Directory
Models and machine learning covers model hubs, research papers, training guides and evaluation tools. The topic mixes practical resources with reference material, so you will find repositories that host downloadable models next to sites that explain how a technique works. Some entries are collections of papers or benchmarks, and others are tutorials that walk through fine-tuning or deployment. It pays to compare the model licence, the training data, the hardware needed and the date of the last update. The field moves quickly, so verify version notes on the source before you build on anything listed here.
VeritasGraph is an open-source knowledge graph and GraphRAG framework. It supports multi-hop reasoning, ontology-aware retrieval, and verifiable attribution over user data, using nodes, edges, RDF, and linked data. The project runs locally or in the cloud.
Some of the python libraries were cut-and-pasted from.
JSMLT - Machine learning toolkit with classification and clustering for Node.js; supports visualization (see ).
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.
vLLM is an open-source library for efficient serving of large language models. It provides an API server, supports offline and online inference, deployment options, and features such as automatic prefix caching, batch invariance, context extension, and speculative decoding. It can be used with GPUs, CPUs, and TPUs.
A comprehensive testing and evaluation framework for voice agents across language models, prompts, and agent personas.
Cloud Native Batch System (Project under CNCF).
Vondy is an AI-powered platform for building apps, websites, dashboards, and internal tools by describing what is needed. It integrates payments, authentication, and GitHub. The site also lists AI generators for images, video, text, and other assets. Paid plans include monthly credits and unlimited downloads.
Vortic provides machine-learning models, research, training resources, or evaluation tools.
Voyager is an LLM-powered embodied agent for Minecraft that explores, acquires skills, and makes discoveries without human intervention. It combines an automatic curriculum, a skill library of executable code, and an iterative prompting mechanism using environment feedback, execution errors, and self-verification. It interacts with GPT-4 through blackbox queries, avoiding model parameter fine-tuning.
We must stop AI replicating the problems of surveillance capitalism provides machine-learning models, research, training resources, or evaluation…
Web provides machine-learning models, research, training resources, or evaluation tools.
CAMEL-AI is an open-source community focused on finding scaling laws of agents for data generation, world simulation, and task automation. It builds multi-agent systems such as CAMEL, OWL, OASIS, and Loong, and conducts foundational research on multi-agent systems to improve reliability, interpretability, and robustness.
WebNN is a web standard that lets web apps and frameworks accelerate deep neural networks using on-device hardware such as GPUs, CPUs, or purpose-built AI accelerators. The introduction states that in-browser inference supports local media sources, keeps user data on-device, and needs no network after initial asset caching.
Weights & Biases is an open-source AI developer platform. It is used to train and fine-tune machine learning models, and manage models across the full lifecycle from experimentation to production. The associated wandb library is hosted in a public GitHub repository.
Weka is open-source machine learning software issued under the GNU General Public License. It runs in Java and is presented as a workbench for data mining, with download, documentation, courses and FAQ resources and datasets available on the project site.
Creates, rewrites or summarizes written content with language models.
WFGY is an open-source project focused on AI reasoning, retrieval-augmented generation, AI agents, and real-world workflows. It is developing the WFGY 5.0 Polaris Protocol, and currently includes components such as the Problem Map, Global Debug Card, 4.0 version, and CFV Easter Egg.
What ChatGPT and generative AI mean for science Nature provides machine-learning models, research, training resources, or evaluation tools.
What We Know About LLMs (Primer) provides machine-learning models, research, training resources, or evaluation tools.
A Gary Marcus article on his Marcus on AI Substack asking when the GenAI bubble might burst and how that could happen within 12 months. It cites survey findings on generative AI and security, notes $50B in and $3B out, and argues that hallucinations remain and that the industry is built on hype.
Where AI evolves from here provides machine-learning models, research, training resources, or evaluation tools.
Who is Afraid of Frankenstein? And of Generative AI?
A Hackaday article by Matthew Carlson explaining why LLaMa matters. It describes LLaMa as a transformer language model from Facebook/Meta research spanning 7 billion to 65 billion parameters, and notes that llama.cpp runs inference with 4-bit quantization.
