Update (Feb. 2, 12:20 am UTC): This article has been updated to add a post by CrossCurve CEO Boris Povar.
Crypto protocol CrossCurve said its cross-chain bridge has been attacked, with $3 million reportedly exploited across multiple networks.
CrossCurve posted to X late on Sunday that its bridge was “under attack, involving the exploitation of a vulnerability in one of the smart contracts used.”
“Please pause all interactions with CrossCurve while the investigation is ongoing,” it added.
Defimon Alerts, an X account linked to the blockchain security company Decurity, reported that CrossCurve was exploited for around $3 million “on several networks.”
It added that one of CrossCurve’s smart contracts allowed anyone to spoof a message to bypass validation and unlock tokens.
“Anyone could call expressExecute on ReceiverAxelar contract with a spoofed cross-chain message, bypassing gateway validation and triggering unlock on PortalV2,” Defimon Alerts said.
Curve Finance, which has partnered with CrossCurve, posted on X that users who allocated to CrossCurve pools “may wish to review their positions and consider removing those votes.”
“We continue to encourage all participants to remain vigilant and make risk-aware decisions when interacting with third-party projects,” it added.
CrossCurve offers 10% bounty if funds returned in 72 hours
In an attempt to contact the attacker, CrossCurve CEO Boris Povar shared 10 addresses he said had received tokens from the exploit and offered a reward for their return within 72 hours.
“These tokens were wrongfully taken from users due to a smart contract exploit. We do not believe this was intentional on your part, and there is no indication of malicious intent,” he said. “We hope for your cooperation in returning the funds.”
Povar offered up to a 10% bounty if the funds were returned within 72 hours of the attack.
“If the funds are not returned or no contact is established within 72 hours, we will have to assume there is malicious intent and treat this as a judicial matter,” he added.
Povar said CrossCurve was prepared to work with law enforcement, file civil lawsuits to recover damages, and coordinate with authorities and other crypto projects to freeze assets if the funds were not returned.
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MATIC shows bearish momentum at $0.38 with RSI at 38. Technical analysis suggests potential recovery to $0.45-$0.52 range within 4-6 weeks if key resistance breaks.
While specific analyst predictions from major crypto KOLs are limited in recent days, available market analysis provides some insight into MATIC’s trajectory. According to Felix Pinkston’s January 6th assessment, “MATIC price prediction targets $0.45-$0.52 recovery within 4-6 weeks, contingent on breaking key $0.58 resistance.”
This Polygon forecast aligns with technical resistance levels identified through on-chain data analysis. According to current market metrics, MATIC remains in a consolidation phase below key moving averages, suggesting cautious optimism among market participants.
MATIC Technical Analysis Breakdown
Polygon’s current technical picture presents a mixed but potentially recovering scenario. At $0.38, MATIC is trading significantly below its key moving averages, with the 20-day SMA at $0.43 and the 50-day SMA at $0.45 acting as immediate resistance levels.
The RSI reading of 38.00 places MATIC in neutral territory, avoiding oversold conditions that could trigger panic selling. However, the MACD histogram at -0.0000 indicates bearish momentum is still present, though weakening.
Polygon’s position within the Bollinger Bands is particularly telling. With a %B position of 0.29, MATIC is trading in the lower portion of the bands but hasn’t touched the lower band at $0.31, suggesting some underlying support remains intact. The middle band at $0.43 represents the first major hurdle for any recovery attempt.
The Stochastic oscillator shows %K at 25.19 and %D at 20.15, indicating MATIC may be approaching oversold territory where buying interest could emerge.
Polygon Price Targets: Bull vs Bear Case
Bullish Scenario
If MATIC can reclaim the 20-day SMA at $0.43, the path opens toward the 50-day SMA at $0.45. A successful break above this level could trigger the recovery scenario outlined in recent analysis, targeting the $0.45-$0.52 range.
The ultimate bullish target sits at the Upper Bollinger Band near $0.56, which coincides with the resistance level mentioned in analyst predictions. For this MATIC price prediction to materialize, Polygon would need to see increased trading volume above the current $1.07 million daily average and positive momentum confirmation through MACD crossover.
Bearish Scenario
Failure to hold current levels could see MATIC test the Lower Bollinger Band at $0.31. This represents approximately 18% downside risk from current prices. A break below this critical support level could trigger further selling pressure, potentially targeting the psychological $0.30 level.
The bearish case is reinforced by MATIC trading below all major moving averages, with the 200-day SMA at $0.69 showing the longer-term downtrend remains intact.
Should You Buy MATIC? Entry Strategy
Current technical conditions suggest a cautious approach to MATIC accumulation. The optimal entry strategy involves:
Risk management remains crucial given the bearish MACD momentum. Position sizing should account for the potential 20% downside to the $0.31 support level.
