Jimmy Song – Jimmy Song's Blog

The waves of technology keep evolving; only by actively embracing change can we continue to create value.

The success or failure of AI applications often lies not in the technology itself, but in the ability to scale delivery and create a closed loop.

Institutionalized open source marks a new starting point for China’s AI Infra, but true breakthroughs and risks lie in the engineering and governance details.

In 2025, the core of software engineering is no longer just about code itself, but about runtime controllability and cost governance.

As a long-time practitioner in the cloud native field, I am increasingly convinced of one thing: AI Agents are not just a change in application form, but a migration of infrastructure paradigms.

The AI Open Source Landscape is not just a project directory, but an innovative attempt to bring transparency and quantifiability to the AI open source ecosystem.

The real turning point for AI in 2026 is not autonomy, but the maturity of infrastructure - where agentic runtimes, GPU efficiency, and organizational design will decide who wins.

Attending COSCon'25 in Beijing, I observed firsthand how open source in China is shifting: AI is now the default context, discussions are grounded in real engineering, and the community is embracing long-term thinking.

Goose is not a project that excites you at first glance in this wave of Agent innovation, but its entry into AAIF signals a deeper shift in how we think about Agentic Runtime and AI-Native infrastructure.

The deep integration of cloud native and AI, with the ARK platform, provides a new paradigm for engineering multi-agent systems.