Peter Song 
As artificial intelligence continues to evolve, agentic AI is emerging as one of the most promising paradigms for building truly autonomous, adaptable, and context-aware systems. But what exactly is agentic AI? More importantly, how is it being applied in real-world settings today? In this article, we’ll explore the definition of agentic AI, highlight its unique … Read more
As artificial intelligence (AI) continues to advance, a new paradigm is emerging that transcends traditional automation. This paradigm is known as agentic workflows. While typical workflows rely on predefined steps and static logic, agentic workflows introduce autonomy, adaptability, and goal-oriented behavior into the execution process. These systems do more than follow instructions—they interpret, plan, and … Read more
In the rapidly evolving field of artificial intelligence (AI), we often encounter terms like autonomous, self-directed, and more recently, agentic. As intelligent systems become more interactive and decision-capable, the agentic definition becomes increasingly relevant. But what does “agentic” actually mean? And how does it relate to machine learning, AI models, and autonomous agents? In this … Read more
As AI agents evolve to mimic human decision-making, one essential advancement is their ability to remember. Without memory, an agent is reactive, stateless, and shallow—limited to single-turn interactions. But with structured memory systems, modern AI agents can retain context, adapt to evolving conversations, and deliver personalized experiences. In this article, we break down the AI … Read more
Agentic AI is a rapidly growing area in AI development where large language models (LLMs) are given autonomy to reason, plan, and execute actions using tools. Frameworks like LangChain, CrewAI, AutoGPT, and OpenAgents empower developers to create intelligent agents capable of complex multi-step tasks. If you’re looking to experiment with these agentic frameworks, Google Colab … Read more
When people think of AI hardware, NVIDIA often comes to mind due to its dominance in machine learning and deep learning applications. However, AMD—traditionally known for CPUs and gaming GPUs—has steadily been expanding its footprint in the AI domain. This leads to a common question among developers and businesses: Does AMD GPU use AI? The … Read more
When it comes to machine learning and deep learning, the GPU (Graphics Processing Unit) is often the heart of the system. For years, NVIDIA has dominated the AI GPU market with its CUDA ecosystem and top-tier performance. However, AMD has increasingly positioned itself as a competitive alternative, offering powerful GPUs with open-source software support and … Read more
The rise of deep learning and data-driven applications has brought a surge in demand for hardware acceleration, especially Graphics Processing Units (GPUs). However, CPUs (Central Processing Units) are still widely used in machine learning workflows—and for good reason. Despite the general preference for GPUs in training complex models, there are many scenarios where using a … Read more
If you’ve started exploring how neural networks are trained, you’ve likely come across the term “iteration.” Often used alongside words like “epoch” and “batch size,” iteration is one of the foundational concepts in machine learning training processes. But what does iteration actually mean in the context of a neural network, and why is it important? … Read more
In machine learning (ML), the pursuit of better accuracy is never-ending. With each new iteration, data scientists and engineers seek to improve predictions, reduce errors, and optimize outcomes. But how do you know that your new model version is actually better? That’s where A/B testing plays a critical role. A/B testing allows teams to compare … Read more