For more aggressive traders, a breakout strategy above $0.43 with volume confirmation could offer better risk-reward ratios, though this requires waiting for technical confirmation.
Conclusion
This MATIC price prediction suggests cautious optimism for Polygon’s recovery potential over the next 4-6 weeks. While current momentum remains bearish, the technical setup doesn’t indicate severe oversold conditions, leaving room for the projected $0.45-$0.52 Polygon forecast to materialize.
The key catalyst will be MATIC’s ability to reclaim the $0.43 resistance level with sustained volume. Until this occurs, traders should expect continued consolidation around current levels.
Disclaimer: Cryptocurrency price predictions are inherently speculative and subject to high volatility. This analysis is for informational purposes only and should not be considered financial advice. Always conduct your own research and risk assessment before making investment decisions.
While specific analyst predictions are limited in recent days, previous forecasts from late January remain relevant to current market conditions. According to blockchain.news reports, analyst Peter Zhang projected Dogecoin could reach $0.16 resistance despite consolidation phases, while Darius Baruo suggested a similar $0.16-$0.175 target range by month-end.
However, these predictions were made when DOGE was trading higher at $0.12-$0.125. With the current price at $0.10, these targets now represent significant upside potential of 60-75% from current levels.
On-chain data from major analytics platforms suggests that despite the recent pullback, Dogecoin’s fundamental metrics remain stable, with trading volume maintaining healthy levels at $225.97 million over the past 24 hours.
DOGE Technical Analysis Breakdown
The current DOGE price prediction hinges on several critical technical indicators painting a mixed but potentially bullish picture for the memecoin.
RSI Oversold Signal: Dogecoin’s RSI has dropped to 28.53, firmly in oversold territory below the traditional 30 threshold. This suggests the recent selling pressure may be exhausted, creating conditions for a potential bounce.
MACD Bearish Momentum: The MACD histogram sits at -0.0000 with both MACD (-0.0069) and signal line (-0.0069) in negative territory, confirming bearish momentum. However, the histogram’s proximity to zero suggests momentum may be stabilizing.
Bollinger Bands Position: With a %B reading of 0.0301, DOGE is trading near the lower Bollinger Band at $0.10, historically a level where oversold bounces occur. The middle band at $0.13 represents the first major resistance target.
Moving Average Structure: All major moving averages remain above current price levels, with SMA 7 at $0.12, SMA 20 at $0.13, and longer-term averages higher still. This creates a clear roadmap for resistance levels during any recovery.
Dogecoin Price Targets: Bull vs Bear Case
Bullish Scenario
The Dogecoin forecast turns optimistic if DOGE can reclaim the $0.11 immediate resistance level. A successful break above this level would target the SMA 7 at $0.12, representing a 20% gain from current levels.
Further upside targets include the SMA 20 at $0.13 and the upper Bollinger Band at $0.15. These levels align with analyst predictions suggesting $0.16 remains achievable within the next 2-4 weeks, representing potential gains of 50-60%.
Technical confirmation for bullish momentum would require RSI to break above 50 and MACD to flip positive, indicating genuine buying pressure rather than a mere oversold bounce.
Bearish Scenario
The downside risk centers around the $0.09 strong support level. A break below this critical threshold could trigger additional selling pressure, potentially targeting the next major support zone around $0.08-$0.085.
Risk factors include broader cryptocurrency market weakness, continued selling pressure from long-term holders, and failure of the RSI to generate a meaningful bounce despite oversold conditions.
The 24-hour low of $0.09 serves as the key line in the sand for maintaining the current bullish DOGE price prediction scenario.
Should You Buy DOGE? Entry Strategy
For traders considering DOGE positions, the current oversold conditions present both opportunity and risk. Conservative entry points include:
Primary Entry Zone: $0.10-$0.105 (current price range) Aggressive Entry: $0.095-$0.10 on any weakness toward strong support
Stop-Loss Strategy: Position stops below $0.088 to limit downside risk to approximately 12% from current levels.
Target Management: Scale out positions at $0.11 (10% gain), $0.12 (20% gain), and hold remaining positions for the $0.14-$0.16 range suggested by recent analyst forecasts.
Risk management remains crucial given DOGE’s volatility, with position sizing limited to amounts traders can afford to lose entirely.
Conclusion
The DOGE price prediction for February 2026 suggests a recovery toward $0.12 represents the most probable near-term scenario, supported by oversold RSI conditions and proximity to lower Bollinger Band support. While recent analyst targets of $0.16-$0.175 remain technically achievable, they now require a 60-75% rally from current depressed levels.
The Dogecoin forecast carries moderate confidence given the clear oversold technical setup, though broader market conditions and the ability to hold $0.09 support will ultimately determine whether this prediction materializes.
