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The rapid advancements in generative AI have created a double-edged sword for organizations seeking to adopt and integrate these technologies. While the potential for innovation is immense, the frequent need to fine-tune models for specific tasks can result in a costly and unsustainable cycle of updates and retraining. As new, more powerful models emerge, businesses may find themselves perpetually chasing the latest technology rather than deriving lasting value from their AI investments. An alternative approach gaining traction is the use of prompt engineering and retrieval-augmented generation (RAG). Unlike traditional model fine-tuning, these methods focus on optimizing how existing models retrieve…

In the early stages of automation, robotic process automation (RPA) combined with low-code platforms and orchestration tools helped organizations improve productivity and scale operations efficiently. Virtual agents and chatbots took this further, enhancing user experiences by introducing conversational interfaces. With the advent of large language models (LLMs), vector databases, retrieval-augmented generation (RAG), and other generative AI technologies, the landscape of automation expanded, enabling capabilities such as summarizing content, generating code with AI copilots, and providing conversational question-and-answer systems. AI agents are poised to take this evolution even further, integrating automation, conversational capabilities, and process orchestration to create the next wave…

A new trend has emerged in the rapidly growing field of AI, one that has seen major players like OpenAI, Google, and Microsoft heavily promote their AI models as “open.” These companies use terms like “open AI” to evoke ideals of transparency, collaboration, and shared progress, which are typically associated with open-source software. However, a closer examination reveals that this promotion of “openness” is often more about branding than actual accessibility, leading to the rise of a phenomenon now known as “open-washing.” Open-washing in AI refers to the practice of companies overstating their commitment to openness while keeping key components…

The Rust Team has unveiled Rust 1.83, the latest update to the memory-safe and thread-safe programming language, which introduces significant enhancements for code running in const contexts. These updates, announced on November 28, expand the capabilities of Rust’s const evaluation, allowing developers more flexibility in what they can express at compile time. Additionally, the Rust Team revealed that the upcoming Rust 2024 edition will bring backwards-incompatible features, such as gen blocks, signaling further evolution in the language. For those already using a previous edition of Rust, the update to version 1.83 can be easily applied through rustup by running the…

Red Hat Ansible Automation Platform Service, designed to simplify and automate the management of hybrid cloud infrastructures, is now available as a managed service on Amazon Web Services (AWS) through the AWS Marketplace. This new offering, announced on December 2, is a significant step toward helping businesses streamline complex cloud operations and reduce the risk of errors that can arise from manual management. By making the Ansible Automation Platform available on AWS Marketplace, Red Hat aims to provide IT teams with a faster, more efficient way to deploy cloud infrastructure automation at scale. The managed service allows organizations to leverage…

Amazon Web Services (AWS) has rolled out new updates to AWS PartyRock, a low-code tool designed to help developers build generative AI applications. These updates include a new app search function and the ability to integrate document processing into applications. This expansion enhances PartyRock’s versatility, allowing developers to experiment with more complex features while still benefiting from its intuitive, mostly free design. Originally introduced as Amazon Bedrock Playground at the previous AWS re:Invent conference, PartyRock has continued to evolve with each new iteration. This year’s updates further empower developers, particularly those who are new to coding, to explore the possibilities…

Amazon Web Services (AWS) has introduced significant updates to Amazon Bedrock, adding new features designed to simplify and enhance the testing of applications prior to deployment. These updates, revealed during the ongoing re:Invent 2024 conference, include a retrieval augmented generation (RAG) evaluation tool within Bedrock Knowledge Bases, providing enterprises with a powerful way to optimize their applications’ performance. Bedrock Knowledge Bases are a key component for enterprises looking to leverage their own data to improve the contextual relevance of large language models (LLMs). By integrating their data, enterprises can fine-tune LLM responses, ensuring better performance for a variety of applications.…

As generative AI models, especially large language models (LLMs) tailored for coding, continue to scale and evolve, the software development life cycle (SDLC) is on the brink of a major transformation. The disruption won’t come from the idea that machines will replace humans, but rather from the fact that many aspects of the SDLC are now perfectly suited for the integration of AI. In particular, LLMs are poised to reshape the way software is developed, with advancements in automation, communication, and decision-making playing a crucial role in this shift. A recent whitepaper by Crowdbotics explores how a complete overhaul of…

Generative AI is no longer just a futuristic concept in software development; it’s actively shaping the industry. With tools like GitHub Copilot, Vercel’s v0, and Cursor, AI is becoming a regular companion in the coding world, helping developers write new code and maintain existing systems. However, the day-to-day responsibilities of developers extend far beyond new development. A significant chunk of their work revolves around refactoring and maintaining legacy code. So, how does the process of refactoring look when the code you’re working with wasn’t written by a human but by an AI tool? As AI tools become more integrated into…

In the tech world, trends can often resemble the ever-changing nature of fashion. Technology decisions frequently mirror the patterns we see in clothing: driven more by what’s trending rather than by practical necessity. The race to adopt the latest “it” technology, such as generative AI or Kubernetes, can often leave organizations jumping on the bandwagon without fully understanding if it fits their needs. For instance, companies are pouring resources into ChatGPT-like tools, convinced by the success stories of others—like the Commonwealth Bank of Australia reducing fraud losses with AI. But while some succeed, this doesn’t guarantee that every company will…