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Bitcoin Traders Brace for Volatility as Sellers Defend $105K Level Despite ‘Crypto Winter’ Fears

Bitcoin hovered around $102,000 on Thursday, as traders struggled to push the price beyond the $105,000 resistance level amid rising sell pressure. Selling Pressure Builds Around $105,000 Data from Cointelegraph Markets Pro and TradingView showed Bitcoin’s rebound losing steam following the daily open. Analyst Skew noted that Bitcoin’s price appeared capped by a cluster of sell orders just above $105,000, adding that this was “not surprising.” He warned that the increase in sell-side liquidity could be a deliberate attempt to suppress prices during Asian trading hours. Trading analytics platform Material Indicators highlighted that the significant ask liquidity had not yet caused a price correction, suggesting the seller could be trying to drive Bitcoin down toward the $98,000 to $93,000 range. “If price hits $105k, I’d expect part if not all of those asks to get pulled,” the group said, noting that Bitcoin’s bounce from its 50-week simple moving average still carries “macro bullish implications.” Traders Eye Potential Dip Market commentator Exitpump described the $105,000 sell wall as “insane,” while other analysts suggested the liquidity might not be genuine. Meanwhile, veteran investor Kyle Chasse cautioned that another short-term price drop could occur, pointing to a buildup of bid liquidity below current levels. “Confidence could get wiped in a heartbeat,” he said, referencing CoinGlass data showing clusters of liquidations awaiting lower price zones. External Market Factors at Play Bitcoin’s latest movements also coincided with cooling momentum in U.S. equities, which have been retreating from all-time highs. Speculation around the Supreme Court possibly overturning international trade tariffs added uncertainty to broader markets. Analysts believe that if the Court strikes down the tariffs, it could trigger a rally in equities — but potentially divert short-term liquidity away from Bitcoin. As of Thursday afternoon, Bitcoin remained volatile, trading narrowly between $101,500 and $103,500, with traders keeping a close watch on the critical $105,000 resistance zone. U.Today

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Revolutionary: Inception’s $50M Breakthrough in Diffusion Models Transforms Code and Text Generation

BitcoinWorld Revolutionary: Inception’s $50M Breakthrough in Diffusion Models Transforms Code and Text Generation Imagine generating complex code and text at speeds previously thought impossible – that’s the groundbreaking promise behind Inception’s recent $50 million seed funding round. This AI startup is challenging the status quo with diffusion models that could revolutionize how developers and businesses approach text and code generation. What Makes Diffusion Models Different? Traditional AI models for text generation use autoregressive approaches, predicting each word sequentially like GPT-5 and Gemini. Inception’s diffusion models take a completely different path, working through iterative refinement that processes information holistically rather than word-by-word. The $50 Million AI Startup Funding That Changes Everything Menlo Ventures led this massive seed round with participation from industry giants including: Mayfield and Innovation Endeavors Nvidia’s NVentures Microsoft’s M12 fund Snowflake Ventures and Databricks Investment Angel investors Andrew Ng and Andrej Karpathy Why Diffusion Models Excel in Code Generation Stefano Ermon, Stanford professor and Inception’s leader, explains that diffusion-based LLMs achieve remarkable efficiency gains in two critical areas: Metric Diffusion Models Traditional Models Latency Over 1,000 tokens/second Significantly slower Compute Cost Much lower Higher resource demands Parallel Processing Built for simultaneous operations Sequential execution The Technical Advantage in AI Efficiency Where autoregressive models must execute operations one after another, diffusion models can process multiple operations simultaneously. This parallel architecture enables significantly lower latency in complex tasks like analyzing large codebases or generating extensive text documents. Real-World Applications and Integration Inception’s Mercury model has already been integrated into development tools including ProxyAI, Buildglare, and Kilo Code. The company’s approach demonstrates how diffusion models can handle large-scale text processing while managing data constraints more effectively than traditional methods. FAQs About Inception and Diffusion Models What companies are backing Inception? The funding round includes Menlo Ventures , Nvidia ‘s NVentures, Microsoft ‘s M12 fund, and angel investors Andrew Ng and Andrej Karpathy . Who leads the Inception project? Stefano Ermon , a Stanford professor specializing in diffusion models, leads the initiative. How do diffusion models compare to autoregressive models? Diffusion models use iterative refinement while autoregressive models work sequentially, making diffusion approaches faster and more efficient for certain tasks. What is the Mercury model? Mercury is Inception’s diffusion model specifically designed for software development, already integrated into multiple development tools. Why are diffusion models better for hardware utilization? Their parallel processing capability allows simultaneous operations, unlike the sequential nature of autoregressive models. Inception’s $50 million funding represents a seismic shift in AI development, proving that innovation in fundamental model architectures can deliver dramatic improvements in speed and efficiency. As diffusion models challenge the dominance of autoregressive approaches, we’re witnessing the beginning of a new era in AI capabilities that could transform how businesses and developers interact with artificial intelligence. To learn more about the latest AI market trends, explore our article on key developments shaping AI models and institutional adoption. This post Revolutionary: Inception’s $50M Breakthrough in Diffusion Models Transforms Code and Text Generation first appeared on BitcoinWorld . U.Today