Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 79
Sort By:
Showing 20445 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
View related business solutions-
Time tracking software for the global workforceIt's easy with Hubstaff, a time-tracking and workforce management platform that automates almost every aspect of running or growing a business. Teams can track time to projects and to-dos using Hubstaff's desktop, web, or mobile applications. You'll be able to see how much time your team spends on different tasks, plus productivity metrics like activity rates and app usage through Hubstaff's online dashboard. Most of the available features are customizable on a per-user basis, so you can create the team management tool you need.
-
No-code automation to improve your process workflowsTransform your financial and HR operations and improve efficiency even remotely with digital, customized workflows that your team can automate and integrate with other software without the need of IT development.
-
1
Daytona
The Open Source Dev Environment Manager
Set up a development environment on any infrastructure, with a single command. -
2
Rawon
A simple powerful Discord music bot built to fulfill your production
A simple powerful Discord music bot built to fulfill your production desires. Easy to use, with no coding required. -
3
Sandbox Agent
Run Coding Agents in Sandboxes
Sandbox Agent by Rivet is an experimental framework for running AI agents in controlled, isolated environments where they can safely execute code, interact with tools, and perform autonomous tasks without risking system integrity. It is designed to provide a secure sandbox that allows agents to test actions, manipulate files, and run commands while enforcing strict boundaries and monitoring capabilities. The project focuses on enabling more reliable and auditable agent behavior by separating execution from the host environment, which is especially important for applications involving automation, code generation, or system-level operations. ... -
4
Microsandbox
Secure local-first microVM sandbox for running untrusted code fast
...It provides a local-first and self-hosted approach, allowing users to maintain full ownership of their execution environment without depending on external cloud services. Microsandbox is particularly geared toward AI agent workflows, offering integrations that enable automated systems to safely run generated code and commands. It also supports standard container images, making it compatible with existing development ecosystems and tooling. -
Apify is a full-stack web scraping and automation platform helping anyone get value from the web.Actors are serverless cloud programs that extract data, automate web tasks, and run AI agents. Developers build them using JavaScript, Python, or Crawlee, Apify's open-source library. Build once, publish to Store, and earn when others use it. Thousands of developers do this - Apify handles infrastructure, billing, and monthly payouts.
-
5
PHP Docs Samples
A collection of samples on how to call Google Cloud services
PHP Docs Samples repository is a broad collection of PHP examples that demonstrate how to call and work with Google Cloud services from PHP applications. It is designed as a companion to Google Cloud documentation, giving developers ready-to-run code patterns they can study, adapt, and integrate into their own projects. Rather than focusing on a single product, the repository spans many services and use cases, making it useful for developers who need a central reference point for PHP-based cloud integrations. The project is structured as a sample library, which means its primary value lies in showing practical API usage, authentication flows, and service interaction patterns instead of shipping a standalone framework. ... -
6
Qodo Cover
AI tool that generates tests to improve code coverage quickly
Qodo Cover is an open source developer tool designed to automate the creation of unit tests using generative AI, helping teams improve code coverage with minimal manual effort. It operates as a command-line interface and can also be integrated into continuous integration workflows, making it adaptable to different development environments. It analyzes an existing codebase, identifies gaps in test coverage, and generates new tests that target uncovered or weakly tested areas. -
7
MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
...RD-Agent can analyze data, generate experimental code, run evaluations, and learn from outcomes to improve future iterations. -
8
Machine learning algorithms
Minimal and clean examples of machine learning algorithms
...The repository includes implementations of both supervised and unsupervised learning techniques, along with dimensionality reduction and clustering methods. Many of the algorithms are written in a simplified style that prioritizes clarity and educational value over production-level optimization. Because the code is compact and easy to follow, it is often used as a learning resource by developers who want to understand how machine learning algorithms are constructed. -
9
Transfer Learning Repo
Transfer learning / domain adaptation / domain generalization
...In addition to academic references, the project provides practical code implementations of many transfer learning algorithms so that researchers can reproduce experiments or build their own applications. The repository also catalogs well-known scholars, research laboratories, and datasets relevant to transfer learning studies. -
SalesTarget.ai | AI-Powered Lead Generation, Email Outreach, and CRMSalesTarget is ideal for B2B sales teams, startup founders, and marketing professionals looking to streamline lead generation and outreach. It also benefits growing SaaS companies and agencies aiming to scale their outbound efforts efficiently.
