Search Results for "engineering" - Page 13
Sort By:
Showing 11438 open source projects for "engineering"
View related business solutions-
Earn up to 16% annual interest with Nexo.Put idle assets to work with competitive interest rates, borrow without selling, and trade with precision. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
1
Zoo Design Studio
The Zoo Design Studio app
Zoo Design Studio is a modern CAD application that combines traditional graphical modeling with fully programmable design through code, creating a hybrid workflow for engineering and design. The platform treats all CAD artifacts as human-readable code, allowing designs to be version-controlled, diffed, and managed using standard software development tools like Git. Users can interact with the system through a familiar point-and-click interface, but every action is translated into code in the underlying modeling language, ensuring consistency between visual and programmatic representations. ... -
2
aie-book
Resources for AI engineers
aie-book is an open-source companion repository that supports a comprehensive guide to building real-world applications with modern AI systems, particularly those based on foundation models. It focuses on bridging the gap between theoretical machine learning knowledge and practical engineering workflows required to deploy AI systems in production. The material emphasizes system design, data pipelines, evaluation strategies, and deployment considerations rather than just model training. It explores how to work with large language models, retrieval systems, and agent-based architectures, providing a practical perspective on how AI is actually used in industry. ... -
3
Machine Learning Study
This repository is for helping those interested in machine learning
...The project compiles notebooks, explanatory documents, and practical code examples that illustrate common machine learning workflows. Topics covered include supervised learning algorithms, feature engineering, model training, and performance evaluation techniques. The repository is structured as a learning resource that guides readers through building machine learning models step by step. It often demonstrates how to implement algorithms using widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Many examples include dataset preparation, visualization of results, and experimentation with different modeling approaches. -
4
Airbyte
Data integration platform for ELT pipelines from APIs, databases
...Moving critical data with Airbyte is as easy and reliable as flipping on a switch. Our teams process more than 300 billion rows each month for ambitious businesses of all sizes. Enable your data engineering teams to focus on projects that are more valuable to your business. Building and maintaining custom connectors have become 5x easier with Airbyte. With an average response rate of 10 minutes or less and a Customer Satisfaction score of 96/100, our team is ready to support your data integration journey all over the world. -
8 Monitoring Tools in One APM. Install in 5 Minutes.AppSignal works out of the box for Ruby, Elixir, Node.js, Python, and more. 30-day free trial, no credit card required.
-
5
Claude Code Skills & Plugins Hub
270+ Claude Code plugins with 739 agent skills
Claude Code Plugins Plus Skills is a large open-source ecosystem of plugins and AI “skills” designed to extend the capabilities of Claude Code development agents. The repository functions as a marketplace-style collection of hundreds of plugins and specialized skills that enable Claude Code to perform complex development, automation, and operational tasks. These plugins cover a wide range of domains including DevOps automation, security testing, API debugging, infrastructure management, and... -
6
Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
...The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. What makes it particularly valuable is its holistic nature: rather than focusing only on modeling or theory, it also addresses the broader lifecycle of data-science work, data ingestion, cleaning, EDA, feature engineering, model building, validation, deployment, etc. -
7
DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving... -
8
AERIS-10
Open-source, low-cost 10.5 GHz PLFM phased array RADAR system
...The project likely implements algorithms for generating, analyzing, and visualizing chirp-based radar signals, enabling experimentation with time-frequency characteristics and signal reconstruction. It is designed to support research or educational use in radar engineering, particularly in understanding how modulated pulses interact with targets and noise. The repository structure suggests an emphasis on simulation rather than hardware integration, allowing users to test radar concepts in a controlled software environment. It likely includes tools for waveform synthesis, matched filtering, and spectral analysis, which are critical for interpreting radar returns. -
9
Spring AI
An Application Framework for AI Engineering
Spring AI is a framework within the Spring ecosystem that simplifies the integration of artificial intelligence capabilities into Java applications by providing abstractions over various AI services and models. It is designed to align with familiar Spring development patterns, allowing developers to incorporate AI features such as chat, embeddings, and prompt handling using consistent APIs. The framework supports multiple providers, including OpenAI, Azure OpenAI, and other compatible... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
Apollo-11
Original Apollo 11 Guidance Computer (AGC) source code
Apollo-11 hosts the original Apollo 11 Guidance Computer (AGC) source code for the Command Module and Lunar Module, faithfully transcribed from historical listings. It is written in AGC assembly and reflects 1960s software engineering practices, complete with comments from the original programmers. The code is both a cultural artifact and a technical reference, illustrating how limited memory and processor constraints shaped algorithms and system design. Developers can examine navigation routines, guidance logic, and task scheduling in an environment with no luxury features—everything is explicit and resource-aware. ... -
11
ML-NLP
This project is a common knowledge point and code implementation
...In addition to technical explanations, the project organizes content into topic areas such as deep learning fundamentals, natural language processing techniques, and algorithm engineering practices. -
12
AgentGuide
AI Agent Development Guide, LangGraph in Action, Advanced RAG
