Is DAI suitable for long-term holding? The answer depends on individual needs and risk preferences. 1. DAI is a decentralized stablecoin, generated by excessive collateral for crypto assets, suitable for users who pursue censorship resistance and transparency; 2. Its stability is slightly inferior to USDC, and may experience slight deansal due to collateral fluctuations; 3. Applicable to lending, pledge and governance scenarios in the DeFi ecosystem; 4. Pay attention to the upgrade and governance risks of MakerDAO system. If you pursue high stability and compliance guarantees, it is recommended to choose USDC; if you attach importance to the concept of decentralization and actively participate in DeFi applications, DAI has long-term value. The combination of the two can also improve the security and flexibility of asset allocation.

USDC is safe. It is jointly issued by Circle and Coinbase. It is regulated by the US FinCEN. Its reserve assets are US dollar cash and US bonds. It is regularly audited independently, with high transparency. 1. USDC has strong compliance and is strictly regulated by the United States; 2. The reserve asset structure is clear, supported by cash and Treasury bonds; 3. The audit frequency is high and transparent; 4. It is widely accepted by institutions in many countries and is suitable for scenarios such as DeFi and compliant payments. In comparison, USDT is issued by Tether, with an offshore registration location, insufficient early disclosure, and reserves with low liquidity assets such as commercial paper. Although the circulation volume is large, the regulatory recognition is slightly low, and it is suitable for users who pay attention to liquidity. Both have their own advantages, and the choice should be determined based on the purpose and preferences of use.

Yes,aPythonclasscanhavemultipleconstructorsthroughalternativetechniques.1.Usedefaultargumentsinthe__init__methodtoallowflexibleinitializationwithvaryingnumbersofparameters.2.Defineclassmethodsasalternativeconstructorsforclearerandscalableobjectcreati

Stable coins are cryptocurrencies with stable value. They maintain price stability by anchoring assets such as the US dollar to solve the volatility problem of crypto markets. Its core role includes providing a medium of transaction, a store of value and an account unit, supporting daily payment and safe-haven needs. There are three main types: 1. Frax (such as USDT and USDC), with a simple mechanism but dependent on centralized institutions; 2. Crypto Assets (such as DAI), with a high degree of decentralization but requiring over-collateralization; 3. Algorithm (such as Frax), which relies on smart contracts to adjust supply, but has a high risk. Stablecoins have become the cornerstone of the DeFi ecosystem and have shown great potential in fields such as cross-border payments.

__post_init__ is used in Python's dataclass to run custom logic after object initialization. The problem it solves is that when you need to perform verification, calculate derivative properties or set internal state after field initialization, you do not need to manually rewrite __init__ and retain the initialization function automatically generated by dataclass. The usage method is to define the __post_init__ method, which Python will automatically call after the default __init__ is executed. Applicable scenarios include field verification, derivative attribute calculation and repeated logic avoidance. Not recommended for initialization that depends on external resources or overly complex. Notes include: __post_init__ does not accept parameters other than self

Function annotations are a feature used in Python to add metadata, which can improve code readability and maintenance. It does not force type checking, but provides type prompts or other information for parameters and return values. Its uses include: 1. Improve code readability and enable developers to clarify the expected input and output of functions; 2. Use it in conjunction with static type checking tools (such as mypy and pyright); 3. Used by frameworks (such as FastAPI) to generate documents or verify requests. Annotations do not affect the operation of the program. For example, name:str and ->str in defgreet(name:str)->str are only additional information, and the actual parameter transmission can still be of other types. Use suggestions include keeping the annotations concise and combining types and types

When do you need to use default_factory? When you want to assign a default mutable object to a field, you should use default_factory. How to set default value with default_factory? You can pass any object that has no arguments to default_factory, such as built-in types, functions, or lambda expressions. Common usages include: 1. Initialize to an empty list: default_factory=list; 2. Initialize to an empty dictionary: default_factory=dict; 3. Initialize to a specific structure: default_factory=lambda:[1,2,3]; 4

The key to using Python to call WebAPI to obtain data is to master the basic processes and common tools. 1. Using requests to initiate HTTP requests is the most direct way. Use the get method to obtain the response and use json() to parse the data; 2. For APIs that need authentication, you can add tokens or keys through headers; 3. You need to check the response status code, it is recommended to use response.raise_for_status() to automatically handle exceptions; 4. Facing the paging interface, you can request different pages in turn and add delays to avoid frequency limitations; 5. When processing the returned JSON data, you need to extract information according to the structure, and complex data can be converted to Data