Disclaimer: Cryptocurrency price predictions involve significant risk and uncertainty. Past performance does not guarantee future results. Always conduct your own research and consider your risk tolerance before making investment decisions.
Bitcoin’s fall of around 7% to $77,000 on Saturday might have marked the low of this cycle, according to Bitcoin analyst PlanC.
It comes as other crypto analysts continue to call for further downside for Bitcoin (BTC) in the coming months.
“Decent chance this will be the deepest pullback opportunity this Bitcoin bull run,” PlanC said in an X post on Saturday.
PlanC compares Bitcoin’s fall to previous bear market cycles
Bitcoin fell 7% to around $77,000 on Saturday and has since slightly moved up to $78,690 at the time of publication, according to CoinMarketCap.
Bitcoin is down 11.44% over the past 30 days. Source: CoinMarketCap
The asset’s price is now down around 38% from its all-time high of $126,100, which it reached on Oct. 5. PlanC said the downtrend Bitcoin has experienced reminds him of past crashes like the 2018 bear market capitulation when Bitcoin fell to $3,000, the March 2020 crash when the asset fell to around $5,100, and the collapse of crypto exchange FTX, which saw Bitcoin dip to around $15,500.
“There is a decent chance we are going through another major capitulation low as we speak,” PlanC said. “It seems like the ultimate low will be between $75,000 and $80,000,” he added.
Meanwhile, Bitcoin advocate and financial accountant Rajat Soni said in an X post on Saturday that the drop down to $77,000 came during one of crypto’s more volatile parts of the week and warned traders against overreacting.
“Never trust a weekend pump OR dump,” Soni said. “Bitcoin will make a comeback when you least expect it,” he added.
Bitcoin $60,000 price level may still be in play
However, some have been speculating that the downfall may go further.
Veteran trader Peter Brandt recently predicted that Bitcoin could fall as low as $60,000 by the third quarter of 2026.
Crypto analyst Benjamin Cowen said Bitcoin’s market cycle low will likely come in early October, but “anticipates plenty of rallies will occur between now and then.”
Meanwhile, Jurrien Timmer, Fidelity’s director of global macroeconomic research, said 2026 could be a “year off” for Bitcoin, with prices potentially falling to as low as $65,000.
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SOL fell to 2026 lows as tech sector layoffs and artificial intelligence revenue concerns hit markets.
Despite the bleak environment, Solana outpaced competitors with network fees jumping 81%, securing its vice-leadership.
Solana’s native token, SOL (SOL), traded down to $100.30 on Saturday, reaching its lowest levels since April 2025. While the 18% price correction over 30 days took traders by surprise, the movement largely mirrored broader altcoin market capitalization trends. A 26% crash in silver prices on Friday further prompted cryptocurrency traders to brace for additional downside.
SOL/USD (orange) vs. altcoin market capitalization (blue). Source: TradingView
SOL was able to reclaim the $102 level on Saturday, but sentiment remained weak after $165 million in leveraged bullish positions were forcefully liquidated. Sentiment worsened following escalating tensions in Iran and fears of an economic downturn after Amazon (AMZN US) announced 16,000 white-collar job cuts on Wednesday.
Investors grew more risk-averse upon learning that OpenAI accounted for 45% of Microsoft’s (MSFT US) Azure cloud computing backlog. Additional tension stemmed from a Wall Street Journal report stating Nvidia (NVDA US) would no longer invest $100 billion in OpenAI. The ChatGPT maker is reportedly expected to face $14 billion in net losses in 2026, according to The Information.
Despite the bleak socio-political environment, Solana onchain activity has outpaced its competitors, consolidating its position as the runner-up in network fees and Total Value Locked (TVL). Healthy onchain metrics provide a dual benefit to the native token: they increase staking returns to incentivize long-term holding while creating constant demand for data processing fees.
Blockchains ranked by 30-day fees vs. recent average. Source: Nansen
Solana network fees jumped 81% above the trend over the past 30 days, according to Nansen data. Additionally, active addresses grew by 62%, and transactions soared to 2.29 billion. In comparison, the Ethereum ecosystem—including layer-2 solutions—totaled 623 million transactions, while Ethereum base layer fees grew by only 11%. Solana remained the clear leader in decentralized application (DApp) activity.
Demand for leveraged bullish positions on SOL vanished as traders sought safety in cash and short-term government bonds. Multi-billion dollar tech companies, including Unity (U US), AppLovin (APP US), Figma, and HubSpot (HUB US), faced price declines of 30% or more within 30 days. Gold, usually perceived as a safe haven, traded down 13% from its $5,600 all-time high reached on Thursday.
SOL perpetual futures annualized funding rate. Source: laevitas.ch
The annualized funding rate on SOL perpetual futures plunged to -17%, meaning shorts (sellers) are paying to keep their positions open. This condition is unusual, rarely lasts long, and indicates an extreme lack of leverage appetite from bulls. The move coincided with political disputes regarding United States government funding.
