Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 39
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Showing 2237 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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Securden Password VaultStore, manage, and share passwords, files, SSH keys, and DevOps secrets among IT teams. Enforce password security best practices. Ensure compliance with industry standards using comprehensive audit trails.
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Monitor production, track downtime and improve OEE.Evocon is a visual and user-friendly OEE software that helps manufacturing companies improve productivity and remove waste as they become better.
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1
Browserbase MCP Server
Allow LLMs to control a browser with Browserbase and Stagehand
Browserbase MCP Server is a server implementation of the Model Context Protocol (MCP) that enables large language models to interact with web browsers programmatically through cloud-based automation. The project provides a standardized interface for connecting AI systems to real-world web environments, allowing them to navigate pages, extract structured data, and perform user-like actions such as clicking, typing, and form submission. It leverages Browserbase infrastructure along with Stagehand to deliver high-performance browser automation with improved speed and efficiency through caching and optimized execution pipelines. The system supports multiple AI models and integrates seamlessly into agent workflows, making it suitable for applications such as web scraping, testing, and intelligent automation. ... -
2
Conversational Health Agents (CHA)
A Personalized LLM-powered Agent Frameworks
CHA, or Conversational Health Agents, is an open-source framework designed to build intelligent healthcare assistants powered by large language models and external data sources. The system enables developers to create personalized AI agents that can interact with users through natural language while performing multi-step reasoning and task execution. It integrates orchestration capabilities that allow the agent to gather information from APIs, knowledge bases, and external services in order to generate more accurate and context-aware responses. ... -
3
TimeMixer
Decomposable Multiscale Mixing for Time Series Forecasting
TimeMixer is a deep learning framework designed for advanced time series forecasting and analysis using a multiscale neural architecture. The model focuses on decomposing time series data into multiple temporal scales in order to capture both short-term seasonal patterns and long-term trends. Instead of relying on traditional recurrent or transformer-based architectures, TimeMixer is implemented as a fully multilayer perceptron–based model that performs temporal mixing across different resolutions of the data. The architecture introduces specialized components such as Past-Decomposable-Mixing blocks, which extract information from historical sequences at different scales, and Future-Multipredictor-Mixing modules that combine predictions from multiple forecasting paths. ... -
4
Text2Code for Jupyter notebook
A proof-of-concept jupyter extension which converts english queries
Text2Code for Jupyter notebook project is a proof-of-concept extension for Jupyter Notebook that allows users to generate Python code directly from natural language queries written in English. The tool is designed to simplify data analysis workflows by enabling users to describe their intended operation in plain language instead of manually writing code. When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. The system uses natural language processing techniques to identify the intent of the query, extract relevant variables, and map the request to predefined code templates. ... -
Digital business card + lead capture + contact enrichmentShare digital business cards, capture leads, and enrich validated contact info - at events, in the field, and beyond. Powered by AI and our proprietary data engine, Popl drives growth for companies around the world, turning every handshake into an opportunity.
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5
TypeChat
Library for building type-safe natural language interfaces with LLMs
...Instead of writing complex prompts, developers define types that represent the intents supported by their applications. It then uses those type definitions to construct prompts for language models and translate user input into structured data that follows the defined schema. -
6
machine learning tutorials
machine learning tutorials (mainly in Python3)
machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis. ... -
7
face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
...The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. The repository supports multiple neural network backbones such as ResNet, DenseNet, MobileNet, and ShuffleNet, enabling experimentation with different architectures depending on performance requirements. It also implements a wide range of loss functions commonly used in face recognition research, including ArcFace, CosFace, Triplet loss, and Softmax variants. ... -
8
AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
...Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. It also promotes disciplined learning routines and project-based practice so learners can develop practical experience and build deployable solutions. -
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
...The repository also highlights important machine learning concepts such as feature engineering, cross-validation strategies, ensemble modeling, and post-processing methods commonly used in winning solutions. Because the content is organized by competition categories such as computer vision, natural language processing, tabular data, and time-series forecasting, users can explore techniques relevant to specific problem types. -
New Relic provides the most powerful cloud-based observability platform built to help companies create more perfect software.Correlate issues across your stack. Debug and collaborate from your IDE. AI assistance at every step. All in one connected experience - not a maze of charts.
