

    0


    Terrill Dicki
    Sep 12, 2025 02:00

    In the interview notes of journalist Faye Xiaofei, Professor Han Feng, in an age of global upheaval, raised his gaze to the stars to discern the tides of history and lowered his eyes to the data to parse their logic, pursuing a question that cuts to the root of civilization itself: When the old gravitational anchors collapse, where should humanity’s wealth be moored?





    Faye’s Lens Turns East

    In her notes, Faye wrote:

    When Nixon announced the Great Decoupling in 1971, China remained outside the gravitational orbit of the world’s main economic system. Yet, almost simultaneously, another story was quietly germinating. Professor Han Feng believes this was one of history’s most unique financial experiments: anchoring wealth not to gold, but to real estate—ignited by the invention of the property title, and launched into motion as the “real estate standard.”

    2. The Genesis Experiment on the Eastern Continent: Land as Foundation

    When Nixon declared the Great Decoupling in 1971, China—this ancient continent—lay outside the gravitational field of the world’s main economic orbit. It resembled an isolated Oort Cloud body, revolving in its own trajectory, with little gravitational exchange with the main galaxy, and thus bore no share of international financial crises.

    Not until 1978 did a grand social project begin—later known as Reform and Opening Up. China, the great ship slumbering for centuries, reignited its engines, altered its course, and set sail toward the starry seas of globalization.

    But it faced a foundational, creation-level problem: the absolute scarcity of credit resources.

    The early development of Western capitalist civilization had been fueled by brutal and bloody “primitive accumulation.” Their fleets acted as colossal resource harvesters, drawing material and energy from across the globe to feed their home economies—the first barrel of oil that fueled the Industrial Revolution. China, by contrast, had no such external inflows. It had to rely on its own endogenous forces—building an oasis in a desert of credit. It was akin to constructing an entire biosphere from scratch on a barren planet.

    At the dawn of Reform, the scarcest resource in China was credit. Without a credible anchor, banks could not extend loans, enterprises could not obtain capital, and markets could not generate liquidity. When the Kuomintang retreated in 1949, it had carried away nearly all of mainland China’s gold reserves to provide the gravitational core for the new Taiwan dollar. The mainland was left like a giant drained of blood: massive in body, but incapable of meaningful economic circulation.

    No one foresaw that a seemingly minor policy change would eventually shift the tectonic plates of the entire continent.

    That change was the birth of the property title deed.

     


     

    The “Real Estate Standard”

    Professor Zhao Yanjing of Xiamen University, an economist whom Han deeply admires, dissected the genetic code of China’s economic miracle with near-clinical precision. He argued that China’s real estate was never a mere commodity—it functioned as the equivalent of “shares issued by a city,” a market-born hard currency unique to China’s historical conditions.

    Before 1998, China had no real estate market. Without clearly defined private ownership, housing could not be credibly traded or used as collateral. Han still recalled being offered in 1993 a two-bedroom apartment inside Beijing’s Second Ring Road for 110,000 yuan—a staggering sum to him at the time, which he promptly declined. Looking back, it was like refusing to buy Bitcoin for a few dollars in its infancy. Yet even had he purchased it, it might not have been wise: in an era of unclear property rights, what one bought might have been no more than a phantom contract—an asset vulnerable to higher-level directives, and potentially null at any time.

    Then came 1998. With the introduction of property title deeds, and later the passage of the Property Law in 2008, a light like Genesis itself dawned. For the first time, land and housing in China were granted clear, tradable, legally protected ownership.

    This was the “Big Bang singularity” of China’s credit universe.

    With clearly defined property rights, banks’ credit engines roared to life at unprecedented power. Ordinary people, enterprises, and local governments—all could now mortgage land and housing to draw liquidity from the vast reservoir of banks. The renminbi finally found its anchoring object. If the Bretton Woods dollar was a “gold standard,” then, as Zhao Yanjing incisively summarized, post-Reform China effectively operated on a “real estate standard.”

     


     

    The Scale of a Miracle

    In barely two or three decades, one of the largest, fastest “wealth-creation movements” in human history unfolded. Millions of people participated in the issuing and trading of this “urban stock” through the buying, selling, and mortgaging of real estate. This was no mere speculation—it was the collective act of hundreds of millions, who committed their lifetime savings and future earnings as votes of confidence in China’s urbanization process, together forging a wealth consensus of unprecedented scale.

    How large was this consensus? At its peak, the total value of China’s real estate market reached an astronomical $65 trillion USD—several times greater than its economic output.

    It was this massive credit foundation of real estate that fueled China’s economic rise over the past twenty years. It funded infrastructure construction and the boom of the internet economy, providing a ceaseless flood of monetary oxygen. The modern skyscrapers, highways, and high-speed railways stretching across China, often surpassing those of Western cities, all trace their funding back to this colossal mortgage machine centered on real estate.

    Even an ordinary working family could, by mortgaging a property, easily obtain millions in funding—enough to send children abroad for education. Before the advent of the “real estate standard,” such a scenario was unimaginable. Han’s fellow Tsinghua alumnus, real estate magnate Lan Chun, once said to him with some frustration: “They all curse us for driving up housing prices. But hasn’t everyone’s wealth increased as a result?

