Search Results for "engineering"
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Showing 53 open source projects for "engineering"
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Loop Engineering
Practical patterns, starters & CLI tools for loop engineering with AI
Loop Engineering is a practical reference repository for designing loop-based workflows with AI coding agents. It focuses on replacing repeated manual prompting with systems that prompt, verify, schedule, and hand off work over time. The project is aimed at developers using tools such as Grok, Claude Code, Codex, Cursor, and other coding agents. -
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Machine Learning Systems
Introduction to Machine Learning Systems
Machine Learning Systems is an open educational repository that serves as the source and learning stack for the Machine Learning Systems textbook, a project focused on teaching how to engineer AI systems that work reliably in real-world environments. Rather than concentrating only on model training, the material emphasizes the broader discipline of AI engineering, covering efficiency, reliability, deployment, and evaluation across the full lifecycle of intelligent systems. The repository includes textbook content, supporting labs, and companion tools such as TinyTorch to help learners move from theory to hands-on experimentation. Its mission is to establish AI systems engineering as a foundational discipline alongside traditional software and computer engineering. -
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GSD Core
Meta-prompting, context engineering, spec-driven development system
GSD Core is the core framework behind Open GSD, a workflow system for AI-assisted software engineering. It is designed to keep agent-driven development organized by turning vague goals into structured research, plans, tasks, implementation phases, verification steps, and handoffs. The project focuses on cleaner context, verified work, and more honest project history when working with coding agents. It supports installation across several AI coding runtimes and offers profiles for different levels of workflow surface area. ... -
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Ponytail
Makes your AI agent think like the laziest senior dev in the room
Ponytail is an AI-agent ruleset and plugin system that pushes coding assistants toward simpler, smaller, and less over-engineered solutions. It is built around the idea that the best code is often the code that does not need to be written. Before generating new code, it encourages the agent to check whether the feature is necessary, whether the standard library already solves it, whether the platform has a native capability, or whether an existing dependency can do the job. The project... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Pro Workflow
Claude Code learns from your corrections: self-correcting memory
Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. The framework includes a collection of prebuilt skills and workflows that have been tested in real-world scenarios, providing a strong starting point for developers. ... -
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TypeChat
Library for building type-safe natural language interfaces with LLMs
...With the rise of large language models, developers can interpret user requests more easily, but they still face challenges related to output reliability, safety, and structured responses. TypeChat addresses these challenges by replacing traditional prompt engineering with a concept called schema engineering. Instead of writing complex prompts, developers define types that represent the intents supported by their applications. It then uses those type definitions to construct prompts for language models and translate user input into structured data that follows the defined schema. -
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Superpowers
An agentic skills framework & software development methodology
Superpowers Framework is a widely-used agentic skills framework and methodology for software development that equips coding agents like Claude Code with structured capabilities to plan, develop, test, and review code systematically. Instead of simply generating code, Superpowers drives an AI through a thoughtful software engineering workflow that starts with clarifying project intent, teasing out a detailed specification, and creating an implementation plan that is readable and actionable. It incorporates test-driven development, task planning, and sub-agent orchestration so that each engineering task is executed, checked, and iterated upon with rigor rather than ad-hoc improvisation. ... -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
...Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. Each lesson includes a short video, a written guide, runnable samples for Azure OpenAI, the GitHub Marketplace Model Catalog, and the OpenAI API, plus a “Keep Learning” section for deeper study. -
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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. ... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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System Prompts Leaks
Collection of extracted System Prompts from popular chatbots
System Prompts Leaks is a curated repository that collects known leaked or publicly exposed system prompts used by large language models, organized so researchers, developers, and AI safety advocates can analyze them in one place. The project highlights how system prompts — instructions that strongly influence model behavior — have been inadvertently shared in forums, datasets, and open repositories, illustrating common patterns and potential vulnerabilities in prompt design and deployment.... -
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Generative AI for beginners with JS
Join a time-traveling adventure where you meet history’s legends
...Each lesson includes written explanations, hands-on exercises, quizzes, and supporting videos to help developers learn the material progressively. Topics covered include prompt engineering, building AI-powered applications, working with structured outputs, integrating retrieval-augmented generation, and enabling tool or function calling in AI systems. The repository focuses specifically on how generative AI can be integrated into web, mobile, or desktop applications using JavaScript frameworks and APIs. -
