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Made With ML is an educational resource by Anyscale that teaches how to responsibly design, develop, deploy and iterate on production machine learning applications. Its lessons cover design, data preparation, model training, development, testing, reproducibility and production.
Magic Patterns is an AI design tool for product teams that turns prompts into production-ready UI. It can use existing components and design rules to generate prototypes, and offers an infinite canvas with collaboration. A changelog describes PDF uploads and export options.
Magic.dev is an artificial intelligence company focused on developing safe artificial general intelligence (AGI) to accelerate progress on high-priority global problems. The company builds frontier code models intended to automate software engineering and AI research workflows. Its technical approach combines large-scale pre-training, domain-specific reinforcement learning, extended context windows, and increased inference-time compute. Magic.dev employs a small team of engineers and researchers working on fundamental AGI-related research problems.
An alternative to Supabase for AI Code editors and Vibe Coding tools.
manifest.build is an open source LLM router for AI agents and related tools. It connects AI agents and harnesses to any provider in seconds, supporting subscription, pay-per-token, local model and custom provider options. The tool offers features including model fallbacks, full request logging, autofix for broken provider calls, an LLM gateway for centralized provider management, and observability tools for tracking usage, reliability and costs. The software is open source, allowing users to self-host it or contribute to its development.
A repository of material for the exercise classes of a Big Data Analytics master course. Hands-on sessions cover Spark and its machine learning library, Map/Reduce implementations of linear regression and alternating least squares, streaming analytics with Recursive Least Squares, and image classification with convolutional neural networks. Material is provided as Jupyter notebooks.
Mastra is a TypeScript AI framework for building agents and AI applications. It includes built-in memory, tool support, MCP compatibility, and observability features to support development from prototype to production. The framework also offers workflow tools, agent factory functionality, cloud deployment servers, and an agent builder for evaluation and iteration.
Matt Harrison is a personal blog authored by Matt Harrison. The blog covers topics including Amazon book piracy, the Effective Pandas book, Python and data science courses, and general software development content.
A registry for discovering Model Context Protocol servers, with links to GitHub and API reference documentation. The site lists recently updated servers and provides a base URL for API access.
Melies is an AI studio for filmmaking that generates images, videos, movie posters, voices, music and sound effects. It offers 30+ AI models and a library of 150+ AI actors. A CLI is available for image, video and movie poster generation from the command line. Plans include monthly credits.
Metabob is a real-time, intelligent code analysis engine that runs in parallel with generative AI coding tools.
Metorial is an agentic infrastructure platform for AI-native companies. It provides a single unified platform for employees, AI agents, and AI clients to connect tools and skills, with built-in governance, access control, and tracing capabilities. The platform supports development of customer-facing agents, internal assistants, and scalable AI workforces.
mexopencv is a collection and development kit of MATLAB MEX functions for the OpenCV library. It provides MATLAB MEX functions interfacing a hundred OpenCV APIs and includes a C++ class converting between MATLAB and OpenCV data types. The package is intended for fast prototyping of OpenCV applications in MATLAB and for developing custom MEX functions.
Documentation for MiniMax, covering setup, features, and practical usage.
Overview of MiniMax AI models and their capabilities.
Self-updating documentation for startups, enterprises, and agents.
ML Resources supports machine learning models, deployment, inspection, datasets, or AI development workflows.
ml5.js is a JavaScript library providing access to machine learning algorithms and models in the browser, building on TensorFlow.js. It aims to make machine learning approachable for artists, creative coders and students, and is supported by code examples, tutorials and sample data sets. The project is described as currently in development.
MLBase (MLBase.jl) is a Julia package hosted in the JuliaStats/MLBase.jl GitHub repository, providing a collection of functions to support the development of machine learning algorithms. It includes accompanying resources and documentation for package users.
MLKit is a simple machine learning framework written in Swift. Currently it features algorithms dealing with regression, with planned expansion to classification, clustering, recommender systems and deep learning. The project is under active development and is not ready for use in commercial or personal projects, and can be installed via CocoaPods as MachineLearningKit.
MLRun is an open source MLOps platform for building and managing continuous machine learning applications across their lifecycle. It integrates with development and CI/CD environments to automate the delivery of production data, ML pipelines, and online applications.
An open standard for connecting AI models to external tools and data sources. MCP Registry opensource.
ModelScope supports machine learning models, deployment, inspection, datasets, or AI development workflows.
ModelScope Finetuning supports machine learning models, deployment, inspection, datasets, or AI development workflows.
