core VS NumPy

Website: terra.money

TerraUSD (UST) was launched in 2020. Like NuBits and other seigniorage algorithmic stablecoins, UST sought to peg one token by moving volatility to a separate token, in this case LUNA, which was also the native token of the Terra blockchain. Users could swap 1 UST for $1 of LUNA and vice versa. When the price of UST rose above $1, holders could profit by swapping $1 of LUNA for 1 UST (which would be worth more than $1), increasing supply and pushing down the price of UST. When UST was below $1, users could profit by swapping 1 UST for $1 of LUNA, reducing the supply of UST. This worked relatively well until a series of events led to confidence in the system collapsing, meaning there was no longer adequate demand for LUNA to stabilize UST. Its failure in May 2022 wiped out tens of billions of dollars of value from the Terra ecosystem, catalyzed the wider crypto bear market, and brought down several large organizations with exposure to it, including crypto hedge fund 3AC and lending company Celsius in the process.

Terra's Official Twitter account post, a newly created account, and a bio that reads "Building a new standard for DAO on Terra"

A final example of a token is a "stablecoin". A stablecoin is a token which is kept by the issuer at a fixed price in terms of a government currency, generally the US dollar. Tether maintains a popular stablecoin known as USDT by keeping reserves matching the supply of the token. Other approaches include that used by MakerDao for its stablecoin Dai, where reserves are decentralized and take the form of tokens or Ethereum. These decentralized stablecoins are referred to as "algorithmic stablecoins". A more unfortunate example is Terra, who's algorithmic stablecoin collapsed to the tune of 60 billion dollars.

Terra

https://www.terra.money/ (this looks exactly like the above)

It’s also on their website https://www.terra.money/

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]