Awesome Open Source AI
Section: 14. Resources & Learning · Open-source textbook and code repository covering AI agent design principles, architectures, and engineering practice. Apache 2.0 licensed.
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Appears in 4 awesome lists
《深入理解 AI Agent:设计原理与工程实践》开源项目是一座将前沿大模型智能体理论转化为可落地代码的工程化知识库,精准击穿了当前开发者在从理论认知迈向实际部署过程中资料割裂、缺乏系统实践指引的痛点。该项目彻底打破了传统技术图书“重概念轻实操”或社区仓库“碎片化拼凑”的固有困境,凭借单一线性叙事构建的知识体系与标准化交付形态,为学习者铺设了一条从底层逻辑到上层应用的平滑过渡路径。其核心优势在于高度一体化的内容编排、严丝合缝的章节配套代码生态以及贯穿始终的工程化思维导向,这不仅有效规避了多源资料整合时的环境冲突风险,更通过标准化的技术栈封装大幅降低了上手门槛,使初学者无需在海量…
Section: 14. Resources & Learning · Open-source textbook and code repository covering AI agent design principles, architectures, and engineering practice. Apache 2.0 licensed.
Section: Other · 《深入理解 AI Agent:设计原理与工程实践》(李博杰 著)开源主仓库:全书正文、编译版 PDF 与按章配套代码
Section: Articles & Books · Open-source book on AI agent design and engineering, with bilingual (Chinese/English) text and per-chapter code covering context engineering, tools, evaluation, and multi-agent systems.
Section: 大语言对话模型及数据 · 《深入理解 AI Agent:设计原理与工程实践》开源项目是一座将前沿大模型智能体理论转化为可落地代码的工程化知识库,精准击穿了当前开发者在从理论认知迈向实际部署过程中资料割裂、缺乏系统实践指引的痛点。该项目彻底打破了传统技术图书“重概念轻实操”或社区仓库“碎片化拼凑”的固有困境,凭借单一线性叙事构建的知识体系与标准化交付形态,为学习者铺设了一条从底层逻辑到上层应用的平滑过渡路径。其核心优势在于高度一体化的内容编排、严丝合缝的章节配套代码生态以及贯穿始终的工程化思维导向,这不仅有效规避了多源资料整合时的环境冲突风险,更通过标准化的技术栈封装大幅降低了上手门槛,使初学者无需在海量…
The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.
books: Freely available programming books
Curated list of artificial intelligence courses, books, video lectures, and papers for developers and researchers. MIT licensed.
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.
🟪 - Another awesome list for mathematics, useful generally but often applicable to programming.
(from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).