The US Senate approved a funding package on Friday, alongside a two-week stopgap measure to allow more time for government funding disputes over Department of Homeland Security funding following Democratic criticism of immigration enforcement. The US House of Representatives must vote on the final version on Monday.
Public companies ranked by total SOL cost, USD. Source: CoinGecko
Solana spot exchange-traded funds (ETFs) saw $11 million in net outflows on Friday, according to CoinGlass. Meanwhile, listed companies using SOL as a corporate reserve strategy are under pressure. Forward Industries (FWDI US), Upexi (UPXI US), and Sharps Technology (STSS US) stocks traded 20% or more below their respective net asset values.
SOL’s path to reclaiming bullish momentum depends largely on renewed confidence in global economic growth and reduced socio-political risks, which may not materialize in the short term.
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. While we strive to provide accurate and timely information, Cointelegraph does not guarantee the accuracy, completeness, or reliability of any information in this article. This article may contain forward-looking statements that are subject to risks and uncertainties. Cointelegraph will not be liable for any loss or damage arising from your reliance on this information.
Bitcoin (BTC) fell to a record low versus gold (XAU) in January, making it a better buying opportunity than what preceded the 2015–2017 bull market, analysts say.
Key takeaways:
BTC vs gold hit a record low, a level that has lined up with past major bottoms.
Some analysts warn that a rotation to BTC from gold is not guaranteed.
Gold-to-Bitcoin rotation could start in February
On Saturday, Bitcoin’s value compared to gold fell to its lowest level ever after adjusting for the global money supply, data from Bitwise Europe showed.
The indicator shows when Bitcoin is unusually strong or weak versus gold. It has now moved near an extreme zone (the -2 level in the chart below) that in the past appeared around BTC market bottoms.
BTC/XAU ratio Z-score. Source: Bitwise
The last time this band fell to similar levels was in 2015, indicating extreme BTC undervaluation to gold. That preceded 11,800% BTC price gains to $20,000 from around $165 within just two years. Analyst Michaël van de Poppe said in a Saturday X post:
“Today represents a better opportunity to be buying Bitcoin than 2017.”
His comment echoed analysts who expect some capital to rotate from gold into Bitcoin this year.
That includes Bitwise European head of research, André Dragosch, and Swyftx lead analyst Pav Hundal. The latter said such rotations could start occurring in February or March.
Capital rotation “might not happen quickly”
The bullish views emerged as gold prices have doubled over the past year, while Bitcoin has fallen by 18% over the same period.
XAU/USD vs. BTC/USD. Source: TradingView
But not everyone agreed that a rotation from precious metals to Bitcoin is imminent, including analyst Benjamin Cowen.
He said Bitcoin’s downtrend may last longer than many holders expect, arguing BTC is “likely going to keep bleeding against the stock market” and that hopes for a “massive rotation” out of gold and silver could be misplaced in the short term.
Citi said that silver could extend its gains in the next few months due to demand from China and a weaker US dollar. Likewise, RBC Capital Markets predicted gold’s price to reach $7,000 by 2026’s end.
Cowen said that even if precious metals stay strong, the move into Bitcoin is “probably not going to happen” quickly.
Bitcoin long-term holders absorb January sell-off
Despite Bitcoin’s sharp pullback in January, on-chain data shows long-term holders are quietly rebuilding positions.
The supply held by Bitcoin’s Long-Term Holders (LTH), entities that hold BTC for over 155 days, began recovering during the January selloff.
Also, the LTH Spent Binary, a metric that shows whether long-term Bitcoin holders are selling or staying put, continued to decline during this period.
In past cycles, recovering LTH supply and declining LTH Spent Binary preceded the formation of durable BTC bottoms, according to analyst Anil.
A recent example came after the April 2025 lows: long-term holder supply began to recover first, and BTC followed with a sharp rebound about a month later, rallying roughly 60% from the lows.
These trends suggest that the more patient holders are taking advantage of BTC’s price drop in January, often the kind of reset that helps Bitcoin build a stronger base for gains in the future.
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. While we strive to provide accurate and timely information, Cointelegraph does not guarantee the accuracy, completeness, or reliability of any information in this article. This article may contain forward-looking statements that are subject to risks and uncertainties. Cointelegraph will not be liable for any loss or damage arising from your reliance on this information.
The United States Treasury has sanctioned two cryptocurrency exchanges linked to Iran’s financial system, marking the first time Washington has directly targeted digital asset platforms as part of its Iran sanctions program.
In a statement on Friday, the Treasury Department’s Office of Foreign Assets Control (OFAC) said the sanctions are part of a wider move against Iranian officials and networks accused of violently suppressing people at home while using alternative financial channels to get around international sanctions.
