Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 39
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8650 projects for "git:/git.code.sf.net/p/docfetcher/code" with 1 filter applied:
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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Ganttic is an online resource planning software, that you can use for managing project portfolios while planning resources with maximum efficiency.Ganttic is a resource management software that excels in high-level resource planning and managing multiple project portfolios at once. In Ganttic, anything and anyone you need to schedule can be a resource – people, rooms, machinery – you name it! The software scales with your business, and you can introduce department after department to Ganttic to utilize all of your resources in the most effective way possible. Using Ganttic, you will have a good grasp on both the allocation and utilization of your resources.
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Dominator
Zero-cost ultra-high-performance declarative DOM library using FRP
...It uses a functional reactive programming model based on signals, allowing UI components to automatically update in response to state changes in an efficient and predictable manner. The library is designed to be “zero-cost,” meaning that abstractions compile down to highly optimized code with no unnecessary runtime overhead. Updates are handled in constant time regardless of application size, making it highly scalable for complex interfaces. Its API is inspired by component-based frameworks like React, but tailored to Rust’s ownership model and compiled to WebAssembly for browser execution. By avoiding the virtual DOM, it eliminates diffing overhead and provides direct control over DOM operations for improved performance. -
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JupyterLite
Wasm powered Jupyter running in the browser
...This architecture eliminates the need for installation or server infrastructure, making it highly accessible for education, demonstrations, and lightweight data science workflows. JupyterLite supports many core Jupyter features, including notebooks, code consoles, and interactive visualizations, while storing files locally using browser storage mechanisms such as IndexedDB. It is designed to be easily deployable as a static website, enabling developers to host fully functional notebook environments on platforms like GitHub Pages. -
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SEO Machine
A specialized Claude Code workspace for creating long-form
SEO Machine is an AI-powered content production system built as a structured workspace for generating long-form, SEO-optimized blog content through automated workflows. It integrates research, writing, analysis, and optimization into a single pipeline, allowing users to produce high-quality articles tailored to search engine performance. The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It... -
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Goose AEO
AEO tracking toolkit (CLI + dashboard) built for AI agents like Claude
Goose AEO is an AI agent orchestration framework focused on automating engineering operations and workflows through autonomous agents. It builds on the concept of local AI agents that can execute complex tasks such as writing code, debugging, managing pipelines, and interacting with APIs without continuous human supervision. The system is designed to be extensible and adaptable, allowing developers to integrate different language models and configure multi-agent workflows tailored to specific use cases. It emphasizes automation of repetitive and complex development tasks, enabling users to focus on higher-level problem solving and innovation. ... -
SOCRadar Extended Threat Intelligence PlatformSOCRadar Extended Threat Intelligence, a natively single platform from its inception that proactively identifies and analyzes cyber threats with contextual and actionable intelligence.
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Sample GenLayer project
Includes the boilerplate code for a GenLayer use case implementation
GenLayer Project Boilerplate is an open-source template designed to accelerate the development of decentralized applications on the GenLayer protocol by providing a fully integrated environment that combines intelligent contract logic with a modern frontend interface. It includes a complete working example of a prediction market application, demonstrating how developers can structure, deploy, and test decentralized logic in a real-world scenario. The project integrates both backend contract... -
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Google Kubernetes Engine (GKE) Samples
Sample applications for Google Kubernetes Engine (GKE)
Google Kubernetes Engine (GKE) Samples repository is a comprehensive collection of sample applications and reference implementations designed to demonstrate how to build, deploy, and manage workloads on Google Kubernetes Engine (GKE). It serves as a practical companion to official GKE tutorials, providing real, runnable code that illustrates how containerized applications are packaged, deployed, and scaled within Kubernetes clusters. The repository is organized into multiple categories such as AI and machine learning, autoscaling, networking, observability, security, and cost optimization, allowing developers to explore specific use cases and architectural patterns. ... -
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Mink
PHP web browser emulator abstraction
...Instead of tying test logic to a specific browser driver, Mink introduces a unified API that can work with multiple drivers such as Goutte, Selenium, ChromeDriver, or BrowserKit. This abstraction enables developers to write tests once and run them across different environments without changing the test code. Mink is commonly used in behavior-driven development workflows, particularly with frameworks like Behat, where it helps simulate real user behavior such as clicking links, filling forms, and navigating pages. The library supports session management, allowing multiple browser sessions to run simultaneously and interact with different pages or environments. -
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AI Notes
Curated AI engineering notes on LLMs, generative models, and tools
...It is designed to help software engineers quickly understand modern AI concepts, tools, and developments through structured documentation and research notes. It functions as a living knowledge base composed of numerous markdown files that organize topics such as text generation, image generation, AI infrastructure, and code generation models. These notes include observations, references, experiments, and summaries of important research and industry developments in AI. ai-notes also contains collections of prompts, curated learning materials, and categorized resources intended to help developers explore AI capabilities and practical applications. -
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TypeAgent Python
Structured RAG: ingest, index, query
...This design allows the system to combine the flexibility of language models with the reliability of traditional programming logic. The repository is intended primarily as a research prototype and sample code rather than a production-ready framework, allowing developers to experiment with building AI agents that maintain structured memory and perform tasks through defined actions. -
Stigg | SaaS Monetization and Entitlements APIA monetization platform is a standalone middleware that sits between your application and your business applications, as part of the modern enterprise billing stack. Stigg unifies all the APIs and abstractions billing and platform engineers had to build and maintain in-house otherwise. Acting as your centralized source of truth, with a highly scalable and flexible entitlements management, rolling out any pricing and packaging change is now a self-service, risk-free, exercise.
