Search Results for "ekho-data" - Page 28
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Showing 3593 open source projects for "ekho-data"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Qlib
Qlib is an AI-oriented quantitative investment platform
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. With Qlib, users can easily try their ideas to create better Quant investment strategies. At the module level, Qlib is a platform that consists of... -
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kg-gen
Knowledge Graph Generation from Any Text
kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing... -
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Memobase
Fast backend for long-term AI user memory via structured profiles
Memobase is an open source backend system that enables long-term user memory functionality for AI applications by capturing and structuring information about users across interactions. Its design centers on creating user profiles and recording event timelines, allowing AI systems to remember, understand, and evolve in their behaviour toward individual users over time. Instead of relying purely on traditional embedding-based retrieval or RAG systems, Memobase uses profile and timeline... -
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pagodo
Automate Google Hacking Database scraping and searching
...To start off, pagodo.py needs a list of all the current Google dorks. The repo contains a dorks/ directory with the current dorks when the ghdb_scraper.py was last run. It's advised to run ghdb_scraper.py to get the freshest data before running pagodo.py. -
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Hindsight
Hindsight: Agent Memory That Learns
...Instead of relying solely on vector similarity or basic retrieval techniques, Hindsight organizes information into distinct categories such as facts, experiences, beliefs, and observations, allowing agents to differentiate between raw data and inferred knowledge. The system operates through three core mechanisms—retain, recall, and reflect—which respectively handle storing information, retrieving relevant context, and generating new insights based on accumulated experience. -
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Python Code Tutorials
The Python Code Tutorials
Python Code Tutorials is a large educational repository that aggregates programming tutorials from the “The Python Code” website into a structured collection of Python projects and learning materials. The repository covers a wide range of programming topics including cybersecurity, networking, web scraping, machine learning, GUI development, and automation scripts. Each tutorial typically includes complete Python code examples and explanations that demonstrate how to build real tools and... -
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LuxTTS
A high-quality rapid TTS voice cloning model
...It implements a lightweight architecture based on ZipVoice and optimized sampling techniques so that it can generate speech at speeds up to roughly 150 times real-time on a single GPU and faster than real-time on CPU, all while producing audio at high fidelity with 48 kHz quality. The project supports zero-shot voice cloning, meaning it can adapt to a reference speaker’s voice with minimal example data, enabling realistic and personalized synthetic speech. Intended for developers, hobbyists, and creators, the repository includes installation instructions, usage examples, and Python APIs that make it feasible to integrate the model in local workflows, web demos, or production systems. Its design emphasizes efficiency and practicality, fitting within modest GPU memory footprints. -
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
...NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. The framework builds on PyTorch Lightning–style modular abstractions, so training scripts are composed from reusable components for data loading, models, optimizers, and schedulers, which simplifies experimentation and adaptation. NeMo is designed to scale: with tools like NeMo-Run, users can orchestrate large-scale experiments across thousands of GPUs. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
DINOv2 is a self-supervised vision learning framework that produces strong, general-purpose image representations without using human labels. It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. ... -
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BlenderProc
Blender pipeline for photorealistic training image generation
A procedural Blender pipeline for photorealistic training image generation. BlenderProc has to be run inside the blender python environment, as only there we can access the blender API. Therefore, instead of running your script with the usual python interpreter, the command line interface of BlenderProc has to be used. In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance,... -
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LingBot-Video
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
...It is designed to connect video synthesis with physical-world understanding instead of generating only visually appealing clips. The project includes dense and MoE model variants for text-to-image, text-to-video, and text-image-to-video workflows. Its training combines large-scale web video data with more than 70,000 hours of embodied data. A multi-reward system emphasizes aesthetics, physical rationality, and task completion. The repository includes models, inference code, prompt rewriting tools, refiner workflows, and single-GPU or multi-GPU scripts. -
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...The models support translation across 33 languages and are designed to follow detailed translation instructions in multiple languages. They can handle tasks involving terminology, style, personalization, delimiters, structured data, and background-aware translation. The repository also provides model links, inference guidance, deployment examples, training documentation, and quantization resources. Overall, Hy-MT2 is useful for developers and researchers who need flexible, instruction-following machine translation models for production, evaluation, or experimentation.