Among those sanctioned was Eskandar Momeni Kalagari, Iran’s minister of the interior, who oversees the country’s Law Enforcement Forces. “Treasury will continue to target Iranian networks and corrupt elites that enrich themselves at the expense of the Iranian people,” Treasury Secretary Scott Bessent said.
OFAC also designated Babak Morteza Zanjani, a well-known Iranian businessman previously convicted of embezzling billions of dollars in oil revenue from Iran’s national oil company. According to the Treasury, Zanjani was released from prison and later used by the Iranian state to help move and launder funds, providing financial support to projects tied to the Islamic Revolutionary Guard Corps (IRGC).
The sanctions break new ground by extending to two UK-registered crypto exchanges, Zedcex Exchange Ltd. and Zedxion Exchange Ltd., which US officials say are linked to Zanjani and have processed large volumes of transactions connected to IRGC-linked entities. OFAC said Zedcex alone has handled more than $94 billion in transactions since its registration in 2022.
“This marks OFAC’s first designation of a digital asset exchange for operating in the financial sector of the Iranian economy,” the Treasury said.
Bessent accused Tehran of diverting oil revenues toward weapons programs and militant proxies instead of supporting its population. He said the United States would continue to target networks that exploit digital assets to bypass restrictions and finance illicit activity.
Beyond the crypto-related designations, OFAC also sanctioned senior IRGC commanders and security officials across multiple provinces, citing evidence of live-fire attacks on protesters, forced burials without funerals and widespread intimidation aimed at crushing dissent.
Iran’s central bank used $500 million in USDt to support rial
Last week, blockchain analytics firm Elliptic said Iran’s central bank accumulated more than $500 million worth of Tether’s USDt (USDT) during a period of severe economic stress, likely using the stablecoin to support the collapsing rial or settle international trade.
The accumulation began as the currency lost roughly half its value in eight months, with Elliptic suggesting the bank used USDT on local exchange Nobitex to buy rials, mirroring traditional central bank market operations through crypto.
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Bitcoin’s break below $84,000 tilts the advantage in favor of the bears, opening the doors for a potential fall to $74,508.
Several major altcoins have slipped below their support levels, signaling that the bears are attempting to take charge.
Bitcoin (BTC) remains under pressure as sellers attempt to sustain the price below the $84,000 level. BTC’s fall near $81,000 caused $1.77 billion in liquidations in the past 24 hours, per CoinGlass data.
Several analysts have turned bearish and expect BTC’s downtrend to continue. They anticipate BTC to fall below the crucial $74,500 low, made in April 2025, following US President Donald Trump’s “Liberation Day” tariff announcement.
Crypto market data daily view. Source: TradingView
However, not everyone is bearish on BTC. Swyftx lead analyst Pav Hundal told Cointelegraph that BTC may form a bottom over the next 40 days if history repeats, as BTC bottoms “have historically lagged gold’s relative strength by about 14 months.”
Could BTC and the major altcoins start a relief rally? Let’s analyze the charts of the top 10 cryptocurrencies to find out.
Bitcoin price prediction
BTC turned down sharply from the 20-day exponential moving average (EMA) ($89,165) on Thursday and fell below the $84,000 support.
The $80,600 level is the crucial support to watch out for in the near term. If bears pull the Bitcoin price below $80,600, the BTC/USDT pair may extend the decline to the critical support at $74,508.
Buyers are likely to have other plans. They will attempt to defend the $80,600 level and push the price above the moving averages. If they do that, it shows that the market has rejected the dip below $84,000. The pair may then surge to the $94,789 to $97,924 resistance zone.
Ether price prediction
Ether (ETH) turned down from the moving averages on Thursday and fell below the $2,787 level, indicating selling on minor rallies.
The downsloping 20-day EMA ($2,999) and the relative strength index (RSI) in negative territory indicate an advantage to sellers. The Ether price may slump to the $2,623 level, which is likely to attract buyers. However, if the bears prevail, the ETH/USDT pair is likely to resume the downtrend toward $2,111.
Time is running out for the bulls. They will have to swiftly push the ETH/USDT pair above the moving averages to signal strength. The pair may then climb to the resistance line.
BNB price prediction
The failure of the bulls to maintain BNB (BNB) above the 20-day EMA ($890) on Thursday triggered selling, which has pulled the price to the uptrend line.
The bulls are expected to vigorously defend the uptrend line, as a close below it may sink the BNB/USDT pair to the $790 level. A break and close below the $790 support risks starting the next leg of the downtrend to $730.
Contrarily, if the BNB price turns up from the uptrend line, it suggests that the bulls remain buyers on dips. The pair may then reach the $928 to $959 overhead resistance zone, where the bears are expected to step in.
XRP price prediction
XRP (XRP) turned down from the moving averages and fell below the $1.77 level, indicating that the bears remain in control.
