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A Collection of Awesome Generative AI Applications.

The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together.

Interactive timeline visualization made by Information Is Beautiful.

Creates, rewrites or summarizes written content with language models.

Fast-stable-diffusion, +25-50% speed increase + memory efficient + DreamBooth.

Agent techniques to augment your LLM and push it beyond its limits.

Creates, rewrites or summarizes written content with language models.

Create. Use. Share. ChatGPT prompts.

Works with speech, voice, music or other audio using machine-learning models.

Tracking AI supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Text Retrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.

Tribuo is a Java ML library for multi-class classification, regression, clustering, anomaly detection and multi-label classification.

A collaborative AI workspace, built on your company context. Build and orchestrate agents right alongside your team.

TrueFoundry supports deploying, serving, monitoring, or operating AI and machine-learning systems.

TruLens instruments your AI agent with OpenTelemetry, scores every step with benchmarked LLM judges, and tells you which version to ship.

Python library for automatic extraction of relevant features from time series.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

A Java implementation of Twitter's text processing library.

Discord invite for joining a related community, support server, updates, or project discussion.

Change just one letter and get github repo context to use as context for your LLM.

Any URL to clean markdown for LLMs. Free API, no signup required. Strips JS/CSS and outputs LLM-ready content.

Works with speech, voice, music or other audio using machine-learning models.

Minimize LLM token complexity to save API costs and model computations.