Crypto Prices Rebound Amid Liquidations

TheNewsCryptoPublicado a 2026-03-24Actualizado a 2026-03-24

Resumen

Crypto prices have rebounded in the last 24 hours, with the total market capitalization rising by 3.48%. Bitcoin is trading around $71,032.59, with expectations to approach $76k, while Ethereum remains relatively flat near $2,158.29. Among top gainers, TAO surged 9.75% to $302.75, followed by RENDER and FIL. Despite the recovery, over $203 million in liquidations occurred, with BTC and ETH accounting for the majority. Meanwhile, Gold and Silver also saw gains, reaching record and multi-year highs, respectively.

Crypto prices have rebounded over the last 24 hours. This comes at a time when liquidations are above $200 million at the time of writing this article. Gold and Silver prices have recovered as well; however, crypto prices are at the center with a possibility to move higher.

Crypto Prices

The collective market cap is up by 3.48%. But, crypto prices are under the light with top tokens like BTC and ETH noting decent upticks. Bitcoin tokens are anticipated to move towards $76k, if not surpass it, in the days to come. For now, it is hovering around $71,032.59, up by 0.79%. ETH has largely remained flat at around $2,100. The current exchange value is $2,158.29, up by 0.17%.

TAO stands out as the AI cryptocurrency has gained 9.75% of value to reach $302.75. It goes on to reflect a weekly uptick of 8.53%. RENDER and FIL are the next notable gainers in the segment. They are up 4.42% and 2.28%, respectively.

Meme coins seem to be lagging behind, even though the likes of DOGE and SHIB have added 0.49% and 1.40% to their respective values.

Crypto Liquidations

Crypto prices commenced upticks roughly when US President Donald Trump announced a 5-day temporary pause in strikes. They have maintained the stance since then. What has happened on the sidelines is the liquidation of around $203.03 million in the last 24 hours. Longs stand at $101.51 million, and shorts come to approximately $101.45 million at the moment.

BTC liquidation comes to a total of $154.20 million, with $121.72 million in short and $32.48 million in long. ETH is next on the list with a small gap below the flagship cryptocurrency. Its combined liquidation comes to $129.52 million, with shorts leading at $94 million and the remaining $35.52 million accounting for longs.

Recovery for Gold and Silver

Gold and Silver were reported to be falling behind BTC and other cryptocurrencies. The trend may start to change, given that Gold and Silver prices are now gaining traction. Gold has jumped by 0.41%, a small margin, but now has a record value of $4,422.95. Silver has surged by 1.66%, outperforming Gold, to $70.34.

Meanwhile, Crude Oil and Brent are reportedly carrying the respective values of $90.56 and $101.96 when the article is being drafted.

Highlighted Crypto News Today:

BitGo has Rolled Out AI-Ready MCP Server

Preguntas relacionadas

QWhat is the total amount of crypto liquidations mentioned in the article?

AThe total crypto liquidations mentioned are $203.03 million in the last 24 hours.

QWhich AI cryptocurrency gained nearly 10% in value according to the report?

ATAO (Bittensor) is the AI cryptocurrency that gained 9.75% in value, reaching $302.75.

QWhat are the current price levels for Bitcoin and Ethereum as stated in the article?

ABitcoin is hovering around $71,032.59 and Ethereum is at $2,158.29.

QHow did the prices of Gold and Silver perform, and what are their new values?

AGold jumped 0.41% to a record $4,422.95, and Silver surged 1.66% to $70.34.

QWhat event does the article suggest contributed to the commencement of crypto price upticks?

AThe article suggests that crypto prices began to rebound roughly when US President Donald Trump announced a 5-day temporary pause in strikes.

Lecturas Relacionadas

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinistHace 49 min(s)

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinistHace 49 min(s)

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbitHace 49 min(s)

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbitHace 49 min(s)

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbitHace 54 min(s)

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbitHace 54 min(s)

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbitHace 1 hora(s)

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbitHace 1 hora(s)

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbitHace 2 hora(s)

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbitHace 2 hora(s)

Trading

Spot