ROOT (ROOT)Price Live Chart, Prediction, Market Cap, Token Unlocks, ROOT Concept Stocks CoinMeta

BTC

$66,905.05

+1.33%

/ROOT Price

ROOT

No.2640

$0.0{4}7208

+1.39%

≈$0.00

Market Cap

$864.98K

Cir. Cap

$279.15K

Cir. Supply

3.87B

Cir. Rate

32.2721%

Total Supply

12B

Max Supply

12B

24h Volume

$5.39B

24h Vol (BTC)

388.62K

24h Turnover

139.215403%

Market Share

Performance

Low

Range

+0%

High

Listing

$0.25

ATH (2026-02-03)

$0.4653

-99.98%

ATL (2023-11-21)

$0.0001594

+-54.78%

Official

Contract

Ethereum: 0xa3d4...C4fa29

Official

Whitepaper

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Chart

Market

XBIT Invite: Earn points & commission

Price

Cap

K-line

Depth

1 Hour

1 Day

TradingView

-1.37%

24H

+1.39%

+27.5%

30D

-29.95%

-31.92%

All

-99.97%

AI Assistant

ROOT下跌原因

ROOT资金流向

ROOT买卖支撑位

ROOT多空比分析

ROOT趋势分析

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ROOT Market

CEX Spot

CEX Derivatives

Pairs Funds

#	Exchange	Pairs	Price	+2%Depth	-2%Depth	Volume (24h)
1	MEXC Global	ROOTUSDT	0.000066	$0	$0	849
2	KuCoin	ROOTUSDT	0.0001232	$0	$0	1,823
3	gate.io	ROOTUSDT	0.0002128	$0	$0	11,104
4	Bybit	ROOTUSDT	0.000123	$0	$0	14,582
5	Hotbit	ROOTUSDT	0.000072	$0	$0	372,597

5 items

Rows

#	Unlock Date	Unlock Amount	Unlock Value	Total Supply %	Unlock Details
No Data

100%

Bullish

Bearish

Community

Han Paopao

4h ago

Spring is coming again, and it's that time of year again for planting. Keep an eye on your vegetable garden, and don't let the pigs root it up!

384

SOL I don't understand

22h ago

AI Booms, Memory Prices Surge First? 40,000 Yuan Per Module: A Frenzy Across the Entire Industry Chain Have you seen this lately, guys? A single memory module is selling for over 40,000 yuan, a whole box worth an apartment in Shanghai? Don't think it's just domestic hype; AI has completely devoured the memory market, a genuine "memory famine"! Today, let's break down this chart and see how AI is consuming memory and which industries it's fueling. I. Why Did Memory Prices Skyrocket When AI Gains Popularity? Merchants in Huaqiangbei are saying they "dare not stockpile"—a single memory module has soared to 40,000+ yuan, a whole box worth 4 million yuan, more valuable than many properties in Shanghai. Many people's first reaction is "speculation"? Wrong! This isn't speculation; AI has completely wiped out the memory market. The root cause is a global memory shortage caused by large AI models: high-end memory production capacity has been monopolized by AI giants, leading to a shortage of ordinary memory as well, and the supply-demand imbalance has driven prices sky-high. Understanding AI in Two or Three Steps: How Exactly Does AI "Eat" Memory? Don't think AI only consumes computing power; it's also a "memory-devouring beast," consuming memory in three steps: Step 1: The Model Itself Takes a Huge Amount of Space Modern large models often have hundreds of billions of parameters, requiring massive amounts of parameters and training data to be crammed into memory upon startup. Not enough memory? The model can't even run—just like a large game crashing on your phone, the same principle applies to AI. Step 2: When It's Actually Working, Memory Consumption Intensifies Conversing with AI for extended periods, having it read long documents, or running complex tasks all require it to take temporary notes in memory. The longer the conversation and the more complex the task, the more memory it consumes; it's practically "the busier it is, the more it eats," and ordinary memory simply can't handle it. Step 3: High-Quality Memory Sold Out To withstand the demands of AI, manufacturers developed ultra-fast, high-end memory (HBM), resulting in almost all production capacity being snapped up by major AI companies. Even ordinary memory was sold out, making shortages a common occurrence. Simply put, AI not only needs to calculate quickly but also remember a lot, and memory is now the lifeblood of AI computing power. III. A Visual Guide: The AI Computing Power Industry Chain and Beneficiary Opportunities From upstream to downstream, this wave of AI computing power has directly restructured the entire industry chain, with opportunities hidden in every link: 🔹 Upstream: Raw Materials and Energy (AI's "Supplies") AI chips cannot function without rare metals such as gallium, germanium, and indium. Fiber optic cables require germanium dioxide, and intelligent computing centers are major electricity consumers, directly driving up electricity demand. Rare Metals: These are the "hard currency" of AI chips, directly benefiting related companies. Carbon Neutrality / Photovoltaics / Electricity: Green electricity is the energy trump card in the AI era. Energy storage, photovoltaics, and the power sector are all "power stations" for AI. 🔹 Midstream: Computing Hardware and Infrastructure (The "Skeleton" of AI) This is the core foundation of AI computing power and the most directly benefiting link: Core Hardware: GPUs/ASICs are the heart of AI computing power, HBM (Hardware Memory) chips are the short-term memory of AI, fiber optics/CPO (Consumer-on-Platform) are its neural networks, and semiconductor equipment is the "shovel seller," even more stable than chip manufacturing. Infrastructure: Data centers/intelligent computing centers are the containers of AI; heat dissipation, liquid cooling, and cloud computing are essential for its operation. Key Point: HBM chips are the direct beneficiaries of the AI "memory shortage," while semiconductor equipment and fiber optic sectors offer a sure-fire way to profit. 🔹 Downstream: Application Implementation (AI's "Hands and Feet") The AI macro-model is the brain, ultimately needing to be applied in various scenarios: AI Macro-model: The underlying technological support, the source of all applications. Smart Manufacturing / Autonomous Driving / Consumer Electronics: Robots are the best carriers of physical AI. AI reduces costs and increases efficiency in content production. Autonomous driving / intelligent transportation are AI's "travel scenarios." Media: AI empowers content creation, boosting the efficiency of the media industry, directly benefiting related companies. This AI boom, simply put, is an industry explosion driven by the dual engines of "computing power + memory." The rising price of memory is just the most obvious signal. From upstream energy and raw materials to midstream hardware infrastructure and downstream application scenarios, every link is being pushed forward by AI. Don't just focus on the sky-high price of memory sticks; behind it lies the opportunity of the entire AI industry chain. Did you guys get it?

