Popular GitHub Repositories for Empirical Software Engineering

21 Lessons, Get Started Building with Generative AI ? https://microsoft.github.io/generative-ai-for-beginners/

? Guides, papers, lecture, notebooks and resources for prompt engineering

? The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

Deepfakes Software For All

? Toolkit to help you get started with Spec-Driven Development

Learn how to design, develop, deploy and iterate on production-grade ML applications.

? Create, share, and use custom AI code assistants with our open-source IDE extensions and hub of models, rules, prompts, docs, and other building blocks

A complete daily plan for studying to become a machine learning engineer.

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

? Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. ?YC W23

Turns Data and AI algorithms into production-ready web applications in no time.

DeepFaceLab is the leading software for creating deepfakes.

Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, Automation, Home Assistant, and Networking.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

Machine Learning Engineering Open Book

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.

A curated list of references for MLOps