Tags: #ai-development

A comprehensive 21-lesson curriculum designed to introduce beginners to Generative AI and guide them through building AI applications.

A unified, open platform for accelerating AI model serving and scaling GenAI deployments with industry-leading performance across various hardware.

A flexible CLI-first framework for building, evaluating, and interacting with sophisticated multimodal LLM agents and workflows, offering comprehensive model and skill support.

LazyLLM simplifies the creation and iterative optimization of multi-agent large language model (LLM) applications with a low-code approach.

A comprehensive, full-stack guide to Retrieval Augmented Generation (RAG) technology for large language model application development, covering theory, practice, and engineering best practices.

A free, all-in-one platform for building, evaluating, and optimizing AI systems, offering tools for RAG, agents, fine-tuning, and synthetic data generation.

A comprehensive guide for Java backend interviews, covering core computer science, databases, distributed systems, high concurrency, system design, and AI application development.

A framework enabling spec-driven development for AI coding assistants to ensure alignment and predictability.

A Markdown-native, Git-integrated CLI tool and web interface for managing project tasks and collaborating with AI agents, enabling spec-driven AI development.

A Google-built open-source framework simplifying the development and deployment of full-stack AI applications across JavaScript, Go, and Python.

An open-source LLMOps platform integrating prompt management, evaluation, and observability to accelerate reliable LLM application development.

A comprehensive repository of best practices and implementation examples for developing with Claude Code's agentic engineering features.