XRP Breaks Down Yet Again, Losing the $2 Mark, with a Potential 10% Drop Looming?

金色财经Pubblicato 2025-12-17Pubblicato ultima volta 2025-12-17

Introduzione

XRP has experienced a sharp decline, falling 4.3% in 24 hours and breaking below the critical $2 support level. The drop was driven by significant selling pressure, with $584 million in long liquidations and a doubling in trading volume to $3.9 billion. Despite the price drop, XRP ETFs have recorded 21 consecutive days of net inflows, indicating continued institutional interest. Key support is now at $1.86. A break below this level could lead to a further 10% decline toward the October 10 low of $1.58. The RSI is at 21.5, indicating extreme oversold conditions, which has previously led to minor rebounds. However, a sustained bounce depends on holding the $1.86 support. Investors are also exploring new opportunities like the presale of meme coin Maxi Doge ($MAXI), which has raised over $4 million. The current situation reflects a battle between institutional accumulation and retail selling. Caution is advised—wait for clear signs of a price bottom before considering entry.

XRP is really falling without any mercy! It plummeted 4.3% in 24 hours, breaking through the crucial $2 support level that had been holding for days. Selling pressure is maxed out, and the bulls look like they're about to give up~ Free group +Q:3260353596

Why such a steep drop? Turns out, $584 million fled overnight, long-term liquidation volumes surged, and many traders got caught completely off guard by this decline. The bulls were fighting hard to defend $2, but buying interest was pitifully low—forget a rebound, it was even hard to stabilize. Even more painful, yesterday's trading volume doubled, hitting $3.9 billion, and after the bears broke through, the selling pressure intensified!

But there's a strange contrast: even though the price is in the gutter, XRP's ETF has seen net inflows for 21 consecutive days! Institutions and long-term holders are quietly buying the dip, showing that big players in the regulated market still have confidence in it~

? Key Levels to Watch! A Break Below $1.86 Could Mean Another 10% Drop?

Looking at the 4-hour chart, after breaking below $2, the focus is squarely on the $1.86 support level. The bulls have no choice but to defend this line now—if it fails, the next stop could be the October 10 low of $1.58, which would mean another 10% drop in the short term. Just thinking about it hurts!

A small silver lining: the RSI has now dropped to 21.5, indicating extreme oversold conditions. The previous two times it hit this level, the price bounced back slightly, so maybe there's a chance for a small dip-buying opportunity this time? But that depends entirely on holding $1.86; otherwise, it's all for nothing.

Also, many crypto investors are starting to shift to new opportunities, like the presale meme coin Maxi Doge ($MAXI), which has already raised over $4 million. Some analysts say it resembles the early days of Dogecoin, so those interested might want to keep an eye on it~

To sum up: XRP is currently a tug-of-war between "institutions buying the dip vs. retail selling." The key is whether $1.86 can hold. Don't blindly buy the dip—wait for clear signs of stabilization before jumping in, or you might get burned again! Free group +Q:3260353596

Crypto di tendenza

Letture associate

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.

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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.

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