Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 28
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Showing 1617 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
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Pylon is an All-in-one B2B Support Platform for modern B2B businesses.We bring together everything a post-sales teams team needs including a ticketing system, B2B omnichannel integrations (Slack Connect, Microsoft Teams), modern chat widget, knowledge base, AI support bot, account management, customer marketing, and more.
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One Unified Time Tracking Software For Projects, Billing, Pay and ComplianceReplicon's time-tracking platform is scalable and configurable to support the diverse needs of small, mid & large businesses with a remote and globally distributed workforce. Replicon’s Time Tracking is a cloud-based, enterprise-grade solution that tracks employee time across projects, tasks, presence, and absence to facilitate client billing, project costing, and compliant payroll processing. The scalable and configurable platform offers seamless integration with common business technology stacks, such as ERP, CRM, Accounting, and payroll solutions. With AI-powered time capture, mobile apps, and labor compliance as a service, Replicon makes time tracking hassle-free.
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Curated Transformers
PyTorch library of curated Transformer models and their components
State-of-the-art transformers, brick by brick. Curated Transformers is a transformer library for PyTorch. It provides state-of-the-art models that are composed of a set of reusable components. Supports state-of-the-art transformer models, including LLMs such as Falcon, Llama, and Dolly v2. Implementing a feature or bugfix benefits all models. For example, all models support 4/8-bit inference through the bitsandbytes library and each model can use the PyTorch meta device to avoid unnecessary... -
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Freqtrade
Free, open source crypto trading bot
...Always start by running a trading bot in Dry-run and do not engage money before you understand how it works and what profit/loss you should expect. We strongly recommend you have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms, and techniques implemented in it. Write your strategy in python, using pandas. Example strategies to inspire you are available in the strategy repository. Download historical data of the exchange and the markets you may want to trade with. Find the best parameters for your strategy using hyper optimization which employs machining learning methods. -
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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... -
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Cofounder
AI tool that generates full-stack web apps with generative UI systems
...Cofounder also provides a dashboard and local API environment that helps manage project generation, iteration, and interaction with the system. Generated projects are stored locally and can be launched using standard development commands, allowing developers to run and modify the code that the system produces. Cofounder relies on configurable nodes and sequences that define how AI operations are executed. -
Employees get more done with RipplingEffortlessly manage the entire employee lifecycle, from hiring to benefits administration. Automate HR tasks, ensure compliance, and streamline approvals. Simplify IT with device management, software access, and compliance monitoring, all from one dashboard. Enjoy timely payroll, real-time financial visibility, and dynamic spend policies. Rippling empowers your business to save time, reduce costs, and enhance efficiency, allowing you to focus on growth. Experience the power of unified management with Rippling today.
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tlm
Local CLI Copilot, powered by Ollama
...This approach allows developers to use powerful open-source models such as Llama, Phi, DeepSeek, and Qwen while maintaining privacy and avoiding external service dependencies. The system supports contextual queries where the AI analyzes files within a directory and generates answers based on project documentation or source code. It also detects the user’s shell environment automatically, allowing it to generate commands tailored to shells such as Bash, Zsh, or PowerShell. -
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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. -
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OpenAI Agent Skills
Skills Catalog for Codex
...It organizes reusable, task-specific workflows, instructions, scripts, and resources into modular skill folders so that an AI agent can reliably perform complex tasks without repeated custom prompting, making agent behavior more predictable and composable. Each skill is defined with clear metadata and instructions organizing how an AI assistant should complete specific tasks ranging from project management to code generation and documentation assistance. The repository supports community contributions, allowing developers to add new skills or update existing ones to keep the catalog relevant and practical for evolving use cases. -
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StatsForecast
Fast forecasting with statistical and econometric models
...The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery of benchmarking and baseline methods, giving users flexibility in selecting forecasting approaches depending on data characteristics (trend, seasonality, intermittent demand, etc.). Its internal implementation leverages numba to compile performance-critical code to optimized machine-level instructions, which makes the models much faster than many traditional Python counterparts. -
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LLaMA Models
Utilities intended for use with Llama models
...The project’s issues and releases reflect an actively used coordination point for the ecosystem, where guidance, utilities, and compatibility notes are published. It complements separate repos that carry code and demos (for example inference kernels or cookbook content) by keeping authoritative metadata and specs here. Model lineages and size variants are documented externally (e.g., Llama 3.x and beyond), with this repo providing the “single source of truth” links and utilities. In practice, teams use llama-models as a reference when selecting variants, aligning licenses, and wiring in helper scripts for deployment. -
Easy management of simple and complex projectsPlan more projects with Worksection. Use Gantt chart and Kanban boards to organize your projects, get your team onboard and assign tasks and due dates.
