Search Results for "frameworks" - Page 14
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Showing 1053 open source projects for "frameworks"
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Determined
Determined, deep learning training platform
The fastest and easiest way to build deep learning models. Distributed training without changing your model code. Determined takes care of provisioning machines, networking, data loading, and fault tolerance. Build more accurate models faster with scalable hyperparameter search, seamlessly orchestrated by Determined. Use state-of-the-art algorithms and explore results with our hyperparameter search visualizations. Interpret your experiment results using the Determined UI and TensorBoard, and... -
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
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Paseo
Orchestrate coding agents remotely from your phone, desktop and CLI
...Paseo is designed to integrate with modern frontend and backend ecosystems, supporting flexible deployment and customization. Its approach prioritizes clarity and efficiency, reducing the cognitive overhead associated with complex frameworks. Overall, Paseo serves as a streamlined development environment that balances simplicity with capability for modern web projects. -
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SuperPrompt
Experimental prompt framework exploring reasoning structures in AI
SuperPrompt is an experimental open source project focused on designing complex prompts intended to help researchers and developers better understand how AI agents reason and respond. It explores structured prompt engineering techniques that combine symbolic expressions, logical constructs, and conceptual frameworks to guide large language models toward deeper reasoning processes. Its main concept revolves around a highly structured prompt format that includes tagged sections for reasoning, analysis, conceptual expansion, and recursive thinking patterns. These sections act as a kind of meta-instruction system intended to influence how an AI model approaches problem solving and conceptual exploration. ... -
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caveman
Why use many token when few token do trick
Caveman is a lightweight and experimental project focused on simplifying backend or full-stack development workflows through minimalistic abstractions and rapid prototyping principles. It is designed to reduce the complexity of modern frameworks by offering a stripped-down approach that prioritizes speed, clarity, and ease of use. The project often serves as a foundation for developers who want to build applications quickly without being constrained by heavy conventions or extensive configuration. It may include utilities for routing, state handling, or simple server logic, depending on its implementation scope. ... -
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Wigolo
The go-to web for your AI coding agent
...Results, embeddings, and configuration are stored locally, and the core tools require no account or paid API key. Optional language models can synthesize cited reports, while Python and TypeScript integrations connect Wigolo to popular agent frameworks. -
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DeepCamera
Open-Source AI Camera. Empower any camera/CCTV
DeepCamera empowers your traditional surveillance cameras and CCTV/NVR with machine learning technologies. It provides open-source facial recognition-based intrusion detection, fall detection, and parking lot monitoring with the inference engine on your local device. SharpAI-hub is the cloud hosting for AI applications that helps you deploy AI applications with your CCTV camera on your edge device in minutes. SharpAI yolov7_reid is an open-source Python application that leverages AI... -
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Cangjie Skill
Distill high-value content like books, long videos, podcasts, and more
...Its goal is structured reuse rather than producing another summary or set of reading notes. The seven-stage RIA-TV++ pipeline analyzes the full source, extracts candidate frameworks, verifies them, constructs skill modules, links related ideas, pressure-tests behavior, and prepares delivery files. Each accepted skill records supporting material, a reconstructed explanation, examples, trigger situations, executable steps, and limitations. Strict verification rejects generic or weak ideas that lack independent evidence, predictive value, or meaningful uniqueness. ... -
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AgentField
Build and run AI agents like microservices
AgentField is an open-source control plane designed to run AI agents as production-grade backend services, applying cloud-native principles similar to Kubernetes to the world of autonomous software. Instead of treating agents as isolated scripts or prototypes, the system elevates them to first-class infrastructure components that can be deployed, orchestrated, and managed at scale across distributed environments. Developers define agents as typed functions, and the platform automatically... -
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Kubeflow Trainer
Distributed AI Model Training and LLM Fine-Tuning on Kubernetes
...It extends the Kubeflow ecosystem by providing a unified framework for orchestrating training workloads using Kubernetes primitives, enabling seamless scaling from single-machine experiments to large production clusters. The platform supports a wide range of machine learning frameworks, including PyTorch, JAX, Hugging Face, DeepSpeed, and XGBoost, making it highly flexible for different AI use cases. One of its key innovations is the integration of MPI-based distributed computing within Kubernetes, allowing efficient communication between nodes for high-performance training. It also includes advanced scheduling capabilities through integrations with tools like Kueue and Volcano, enabling topology-aware resource allocation and multi-cluster job orchestration. -
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Shadcn UI v4 MCP Server
A mcp server to allow LLMS gain context about shadcn ui component
...It provides structured access to component source code, demos, metadata, and reusable UI blocks, allowing AI agents to generate accurate and production-ready interface implementations. The server supports multiple frontend frameworks including React, Svelte, Vue, and React Native, making it highly versatile for cross-platform development. It includes smart caching and efficient GitHub API usage to optimize performance and handle rate limits during component retrieval. The system also supports multiple transport modes such as standard input/output and Server-Sent Events, enabling both local and distributed deployments. -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. The framework was originally developed for high-energy physics experiments where real-time decision systems must process large volumes of data with strict latency constraints. ... -
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Memori
SQL-native memory layer enabling persistent context for AI agents
