Ignite is a high-level library for training and evaluating neural networks in PyTorch. It provides a simple engine and event system, out-of-the-box metrics to evaluate models, and built-in handlers for composing training pipelines and saving artifacts. The library aims to require less code than pure PyTorch while keeping maximum control, with an extensible API for metrics and experiment managers.
A personal notes site with writings on topics the author is thinking about, learning, and working on. It includes an upcoming entry about building Atlas, described as an AI tool for thought, and an archive of earlier posts.
Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
Lemonade Server is a lightweight, open-source local LLM server for running and managing multiple AI applications on a local machine. It provides a simple CLI for managing applications and supports various LLMs, and offers endpoints compatible with OpenAI, Ollama, Anthropic and others, plus a local-first API and MCP gateway.
Architecture Helper is an AI-powered architecture design analysis tool. It can analyze building photos for styles, history and design details, and re-render a photo of a home or room in a chosen style. It also offers city architecture guides. The first analysis and the first restyle are free, with no account needed.
Neuroph is a lightweight, open source Java neural network framework released under the Apache 2.0 license. It provides a Java library and a GUI editor, Neuroph Studio, based on the NetBeans Platform, for creating, training and saving neural networks. It supports architectures including Perceptron, Multi Layer Perceptron, Hopfield, Kohonen and RBF networks, and both supervised and unsupervised learning rules.
VLFeat is an open-source library of computer vision algorithms focused on image understanding and local feature extraction and matching. Algorithms include SIFT, MSER, Fisher Vector, VLAD, k-means, SLIC superpixels, and SVM training. It is written in C with MATLAB interfaces and supports Windows, Mac OS X, and Linux.
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A personal notes site with writings on topics the author is thinking about, learning, and working on. It includes an upcoming entry about building Atlas, described as an AI tool for thought, and an archive of earlier posts.
Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
Lemonade Server is a lightweight, open-source local LLM server for running and managing multiple AI applications on a local machine. It provides a simple CLI for managing applications and supports various LLMs, and offers endpoints compatible with OpenAI, Ollama, Anthropic and others, plus a local-first API and MCP gateway.
Architecture Helper is an AI-powered architecture design analysis tool. It can analyze building photos for styles, history and design details, and re-render a photo of a home or room in a chosen style. It also offers city architecture guides. The first analysis and the first restyle are free, with no account needed.
Neuroph is a lightweight, open source Java neural network framework released under the Apache 2.0 license. It provides a Java library and a GUI editor, Neuroph Studio, based on the NetBeans Platform, for creating, training and saving neural networks. It supports architectures including Perceptron, Multi Layer Perceptron, Hopfield, Kohonen and RBF networks, and both supervised and unsupervised learning rules.
VLFeat is an open-source library of computer vision algorithms focused on image understanding and local feature extraction and matching. Algorithms include SIFT, MSER, Fisher Vector, VLAD, k-means, SLIC superpixels, and SVM training. It is written in C with MATLAB interfaces and supports Windows, Mac OS X, and Linux.
