Luna Classic ($LUNC) 代币在 2022 年遭遇灾难性崩盘，一天之内从 119 美元暴跌至 0.00001 美元。事情是这样的：

Luna Classic ($LUNC) 代币在 2022 年遭遇灾难性崩盘，一天之内从 119 美元暴跌至 0.00001 美元。事情是这样的：

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) 代币在 2022 年经历了灾难性的崩盘，一天之内从 119 美元暴跌至 0.00001 美元。这次崩盘是由 Terra 的算法稳定币 UST 与美元脱钩引发的。

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.

结果，出现了“死亡螺旋”，UST 的不稳定导致 LUNA（现为 LUNC）大规模抛售以维持其挂钩。为了拯救 UST，LUNA 以不可持续的速度铸造，导致市场恶性通货膨胀。

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

最终，对生态系统的信心丧失导致了整个市场的抛售和价格的急剧下跌。

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.

现在，市场趋势表明，在下一个山寨币季节，低市值代币可能会激增。为了解决这个问题，战略举措包括投资组合多元化、美元成本平均以及对基本面的关注。

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

此外，Polkadot 和 Cosmos 等新兴生态系统也为精明的投资者提供了机会。

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.

在其他新闻中，DIN（动态输入规范化）正在以其模块化性彻底改变人工智能数据处理。作为第一个人工智能原生预处理层，DIN 适应不同的数据管道并实时优化机器学习和人工智能的数据。

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.

这种模块化设计可以无缝集成到任何数据工作流程中，无需手动调整即可处理结构化、非结构化或半结构化数据。与静态预处理方法不同，DIN 确保 AI 模型接收高质量、随时可用的数据，无论输入变化如何。

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

这种效率和灵活性消除了预处理瓶颈并降低了数据不一致的风险，最终带来更好的模型性能。

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.

由于数据工作流程的未来需要模块化，DIN 的 AI 原生设计将成为可扩展 AI 系统的基石。结合 Binance Web3 Airdrop 等机会，DIN 正在塑造 AI 数据智能的未来。