Software Engineering Daily

Volatility is the degree of fluctuation of something’s price. Highly volatile assets may see rapid and large price changes, while less volatile assets will maintain a steady price. This concept is important in decentralized finance because cryptocurren...

A decentralized exchange, usually referred to as a DEX, is a platform for exchanging cryptocurrencies. Depending on trading volume for different coins, some DEXs are more liquid than others. On the one hand you can freely swap unlisted tokens and maint...

A data warehouse is a data management system that often contains large amounts of historical data and is used for business intelligence activities like analytics. It centralizes customer data from multiple sources to be an organization’s single source ...

IT infrastructure are the components required to operate IT environments, like networks, virtual machines or containers, an operating system, hardware, data storage, etc…. As companies build out different deployment environments with infrastructure con...

Decentralized applications, termed “dApps,” are applications that feel like normal apps but are actually deployed (mostly) on the Ethereum blockchain. This means dApps can’t be taken down, can’t be censored or blocked,

A ‘token’ can represent almost anything in Ethereum, according to Ethereum.org: Lottery tickets, points in an online platform, fiat currency, and much more. These tokens must follow a standard called ECR-20 to have the same type and value of any other ...

In decentralized finance (DeFi) a liquidity pool is a collection of cryptocurrency funds created from the deposits of many users and usually multiple different currencies. There are 2 main types of pools: custodial and non-custodial.

A smart contract contains the “terms” of a blockchain transaction between a buyer and a seller as well as the capabilities to execute those terms. In order for smart contracts to include outside data from the world, such as stock market data, weather,

Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms.

The typical procedure many companies follow to reach production-level code is design the program, code and test it in different environments, and put it in a pipeline to deploy to production. Developers can make it pretty far into building their core f...