    His words revealed the core of the model: credit expansion driven by rising asset prices, creating enough “money” to propel economic growth. It was like a star expanding through its own nuclear fusion, casting light across an entire galaxy.

     


     

    The Limits of the Model

    Yet just as any civilization dependent on a single energy source eventually faces depletion, China’s “real estate standard” reached its physical limits.

    First, it created vast social problems. Rising housing prices became an invisible gravitational field, binding the life energy of an entire younger generation to narrow parcels of land. Families poured their life savings and future earnings into apartments, leaving little capacity for risk-taking or innovation. The unrest in Hong Kong in recent years stemmed in part from this generational despair born of unaffordable housing—a civilizational decline in vitality.

    Second, this wealth consensus was insular—incapable of globalization. China’s real estate is among the most expensive assets in the world, yet its consensus ends at national borders. You cannot mortgage a Beijing apartment to a New York bank to access global liquidity. Thus, despite massive renminbi issuance, the currency’s internationalization lagged far behind the size of its economy. Real estate was a powerful local gravitational field, but it could not radiate across the universe.

    The central government had long recognized the risks of this model. The policy of “houses are for living in, not for speculation” was an attempt to cool an overheating engine. But the greater crisis lies ahead: what happens when this engine stops—or reverses?

    When property prices stop climbing, or begin to fall, the entire credit chain built upon them unravels in cascading collapse. Evergrande’s 2 trillion-yuan debt black hole, the tens of trillions in hidden local government debt—all were predicated on the fragile assumption of “land values only rise.” Once that assumption fails, banks stop lending, businesses and households stop borrowing, liquidity vanishes as though sucked into a black hole, and the system drifts toward heat death.

    Japan has already rehearsed such a future. After its property bubble burst, Japan endured decades of stagnation: social vitality drained, young people choosing not to buy homes, not to marry, not to have children. Civilization entered a “low-desire equilibrium”—a slow, dignified decay.

    Now, China stands at this crossroads. The great vessel of real estate can no longer carry the mission of national rejuvenation. It has fulfilled its glorious and heavy historical task. Now, it drifts slowly toward the shoals.

     


     

    Faye’s Observations and Summary

    In her annotations, Faye wrote:

    Real estate was indeed a civilization-level credit ignition experiment, solving the problem of how a China without gold reserves could integrate into the global economy.

    But it left behind an even larger question: When property stops rising, what will propel the great vessel forward?

     


     

    Suspense

    At the chapter’s close, Faye left one question hanging:

    If gold is too heavy, real estate cannot globalize, and dollar credit is exhausted—then who will become the “hard currency” of the 21st century?

    Preview | Episode 4: Paradigm Shift — A Power Holder of the Old World Turns.

    Peview | Episode 3: Hard Currency of the Information Age — The Rise of Quantum Gold (Bitcoin, Trump’s pivot, and the Harvard NBW experiment).

    Rreview | Episode 1:  A Prophecy Beneath the Balinese Sky

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    Auditor flagged issue before $2.59M Nemo hack, team admits

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    Sui-based yield trading protocol Nemo lost about $2.59 million due to a known vulnerability introduced by non-audited code being deployed, according to the project.

    According to Nemo’s post-mortem analysis of the Sept. 7 hack, a flaw in a function intended to reduce slippage allowed the attacker to change the state of the protocol. This function, named “get_sy_amount_in_for_exact_py_out,” was pushed onchain without being audited by smart contract auditor Asymptotic.

    Furthermore, Asymptotic’s team identified the issue in a preliminary report. Still, the Nemo team admits that its “team did not adequately address this security concern in a timely manner.”

    Deploying new code only required a signature from a single address, allowing the developer to push unaudited code onchain without disclosing the changes. Furthermore, he did not use the confirmation hash provided in the audit for the deployment, breaking the procedure.

    This is not the first time a hack was revealed to have been easily preventable. The report follows NFT trading platform SuperRare suffering a $730,000 exploit in late July due to a basic smart contract bug that experts say could have easily been prevented with standard testing practices.

    Related: Bubblemaps alleges largest Sybil attack in crypto history on MYX airdrop

    Security procedures changed too late

    The vulnerable code was pushed onchain in early January. The upgrade procedure, which would likely have prevented the unaudited code from being deployed onchain, was implemented in April.

    Despite the upgrade, the vulnerability had already made its way into the production environment. Asymptotic warned Nemo of the vulnerability on Aug. 11, but the project said it was focused on other issues and failed to address it before the exploit.

    Related: Failed NPM exploit highlights looming threat to crypto security: Exec

    Nemo pauses protocol, prepares patch

    According to the analysis, Nemo’s protocol core functions are now paused to prevent further losses. The team is collaborating with multiple security teams and providing all relevant addresses to assist in freezing assets on centralized exchanges.

    A patch has now been developed, and Asymptotic is auditing the new code. The project said it removed its flash loan function, fixed the vulnerable code and added a manual-reset feature to restore affected values. Nemo is also designing a compensation plan for users, including debt structuring at the tokenomics level.

    “The core team is formulating a detailed user compensation plan, including a debt-structuring design at the tokenomics level.“

    Nemo apologized to its users and claims to have learned that “security and risk management demand constant vigilance.” The team also promised to improve its defences and apply stricter protocol control.