The latest News and Information on DevOps, CI/CD, Automation and related technologies.
As we’ve talked about before, our app is a monolith: all our backend code lives together and gets compiled into a single binary. One of the reasons I prefer monolithic architectures is that they make it much easier to focus on shipping features without having to spend much time thinking about where code should live and how to get all the data you need together quickly. However, I’m not going to claim there aren’t disadvantages too. One of those is compile times.
$575 million was the cost of a huge IT incident that hit Equifax, one of the largest credit reporting agencies in the U.S. In September 2017, Equifax announced a data breach that impacted approximately 147 million consumers. The breach occurred due to a vulnerability in the Apache Struts web application framework, which Equifax failed to patch in time. This vulnerability allowed hackers to access the company's systems and exfiltrate sensitive data.
DevOps has accelerated the delivery of software, but it has also made it more difficult to stay on top of compliance issues and security threats. When applications, environments and infrastructure are constantly changing it becomes increasingly difficult to maintain a handle on compliance and security. For fast-moving teams, real time security monitoring has become essential for quickly identifying risky changes so they can be remediated before they result in security failure.
Modern software delivery teams find themselves under constant pressure to maintain security and compliance without slowing down the speed of development. This usually means that they have to find a way of using automation to ensure robust governance processes that can adapt to evolving cyber threats and new regulatory requirements.
Prompt engineering is the practice of crafting input queries or instructions to elicit more accurate and desirable outputs from large language models (LLMs). It is a crucial skill for working with artificial intelligence (AI) applications, helping developers achieve better results from language models. Prompt engineering involves strategically shaping input prompts, exploring the nuances of language, and experimenting with diverse prompts to fine-tune model output and address potential biases.