Welcome to part 4 of the AI content creation assistant series. In this series, we are building a full-stack generative AI application using TypeScript and Python. In the last part, we improved our GenAI workflows running in FastAPI by adding WebSockets and persistent storage. In this part, we will expand

Welcome to part 3 of this series. In the previous post, we adapted our Jupyter Notebook implementation from part 1 into a FastAPI server that can serve users. We learned how to set up FastAPI and configure settings, as well as how to trigger workflows from REST endpoints. Previously, we

Welcome back to another installment of creating an AI-powered content creation assistant. In the previous part of this series, we learned about Jupyter Notebooks, LlamaIndex Workflows, and OpenAI’s models for text and image generation. We built an example workflow that created a blog post, a social media post and

Welcome to the Journey of Creating a Content Creation Assistant! The demand for fresh, relevant, and visually engaging content has remained unchanged. Yet, for many content creators, managing the sheer volume and variety of content required across different platforms can be overwhelming. Enter the Content Creation Assistant — an innovative solution