Showing 11438 open source projects for "engineering"

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  • 1
    Prompt Poet

    Prompt Poet

    Streamlines and simplifies prompt design for both developers

    Prompt Poet is an open-source framework designed to simplify the creation, organization, and maintenance of prompts for large language model applications. The project focuses on transforming prompt engineering into a structured design process rather than ad-hoc string manipulation within application code. It allows developers and non-technical users to build prompts using templated configurations based on YAML and Jinja2, which makes prompts easier to compose, reuse, and modify across different environments. By separating prompt structure from program logic, Prompt Poet encourages iterative prompt design and experimentation without requiring constant changes to application code. ...
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  • 2
    Fulling

    Fulling

    Full-stack Engineer Agent. Built with Next.js, Claude, shadcn/ui

    Fulling is an open-source AI-powered development environment designed to function as an autonomous full-stack engineering assistant. The platform provides a sandboxed workspace where developers can build complete applications with the help of an integrated AI coding agent. Instead of manually configuring development environments, the system automatically provisions the required infrastructure including a Linux environment, database services, and development tools.
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  • 3
    Daily Interview

    Daily Interview

    Datawhale members have compiled a book covering machine learning

    daily-interview is an open-source educational repository designed to help software engineers prepare for technical interviews through daily practice questions and curated learning materials. The project collects a wide range of interview questions related to algorithms, data structures, system design, and core computer science topics commonly tested by technology companies. The repository is organized in a structured format that encourages developers to practice solving problems regularly...
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  • 4
    PyTorch-Tutorial-2nd

    PyTorch-Tutorial-2nd

    CV, NLP, LLM project applications, and advanced engineering deployment

    PyTorch-Tutorial-2nd is an open-source educational repository that provides structured tutorials for learning deep learning with the PyTorch framework. The project serves as a practical companion to a second edition of a PyTorch learning guide and is designed to help learners understand neural network concepts through hands-on coding examples. The repository covers a wide range of topics including tensor operations, neural network construction, model training workflows, and optimization...
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  • 5
    LearnLLM.AI

    LearnLLM.AI

    Sharing knowledge about big models that everyone can understand

    LLMForEverybody is an open-source educational repository designed to make large language model concepts accessible to a broad audience, including beginners, developers, and job candidates preparing for AI-related interviews. The project organizes knowledge about LLMs into a structured learning path that begins with foundational research papers and progresses through the evolution of modern model architectures. It covers a wide range of topics including attention mechanisms, tokenization...
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  • 6
    Web Quality Skills

    Web Quality Skills

    Agent Skills for optimizing web quality based on Lighthouse

    This repository is a curated set of AI agent skills that encapsulate best practices for improving web quality, performance, accessibility, search engine optimization, and general best practices for web projects. It encodes knowledge drawn from Google Lighthouse audits, Core Web Vitals heuristics, WCAG accessibility guidelines, and real-world engineering experience, allowing coding agents to automatically assess and suggest improvements. These skills are framework-agnostic, meaning they apply to React, Vue, Svelte, Angular, Astro, or even plain HTML projects. For example, an agent can use these skills to audit a page’s performance, identify bottlenecks in loading speed, fix layout shift issues, suggest accessibility enhancements, or recommend SEO improvements. ...
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  • 7
    VibeTensor

    VibeTensor

    Our first fully AI generated deep learning system

    VibeTensor is a groundbreaking open-source research system software stack for deep learning that was uniquely generated almost entirely by AI coding agents under guided human supervision, demonstrating a new frontier in AI-assisted software engineering. It implements a PyTorch-style eager tensor library with a modern C++20 core that supports both CPU and CUDA backends, giving it the ability to manage tensors, automatic differentiation (autograd), and complex computation flows similar to mainstream frameworks. What makes VibeTensor remarkable is that every major component, from core libraries and dispatch systems to CUDA runtime support, caching allocators, and language bindings, was created and validated by coding agents using automated builds and tests rather than manual line-by-line human coding. ...
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  • 8
    AI Agents Masterclass