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Get Shit Done
A light-weight and powerful meta-prompting, context engineering
Get Shit Done is a high-impact, open-source meta-prompting and spec-driven development system designed to streamline building software with AI assistants like Claude Code, OpenCode, and Gemini CLI. It solves “context rot” — the degradation of AI quality as a chat session grows — by structuring your idea into precise, context-engineered steps that are researched, scoped, planned, executed, and verified with clear commands and outputs instead of ad-hoc prompts. The project emphasizes... -
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designlang
Extract any website's complete design system with one command
...The system includes accessibility analysis features, such as WCAG compliance checks and CSS health audits, helping developers improve usability and standards compliance. It can be used via CLI or browser extension, making it flexible for different workflows. Overall, design-extract automates the process of reverse-engineering design systems, significantly accelerating frontend development. -
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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
HumanLayer is an open source development environment designed to help developers orchestrate and manage AI coding agents working within complex software projects. It provides a framework and tooling that allow AI agents to research, plan, and implement changes in large codebases while maintaining structured workflows. It focuses on enabling AI-assisted development through coordinated agent workflows rather than isolated code generation tasks. HumanLayer integrates with modern AI models and... -
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Easy-Vibe
Tutorial on Product Prototype, AI Capability Integration
...The learning path is divided into progressive stages that cover beginner concepts, full-stack development, and advanced multi-platform application development. Throughout the curriculum, learners explore topics such as prompt engineering, AI tool integration, product prototyping, and deployment strategies for AI-enabled applications. -
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Devon
Open source AI pair programmer for coding, debugging, automation
Devon is an open source AI-powered pair programming tool designed to assist developers with software engineering tasks through natural language interaction. It operates as an agent-based system that can explore codebases, edit files, and execute development workflows with minimal manual intervention. Devon uses a client-server architecture with a Python backend and multiple user interfaces, including a terminal interface and an Electron-based desktop application. -
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GNNPCSAFT Web App
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository. -
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GNNPCSAFT Chat
Chatbot with GNNPCSAFT
The GNNPCSAFT Chat is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, you can chat with LLM models (Gemini or Ollama) with GNNPCSAFT tools, allowing you to ask questions about the PC-SAFT parameters of various compounds, predict thermodynamic properties, and get insights into the... -
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Text-to-image Playground
A playground to generate images from any text prompt using SD
...Originally built around DALL-E Mini, the project later transitioned to using Stable Diffusion, enabling more detailed and higher-quality image synthesis. The system combines a backend machine learning service with a browser-based frontend interface that lets users experiment interactively with prompt engineering and generative AI. Developers can run the application locally or deploy it using cloud infrastructure, making it accessible both for experimentation and educational use. The platform demonstrates how large generative models can be integrated into user-friendly tools for creative exploration and rapid prototyping. It also serves as a reference architecture for building full-stack generative AI applications that connect model inference pipelines with web interfaces. -
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Null-CLi
Secure The Unknown
NullSquare is an AI-powered security platform that can do both penetration testing and compliance auditing. -
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dashAI
dashAI: an interactive platform for training, evaluating and deploying
dashAI is an open-source, No-code workbench for Exploratory Data Analysis and classical ML. Visual data preparation, multi-model experiments, XAI explainability, and a plugin-based extensible catalog. The platform guides users through a complete, traceable workflow — data ingestion → visual exploration → preprocessing → model training → evaluation → explainability — without writing a single line of code. Each step is explicit and reversible, keeping the user in control rather than... -
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CodinIT.dev
Free, local, open-source AI app builder
CodinIT.dev is a free, local, open source AI app builder that lets you go from idea to full-stack application entirely on your machine, no coding required, just chat with AI. You can build unlimited apps with real-time previews, instant undo, and responsive, frictionless workflows. Deep Supabase integration means you can create UI and backend logic in one cohesive environment, while the model-agnostic architecture lets you connect to any AI, whether cloud-based (Gemini 3 Pro, GPT-5,... -
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pipeless
A computer vision framework to create and deploy apps in minutes
Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new... -
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fe4ml-zh
Feature Engineering for Machine Learning
fe4ml-zh is an open-source project that provides a Chinese translation and structured documentation of the book Feature Engineering for Machine Learning. The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. ... -
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Learn Prompting
This website is a free, open-source guide on prompt engineering
...Coding is a great skill to learn alongside prompt engineering. We recommend learning Python, as it is a popular language for AI and machine learning. Be among the first to access the certification program as soon as it launches.