A semantic forking mechanism is a process that allows a system or software to split into different versions based on changes in meaning or interpretation, not just changes in code. It helps maintain compatibility or create new features by branching off when the intended use or definition of data or functions diverges. This mechanism is…
Sharding is a method used to split data into smaller, more manageable pieces called shards. Each shard contains a subset of the total data and can be stored on a separate server or database. This approach helps systems handle larger amounts of data and traffic by spreading the workload across multiple machines.
Hash rate is a measure of how quickly a computer or network can perform cryptographic calculations, called hashes, each second. In cryptocurrency mining, a higher hash rate means more attempts to solve the mathematical puzzles needed to add new blocks to the blockchain. This metric is important because it reflects the overall processing power and…
A validator node is a computer in a blockchain network that checks and approves transactions to ensure they follow the rules. Validator nodes help keep the network secure and trustworthy by confirming that each transaction is valid before it is added to the blockchain. People or organisations who run validator nodes are often rewarded for…
A Layer 1 protocol is the fundamental set of rules and technologies that make a blockchain network work. It handles how transactions are processed, how data is stored, and how computers in the network agree on what is true. Examples include Bitcoin, Ethereum, and Solana, which each have their own Layer 1 protocols. These protocols…
IT Infrastructure as Code is a way to manage and set up computer servers, networks, and other technology resources by writing code, rather than doing everything manually. This code describes how the infrastructure should look and behave, allowing teams to create, change, or remove resources quickly and reliably. By treating infrastructure like software, organisations can…
Model monitoring is the process of regularly checking how a machine learning or statistical model is performing after it has been put into use. It involves tracking key metrics, such as accuracy or error rates, to ensure the model continues to make reliable predictions. If problems are found, such as a drop in performance or…