    AI Agents Masterclass

    Follow along with my AI Agents Masterclass videos

    AI Agents Masterclass is an educational open-source repository designed to teach developers how to build, train, and deploy intelligent AI agents using modern tooling and workflow patterns. The project includes structured lessons, code examples, and practical exercises that cover foundational concepts like prompt engineering, chaining agents, tool usage, plan execution, evaluation, and safety considerations. It breaks down how autonomous agents interact with external systems, handle iterative reasoning, and integrate with third-party services or APIs to perform real tasks — for example, web search, browsing, scheduling, or coding assistance. Students of the masterclass can follow written modules or Jupyter notebooks that illustrate concepts step by step and progressively build more capable agents. ...
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  • 9
    PythonPark

    PythonPark

    Python open source project "The Road to Self-Study Programming"

    PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. Because of this breadth, PythonPark serves both as a reference library (for quick lookup) and as a structured learning path for beginners and intermediate learners in Python. ...
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  • 10
    Android Interview Questions

    Android Interview Questions

    Your Cheat Sheet For Android Interview

    ...The content favors concise, practical answers that interviewers expect, helping candidates rehearse concepts and trade-offs rather than memorize trivia. It’s equally useful for brushing up before an interview and for leveling up day-to-day Android engineering knowledge.
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  • 11
    Node.js Best Practices

    Node.js Best Practices

    The Node.js best practices list (July 2024)

    ...It is designed for real-world constraints: production deployment, observability, CI/CD, containerization, and cross-functional collaboration. Because it’s continuously updated, the guide helps keep engineering standards aligned with evolving Node versions and tooling, turning tribal knowledge into a shared, documented baseline for teams.
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  • 12
    Serena

    Serena

    Agent toolkit providing semantic retrieval and editing capabilities

    Serena is a coding-focused agent toolkit that turns an LLM into a practical software-engineering agent with semantic retrieval and editing over real repositories. It operates as an MCP server (and other integrations), exposing IDE-like tools so agents can locate symbols, reason about code structure, make targeted edits, and validate changes. The toolkit is LLM-agnostic and framework-agnostic, positioning itself as a drop-in capability for different chat UIs, orchestrators, or custom agent stacks. ...
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  • 13
    TraceRoot

    TraceRoot

    Find the Root Cause in Your Code's Trace

    TraceRoot.AI is an open source, AI-native observability and debugging platform designed to help engineering teams resolve production issues faster. It consolidates telemetry into a single correlated execution tree that provides causal context for failures. AI agents operate over this structured view to summarize issues, pinpoint likely root causes, and even suggest actionable fixes or draft GitHub issues and pull requests. It offers interactive trace exploration with zoomable log clusters, span and latency views, and code-linked insights. ...
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  • 14
    Tegon

    Tegon

    Tegon is an open-source, AI-first alternative to Jira, Linear

    ...Issue tracking tools serve as a task database, directing engineers on what to work on but not aiding in faster task completion. Existing tools don't effectively assist Engineering Managers in the real-time task, feature, or bug prioritization.
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  • 15
    Svelto.ECS

    Svelto.ECS

    Svelto ECS C# Entity Component System

    ...The hardest problem to overcome is usually to shift mentality from OOP programming to ECS programming more than using the framework itself. Svelto.ECS wasn't born just from the needs of a large team, but also as a result of years of reasoning behind software engineering applied to game development. Svelto.ECS hasn't been written just to develop faster code, it has been designed to help develop better code. Performance gains is just one of the benefits in using Svelto.ECS, as ECS is a great way to write cache-friendly code.
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  • 16
    Great Expectations

    Great Expectations

    Always know what to expect from your data

    ...Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations are a great start, but it takes more to get to production-ready data validation. Where are Expectations stored? How do they get updated? How do you securely connect to production data systems? ...
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  • 17
    IQuest-Coder-V1 Model Family

    IQuest-Coder-V1 Model Family

    New family of code large language models (LLMs)

