12 projects for "python ai" with 2 filters applied:

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    Protect your business with AI policies and data loss prevention in the browser

    Make AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
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  • Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

    The database for AI-powered applications.

    MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
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  • 1
    Ollama Python

    Ollama Python

    Ollama Python library

    ...This tool is ideal for those building AI-driven apps with local model deployment.
    Downloads: 6 This Week
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  • 2
    Mistral Inference

    Mistral Inference

    Official inference library for Mistral models

    Open and portable generative AI for devs and businesses. We release open-weight models for everyone to customize and deploy where they want it. Our super-efficient model Mistral Nemo is available under Apache 2.0, while Mistral Large 2 is available through both a free non-commercial license, and a commercial license.
    Downloads: 1 This Week
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  • 3
    GenAI Processors

    GenAI Processors

    GenAI Processors is a lightweight Python library

    GenAI Processors is a lightweight Python library for building modular, asynchronous, and composable AI pipelines around Gemini. Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which makes sophisticated agent behaviors easy to express with simple operators. ...
    Downloads: 0 This Week
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  • 4
    EKS Best Practices

    EKS Best Practices

    A best practices guide for day 2 operations

    The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open to...
    Downloads: 3 This Week
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    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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  • 5
    Awesome Free ChatGPT

    Awesome Free ChatGPT

    List of free ChatGPT mirror sites, continuously updated

    This is a curated directory of freely accessible ChatGPT-style services and mirror sites that offer AI chatbot interfaces without login or payment requirements. Resources often support multiple models like GPT-4, Claude, Gemini, and more. Data collected from multiple independent sites with descriptions and tags. Includes services with image upload and drawing capabilities. Aggregates free, no-login-required ChatGPT-like web services. Continually updated mirror list to maintain availability.
    Downloads: 0 This Week
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  • 6
    Courses (Anthropic)

    Courses (Anthropic)

    Anthropic's educational courses

    Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured...
    Downloads: 1 This Week
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  • 7
    FlashMLA

    FlashMLA

    FlashMLA: Efficient Multi-head Latent Attention Kernels

    FlashMLA is a high-performance decoding kernel library designed especially for Multi-Head Latent Attention (MLA) workloads, targeting NVIDIA Hopper GPU architectures. It provides optimized kernels for MLA decoding, including support for variable-length sequences, helping reduce latency and increase throughput in model inference systems using that attention style. The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to...
    Downloads: 0 This Week
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  • 8
    CoreNet

    CoreNet

    CoreNet: A library for training deep neural networks

    CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed...
    Downloads: 0 This Week
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  • 9
    DeepMind Research

    DeepMind Research

    Implementations and code to accompany DeepMind publications

    This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics,...
    Downloads: 0 This Week
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  • Total Network Visibility for Network Engineers and IT Managers Icon
    Total Network Visibility for Network Engineers and IT Managers

    Network monitoring and troubleshooting is hard. TotalView makes it easy.

    This means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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  • 10
    Pythonidae

    Pythonidae

    Curated decibans of scientific programming resources in Python

    Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities,...
    Downloads: 3 This Week
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  • 11
    PyTorchVideo

    PyTorchVideo

    A deep learning library for video understanding research

    PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research...
    Downloads: 0 This Week
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  • 12
    Functional, Data Science Intro To Python

    Functional, Data Science Intro To Python

    [tutorial]A functional, Data Science focused introduction to Python

    The first section is an intentionally brief, functional, data science-centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing.
    Downloads: 0 This Week
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