Reinforcement Learning’s Role in Fine-Tuning LLMs Reinforcement learning has emerged as a powerful approach to fine-tune large language models (LLMs) for more intelligent behavior. These models are already capable of performing a wide range of tasks, from summarization to code…

Suppose you were shown that an artificial intelligence tool offers accurate predictions about some stocks you own. How would you feel about using it? Now, suppose you are applying for a job at a company where the HR department uses…

In this tutorial, we’ll learn how to harness the power of Google’s Gemini models alongside the flexibility of Pandas. We will perform both straightforward and sophisticated data analyses on the classic Titanic dataset. By combining the ChatGoogleGenerativeAI client with LangChain’s…

As AI-first development redefines how software is built, “vibe coding” has emerged as a paradigm-shifting approach where developers simply say what they want, and an agent builds it. Coined by Andrej Karpathy, the term reflects a shift from code-heavy workflows…

Early large language models (LLMs) excelled at generating coherent text; however, they struggled with tasks that required precise operations, such as arithmetic calculations or real-time data lookups. The emergence of tool-augmented agents has bridged this gap by endowing LLMs with…

Bridging Perception and Action in Robotics Multimodal Large Language Models (MLLMs) hold promise for enabling machines, such as robotic arms and legged robots, to perceive their surroundings, interpret scenarios, and take meaningful actions. The integration of such intelligence into physical…