...By recalling relevant context during future model calls, Memori helps AI agents produce more consistent and context-aware responses while reducing the need to repeatedly provide background information. Memori is designed to work with multiple LLM providers, data stores, and AI frameworks, allowing it to integrate into existing software architectures without requiring major changes. -
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Shell-AI
LangChain powered shell command generator and runner CLI
...Instead of requiring users to remember complex command syntax, the tool lets them describe their intent in plain English and automatically suggests commands that accomplish the task. The system is powered by large language models and integrates with frameworks such as LangChain to interpret user requests and translate them into executable shell instructions. Users interact with the program through an interactive terminal interface where multiple command suggestions are presented for review before execution. This approach improves productivity for developers and system administrators who frequently use terminal environments but may not recall every command variation. ... -
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GitClaw
A universal git-native AI agent framework
GitClaw is an open-source framework for building AI agents whose entire identity, configuration, memory, and capabilities live inside a Git repository. Instead of storing agent state in databases or application code, the framework treats a repository itself as the agent’s environment, allowing developers to version, inspect, and collaborate on agents using standard Git workflows. The system defines structured files that represent the agent’s personality, rules, configuration, and operational... -
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swark.io
Create architecture diagrams from code automatically using LLMs
...The tool integrates with GitHub Copilot and the VS Code environment, allowing developers to generate diagrams with minimal setup and without requiring additional authentication or API configuration. Because the logic of understanding code structure is handled by an LLM, Swark can support many programming languages and frameworks without requiring custom rules for each language. -
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LLM Guard
The Security Toolkit for LLM Interactions
...LLM Guard supports both input and output filtering pipelines, allowing developers to sanitize prompts and validate generated responses in real time. The library integrates easily with existing AI frameworks and can be deployed in production environments to enhance the security posture of LLM-based applications. -
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RubyLLM
One beautiful Ruby API for OpenAI, Anthropic, Gemini, Bedrock
...The library supports advanced capabilities such as tool calling, structured responses, and schema-based outputs that enable developers to build more reliable AI-driven applications. RubyLLM also integrates smoothly with modern Ruby frameworks and development workflows, making it easier to embed AI functionality into web services, background jobs, and automation scripts. -
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Anomaly Detection Learning Resources
Anomaly detection related books, papers, videos, and toolboxes
...The project serves as a centralized index for researchers and practitioners who want to explore algorithms, datasets, and publications associated with detecting unusual patterns in data. The repository organizes resources into structured categories such as books, tutorials, academic papers, datasets, benchmark frameworks, and open-source toolkits. It includes materials covering a wide range of anomaly detection domains, including time series data, graph data, tabular datasets, and real-time monitoring systems. By compiling resources from multiple programming ecosystems such as Python, R, and other machine learning platforms, the repository allows users to discover both research papers and practical implementations. -
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self-llm
Tutorial tailored for Chinese babies on rapid fine-tuning
...It provides step-by-step tutorials covering environment setup, model deployment, inference workflows, and efficient fine-tuning techniques such as LoRA and parameter-efficient training. The project also includes guides for integrating models into real applications, including command-line interfaces, web demos, and frameworks like LangChain. By combining theory, configuration instructions, and runnable examples, self-llm lowers the barrier to entry for students and engineers who want to experiment with open-source models. -
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NagaAgent
A simple yet powerful agent framework for personal assistants
NagaAgent is an experimental framework for building interactive virtual agents capable of autonomous reasoning, dialog, and task execution using components that mirror human cognitive patterns. It provides abstractions for representing goals, context, and state so that agents can plan sequences of actions, evaluate outcomes, and adjust behavior over time. The project includes mechanisms for semantic memory, reasoning pipelines, and integration points with external data sources and language... -
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UCP Python SDK
The official Python SDK for UCP
UCP Python SDK repository for the Universal Commerce Protocol (UCP) delivers an official Python client library that simplifies building UCP-compliant applications in Python. UCP itself is a modern, open-source standard that empowers seamless commerce interactions between platforms, AI agents, merchants, and payment providers without requiring bespoke integrations for every participant in the commerce ecosystem. This SDK provides Pydantic models for UCP schemas, making it easy for Python... -
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Open Model Zoo
Pre-trained Deep Learning models and demos
Open Model Zoo is a large repository of high-quality pre-trained deep learning models and demonstration applications designed to work with the OpenVINO™ toolkit, offering a comprehensive starting point for a wide range of AI and computer vision workloads. It includes hundreds of models covering object detection, classification, segmentation, pose estimation, speech recognition, text-to-speech, and more, many of which are already converted into formats optimized for inference on CPUs, GPUs,... -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well... -
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DLRM
An implementation of a deep learning recommendation model (DLRM)
DLRM (Deep Learning Recommendation Model) is Meta’s open-source reference implementation for large-scale recommendation systems built to handle extremely high-dimensional sparse features and embedding tables. The architecture combines dense (MLP) and sparse (embedding) branches, then interacts features via dot product or feature interactions before passing through further dense layers to predict click-through, ranking scores, or conversion probabilities. The implementation is optimized for...
