Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 29
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Showing 1619 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
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The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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Slack MCP Server
The most powerful MCP Slack Server with no permission requirements
...Through this architecture, AI assistants can read message histories, interact with channels, and retrieve contextual information from Slack conversations in order to perform tasks such as automated analysis, collaboration assistance, or contextual code review. The server supports multiple communication transports, including standard input/output streams, HTTP, and Server-Sent Events, allowing flexible integration with different AI client environments. -
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verl
Volcano Engine Reinforcement Learning for LLMs
...It brings together supervised fine-tuning, preference modeling, and online RL into one coherent training stack so teams can move from raw data to aligned policies with minimal glue code. The library focuses on scalability and efficiency, offering distributed training loops, mixed precision, and replay/buffering utilities that keep accelerators busy. It ships with reference implementations of popular alignment algorithms and clear examples that make it straightforward to reproduce baselines before customizing. Data pipelines treat human feedback, simulated environments, and synthetic preferences as interchangeable sources, which helps with rapid experimentation. ... -
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OSS-Fuzz Gen
LLM powered fuzzing via OSS-Fuzz
OSS-Fuzz-Gen is a companion project that helps automatically create or improve fuzz targets for open-source codebases, aiming to increase coverage in OSS-Fuzz with minimal maintainer effort. It analyses a library’s APIs, examples, and tests to propose harnesses that exercise parsers, decoders, or protocol handlers—precisely the code where fuzzing pays off. The system integrates with modern LLM-assisted workflows to draft harness code and then iterates based on build errors or low coverage signals. Importantly, it aligns with OSS-Fuzz conventions, generating corpus seeds, build rules, and sanitizer settings so projects can plug in quickly. Reports highlight what functions were targeted, how coverage evolved, and where manual hints could unlock more paths. ... -
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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
Inventory and Order Management Software for Multichannel SellersWe are the most powerful inventory and order management platform for Amazon, Walmart, and multichannel product sellers. Centralize orders, product information, and fulfillment operations to run more efficiently, sell more products, and stay compliant with marketplace requirements so you can grow profitably.
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Unla
Gateway service that instantly transforms existing MCP Servers
Unla is a lightweight, highly available MCP gateway written in Go that turns existing MCP servers or ordinary HTTP APIs into MCP-compliant services through configuration, not code changes. Its goal is to let teams “wire up” tools they already run—internal REST endpoints, third-party APIs, or local MCP servers—and present a single, reliable MCP interface to clients like Claude Desktop, Cursor, and IDEs. The gateway focuses on operational concerns you’d expect in production: multi-instance availability, health checking, and declarative routing that maps upstreams to MCP tools and resources. ... -
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Qwen3-VL
Qwen3-VL, the multimodal large language model series by Alibaba Cloud
Qwen3-VL is the latest multimodal large language model series from Alibaba Cloud’s Qwen team, designed to integrate advanced vision and language understanding. It represents a major upgrade in the Qwen lineup, with stronger text generation, deeper visual reasoning, and expanded multimodal comprehension. The model supports dense and Mixture-of-Experts (MoE) architectures, making it scalable from edge devices to cloud deployments, and is available in both instruction-tuned and... -
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
...It is evaluated on many benchmarks (speech recognition, translation, sound classification, emotion, etc.), and offers pretrained models (e.g. 7B) released via ModelScope and Hugging Face. Code & examples provided with Hugging Face transformers, and usage via AutoProcessor, model classes etc. High performance on many standard benchmarks: ASR, speech-emotion recognition, vocal sound classification, speech translation etc. -
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lightning AI
The most intuitive, flexible, way for researchers to build models
...Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging... -
SoftCo: Enterprise Invoice and P2P Automation SoftwareSoftCo Accounts Payable Automation processes all PO and non-PO supplier invoices electronically from capture and matching through to invoice approval and query management. SoftCoAP delivers unparalleled touchless automation by embedding AI across matching, coding, routing, and exception handling to minimize the number of supplier invoices requiring manual intervention. The result is 89% processing savings, supported by a context-aware AI Assistant that helps users understand exceptions, answer questions, and take the right action faster.