359

Mark Gadala-Maria

03-27 23:08

He experienced 119 episodes of sleep apnea each night, with his blood oxygen saturation dropping to 78%. His family joked about his snoring, while doctors attributed it to "old age." For 25 years, four specialists—nephrologists, neurologists, pulmonologists, and otolaryngologists—had examined him, yet none could connect the problems. Claude found the answer in just one conversation. He was ultimately diagnosed with severe sleep apnea. This could have caused his high blood pressure, exacerbated his history of stroke, and confirmed to be the root cause of his daily headaches. The sleep research results were alarming by any clinical standard. The solution was a CPAP machine costing 30,000 rupees (approximately $360). Claude didn't replace his doctors; instead, he did what they didn't: he simultaneously reviewed data from various specialties, constructed a diagnostic roadmap, identified the first specialist to consult, drafted the questions to ask, selected the appropriate machine settings, and wrote maintenance instructions in Gujarati so the whole family could use it. Years of specialist visits, hundreds of appointments, and numerous near-misdiagnoses. A chatbot and a $360 machine solved the problem. The problem with the healthcare system isn't poor doctors, but that no one is truly taking responsibility for considering the patient's situation holistically. Reddit post:

326

Cosplay (Yu Xian) 😶‍🌫️

03-27 14:08

Vulnerability 0x03D8096377Ea7683d840E395d72439F7B6415Abe has been exploited. Powered by SlowMist AI 👇 Attack Overview Attack Type: Oracle Manipulation (AMM Spot Price Manipulation) + Staking Reward Distribution Logic Flaw (Missing rewardDebt Update) + EIP-7702 Account EOA Restriction Bypass Victim Contract: Stake (0x03d8096377ea7683d840e395d72439f7b6415abe) Attacker Address (EIP-7702): 0xc93a5ab3737081f00788b61da42281955d3df692 Assisting Account (EIP-7702): 0xfd11c78a2ffc9102080f1accfb2c9cd2ce2aceab 0x9007983c0b1db337e3c0ff29771027b8e2be550b Total Profit: Approximately 209,793 USDT (of which 133,490 USDT is guaranteed by 0xef670d9c2e24d1788f39ad35c70f4cc51b4e5898, and 76,303 USDT is guaranteed by 0x972bfaae4093baf00bd5b4db2e11d143adc16f97) Flash Loan Source: Moolah Protocol (0x8f73b65b4caaf64fba2af91cc5d4a2a1318e5d8c), loan amount 1,900,000 USDT Root Cause Analysis Major Vulnerability — Missing Reward Debt Update During Referral Reward Distribution Period Contract: Stake (0x03d8096377ea7683d840e395d72439f7b6415abe) Function: _distributeRefPower(address user, uint256 power) Minor Vulnerability — Exploiting AMM Spot Price Oracles Contract: Stake (0x03d8096377ea7683d840e395d72439f7b6415abe) Function: getPowerAmount(uint256 amount) → getTURPrice() / getNobelPrice() Both price sources rely on getReserves() (AMM spot price), which can be manipulated through flash swaps or large-scale swaps in a single transaction. This allows attackers to artificially increase the _power value calculated by getPowerAmount(), thereby amplifying the amount of power allocated to the referencer via _distributeRefPower.

