The widespread excitement around AI—specifically the consumer-facing large language models (LLMs) like ChatGPT—has raised the profile of AI tools in popular consciousness. It has also prompted many ...

Most organizations spend a tremendous amount of resources, time and money to protect their network perimeters from Internet-borne threats and hackers. But no matter how good a defense may be, it ...

Acceptance test driven development, or ATDD, is a collaborative practice wherein application developers, software users, and business analysts define automated acceptance criteria very early in the ...

2023 was truly the year of AI. Last year marked a significant turning point when artificial intelligence (AI) showcased its potential to revolutionize various industries and applications. Innovations ...

Modern software development teams have adopted a continuous delivery approach based upon DevOps and agile development techniques. The small and frequent code changes that result from such ...

SANTA CLARA, Calif.--(BUSINESS WIRE)--DeGirum Corp. today introduced DeLight, a cloud platform designed to overcome the major challenges faced by Edge Artificial Intelligence (AI) application ...

Blockchain application development refers to the process of creating a blockchain-native application. Blockchain development differs from traditional application development in several important ways.

As more and more applications and application development move to the cloud, traditional security roles and organizational structures are being shaken up. Why is that, and what are the benefits of a ...

In today’s world, it unfortunately takes more than a chivalrous heart to attain the Holy Grail of software development: productive development of high-quality applications. I recently spent some time ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...

Back in the ancient days of machine learning, before you could use large language models (LLMs) as foundations for tuned models, you essentially had to train every possible machine learning model on ...