Core Insights - The report titled "Global Large Model Open Source Development Ecosystem Panorama and Trends" was released by Ant Group and Inclusion AI, revealing the current state and future trends of the AI open-source field [1][3] - The report highlights China's significant position in the AI open-source ecosystem, with a data-driven approach to present the real status of global AI open-source development [3] Development Trends - The report includes 114 notable open-source projects across 22 technical fields, categorized into AI Agent and AI Infra [3] - 62% of the open-source projects in the large model ecosystem were created after the "GPT moment" in October 2022, indicating a rapid iteration characteristic of the AI open-source ecosystem [3][4] Developer Participation - Among approximately 360,000 global developers involved in the projects, 24% are from the United States, 18% from China, followed by India (8%), Germany (6%), and the UK (5%), with the US and China contributing over 40% of the core development force [4] Open Source Strategies - Chinese companies tend to favor open-weight models, while leading US firms often adopt closed-source strategies, reflecting a divergence in approaches to large model open-source development [4][8] AI Coding Tools Growth - There is a significant surge in AI programming tools that automate code generation and modification, enhancing developer efficiency and becoming a hot topic in the open-source community [5] - Tools are categorized into command-line tools (e.g., Gemini CLI) and integrated development environment plugins, each catering to different developer needs [5] Future of Software Development - The demand for AI assistants among global developers is rising, with a trend towards delegating repetitive tasks to AI tools, allowing programmers to focus on creative design and complex problem-solving [5] Timeline of Large Model Development - A timeline of large model releases from major domestic and international companies was published, detailing both open and closed models along with key parameters and modalities [6][8] - Key directions for large model development include a clear divergence between open-source and closed-source strategies in China and the US, a trend towards scaling model parameters under MoE architecture, and the rise of multi-modal models [8]