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Source channel @githubtrending · Post #14974 · Jul 19

#cplusplus ik_llama.cpp is an improved version of llama.cpp that runs faster on CPUs and hybrid GPU/CPU setups. It supports many new advanced quantization methods, which help models use less memory and run more efficiently. It also offers better performance for special models like DeepSeek and MoE, with faster prompt processing and token generation. You can run it on various hardware, including Android, and it has features to control where model data is stored (CPU or GPU). This means you get quicker AI responses and can handle bigger or more complex models smoothly on your computer or device[2][1][4]. https://github.com/ikawrakow/ik_llama.cpp

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Crypto M - Crypto News

@CryptoM · Post #65372 · 04/13/2026, 02:55 AM

🚀 Asian Markets Show Resilience Amid Geopolitical Tensions Asian stock markets continued their 'low open-recovery' trend on April 13, with Japan's Nikkei 225 and South Korea's KOSPI initially experiencing significant declines before quickly narrowing their losses. According to BlockBeats, this pattern reflects a decreasing sensitivity to geopolitical conflicts, such as the ongoing tensions involving Iran. Since the escalation of the Iran conflict, the market's Monday corrections have shown a consistent reduction in magnitude, moving from near double-digit losses to more moderate adjustments. This indicates a diminishing impact of geopolitical shocks on market sentiment. In trading dynamics, the 'Trump model' is being systematically priced in. This involves creating uncertainty through policy signals or strong statements during the week, followed by a softening stance or 'victory' outcomes over the weekend, which helps to restore expectations and drive a rebound in risk assets. As this framework is repeatedly validated, investors' willingness to trade on early-week volatility has decreased, leading to reduced selling pressure and a convergence in volatility patterns. #AsianMarkets#GeopoliticalTensions#Nikkei225#KOSPI#IranConflict#MarketResilience#TrumpModel#Volatility#RiskAssets#MarketSentiment#TradingDynamics#InvestorBehavior