-
10
Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
Learn PyTorch for Deep Learning is an open-source educational repository that provides the full learning materials for the “Learn PyTorch for Deep Learning: Zero to Mastery” course created by Daniel Bourke. The project is designed to teach beginners how to build deep learning models using PyTorch through a hands-on, code-first learning approach. Instead of focusing heavily on theory alone, the repository encourages learners to experiment with code and develop practical machine learning skills through guided examples and exercises. The materials include Jupyter notebooks, explanations of core deep learning concepts, and step-by-step demonstrations of building and training neural networks. ... -
11
LongBench
LongBench v2 and LongBench (ACL 25'&24')
...Traditional language model benchmarks typically evaluate tasks involving relatively short inputs, which does not reflect many real-world applications such as analyzing large documents or entire code repositories. LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. ... -
12
Unstract
No-code LLM Platform to launch APIs and ETL Pipelines
Unstract is a powerful open-source, no-code platform built to automate the extraction and structuring of unstructured documents using large language models and flexible workflows, enabling developers and data teams to turn messy files into organized JSON content without complex coding. It integrates a visual Prompt Studio environment where users can iteratively design extraction schemas, compare outputs from different models, and monitor costs and accuracy side by side, making it easier to refine prompts and extraction logic before deploying at scale. ... -
13
AWS Agent Skills
AWS Skills for Agents
...The skills cover critical AWS services such as IAM, Lambda, DynamoDB, S3, API Gateway, EKS, and many more, letting agents offer actionable advice on infrastructure as code, debugging, security configurations, and architectural workflows. Skills are kept up to date with weekly documentation checks, ensuring they reflect current AWS patterns and service changes. -
14
Interface Design
Design engineering for Claude Code
Interface Design is a Claude Code plugin and design-engineering toolkit that helps developers and designers retain consistent UI design decisions across sessions by codifying design principles and remembering them for future use. It’s built around the idea that when building UI (dashboards, apps, admin panels), design decisions like spacing, colors, depth, and component patterns can drift over time, so Interface-Design captures and stores those decisions in a system MD file that’s loaded automatically each session. ... -
15
100 proyectos de JavaScript
100 JavaScript projects with code and videos for free
100 proyectos de JavaScript is a large collection of 100 JavaScript projects, each with source code and an accompanying video tutorial, offered entirely for free. The repository is tied to the website javascript100.dev, which presents the projects as a structured learning path so learners can progressively build their skills through practice.Each project lives in its own numbered folder (for example, 01-tinder-swipe), and the README explains how to run them via the VS Code Live Preview extension, making it easy for beginners to get them running without complex tooling. ... -
16
From Java To Kotlin
Your Cheat Sheet For Java To Kotlin
...It also discusses interoperability and migration strategies so mixed-language projects remain stable during the transition. The result is a concise playbook that helps teams adopt Kotlin with confidence, writing safer and more maintainable code without losing touch with the underlying platform. -
17
Backbone
Give your JS app some Backbone with models, views, and collections
...It helps developers avoid "spaghetti code" by separating concerns and centralising data-bindings and event flows. Even though newer frameworks have largely overtaken it in popularity, Backbone remains a valuable historical and educational reference. -
18
Atheris
A Coverage-Guided, Native Python Fuzzer
Atheris is a coverage-guided fuzzer for CPython that treats Python as a first-class fuzzing target, enabling rapid discovery of crashes and logic errors in pure-Python code and native extensions. It hooks into Python’s interpreter to collect fine-grained coverage and uses that signal to evolve inputs, pushing programs into previously unexplored code paths. Because many Python libraries are thin wrappers over C/C++ code, Atheris is equally adept at surfacing memory safety issues in extension modules compiled with sanitizers. ... -
19
RustPython
A Python Interpreter written in Rust
...The interpreter aims to support the Python standard library, dynamic typing, garbage collection, and common builtins, although full compatibility is a work in progress. It also supports bytecode compilation and execution, enabling faster startup and reuse of parsed code. By bridging the two language ecosystems, RustPython offers a compelling path for projects that want Python flexibility but desire Rust’s performance and safety discipline. -
20
Prompt Declaration Language
Prompt Declaration Language is a declarative prompt programming lang
LLMs will continue to change the way we build software systems. They are not only useful as coding assistants, providing snipets of code, explanations, and code transformations, but they can also help replace components that could only previously be achieved with rule-based systems. Whether LLMs are used as coding assistants or software components, reliability remains an important concern. LLMs have a textual interface and the structure of useful prompts is not captured formally. ... -
21
Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
...This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. Testing multiple pipelines requires many lines of code, and writing it all in the same notebook often makes it long and cluttered. On the other hand, using multiple notebooks makes it harder to compare the results and to keep an overview. On top of that, refactoring the code for every test can be quite time-consuming. How many times have you conducted the same action to pre-process a raw dataset? ... -
22
GraphQL Manifold
Manifold is a Java compiler plugin, features include Metaprogramming
...Use the framework to gain direct, type-safe access to any type of resource, such as GraphQL, JSON, XML, YAML, CSV, and even other languages such as JavaScript. Remove the code gen step in your build process. Use types defined in .graphql files directly, no code gen steps! Make GraphQL changes and immediately use them with code completion. -
23
qwik
The HTML-first framework. Instant apps of any size with
...Then Qwik can resume execution where the server left off in the browser because all the data Qwik needs is in HTML. No JS needs to be downloaded or executed until it is needed to handle user interaction or rendering. Never think about lazy-loading of your code again. Qwik was built around lazy loading as its core primitive and took it to a whole new level with zero effort for the developer. Event listeners, rendering function, side-effects, creation blocks, it all gets lazy-loaded on an as-needed basis. -
24
Opacus
Training PyTorch models with differential privacy
...Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
25
ClearML
Streamline your ML workflow
ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML powerful and versatile set of classes and methods. The ClearML Server storing experiment, model, and workflow data, and supports the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. ...
