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  • 1
    OpenClaw-RL

    OpenClaw-RL

    Train any agents simply by 'talking'

    OpenClaw-RL is an open-source reinforcement learning framework designed to train and personalize AI agents built on the OpenClaw ecosystem. The project focuses on enabling agents to improve their behavior through interactive learning rather than relying solely on static prompts or predefined skills. One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process.
    Downloads: 6 This Week
    Last Update:
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  • 2
    Porcupine

    Porcupine

    On-device wake word detection powered by deep learning

    ...Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64). Scalable. It can detect multiple always-listening voice commands with no added runtime footprint. Self-service. Developers can train custom wake word models using Picovoice Console. Porcupine is the right product if you need to detect one or a few static (always-listening) voice commands. If you want to create voice experiences similar to Alexa or Google, see the Picovoice platform.
    Downloads: 9 This Week
    Last Update:
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  • 3
    OpenPipe

    OpenPipe

    Turn expensive prompts into cheap fine-tuned models

    ...It provides tools for training language models and agents using real-world feedback, enabling systems to learn from interactions and improve over time rather than relying solely on static prompts. One of its core components, the Agent Reinforcement Trainer, allows developers to train multi-step agents using reinforcement learning techniques such as GRPO, enhancing their ability to perform complex, sequential tasks. OpenPipe emphasizes cost reduction by enabling organizations to distill high-cost inference workflows into smaller, specialized models that can run more efficiently at scale.
    Downloads: 0 This Week
    Last Update:
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  • 4
    diff2html

    diff2html

    Pretty diff to html javascript library (diff2html)

    ...Similar lines are paired, allowing for easier change tracking. We work hard to make sure you can have your diffs in a simple and flexible way. The AI community building the future. Build, train and deploy state of the art models powered by the reference open source in natural language processing. Wrapper and helper adding syntax highlight, synchronized scroll, and other nice features. You can use it without syntax highlight or by passing your own implementation with the languages you prefer. Diff2Html can be used in various ways as listed in the distributions section.
    Downloads: 1 This Week
    Last Update:
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    Go from Code to Production URL in Seconds

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  • 5
    onepoint

    onepoint

    Assistant tool that integrates coding, writing, and reading functions

    ...Its initial goal was to develop a smart floating window similar to Apple's intelligent assistant that does not take up desktop space or system performance and can be quickly accessed through global hotkeys for user convenience. With ChatGPT technology, users can continuously train onepoint to generate and reconstruct content with greater accuracy (onpoint), thereby improving efficiency. Onepoint currently supports various editing scenarios such as VSCode, Pages, Microsoft Word, Email etc, as well as reading scenarios like Safari and Chrome, achieving true full-scene intelligent coverage.
    Downloads: 0 This Week
    Last Update:
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  • 6
    TensorFlow.js models

    TensorFlow.js models

    Pretrained models for TensorFlow.js

    ...You can run the unit tests for any of the models by running "yarn test" inside a directory. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Build and train models directly in JavaScript using flexible and intuitive APIs. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.
    Downloads: 0 This Week
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  • 7
    Cloud Annotations

    Cloud Annotations

    A fast, easy and collaborative open source image annotation tool

    ...Creating image annotations for your project is easy inside CV Studio. For classification projects, just select and label your images. For object detection, use the integrated tool to highlight target elements in your images. Train your model using the image annotations from the previous step. Practice using cutting-edge tools like Jupyter Notebook, Watson Machine Learning, Elyra, and more.
    Downloads: 4 This Week
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  • 8
    Pipcook

    Pipcook

    Machine learning platform for Web developers

    A JavaScript application framework for machine learning and its engineering. With the mission of enabling JavaScript engineers to utilize the power of machine learning without any prerequisites and the vision to lead the front-end technical field to intelligence. Pipcook is to become the JavaScript application framework for the cross-cutting area of machine learning and front-end interaction. We are truly to design Pipcook's API for front-end and machine learning applications, and focusing...
    Downloads: 1 This Week
    Last Update:
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  • 9
    node-markov-generator

    node-markov-generator

    Generates simple sentences based on given text corpus

    ...And then, for any given word it tries to predict what the next one might be. Here you create an instance of TextGenerator passing an array of strings to it - it represents your text corpus which will be used to "train" the generator. The more strings/sentences you pass, the more diverse results you get, so you'd better pass like hundreds of them, or even more! If you have your texts in an external file, you can pass the path to it as an argument for TextGenerator's constructor.
    Downloads: 0 This Week
    Last Update:
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    Gemini 3 and 200+ AI Models on One Platform

    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

    Build generative AI apps with Vertex AI. Switch between models without switching platforms.
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  • 10
    Facets

    Facets

    Visualizations for machine learning datasets

    ...Overview gives users a quick understanding of the distribution of values across the features of their dataset(s). Uncover several uncommon and common issues such as unexpected feature values, missing feature values for a large number of observation, training/serving skew and train/test/validation set skew.
    Downloads: 0 This Week
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