這對SOMI是中性偏樂觀的，因爲INDODAX每月超過1000萬的訪問者可能會提高流動性和零售採用，儘管初始交易量（₽7 | 幣安廣場上的 Harley Hewlett niuv

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Harley Hewlett niuv

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這對SOMI是中性偏樂觀的，因爲INDODAX每月超過1000萬的訪問者可能會提高流動性和零售採用，儘管初始交易量（₽74M/$4.8M）表明需求溫和。上市是在SOMI的30天價格下降49%之後，可能提供一個穩定的催化劑$SOMI 
#Somnia  
@Somnia Official 

免責聲明：包含來自第三方的見解。非財務建議。可能包含贊助內容。請參閱條款。

SOMI

0.5201

+1.20%

19

0

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Plume Network registered as a transfer agent with the U.S. SEC, enabling onchain management of securities ownership and integration with traditional finance infrastructure like DTCC. This approval positions Plume as a compliant gateway for tokenized assets such as private credit and Treasurys. $PLUME #plume @Plume - RWA Chain

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OpenLedger launched SenseMap, a decentralized mapping network that pays users for verified, real-time data (e.g., crowd levels, safety). Contributors stake reputation to validate entries, with spam penalized. Alphabet’s $82.5B Q2 2025 Google Services revenue highlights the market gap SenseMap targets. $OPEN #OpenLedger @OpenLedger

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The consensus on $MITO is mixed – bullish on novel liquidity mechanisms and exchange listings, bearish on tokenomics (18% circulating supply) and unlock schedules. Watch the 30-day price change (-20% as of Oct 8) against 24h volume spikes ($18M, +36.6% YoY) for momentum shifts. Will the EOL model’s yield campaigns offset dilution risks? $MITO #Mitosis @Mitosis Official

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Pyth Network, alongside Chainlink, was chosen by the U.S. Department of Commerce as one of the oracles to post real gross domestic product (GDP) data on the blockchain and the decentralized blockchain-based distribution platform aims to further scale its data business. $PYTH #PythRoadmap @Pyth Network

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Foundation models for computer vision built on Vision Transformer (ViT) architectures have become increasingly widespread. However, their fine-tuning process is resource-intensive, slowing their adoption in edge or low-energy applications. We introduce ALaST (Adaptive Layer Selection for ViT Fine-Tuning), a novel approach that dynamically optimizes the fine-tuning process to significantly reduce computational cost, memory consumption, and training time. Our method is founded on the critical observation that during fine-tuning $LAYER #BuiltonSolayer @Solayer

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