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10
FlexLLMGen
Running large language models on a single GPU
FlexLLMGen is an open-source inference engine designed to run large language models efficiently on limited hardware resources such as a single GPU. The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. The architecture distributes computation and memory usage across the GPU, CPU, and disk in order to maximize the number of tokens processed during inference. ... -
11
MyScaleDB
A @ClickHouse fork that supports high-performance vector search
...The system is built on top of the ClickHouse database engine and extends it with specialized indexing and search capabilities optimized for vector embeddings. This design allows developers to store structured data, unstructured text, and high-dimensional vector embeddings within a single database platform. MyScaleDB enables developers to perform vector similarity searches using standard SQL syntax, eliminating the need to learn specialized vector database query languages. The database is optimized for high performance and scalability, allowing it to handle extremely large datasets and high query loads typical of production AI applications. -
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UCCL
UCCL is an efficient communication library for GPUs
UCCL is a high-performance GPU communication library designed to support distributed machine learning workloads and large-scale AI systems. The library focuses on enabling efficient data transfer and collective communication between GPUs during training and inference processes. It supports a variety of communication patterns including collective operations such as all-reduce as well as peer-to-peer transfers that are commonly used in modern machine learning architectures. UCCL is designed to work with heterogeneous hardware environments, allowing GPUs from different vendors and network interfaces to communicate efficiently without vendor lock-in. ... -
13
Korvus
Korvus is a search SDK that unifies the entire RAG pipeline
...By leveraging PostgresML and vector extensions such as pgvector, Korvus eliminates the need for external microservices typically used for AI search architectures, reducing both system complexity and latency. The architecture enables machine learning operations to occur directly in the database, minimizing data transfer between services and improving overall performance for large datasets. -
14
Daily Interview
Datawhale members have compiled a book covering machine learning
daily-interview is an open-source educational repository designed to help software engineers prepare for technical interviews through daily practice questions and curated learning materials. The project collects a wide range of interview questions related to algorithms, data structures, system design, and core computer science topics commonly tested by technology companies. The repository is organized in a structured format that encourages developers to practice solving problems regularly while building a deeper understanding of underlying concepts. Many of the problems include explanations, references, or links to additional learning resources that help users study relevant theory and improve problem-solving skills. ... -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
DeepSearcher is an open-source “deep research” style system that combines retrieval with evaluation and reasoning to answer complex questions using private or enterprise data. It is designed around the idea that high-quality answers require more than top-k retrieval, so it orchestrates multi-step search, evidence collection, and synthesis into a comprehensive response. The project integrates with vector databases (including Milvus and related options) so organizations can index internal documents and query them with semantic retrieval. ... -
16
Secure OpenClaw
A personal 24x7 AI assistant like OpenClaw
...It leverages Claude and other models to interpret messages and is built to manage persistent memory, scheduled reminders, and integration with hundreds of third-party apps to automate workflows without leaving your chat. The design emphasizes security and autonomy by allowing you to maintain ownership of your data and run the system within your trusted environment, while still harnessing advanced AI capabilities. -
17
VGGT
[CVPR 2025 Best Paper Award] VGGT
...The repo provides inference pipelines to estimate geometry from monocular inputs, stereo pairs, or brief sequences, together with evaluation harnesses for common geometry benchmarks. Training utilities highlight data curation and augmentations that preserve geometric cues while improving generalization across scenes and cameras. -
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Mistral Finetune
Memory-efficient and performant finetuning of Mistral's models
...It builds on techniques like LoRA (Low-Rank Adaptation) to allow customizing models without full parameter updates, which reduces GPU memory footprint and training cost. The repo includes utilities for data preprocessing (e.g. reformat_data.py), validation scripts, and example YAML configs for training variants like 7B base or instruct models. It supports function-calling style datasets (via "messages" keys) as well as plain text formats, with guidelines on formatting, tokenization, and vocabulary extension (e.g. extending vocab to 32768 for some models) before finetuning. ... -
19
Void Editor
Open source AI IDE and Cursor alternative
...Designed as a fully transparent and privacy-focused alternative to Cursor or GitHub Copilot, it lets you use AI models locally or via APIs (OpenAI, Claude, Gemini, Ollama, etc.)—without routing data through proprietary servers. Developed by YC-backed startup Glass Devtools, it supports traditional coding features inherited from VS Code, enhanced with in-editor LLM capabilities—autocomplete, inline quick edits, and chat agents, all while giving developers total prompt and data control. -
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Kubeflow pipelines
Machine Learning Pipelines for Kubeflow
...A pipeline component is a self-contained set of user code, packaged as a Docker image, that performs one step in the pipeline. For example, a component can be responsible for data preprocessing, data transformation, model training, and so on. -
21
TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
...TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. TFP is open source and available on GitHub. ... -
22
Interpretable machine learning
Book about interpretable machine learning
...As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. ... -
23
DeepCTR
Package of deep-learning based CTR models
...You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique. -
24
PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
...For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. AI analysis runs read-only, and all suggestions must be reviewed before being applied. AI File Sorter can run fully offline using local models like Mistral or LLaMA, so files and metadata stay on your device unless you configure a remote endpoint.
