Latest Tutorials

Explore how dynamic context in large language models enhances efficiency and accuracy by prioritizing relevant information for improved performance.

Explore how LLM-based error detection enhances automation, accuracy, and compliance across industries like banking, healthcare, and e-commerce.

Ever dreamed of quickly building full apps and websites from scratch without sweating over every line of code? Imagine just describing your idea — like “I want a to-do list app with a sleek login page and task sorting” — and watching AI whip up the code for you. Sounds like magic, right? That’s vibe coding , and it’s quickly gaining popularity. But here’s the part most people miss: Vibe coding isn’t just blindly prompting ChatGPT and hoping for the best. It’s a structured workflow — a clear, repeatable process that helps you build real, functional apps with the help of AI. The most successful vibe coders still think like builders. They plan their project, gather design and UX inspiration, use AI tools smartly, and refine the output to match their vision. This structured approach is what separates good AI-assisted apps from messy, half-baked ones.

Did you know that THE most common use case for ChatGPT —and LLMs in general—in 2025 is for therapy and companionship? (Yes, they have them linked as one category). I’m not making this stuff up or picking it from a random dude on X / Twitter . It’s from Harvard Business Review .

Explore the techniques and benefits of cross-lingual fine-tuning for transformer models, enhancing multilingual AI capabilities efficiently.