The XRP/USDT pair is likely to descend to $1.61, which is a critical level to watch out for. If sellers yank the XRP price below the $1.61 support, the pair risks falling to the support line of the descending channel pattern.
Instead, if the price turns up from $1.61, it is expected to face selling at the moving averages. If buyers overcome the hurdle, the pair may reach the downtrend line. A close above the downtrend line suggests that the bulls are back in the driver’s seat.
Solana price prediction
Solana’s (SOL) range-bound action from $117 to $147 resolved to the downside on Thursday, signaling that the bears are attempting to take charge.
If the Solana price closes below $117, the SOL/USDT pair risks falling to the $95 support. Buyers are expected to mount a strong defense at the $95 level, as a break below it may sink the pair to $79.
The bulls will have to push the price back above the moving averages to suggest that the break below $117 may have been a bear trap. The pair may then ascend to the $147 resistance.
Dogecoin price prediction
Dogecoin (DOGE) closed below the $0.12 support on Thursday, signaling the resumption of the downtrend.
The bulls will attempt to push the Dogecoin price back above the breakdown level of $0.12 but are expected to face solid resistance from the bears. If the price turns down from the $0.12 level or the moving averages, it heightens the risk of a collapse to the Oct. 10, 2025, low of $0.10.
This negative view will be invalidated in the near term if the DOGE/USDT pair turns up and breaks above the moving averages. That suggests solid buying at lower levels, opening the gates for a potential rally to $0.16.
Cardano price prediction
Cardano (ADA) is witnessing a tough battle between the buyers and sellers at the $0.33 level.
If the Cardano price closes below the $0.33 support, the ADA/USDT pair may decline to the support line of the descending channel pattern. The bulls are expected to defend the support line, which is close to the Oct. 10, 2025, low of $0.27.
Contrary to this assumption, if the price turns up from the current level and breaks above the downtrend line, it signals that the bulls are active at lower levels. That opens the doors for a rally to the breakdown level of $0.50.
The bulls will attempt to push the Bitcoin Cash price back above the $563 level but are expected to face solid resistance from the bears. If the price turns down from $563, it suggests that the bears have flipped the level into resistance. That increases the likelihood of a drop to $518 and thereafter to the pattern target of $456.
This bearish view will be negated in the short term if buyers drive the price above the $604 resistance. The BCH/USDT pair may then jump to $631 and subsequently to $670.
Hyperliquid price prediction
Hyperliquid (HYPE) turned down from the breakdown level of $35.50 on Thursday, indicating that the bears are fiercely defending the level.
The 20-day EMA ($26.36) is the critical support to watch out for on the downside. If the price turns up from the 20-day EMA, the bulls will again attempt to propel the HYPE/USDT pair above $35.50. If they succeed, the pair may rally to $44.
Conversely, if the Hyperliquid price breaks below the moving averages, the pair may consolidate from $35.50 to $20.82 for a while longer. The downtrend may resume on a break below $20.82.
Monero price prediction
The failure of the bulls to push Monero (XMR) above the 50-day simple moving average (SMA) ($482) shows that the bears are selling on every minor rise.
The bulls are attempting to defend the $417 support as seen from the long tail on the candlestick. The relief rally is expected to face selling at the moving averages. If the price turns down from the moving averages, the risk of a break below the $417 level increases. The XMR/USDT pair may then nosedive to $360.
Buyers have an uphill task ahead of them. They will have to drive the Monero price above the 20-day EMA ($501) to signal a comeback. The pair may then march toward $546, where the sellers are expected to step in.
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. While we strive to provide accurate and timely information, Cointelegraph does not guarantee the accuracy, completeness, or reliability of any information in this article. This article may contain forward-looking statements that are subject to risks and uncertainties. Cointelegraph will not be liable for any loss or damage arising from your reliance on this information.
At its core, proof-of-reserves is a public demonstration that a custodian holds the assets it claims to hold on behalf of users, typically using cryptographic methods and onchain transparency.
The truth is that proof-of-reserves is not a trust guarantee. It shows whether verifiable assets exist on a platform at a single point in time, but it does not confirm that the platform is solvent, liquid or governed by controls that prevent hidden risk.
But even when executed properly, PoR is often a point-in-time snapshot that can miss what happened before and after the reporting moment.
Without a credible view of liabilities, PoR cannot prove solvency, which is what users actually need during periods of withdrawal stress.
Did you know? On Dec. 31, 2025, Binance’s CEO wrote that the platform’s user asset balances publicly verified through proof-of-reserves had reached $162.8 billion.
What PoR proves and how it is usually done
In practice, PoR involves two checks: assets and, ideally, liabilities.
On the asset side, an exchange shows that it controls certain wallets, usually by publishing addresses or signing messages.
