47,133 repositories indexed
1–20 of 20 · Most stars
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Learn AI Engineering! Learn it. Build it. Ship it for others.
The world's simplest facial recognition api for Python and the command line
DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/.
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
A curated list for awesome kubernetes sources :ship::tada:
Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
Open Source Marketplace For Developer Tools
:zap: A new cd command that helps you navigate faster by learning your habits.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Jump helps you navigate faster by learning your habits. ✌️
ilastik-shell, applets, and workflows to string them together.
Walk through a diff the way a senior engineer reads it. A read-only TUI that turns a git change into a guided tour.
A command-line utility for quick math.
A comfortable CLI tool to use ChatGPT service🔥
Go's answer to pandas + scikit-learn: a batteries-included data analysis library with dataframes, statistics, machine learning, quant, and GPU acceleration. Run Python with seamless two-way variable sharing, or explore data straight from the Insyra CLI.
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
the goal of this kata is to learn to work incrementally by implementing a cli arguments parser.
Set up and maintain a machine learning project in Tensorflow with a single script.