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10
Rubix ML
A high-level machine learning and deep learning library for PHP
...In addition, we provide tutorials and other educational content to help you get started using ML in your projects. Our intuitive interface is quick to grasp while hiding alot of power and complexity. Write less code and iterate faster leaving the hard stuff to us. Rubix ML utilizes a versatile modular architecture that is defined by a few key abstractions and their types and interfaces. Train models in a fraction of the time by installing the optional Tensor extension powered by C. Learners such as neural networks will automatically get a performance boost. -
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Genkit
An open source framework for building AI-powered apps
Genkit is an open-source framework developed by Firebase for building AI-powered applications using familiar code-centric patterns. It simplifies the development, integration, and testing of AI features, providing observability and evaluation tools, and supports various models and platforms for versatile AI application development. -
12
langrocks
Tools like web browser, computer access and code runner for LLMs
Langrocks is a programming language experimentation toolkit that enables developers to create, test, and optimize custom programming languages. -
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AGiXT
AGiXT is a dynamic AI Automation Platform
...By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology. -
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WhatsApp MCP Server
WhatsApp MCP server enabling AI access to chats and messaging
whatsapp-mcp is an open source Model Context Protocol (MCP) server that enables AI agents to interact directly with a user’s WhatsApp account through a structured interface. It acts as a bridge between WhatsApp and large language models, allowing controlled access to messages, chats, and contacts. whatsapp-mcp is composed of two main components: a Go-based bridge that connects to the WhatsApp Web API and stores data locally, and a Python-based MCP server that exposes tools for AI... -
15
OpenClaw CN
The Chinese version of OpenClaw
...Beyond simple translation, the project often curates region-specific integrations or tooling recommendations that resonate with local developer environments and platforms. It helps accelerate adoption by providing readable guides, sample configurations, and annotated code that aligns with Chinese developer preferences and tooling conventions. -
16
Claude-Flow
The leading agent orchestration platform for Claude
...At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
17
Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A... -
18
Instructor
Structured outputs for llms
...Instructor is trusted by engineers from platforms like Langflow, underscoring its reliability and effectiveness in managing structured outputs powered by LLMs. Instructor is powered by Pydantic, which is powered by type hints. Schema validation and prompting are controlled by type annotations; less to learn, and less code to write, and it integrates with your IDE. -
19
Telegram.Bot
.NET Client for Telegram Bot API
...In fact, our test cases are self-documenting and serve as examples for Bot API methods. Once you learn the basics of Telegram chatbots, you will be able to easily understand the code in examples and use it in your own bot program. -
20
Vidi2
Large Multimodal Models for Video Understanding and Editing
...Vidi targets applications like intelligent video editing, automated video search, content analysis, and editing assistance, enabling users to efficiently locate relevant segments and objects in hours-long footage. The system is built with open-source release in mind, giving developers access to model code, inference scripts, and evaluation pipelines so they can reproduce research results or integrate Vidi into their own video-processing workflows. -
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MiniMax-M2
MiniMax-M2, a model built for Max coding & agentic workflows
...The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real repositories and diverse programming languages. It is also optimized for multi-step agent tasks, planning and executing long toolchains that span shell commands, browsers, retrieval systems, and code runners. Benchmarks show that it achieves highly competitive scores on a wide range of intelligence and agent benchmarks, including SWE-Bench variants, Terminal-Bench, BrowseComp, GAIA, and several long-context reasoning suites. -
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files-to-prompt
Concatenate a directory full of files into a single prompt
...It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. The output format is flexible: you can emit plain text, Markdown with fenced code blocks, or a Claude-XML style format designed for structured multi-file prompts. It can read file paths from stdin (including NUL-separated paths), which makes it easy to combine with find, rg, or other shell tools. -
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AWS Neuron
Powering Amazon custom machine learning chips
...Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can continue to use the same ML frameworks you use today and migrate your software onto Inf1 instances with minimal code changes and without tie-in to vendor-specific solutions. Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization. -
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Milvus Bootcamp
Dealing with all unstructured data, such as reverse image search
Milvus Bootcamp is a collection of tutorials, examples, and best practices for using Milvus, an open-source vector database designed for AI-powered similarity search and retrieval applications. -
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Vibe Vibe
The First Systematic Vibe Coding Tutorial
...It introduces a paradigm shift where coding becomes a conversational process, allowing users to build software by describing their intent rather than writing every line of code manually. The repository is organized into multiple learning stages, including foundational concepts, advanced techniques, and hands-on projects that reinforce practical skills. It also includes deployment instructions and optional local hosting through Docker, making the platform accessible in both online and offline environments.