454

Solana Foundation

03-26 00:25

"Agents love APIs, documentation, and skills. So the Solana Foundation was actually the first blockchain to put skills documentation in the root directory of its website...it teaches them what a Solana wallet is and how to transact." @vibhu @solanafndn

389

0xNobler

03-25 01:14

🚨The situation is dire. The US housing market has just hit an all-time low. Worse than the 2008 financial crisis. If you think this is just limited to the housing market… You're sorely mistaken. It will affect all markets, including stocks, precious metals, and cryptocurrencies: This isn't just a housing market problem. It's a credit crisis. It's a consumer crunch. It's a liquidity crisis. More importantly… It's a global ripple effect. And this is precisely what most people don't understand. The median home price in the US is currently around $415,000. Just five years ago, that figure was closer to $270,000. An increase of over 50%. Meanwhile, wages have only increased by about 30%. The real pressure is building in this gap. And then comes the second blow: Mortgage rates. Mortgage rates have surged from about 2.7% to about 6.3%. Therefore, even before the price adjustment, monthly mortgage payments had already increased significantly. Now think about what this means. Today, a family needs an annual income of over $125,000 to afford a median-priced home. The median family income is approximately $80,000. Think about it. Right now, about three-quarters of homes are out of reach for the average American. This imbalance alone explains everything. Because the housing market doesn't collapse overnight. It declines quietly. Buyers disappear first. Next comes a sharp drop in sales volume. And that's exactly what's happening. The number of homes for sale has just fallen to an all-time low. Even below 2008 levels. This isn't a "cooling down." This is a collapse in demand. Remember: The number of homes for sale leads the market. They reflect pre-sale demand. They reflect pre-price reaction demand. They reflect pre-price reaction demand. It's demand before news reports catch up with actual demand. The reason is simple: Monthly mortgage payments are too high. Even with mortgage rates around 6%, years of price increases are enough to keep purchasing power persistently low. This is why people misunderstand. They see stable prices and assume the market is strong. But the real estate market goes through the following stages: → Homebuying pressure → Repayment pressure → Transaction volume collapse First. Then everything else follows. This is the root of the problem's spread. The real estate market directly impacts: → Bank loans → Credit creation → Construction activity → Global demand for building materials → Consumer spending When the US real estate market slows, its impact isn't limited to the US. It affects: → European banks affected by global credit → Emerging markets linked to dollar liquidity → Commodity demand (steel, copper, timber) → Global stock markets dependent on economic growth Real estate is more than just an industry. It is the core engine of the financial system. When the engine stalls: → Credit tightens → Liquidity shrinks → Risky assets begin to experience unpredictable volatility This is the warning sign. Slow markets are the most dangerous. They don't show panic first. They deteriorate silently… …by the time people realize the problem, the damage has already spread throughout the market. I have been studying markets for ten years and have almost correctly predicted the tops of every major market.

481

Uncle Cow

03-24 23:41

Today, the most valuable aspect of the Agent field isn't slogans, but rather who actually starts running the task loop. Cryptocurrency exchange Binance launched an AI search tool, allowing users to obtain personalized trading advice and market insights by directly asking questions. Hugging Face researchers released EVA, a new framework specifically for evaluating voice agents. Nvidia CEO Jensen Huang recently pointed out that the root cause of the "illusion" problem in AI models lies in the quality of training data.

