Coinbase Users Locked Out: Unable To Buy, Sell, Or Transfer Crypto

bitcoinistОпубліковано о 2026-02-12Востаннє оновлено о 2026-02-12

Анотація

Coinbase experienced a significant service disruption on Wednesday, preventing users from buying, selling, or transferring cryptocurrency. The outage occurred just before the company's Q4 2025 earnings report, causing its stock (COIN) to drop 8% to $140. Coinbase acknowledged the issue on social media, deployed a fix, and assured users that funds were secure, but did not disclose the root cause. Ahead of earnings, Monness Crespi double-downgraded COIN from buy to sell, citing a $120 price target and predicting continued weakness in the crypto market.

Cryptocurrency exchange Coinbase (COIN) experienced an unexpected service disruption on Wednesday, just hours before the company is scheduled to report its fourth‐quarter 2025 earnings.

The outage left users temporarily unable to buy, sell, or transfer digital assets on the platform, triggering concern among customers and adding pressure to the company’s stock.

Platform Disruption Hits Coinbase

In a post on X (previously Twitter) Coinbase acknowledged the issue, stating that some customers were unable to conduct transactions on the platform. The company assured users that it was investigating the problem and emphasized that customer funds remained secure.

Shortly afterward, Coinbase Support announced that a fix had been deployed and that teams were monitoring the platform to ensure services were fully restored. However, the company did not provide details about the root cause of the disruption or explain what led to the interruption in trading activity.

Coinbase shares (COIN) fell sharply during Wednesday’s trading session. As of this writing, the stock is trading at $140, marking an 8% decline over the past several hours. The drop comes as analysts prepare for what many expect to be a challenging fourth‐quarter report.

The daily chart shows COIN’s valuation drop. Source: COIN on TradingView.com

$120 Price Target Issued For COIN

Research firm Monness Crespi took a notably cautious stance ahead of the earnings announcement. The firm issued a double downgrade on Coinbase stock, moving its rating from buy to sell.

Analyst Gus Gala described earlier expectations of a steady recovery through 2026 as “foolish,” citing the historical depth and duration of crypto bear markets.

Monness Crespi now anticipates continued weakness through the first half of the year and has revised its 2026 and 2027 projections to levels below Wall Street consensus estimates. Gala also set a $120 price target for the stock, suggesting that more attractive entry points may emerge later.

Featured image from OpenArt, chart from TradingView.com

Пов'язані питання

QWhat was the main issue that Coinbase users experienced according to the article?

ACoinbase users were temporarily unable to buy, sell, or transfer digital assets on the platform due to an unexpected service disruption.

QWhat action did Coinbase take to address the service disruption?

ACoinbase acknowledged the issue on X, investigated the problem, deployed a fix, and had teams monitoring the platform to ensure services were fully restored.

QHow did the service disruption affect Coinbase's stock (COIN) price?

ACoinbase shares fell sharply, trading at $140 and marking an 8% decline during Wednesday's trading session.

QWhich research firm issued a double downgrade and a $120 price target for Coinbase stock?

AResearch firm Monness Crespi issued a double downgrade, moving its rating from buy to sell, and set a $120 price target for the stock.

QWhat reason did analyst Gus Gala give for the cautious stance on Coinbase's recovery expectations?

AAnalyst Gus Gala described earlier expectations of a steady recovery as 'foolish,' citing the historical depth and duration of crypto bear markets.

Пов'язані матеріали

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

NVIDIA's Rubin Ultra, the top-tier variant of the newly announced Rubin AI accelerators, has reportedly seen significant specification downgrades, according to an industry report from SemiAnalysis. Initially designed with four compute dies (4-die), the Rubin Ultra is now said to be reduced to a 2-die design. Key changes highlighted in the report include: * **No increase in peak theoretical compute performance**, remaining at 35 PFLOPs like the standard Rubin. * **Severe reduction in memory capacity** to 192GB using 8-Hi HBM stacks, which is less than the standard Rubin's 288GB using 12-Hi stacks. * **Negligible memory bandwidth improvement** of only 1 TB/s. * **Slightly higher chip-level power consumption**. * The **primary upgrade is a massive increase in scale-up interconnect capacity**, supporting connections for up to 576 GPUs via NVLink, compared to 72 for the standard Rubin. The report suggests the redesign is primarily a cost-optimization move driven by the sharp rise in HBM (High-Bandwidth Memory) prices. By reducing the expensive HBM content and shifting investment towards enhanced system-scale networking, NVIDIA aims to maintain the platform's value for large-scale AI training clusters while managing soaring material costs. The news reportedly triggered a sell-off in South Korean memory stocks, with SK Hynix and Samsung shares falling around 8%, as markets grew concerned that NVIDIA—a major HBM buyer—might be reducing its reliance on high-capacity memory, potentially capping future pricing power for memory makers.

Odaily星球日报12 хв тому

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

Odaily星球日报12 хв тому

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.

marsbit1 год тому

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

marsbit1 год тому

Торгівля

Спот
活动图片