Search Results for "linux ai"
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Showing 6 open source projects for "linux ai"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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AI Researcher
An autonomous AI researcher
AI Researcher is an experimental open-source project that demonstrates how multiple AI agents can collaborate to conduct complex research tasks from start to finish with minimal human intervention. It orchestrates agents that can generate research questions, perform literature reviews, execute experiments, analyze results, and synthesize findings into structured outputs like reports or code. Each agent operates with clear roles — such as researcher, analyst, and summarizer — and they... -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run... -
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Local Deep Research
95% on SimpleQA (e.g. Qwen3.6-27B on a 3090)
Local Deep Research is an open-source AI-powered research assistant designed to perform deep, iterative investigations by combining large language models with multi-source search capabilities. It runs locally, giving users full control over their data, privacy, and infrastructure while supporting both local and cloud-based LLMs. The system breaks down complex queries into smaller steps, performs parallel searches across web and academic sources, and generates structured, citation-backed... -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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PyTorch GAN Zoo
A mix of GAN implementations including progressive growing
PyTorch GAN Zoo is a comprehensive open research toolbox designed for experimenting with and developing Generative Adversarial Networks (GANs) using PyTorch. The project provides modular implementations of popular GAN architectures, including Progressive Growing of GANs (PGAN), DCGAN, and an experimental StyleGAN version. It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD,... -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging...
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