415

KK.aWSB

03-24 23:00

In the 1930s, the United States was suffering from an unprecedented ecological disaster. Across the Great Plains of the Midwest, towering dust storms swept across the entire region. Farmland was buried, livestock suffocated, and millions were displaced. This disaster had a devastating name—"The Black Storm." But few know that the root of this disaster lay in a well-intentioned policy. In 1862, President Lincoln signed the Homestead Act. The act was remarkably generous: Any American citizen who cleared wasteland in the West and cultivated it for five years would receive 160 acres of land free of charge. The government's initial intentions were noble: To give the poor land to cultivate, to turn wasteland into fertile fields, and to expand the nation westward. This policy indeed attracted millions of families to the Great Plains. They used plows to turn over grasslands untouched for millennia. Wheat was planted field after field, yielding bountiful harvests year after year. Everyone believed this land was a gift from God. But no one noticed a crucial fact. The Great Plains weren't grasslands because no one farmed them. It was because the annual rainfall was simply unsuitable for agriculture. It was the deep-rooted grass that had spent tens of thousands of years holding the loose sand together. The moment the plow turned the sod, the land's fate was sealed. In 1931, a severe drought arrived as expected. Without the protection of the grass roots, the topsoil quickly cracked and crumbled under the scorching sun. Then, the winds came. The first black dust storm erupted in 1932. The dust, obscuring the sky, swept from Texas all the way to Chicago. It even reached New York and Washington, D.C., settling on the windowsills of the White House. The entire Great Plains became a dead land. More than 2.5 million people were forced to leave their homes. The federal government spent decades and billions of dollars to barely restore some of the land. 🖊️This is the famous "dust storm paradox" in ecological economics. A policy that promised a comfortable life for millions ultimately left millions homeless. When you only see the productive value of the land, but ignore its ecological value. Short-term prosperity often comes at the cost of a bill you were unaware of.

422

Not Elon Musk

03-24 06:37

Introducing PlayerZero The world's first engineering world model, automating your code debugging, fixing, and testing. We've raised $20 million from a wide range of investors including Foundation Capital, @matei_zaharia (Databricks), @pbailis (Workday), @rauchg (Vercel), @zoink (Figma), and @drewhouston (Dropbox). PlayerZero frees up 30% of your engineering resources by: 1. Finding the root causes of bugs and incidents that would take engineering teams days to identify in minutes. 2. Predicting extreme issues that would take a 300-person QA team weeks to discover in minutes. ------ Here's why: No one in your organization has a complete picture of how your production software is actually running. Support staff look at tickets. SREs look at the infrastructure. Developers look at the code. Each team builds its own fragmented view—and these systems don't communicate with each other. When a problem arises, everyone is scrambling to piece together a complete picture manually. PlayerZero integrates all this information into a unified context graph: → Your manager mentions in a Slack thread, "We chose X because Y crashed in production last time." → Records of engineers explaining trade-offs in PR reviews → Lifecycle history of your CI/CD pipeline, observability stack, events, and support tickets Therefore, you can traverse all silos and track the root cause of any problem. And this advantage accumulates. Each diagnosed event adds new knowledge to the model. The longer the model runs, the deeper the understanding—which code paths are risky, which configurations are vulnerable, and which variables are more likely to disrupt which customer processes. Therefore, when you tackle a live issue, collective reasoning and production experience across the organization immediately support you. ------ Zuora, Georgia-Pacific, and Nylas have reduced issue resolution time by 90% and captured 95% of major changes, freeing up an average of $30 million worth of engineering bandwidth. ----- Our Guarantee: If we cannot increase your engineering bandwidth by at least 20% within one week, we will donate $10. Donate $000 to your chosen open-source project. Schedule a Demo -

327

Cosplay (Yu Xian) 😶‍🌫️

03-23 19:20

Hello @Cyrus_finance. Powered by SlowMist AI 👇 ### [Attack Overview] - **Attack Type**: Price Manipulation (Flash Loan + Instant AMM Price Manipulation) → Excessive Liquidity Withdrawal - **Victim Contract**: CyrusTreasury (`0xb042ea7b35826e6e537a63bb9fc9fb06b50ae10b`) and its managed PancakeSwap V3 liquidity pool (`0x9f599f3d64a9d99ea21e68127bb6ce99f893da61`, WETH/USDT fee = 100) - **Attacker Address**: `0xf96eb14171b71ac16200013753dff3e91043b63b` (EOA) - **Attack Contract**: `0x938dbbb69e71d00f52d5ed5d69ba892fa1448a7b` - **Profit**: Approximately 28.14 WETH + 454,169,217 USDT (Net profit after deducting 1.079 ETH flash loan fee) --- ### [Root Cause Analysis] **Contract**: CyrusTreasury (`0xb042ea7b35826e6e537a63bb9fc9fb06b50ae10b`) **Function**: `withdrawUSDTFromAny(uint256 usdtAmountWithSlippage, address to)` **Vulnerability**: This function obtains the **instantaneous price** by calling `sqrtPriceX96`. The function `IPancakePool(pool).slot0()` calculates the current available USDT in the LP position using that price. When an attacker manipulates the spot price of the pool using flash loans, the calculated `availableUSDT` becomes extremely small, satisfying the condition `availableUSDT < remaining`. Therefore, the function continues to extract **all liquidity** (`liquidityToUse = liquid`). This allows an attacker to extract hundreds of millions of dollars worth of LP liquidity (including large amounts of WETH and USDT) with only 1,707,055 USDT in collateral.

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