Liabilities are trickier. Most exchanges take a snapshot of user balances and commit it to a Merkle tree, often a Merkle-sum tree. Users can then confirm that their balance is included using an inclusion proof, without everyone’s balances being made public.
When done properly, PoR shows whether onchain assets cover customer balances at a specific moment.
Did you know? Binance lets each user independently verify their inclusion in its PoR snapshot. Through its verification page, Binance generates a cryptographic proof based on a Merkle tree of user balances, allowing users to confirm that their account was counted without revealing anyone else’s data or balances.
How an exchange can “pass PoR” and still be risky
PoR can improve transparency, but it shouldn’t be relied on as the sole measure of a company’s financial health.
Of course, a report on assets without full liabilities does not demonstrate solvency. Even if onchain wallets appear strong, liabilities can be incomplete or selectively defined, missing items such as loans, derivatives exposure, legal claims or offchain payables. That can show funds exist without proving the business can meet all of its obligations.
Also, a single attestation does not reveal what the balance sheet looked like last week or what it looks like the day after the report. In theory, assets can be temporarily borrowed to improve the snapshot, then moved back out afterward.
Next, encumbrances often do not show up. PoR typically cannot tell you whether assets are pledged as collateral, lent out or otherwise tied up, meaning they may not be available when withdrawals spike.
Liquidity and valuation can also be misleading. Holding assets is not the same as being able to liquidate them quickly and at scale during periods of stress, especially if reserves are concentrated in thinly traded tokens. PoR does not address this issue; clearer risk and liquidity disclosures might.
PoR isn’t the same as an audit
A lot of the trust problem comes from a mismatch in expectations.
Many users treat PoR like a safety certificate. In reality, many PoR engagements resemble agreed-upon procedures (AUPs). In these cases, the practitioner performs specific checks and reports what was found without providing an audit-style opinion on the company’s overall health.
Indeed, an audit or even a review is designed to deliver an assurance conclusion within a formal framework. AUP reporting is narrower. It explains what was tested and what was observed, then leaves interpretation to the reader. Under International Standard on Related Services (ISRS) 4400, an AUP engagement is not an assurance engagement and does not express an opinion.
Regulators have highlighted this gap. The Public Company Accounting Oversight Board has warned that PoR reports are inherently limited and should not be treated as proof that an exchange has sufficient assets to meet its liabilities, especially given the lack of consistency in how PoR work is performed and described.
This is also why PoR drew increased scrutiny after 2022. Mazars paused work for crypto clients, citing concerns about how PoR-style reports were being presented and how the public might interpret them.
What’s a practical trust stack, then?
PoR can be a starting point, but real trust comes from pairing transparency with proof of solvency, strong governance and clear operational controls.
Start with solvency. The real step up is showing assets versus a complete set of liabilities, ensuring assets are greater than or equal to liabilities. Merkle-based liability proofs, along with newer zero-knowledge approaches, aim to close that gap without exposing individual balances.
Next, add assurance around how the exchange actually operates. A snapshot does not reveal whether the platform has disciplined controls such as key management, access permissions, change management, incident response, segregation of duties and custody workflows. This is why institutional due diligence often relies on System and Organization Controls (SOC)-style reporting and similar frameworks that measure controls over time, not just a balance at a single moment.
Make liquidity and encumbrance visible. Solvency on paper does not guarantee that an exchange can survive a run. Users need clarity on whether reserves are unencumbered and how quickly holdings can be converted into liquid assets at scale.
Anchor it in governance and disclosure. Credible oversight depends on clear custody frameworks, conflict management and consistent disclosures, especially for products that introduce additional obligations such as yield, margin and lending.
PoR helps, but it can’t replace accountability
PoR is better than nothing, but it remains a narrow, point-in-time check (even though it’s often marketed like a safety certificate).
On its own, PoR does not prove solvency, liquidity or control quality. So, before treating a PoR badge as “safe,” consider the following:
Are liabilities included, or is it assets only? Assets-only reporting cannot demonstrate solvency.
What is in scope? Are margin, yield products, loans or offchain obligations excluded?
Is it reporting a snapshot or ongoing? A single date can be dressed up. Consistency matters.
Are reserves unencumbered? “Held” is not the same as “available during stress.”
What kind of engagement is it? Many PoR reports are limited in scope and should not be read like an audit opinion.
Decentralized GPU networks are pitching themselves as a lower-cost layer for running AI workloads, while training the latest models remains concentrated inside hyperscale data centers.
Frontier AI training involves building the largest and most advanced systems, a process that requires thousands of GPUs to operate in tight synchronization.
That level of coordination makes decentralized networks impractical for top-end AI training, where internet latency and reliability cannot match the tightly coupled hardware in centralized data centers.
Most AI workloads in production do not resemble large-scale model training, opening space for decentralized networks to handle inference and everyday tasks.
