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TRAFFICVIS is an interactive visualization tool that leverages advertisement metadata to quickly identify and label suspicious clusters of ads as potential evidence of human trafficking, offering a new approach to detecting and combating the issue with a meta-clustering technique and an efficient and streamlined labeling process.

tRPC is a lightweight library for remotely calling backend functions on the client side. It acts like a personal guide, providing autocompletion and best practices to help developers create typesafe and efficient APIs, in a closer relationship between frontend and backend, and without any code generation or runtime bloat.

Deep dive into Stable Diffusion and the components that power it's image generation

This article provides a tutorial on how to deploy a pre-trained machine learning model for image classification (classifying images of birds, cats, and dogs) using TensorFlow Serving. The steps include installing TensorFlow Serving, loading a pre-trained model, saving it in the format required by TensorFlow Serving, spinning up a web server using TensorFlow Serving to accept HTTP requests, interacting with the model via a REST API, and learning about model versioning. The tutorial is written in Python and uses TensorFlow and its associated packages. The tutorial also uses Google Colab as a platform to follow along.

Learn how Docker and Tensorflow serving can be leveraged to serve your Machine Learning models for inference.

Learn how to manage state in React for better performance and scalability. We'll cover topics such as lifting state up, component composition, and React's Context API.

React components are functions which return JSX, and there are two important concepts to understand when working with React: props and state. Props are the input arguments of a React function component, while state is data that changes over the lifetime of a React component instance. These concepts are essential to understand in order to write effective React components.