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
...It also employs connectionist temporal classification (CTC) to align predicted character sequences with input images without requiring character-level segmentation. The repository provides code for training models, performing inference on handwritten text images, and evaluating recognition accuracy. SimpleHTR is commonly used as an educational example for understanding how modern handwriting recognition systems operate. -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
...The repository includes notebooks that demonstrate classic algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, support vector machines, and clustering techniques. Each notebook typically combines explanatory text, Python code, and visualizations to illustrate how the algorithm operates and how it can be applied to datasets. -
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rebroswer-patches
Patches for Puppeteer and Playwright to reduce automation detection
rebrowser-patches is an open source collection of patches designed to improve the stealth capabilities of browser automation frameworks. It focuses primarily on enhancing Puppeteer and Playwright by modifying parts of their source code that may reveal automation activity to websites. Many modern websites rely on bot detection mechanisms that identify automation through behavioral or technical signals, and these patches aim to reduce those detection vectors. By applying targeted fixes, the project helps developers minimize automation leaks that are difficult or impossible to address through configuration options alone. ... -
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GPU Puzzles
Solve puzzles. Learn CUDA
...Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively more complex puzzles, learners gain a practical understanding of how parallel algorithms operate on graphics processing units. The project emphasizes experimentation and problem solving, encouraging learners to discover GPU programming techniques through trial and exploration. ... -
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LLMs-Zero-to-Hero
From nobody to big model (LLM) hero
LLMs-Zero-to-Hero is an open-source educational project designed to guide learners through the complete process of understanding and building large language models from the ground up. The repository presents a structured learning pathway that begins with fundamental concepts in machine learning and progresses toward advanced topics such as model pre-training, fine-tuning, and deployment. Rather than relying entirely on existing frameworks, the project encourages readers to implement... -
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Floneum
Instant, controllable, local pre-trained AI models in Rust
Floneum is an open-source platform for building AI-powered workflows using large language models through a visual and extensible interface. The system allows users to design complex AI pipelines using a drag-and-drop workflow builder rather than writing extensive code. It focuses on enabling developers and researchers to create language model applications that combine different tools, data sources, and AI capabilities into automated workflows. Floneum supports a plugin architecture that allows external components to extend the platform while maintaining isolation and security. Many plugins can be written in different programming languages and compiled to WebAssembly modules, allowing them to run safely within the system. ... -
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KG-LLM-Papers
Papers integrating knowledge graphs (KGs) and large language models
KG-LLM-Papers is a curated academic resource that collects and organizes research papers exploring the intersection between knowledge graphs and large language models. The repository functions as a continuously updated index of scholarly work that investigates how structured knowledge representations can enhance the reasoning, factual accuracy, and interpretability of language models. It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge... -
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AI Engineering Transition Path
Research papers and blogs to transition to AI Engineering
...Instead of presenting isolated tutorials, the repository provides a structured pathway that guides engineers through the technical knowledge needed to build and deploy large language model systems. The materials include curated research papers, blog posts, and code examples that explain both theoretical foundations and practical implementation strategies. By consolidating these resources into a single repository, the project helps developers navigate the rapidly expanding AI ecosystem without needing to search through scattered materials. -
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Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer... -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
...Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile. -
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SmartEnum
A base class for creating strongly typed enum replacements in C#
...The project also includes ecosystem support for serialization, Entity Framework persistence, and validation attributes, making it suitable for real-world enterprise applications. Overall, SmartEnum helps teams write more maintainable and intention-revealing domain code by replacing brittle primitive enums with extensible types. -
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papermill
Parameterize, execute, and analyze notebooks
...This capability is particularly useful in data science and analytics, where a template notebook might be reused for batching reports across dates, customers, or other variables without rewriting code or duplicating notebooks. Papermill supports both Python API usage and a command-line interface, making it flexible for integration with CI/CD systems, shells, and workflow orchestration tools like Airflow. -
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Trellis AI
All-in-one AI framework & toolkit for Claude Code & Cursor
Trellis is an advanced workflow and agent orchestration framework designed for building, managing, and scaling intelligent applications that coordinate numerous autonomous components. At its core, Trellis lets developers define units of work — called tasks or agents — and compose them into rich workflows that can operate with concurrency, conditional logic, and dynamic branching, all without sacrificing readability or control. It emphasizes modular design, encouraging users to encapsulate... -
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ZAPI
ZAPI by Adopt AI is an open-source Python library
...It integrates smoothly into modern development stacks, supports hot reloading for rapid iteration, and includes a command-line toolchain for scaffolding new endpoints or services with sensible defaults. The framework also supports plugin extensions that add things like rate limiting, caching layers, and telemetry without cluttering core code. -
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AI Researcher
An autonomous AI researcher
...It orchestrates agents that can generate research questions, perform literature reviews, execute experiments, analyze results, and synthesize findings into structured outputs like reports or code. Each agent operates with clear roles — such as researcher, analyst, and summarizer — and they communicate through a task-management interface that ensures progress tracking and iterative refinement. The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. ... -
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TONL
TONL (Token-Optimized Notation Language)
...It provides a serialization format that significantly reduces token usage compared with traditional JSON, which can result in lower costs and more efficient prompt size utilization in LLM-driven systems. TONL isn’t just a format — it includes a rich API for querying, indexing, modifying, and streaming data, along with tools for schema validation and TypeScript code generation. The platform comes with a complete command-line interface that supports interactive dashboards and cross-platform usage in browsers and server environments, and its high test coverage gives developers confidence in stability.