“What we are beginning to see is that many open-source and other models are becoming compact enough and sufficiently optimized to run very efficiently on consumer GPUs,” Mitch Liu, co-founder and CEO of Theta Network, told Cointelegraph. “This is creating a shift toward open-source, more efficient models and more economical processing approaches.”
Training frontier AI models is highly GPU-intensive and remains concentrated in hyperscale data centers. Source: Derya Unutmaz
From frontier AI training to everyday inference
Frontier training is concentrated among a few hyperscale operators, as running large training jobs is expensive and complex. The latest AI hardware, like Nvidia’s Vera Rubin, is designed to optimize performance inside integrated data center environments.
“You can think of frontier AI model training like building a skyscraper,” Nökkvi Dan Ellidason, CEO of infrastructure company Ovia Systems (formerly Gaimin), told Cointelegraph. “In a centralized data center, all the workers are on the same scaffold, passing bricks by hand.”
That level of integration leaves little room for the loose coordination and variable latency typical of distributed networks.
“To build the same skyscraper [in a decentralized network], they have to mail each brick to one another over the open internet, which is highly inefficient,” Ellidason continued.
AI giants continue to absorb a growing share of global GPU supply. Source: Sam Altman
Meta trained its Llama 4 AI model using a cluster of more than 100,000 Nvidia H100 GPUs. OpenAI does not disclose the size of the GPU clusters used to train its models, but infrastructure lead Anuj Saharan said GPT-5 was launched with support from more than 200,000 GPUs, without specifying how much of that capacity was used for training versus inference or other workloads.
Inference refers to running trained models to generate responses for users and applications. Ellidason said the AI market has reached an “inference tipping point.” While training dominated GPU demand as recently as 2024, he estimated that as much as 70% of demand is driven by inference, agents and prediction workloads in 2026.
“This has turned compute from a research cost into a continuous, scaling utility cost,” Ellidason said. “Thus, the demand multiplier through internal loops makes decentralized computing a viable option in the hybrid compute conversation.”
Decentralized GPU networks are best suited to workloads that can be split, routed and executed independently, without requiring constant synchronization between machines.
“Inference is the volume business, and it scales with every deployed model and agent loop,” Evgeny Ponomarev, co-founder of decentralized computing platform Fluence, told Cointelegraph. “That is where cost, elasticity and geographic spread matter more than perfect interconnects.”
In practice, that makes decentralized and gaming-grade GPUs in consumer environments a better fit for production workloads that prioritize throughput and flexibility over tight coordination.
Low hourly prices for consumer GPUs illustrate why decentralized networks target inference rather than large-scale model training. Source: Salad.com
“Consumer GPUs, with lower VRAM and home internet connections, do not make sense for training or workloads that are highly sensitive to latency,” Bob Miles, CEO of Salad Technologies — an aggregator for idle consumer GPUs — told Cointelegraph.
“Today, they are more suited to AI drug discovery, text-to-image/video and large scale data processing pipelines — any workload that is cost sensitive, consumer GPUs excel on price performance.”
Decentralized GPU networks are also well-suited to tasks such as collecting, cleaning and preparing data for model training. Such tasks often require broad access to the open web and can be run in parallel without tight coordination.
This type of work is difficult to run efficiently inside hyperscale data centers without extensive proxy infrastructure, Miles said.
When serving users all around the world, a decentralized model can have a geographic advantage, as it can reduce the distances requests have to travel and multiple network hops before reaching a data center, which can increase latency.
“In a decentralized model, GPUs are distributed across many locations globally, often much closer to end users. As a result, the latency between the user and the GPU can be significantly lower compared to routing traffic to a centralized data center,” said Liu of Theta Network.
Theta Network is facing a lawsuit filed in Los Angeles in December 2025 by two former employees alleging fraud and token manipulation. Liu said he could not comment on the matter because it is pending litigation. Theta has previously denied the allegations.
Frontier AI training will remain centralized for the foreseeable future, but AI computing is shifting away to inference, agents and production workloads that require looser coordination. Those workloads reward cost efficiency, geographic distribution and elasticity.
“This cycle has seen the rise of many open-source models that are not at the scale of systems like ChatGPT, but are still capable enough to run on personal computers equipped with GPUs such as the RTX 4090 or 5090,” Liu’s co-founder and Theta tech chief Jieyi Long, told Cointelegraph.
With that level of hardware, users can run diffusion models, 3D reconstruction models and other meaningful workloads locally, creating an opportunity for retail users to share their GPU resources, according to Long.
Decentralized GPU networks are not a replacement for hyperscalers, but they are becoming a complementary layer.
As consumer hardware grows more capable and open-source models become more efficient, a widening class of AI tasks can move outside centralized data centers, allowing decentralized models to fit in the AI stack.
