Last week, a C-suite executive from a large organization asked us, ‘What should our Gen AI strategy be?’ While we provided an on-the-spot answer, the question made me pause and reflect. There is an overwhelming amount of noise surrounding Gen AI—new announcements seem to emerge every few hours, much of it filled with hype, or even smoke and mirrors. In the midst of this commotion, the true value of Gen AI often gets lost.

After having tried UV as the package manager for Python, I now regret having wasted so much of my life waiting for pip installs to complete. If you’re tired of slow package installations and want to supercharge your Python development workflow, it’s time to switch to UV.

Artificial General Intelligence (AGI) has long been considered the holy grail of AI research. The prospect of creating a machine that can think and learn like humans has captivated scientists, entrepreneurs, and enthusiasts alike. Recently, OpenAI’s CEO, Sam Altman, emphasized the urgency and importance of achieving AGI, hinting at the possibility of a single, monolithic model capable of answering any question or solving any problem.

Large Language Models (LLMs) have revolutionized natural language processing, but their size and computational requirements often make them impractical for edge devices or resource-constrained environments. This post explores techniques to optimize LLMs for these scenarios, enabling AI capabilities on smartphones, IoT devices, and other platforms with limited resources.

As you can tell artificial intelligence (AI) is making significant strides in various domains, and software development is no exception. One of the most fascinating developments is AI-assisted code generation, which promises to revolutionize how we write and think about code. This article explores some key aspects of AI’s role in software development, from enhancing syntactic simplicity to reshaping development workflows.

The world of AI agents is booming with applications ranging from chatbots to virtual assistants and even self-driving cars. But how do we build these intelligent entities? Two main approaches are taking center stage: controlled agents built with Lang Graph and autonomous agents powered by tools like Crew AI. Let’s delve into the pros and cons of each approach to see which might be the better fit for your project.

Artificial intelligence (AI) has become an integral part of technological advancement, influencing countless applications from machine learning algorithms to autonomous systems. In this context, Single Agent Architectures (SSAs) and Multi-Agent Architectures (MAAs) play pivotal roles in elevating the capabilities of AI systems. Understanding the nuances between these two architectural frameworks is essential for businesses and developers aiming to leverage AI effectively in their solutions.

As the digital landscape continues to evolve, so do the mechanisms for surfacing and disseminating information. Search Engine Optimization (SEO) has long been the cornerstone of digital marketing strategies, aimed at improving a website’s visibility and ranking on search engine results pages. However, with the rise of Generation AI (Gen AI) and the increasing prevalence of AI-powered search agents, a new paradigm is emerging – Search Agent Optimization (SAO). In this blog post, we’ll delve into the transition from SEO to SAO and explore how content creators and digital marketers can adapt their strategies to thrive in this new era.

If you are an engineer earger to get started on building Generative AI applications, the current information overload and hype cycle currently associated with Generative AI could get overwhelming for many.

Since the dawn of the microprocessors and assembly language, 99% of all the applications we have been building and using fall in the category of Deterministic applications.