    IQuest-Coder-V1 is a cutting-edge family of open-source large language models specifically engineered for code generation, deep code understanding, and autonomous software engineering tasks. These models range from tens of billions to smaller footprints and are trained on a novel code-flow multi-stage paradigm that captures how real software evolves over time — not just static code snapshots — giving them a deeper semantic understanding of programming logic. They support native long contexts up to 128K tokens, enabling them to reason across large codebases and multi-file interactions without context fragmentation, and include “Thinking” variants optimized for complex reasoning and “Loop” variants with recurrent mechanisms to improve inference efficiency. ...
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  • 18
    Acontext

    Acontext

    Context data platform for building observable, self-learning AI agents

    Acontext is a cloud-native context data platform designed to support the development and operation of advanced AI agents. It provides a unified system to store and manage contexts, multimodal messages, artifacts, and task workflows, enabling developers to engineer context effectively for their agent products. The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also...
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  • 19
    iOS Interview Questions

    iOS Interview Questions

    iOS interview questions

    This repository collects a broad, categorized list of interview questions (with expected answers) that are commonly asked in iOS or Swift engineering interviews. Topics range from language fundamentals (memory management, ARC, value vs. reference types), to UIKit/SwiftUI, concurrency, Grand Central Dispatch, background execution, networking, design patterns, system frameworks, and architecture decisions. Each question is usually backed by sample explanations or code snippets, helping candidates understand not just what to answer but why it matters and how to reason about tradeoffs. ...
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  • 20
    JeecgBoot

    JeecgBoot

    Low-code enterprise web development platform

    ...Multi-tenancy, API scaffolding, and pluginable modules make it suitable for both monolith and microservice deployments. In practice, it shortens the path from schema to usable back-office systems, leaving custom logic and integrations as the primary engineering focus.
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  • 21
    APIAuto

    APIAuto

    The most advanced tool for HTTP API

    ...Support GET, POST, PUT, PATCH, DELETE, HEAD and other HTTP Method and Content-Type, URL /{Path}/{Variable}. Tencent's internal users include IEG Interactive Entertainment Business Group, TEG Technology Engineering Business Group, CSIG Cloud and Smart Business Group, etc.
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  • 22
    The Grand Complete Data Science Guide

    The Grand Complete Data Science Guide

    Data Science Guide With Videos And Materials

    The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project...
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  • 23
    CodeBehind Framework

    CodeBehind Framework

    CodeBehind is a modern back-end framework under ASP.NET Core.

    CodeBehind is a modern back-end framework under ASP.NET Core. CodeBehind was developed by Elanat in 2023 and competes with Microsoft's default web frameworks (ASP.NET Core MVC and Razor Pages and Blazor). CodeBehind is an engineering masterpiece that simultaneously provides the possibility of development based on MVC, Model-View, Controller-View, only View, and Web-Forms. The type of structure and naming in CodeBehind is nostalgia and reminds me of former Microsoft Web-Forms. The aspx extension is the files of the view section in the CodeBehind framework and they support standard syntax (<%=Standard%>) and Razor syntax (@Razor). ...
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  • 24
    Design Patterns Library

    Design Patterns Library

    A comprehensive design patterns library implemented in C#

    A comprehensive design patterns library implemented in C#, which covers various design patterns from the most commonly used ones to the lesser-known ones. Get familiar with and learn design patterns through moderately realistic examples. In software engineering, a design pattern is a general repeatable solution to a commonly occurring problem in software design. A design pattern isn't a finished design that can be transformed directly into code. It is a description or template for how to solve a problem that can be used in many different situations. In addition, design patterns allow developers to communicate using well-known, well-understood names for software interactions. ...
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  • 25
    GeoServer
    GeoServer is an open source software server written in Java that allows users to share and edit geospatial data. Designed for interoperability, it publishes data from any major spatial data source using open standards: WMS, WFS, WCS, WPS and REST
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    Downloads: 44,666 This Week
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