What is ort

The ort project is an unofficial Rust wrapper for ONNX Runtime 1.19, built on the foundation of the now inactive onnxruntime-rs. It leverages ONNX Runtime to accelerate machine learning inference and training on both CPU and GPU. This wrapper provides a robust interface for integrating ML capabilities into Rust applications, making it a valuable tool for developers looking to enhance their projects with efficient ML operations. With extensive documentation, active support channels, and adoption by several notable projects like Twitter, Bloop, and Supabase, ort is definitely worth exploring for anyone interested in integrating ML into their Rust projects.

What is Fortuna

Fortuna is a library designed for uncertainty quantification, a crucial aspect in applications where critical decisions are made. It helps in the proper estimation of predictive uncertainty, allowing users to assess the reliability of model predictions and trigger human intervention when necessary. By providing tools to quantify and manage uncertainty, Fortuna enhances the trustworthiness and accuracy of predictive models, making it a valuable resource for developers and researchers in various fields. Exploring Fortuna can significantly improve the robustness and reliability of your predictive models.

What is Tutor-GPT

Tutor-GPT, developed by Plastic Labs, is a LangChain LLM application designed to dynamically assess and address your learning needs. It updates its own prompts to provide the most effective support, making it more than just a literacy tutor but an expansive learning companion. Inspired by theory of mind experiments, Tutor-GPT offers a personalized learning experience. You can try the hosted version, called Bloom, at chat.bloombot.ai or join the Discord to use it for free. Alternatively, you can run your own instance of the bot by following the provided instructions. The project is structured into multiple modules for different clients, with the core logic and prompting located in the agent/ directory.

Project Overview

Introduction

The Neon Image Search project is an innovative image search application built using NeonDB and Vertex AI. This app converts data images into high-dimensional vectors, known as embeddings, using Google Cloud’s Vertex AI. These embeddings are then stored in NeonDB, a database that supports vector search through the PGVector extension.

Project Overview

Introduction

The torch-log-wmse project provides a PyTorch implementation of the logWMSE (logarithm of frequency-weighted Mean Squared Error) metric, originally developed by Iver Jordal of Nomono. This metric and loss function are designed to evaluate and improve the quality of audio signals, particularly addressing the limitations of traditional audio metrics, such as the inability to handle digital silence targets. It can be used both as a metric to assess audio quality and as a loss function for training audio separation and denoising models. Installing it is straightforward with pip install torch-log-wmse, making it a valuable tool for audio processing tasks.

Project Overview

Introduction

PromptML, or Prompt Markup Language, is a simple and elegant markup language designed to define AI prompts in a structured and deterministic way. It allows prompt engineers to break down complex prompts into clear sections such as context, objective, instructions, and metadata, making the information explicit and manageable. This Domain Specific Language (DSL) enables AI agents to automatically generate prompts for other AI systems. With its straightforward syntax using @ section annotations and # comments, PromptML simplifies the process of creating and managing AI prompts, making it a valuable tool for those working in the field of AI prompt engineering.