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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... -
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
...TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++ API that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. ... -
12
JMusicBot
A Discord music bot that's easy to set up and run yourself
...Supports many sites, including Youtube, Soundcloud, and more. Supports many online radio/streams. Supports local files. Playlist support (both web/youtube and local) This bot (and the source code here) might not be easy to edit for inexperienced programmers. The main purpose of having the source public is to show the capabilities of the libraries, to allow others to understand how the bot works, and to allow those knowledgeable about java, JDA, and Discord bot development to contribute. -
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OpenFold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction
OpenFold carefully reproduces (almost) all of the features of the original open source inference code (v2.0.1). The sole exception is model ensembling, which fared poorly in DeepMind's own ablation testing and is being phased out in future DeepMind experiments. It is omitted here for the sake of reducing clutter. In cases where the Nature paper differs from the source, we always defer to the latter. OpenFold is trainable in full precision, half precision, or bfloat16 with or without DeepSpeed, and we've trained it from scratch, matching the performance of the original. ... -
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Face Alignment
2D and 3D Face alignment library build using pytorch
...However, the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes. While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA-enabled GPU. While here the work is presented as a black box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my webpage. -
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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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Python Programming Hub
Learn Python and Machine Learning from scratch
...The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. Many sections are implemented as Jupyter notebooks, allowing learners to run code interactively while reading explanations of the concepts involved. The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. It also includes examples of commonly used libraries such as NumPy, Pandas, and other tools used in data science workflows. -
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Plano
Delivery infrastructure for agentic apps
Plano is an AI-native proxy and data plane designed to simplify the infrastructure required to deploy and operate agentic applications in production environments. It removes repetitive plumbing work from application code by centralizing capabilities such as agent routing, orchestration, guardrails, observability, and model selection. Built on modern proxy technology and compatible with any language or AI framework, Plano enables developers to focus on core agent logic instead of infrastructure complexity. The system provides intelligent LLM routing APIs that support model agility, along with filter chains for safety, moderation, and memory hooks. ... -
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Automaton
The first AI that can earn its own existence, replicate, and evolve
Automaton is an open-source project designed to provide a flexible framework for building and simulating computational automata and related formal systems. The repository focuses on giving developers and researchers a programmable environment to experiment with state machines, language recognition models, and algorithmic behaviors that mirror theoretical computer science constructs. Its architecture emphasizes modularity so users can extend or customize automaton types without rewriting core... -
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Computer Vision in Action
A computer vision closed-loop learning platform
...It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. The repository includes structured code examples, scripts, and notebooks that cover pipeline construction, preprocessing, model inference, and visual output rendering, making it easy for newcomers or intermediate practitioners to adapt patterns to their own projects. It also explores how to combine classical computer vision techniques with modern neural network-based models, offering insight into when each approach is most effective. -
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Self-Hosted AI Starter Kit
Template that quickly sets up a local AI environment
The Self-Hosted AI Starter Kit is an open-source framework designed to help developers and teams quickly provision a local AI development environment that emphasizes control, privacy, and flexibility rather than dependence on external cloud APIs. At its core, the starter kit uses Docker Compose to orchestrate essential components like an AI workflow engine, vector database, local LLM server, and persistent storage, making it suitable for prototyping AI-driven applications without exposing... -
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Model Zoo
Please do not feed the models
FluxML Model Zoo is a collection of demonstration models built with the Flux machine learning library in Julia. The repository provides ready-to-run implementations across multiple domains, including computer vision, natural language processing, and reinforcement learning. Each model is organized into its own project folder with pinned package versions, ensuring reproducibility and stability. The examples serve both as educational tools for learning Flux and as practical starting points for... -
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Better Chatbot
Just a Better Chatbot. Powered by MCP Client & Workflows
Better‑chatbot is an AI chatbot framework powered by MCP protocols and workflows, allowing developers to deploy and integrate AI-powered chat systems with ease. Integrates all major LLMs: OpenAI, Anthropic, Google, xAI, Ollama, and more. MCP protocol, web search, JS/Python code execution, data visualization. Custom agents, visual workflows, artifact generation. Custom agents, visual workflows, artifact generation. Realtime voice chat with full MCP tool integration.
