Luna Classic ($LUNC) 代幣在 2022 年遭遇災難性崩盤，一天之內從 119 美元暴跌至 0.00001 美元。事情是這樣的：

Luna Classic ($LUNC) 代幣在 2022 年遭遇災難性崩盤，一天之內從 119 美元暴跌至 0.00001 美元。事情是這樣的：

The Luna Classic ($LUNC) token experienced a catastrophic crash in 2022, plummeting from $119 to $0.00001 in just a day. This crash was triggered by the de-pegging of Terra's algorithmic stablecoin, UST, from the US dollar.

Luna Classic ($LUNC) 代幣在 2022 年經歷了災難性的崩盤，一天之內從 119 美元暴跌至 0.00001 美元。這次崩盤是由 Terra 的演算法穩定幣 UST 與美元脫鉤引發的。

As a result, a "death spiral" was created, where UST's instability led to massive sell-offs of LUNA (now LUNC) to maintain its peg. In an attempt to save UST, LUNA was minted at an unsustainable rate, causing hyperinflation in the market.

結果，出現了“死亡螺旋”，UST 的不穩定性導致 LUNA（現為 LUNC）大規模拋售以維持其掛鉤。為了拯救 UST，LUNA 以不可持續的速度鑄造，導致市場惡性通貨膨脹。

Ultimately, this loss of confidence in the ecosystem led to a market-wide sell-off and a dramatic price collapse.

最終，對生態系統的信心喪失導致了整個市場的拋售和價格的急劇下跌。

Now, market trends suggest a potential surge in low-cap tokens during the next altcoin season. To navigate this, strategic moves include portfolio diversification, dollar-cost averaging, and a focus on fundamentals.

現在，市場趨勢表明，在下一個山寨幣季節，低市值代幣可能會激增。為了解決這個問題，策略性舉措包括投資組合多元化、美元成本平均以及對基本面的關注。

Additionally, emerging ecosystems like Polkadot and Cosmos are presenting opportunities for savvy investors.

此外，Polkadot 和 Cosmos 等新興生態系統也為精明的投資者提供了機會。

In other news, DIN (Dynamic Input Normalization) is revolutionizing AI data processing with its modularity. As the first AI-native preprocessing layer, DIN adapts to diverse data pipelines and optimizes data in real-time for machine learning and AI.

在其他新聞中，DIN（動態輸入規範化）正在以其模組化性徹底改變人工智慧資料處理。作為第一個人工智慧原生預處理層，DIN 適應不同的資料管道並即時優化機器學習和人工智慧的資料。

This modular design enables seamless integration into any data workflow, handling structured, unstructured, or semi-structured data without manual adjustments. Unlike static preprocessing methods, DIN ensures AI models receive high-quality, ready-to-use data regardless of input variability.

這種模組化設計可以無縫整合到任何資料工作流程中，無需手動調整即可處理結構化、非結構化或半結構化資料。與靜態預處理方法不同，DIN 確保 AI 模型接收高品質、隨時可用的數據，無論輸入變化如何。

This efficiency and flexibility eliminate preprocessing bottlenecks and reduce risks of data misalignment, ultimately leading to better model performance.

這種效率和靈活性消除了預處理瓶頸並降低了資料不一致的風險，最終帶來更好的模型效能。

As the future of data workflows demands modularity, DIN's AI-native design is set to become a cornerstone for scalable AI systems. Combined with opportunities like the Binance Web3 Airdrop, DIN is shaping the future of AI data intelligence.

由於資料工作流程的未來需要模組化，DIN 的 AI 原生設計將成為可擴展 AI 系統的基石。結合 Binance Web3 Airdrop 等機會，DIN 正在塑造 AI 資料智慧的未來。