...The guide covers topics such as agent frameworks, retrieval-augmented generation systems, multi-agent collaboration, memory management, and tool usage. It also includes practical projects, interview preparation materials, and curated research papers related to AI agents and LLM engineering. The project is designed not only for learning but also for career preparation, helping developers understand how to build portfolio projects and prepare for AI engineering roles. -
13
llm_interview_note
Mainly record the knowledge and interview questions
...It covers fundamental topics such as the historical evolution of language models, tokenization methods, word embeddings, and the architectural foundations of transformer-based models. The repository also explores practical engineering concerns including distributed training strategies, dataset construction, model parameters, and scaling techniques used in large-scale machine learning systems. By organizing topics in a hierarchical documentation format, it enables readers to progress from basic NLP concepts to advanced topics like mixture-of-experts architectures and large-scale training frameworks. -
14
Fish Skin AI Knowledge Base
Programmer Fish Skin's AI Resource Guide
Fish Skin AI Knowledge Base is a comprehensive open knowledge base and tutorial collection that helps developers quickly learn, evaluate, and apply modern AI technologies, especially in the context of “vibe coding” and practical AI product development. The project curates structured learning paths covering model selection, AI coding tools, agent platforms, prompt engineering, and full-stack AI application workflows. It combines beginner-friendly introductions with advanced guides on context management, hallucination mitigation, and production-quality code practices. The repository also includes hands-on project tutorials that walk users from zero to deployable AI products across multiple application types. ... -
15
CBIG
Computational Brain Imaging Group tools
CBIG is a comprehensive toolkit maintained by Thomas Yeo’s Computational Brain Imaging Group containing tools for processing and analyzing neuroimaging data—including fMRI preprocessing pipelines, brain parcellation algorithms, mental disorder subtyping models, fMRI dynamic models, registrations between brain spaces, and phenotypic prediction algorithms. After cloning/downloading this repository, please see README inside setup directory to see instructions on how to set up your local... -
16
ChefKiss Inferno
Emulating Apple Silicon devices
...Inferno is especially relevant for developers working on emulation, virtualization, or cross-platform graphics stacks, as it attempts to expose native GPU functionality in unconventional contexts. Its development reflects a deep understanding of Apple’s proprietary hardware and driver model, often requiring reverse engineering and experimentation. -
17
Jazz2
Jazz² Resurrection: Open-source reimplementation of Jazz Jackrabbit 2
jazz2 is a reverse engineering and decompilation project of Jazz Jackrabbit 2, the iconic 2D platformer developed by Epic MegaGames in the late 1990s. This ambitious project aims to recreate the original game from its binary form into readable and compilable C++ code. By doing so, it opens the door for native builds, modding, bug fixing, and potential enhancements beyond the limitations of the original closed-source game. -
18
MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,... -
19
Anything Analyzer
AI Agent/IDE | All-in-one protocol analysis toolkit
...The system is built to connect seamlessly with AI agents and development environments through MCP server integration. It is particularly useful for security research, debugging, and reverse engineering workflows. Overall, it transforms complex network inspection into an extensible and intelligent analysis platform. -
20
Polymarket Data
Polymarket Data Retriever that fetches, processes, and structures data
Polymarket Data is a comprehensive data engineering pipeline designed to collect, process, and structure trading activity from the Polymarket prediction market ecosystem into analyzable datasets. The system operates as a multi-stage pipeline that integrates data from both off-chain APIs and on-chain event sources, enabling users to reconstruct full trading activity including markets, order events, and executed trades. -
21
GSD 2
A powerful meta-prompting, context engineering
GSD 2 is a project focused on automating and streamlining development workflows through structured build systems and tooling. It aims to simplify the process of configuring, building, and deploying applications by providing predefined templates and automation scripts. The system is designed to reduce manual setup and improve consistency across development environments. It supports modular configurations, allowing users to adapt the build process to different project requirements. The project... -
22
Synkra AIOS
AI-Orchestrated System for Full Stack Development
...The framework includes features for project setup, environment configuration, deployment scaffolding, and intelligent code generation, potentially reducing time spent on boilerplate and repetitive engineering tasks. Synkra AIOS positions itself as a productivity middleware that integrates AI reasoning within traditional devops, front-end, and back-end workflows, allowing teams to produce production-ready applications faster. -
23
NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers
...Built on top of the SciML ecosystem, it provides a flexible and composable interface for defining PDEs and training neural networks to approximate their solutions. NeuralPDE.jl enables hybrid modeling, data-driven discovery, and fast PDE solvers in high dimensions, making it suitable for scientific research and engineering applications. -
24
Porcupine
On-device wake word detection powered by deep learning
Build always-listening yet private voice applications. Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux... -
25
PySINDy
A package for the sparse identification of nonlinear dynamical systems
...Instead of fitting a purely predictive machine learning model, PySINDy attempts to recover interpretable differential equations that explain how a system evolves over time. This approach is particularly valuable in scientific fields such as physics, engineering, and biology where researchers seek both predictive accuracy and theoretical insight. The library provides tools for constructing libraries of candidate functions, performing sparse regression, and validating discovered models against observed data. It integrates with standard Python scientific computing libraries, making it easy to apply to experimental datasets or simulated systems.
