What Is OnChain Insurance?
Life is like a rolling dice, full of uncertainties and risks. On this principle, the concept of insurance was developed to step in to significantly cover for some of these unplanned eventualities.
What is Yield Farming?
Yield farming is a set of strategies to maximize returned yield (APY) from multiple aggregated protocols that usually are stacked on one another and compounded. In this blog post we’ll walk you through how yield farming works, its protocols and platforms, and some of the risks involved in using it.
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Terraform vs Crossplane - How to provide IaC with the environment state observability
Infrastructure as Code (IaC) is the managing and provisioning of infrastructure through code instead of through manual processes. It enables the automated and repeatable creation and maintenance of all virtualized infrastructures.
What Are the Risks Associated With The Binance Smart Chain?
Perfection is a myth and every invention no matter how highly placed is bound to have a measure of imperfections. Based on this premise, Peter Drucker famously declared, “Innovate or Die!” and this served as the guiding principle for the development of the Binance Chain and ultimately Binance Smart Chain.
How an Internship can be the first step to a career in recruitment
When Paulina first joined 10Clouds about 10 months ago, she had already finished two internships in HR: first a year in HR administration and then another one in Employer Branding. Still, recruiting was an undiscovered area and one she’s always wanted to work in.
Kubernetes Environment for Developers - Comparison of Kind, Minikube and Skaffold
Companies have shifted to Kubernetes in production, but most developers are still using tools like 'Docker Compose' to run and test their applications. As the DevOps approach is becoming a new normal, developers need to consider Kubernetes development environment.
What Data You Need to Collect in Your Company to Build an MLOps Infrastructure
We know that machine learning has inspired a lot of industries and has actually deep penetrated into their everyday business operations. With implementing ML culture, comes a huge responsibility of designing, implementing, training, testing, deploying, monitoring, and evaluating machine learning models in order to seamlessly experience the end-to-end execution of ML models in the production environment.