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Hiring Agent
AI agent to evaluate and score resumes
...It reads a resume PDF and converts the content into Markdown-like text. It then uses a local or hosted language model to extract structured candidate information into sectioned JSON. The system can enrich that resume data with GitHub profile and repository signals when a profile is available. After the data is collected, it produces an explainable evaluation with category scores, supporting evidence, bonus points, and deductions. It can run locally with Ollama or use Google Gemini, which makes it flexible for teams that want either private local processing or hosted model access. -
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Alpamayo 1
Bridging Reasoning and Action Prediction
...The model is designed as a foundational component rather than a complete driving stack, allowing developers to build custom autonomous vehicle applications on top of it. It incorporates vision-language-action modeling, enabling it to process sensor data and contextual information simultaneously. Alpamayo supports tasks such as trajectory prediction, auto-labeling, and reasoning-based decision making. The system is optimized for high-performance GPU environments and is intended primarily for experimentation and benchmarking. Overall, it represents an advanced step toward integrating reasoning into autonomous driving pipelines. -
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Cal.diy
Scheduling infrastructure for absolutely everyone
...Built with modern web technologies such as Next.js, React, and PostgreSQL, it offers a robust foundation for building custom scheduling workflows. The system is designed for developers and advanced users who prefer to host and secure their own data rather than depend on third-party providers. It also supports integration with external tools through APIs and authentication systems, allowing flexible customization. -
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autoresearch-win-rtx
AI agents running research on single-GPU nanochat training
...It adapts the original autoresearch concept to a Windows environment, enabling users to perform iterative machine learning optimization without requiring specialized Linux or data center setups. The system revolves around a small set of core files, including a training script that is continuously modified by an AI agent, along with supporting utilities for data preparation and evaluation. Experiments are executed within a fixed time budget, ensuring consistent benchmarking across iterations and allowing the agent to focus on incremental improvements. ... -
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OpenRecall
OpenRecall is a fully open-source, privacy-first alternative
...It works by taking periodic screenshots of a user’s screen and applying local AI processing, including OCR and semantic analysis, to extract and structure information from both text and images. This data is then indexed into a searchable database, allowing users to retrieve past information quickly using natural language queries. Unlike proprietary alternatives, OpenRecall operates entirely locally, ensuring that all captured data remains on the user’s device and is never transmitted to external servers. The platform supports multiple operating systems, including Windows, macOS, and Linux, making it widely accessible across different environments. -
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LinkdAPI Python
Powerful, lightweight B2B Data API for enriching professional profiles
LinkdAPI Python is a software development kit designed to simplify integration with the LinkdAPI platform, enabling developers to connect applications with external services through a unified API layer. It provides structured tools, libraries, and documentation that streamline authentication, request handling, and data exchange between systems. The SDK is built to reduce the complexity of working with multiple APIs by offering standardized methods for interacting with different endpoints. It supports rapid development by abstracting low-level implementation details, allowing developers to focus on building features rather than managing connectivity. ... -
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NeMo Retriever Library
Document content and metadata extraction microservice
...It processes various document types by splitting them into components such as text, tables, charts, and images, and then applies OCR and contextual analysis to convert them into structured data formats. The system is built on NVIDIA NIM microservices, enabling high-performance parallel processing and efficient handling of large datasets. It supports multiple extraction strategies for different document formats, balancing accuracy and throughput depending on the use case. Additionally, it can generate embeddings for extracted content and integrate with vector databases like Milvus, making it well-suited for retrieval-augmented generation pipelines. -
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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. -
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pywinauto
Windows GUI Automation with Python (based on text properties)
pywinauto is a set of Python modules to automate the Microsoft Windows GUI. At its simplest it allows you to send mouse and keyboard actions to Windows dialogs and controls, but it has support for more complex actions like getting text data. -
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PyExcelerate
Accelerated Excel XLSX Writing Library for Python 2/3
Accelerated Excel XLSX writing library for Python. PyExcelerate is a Python for writing Excel-compatible XLSX spreadsheet files, with an emphasis on speed. -
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bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
Make drawing and labeling bounding boxes easy as cake. This package helps users draw bounding boxes around objects, without doing the clumsy math that you'd need to do for positioning the labels. It also has a few different types of visualizations you can use for labeling objects after identifying them. There are optional functions that can draw multiple bounding boxes and/or write multiple labels on the same image, but it is advisable to use the above functions in a loop in order to have... -
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redshift_connector
Amazon Redshift connector for Python
redshift_connector is the Amazon Redshift connector for Python. Easy integration with pandas and numpy, as well as support for numerous Amazon Redshift-specific features help you get the most out of your data. redshift_connector integrates with various open-source projects to provide an interface to Amazon Redshift. Please open an issue with our project to request new integrations or get support for a redshift_connector issue seen in an existing integration. Following the DB-API specification, autocommit is off by default. It can be turned on by using the autocommit property of the connection. ... -
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SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
...After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. A SageMaker Model contains references to a model.tar.gz file in S3 containing serialized model data, and a Docker image used to serve predictions with that model. A Batch Transform job runs an offline-inference job using your TensorFlow Serving model. Input data in S3 is converted to HTTP requests, and responses are saved to an output bucket in S3.