What Is AI-Native Infrastructure?
Core definition, boundaries, and evaluation criteria for AI-native infrastructure, focusing on model behavior, compute scarcity, and uncertainty governance.
Recent content in Jimmy Song 的博客 on Jimmy Song
Core definition, boundaries, and evaluation criteria for AI-native infrastructure, focusing on model behavior, compute scarcity, and uncertainty governance.
Three planes (Intent, Execution, Governance) + closed-loop feedback for AI-native infrastructure architecture alignment.
Discussing Intent vs Consequence, why compute and cost are the first-order constraints of AI-native infrastructure.
Analyzing the closed-loop governance of metrics, budgets, isolation, and sharing in AI-native infrastructure, and explaining how SLO maps to cost and risk.
Redrawing boundaries across platform, infra, ML, and security, and transforming accountability and collaboration in the AI era.
An actionable roadmap for AI-native migration, covering bypass pilot, domain isolation, AI-first refactoring, and anti-patterns, with focus on governance loops and organizational contracts.
Bilingual glossary of core AI-native infrastructure terminology for aligning organizational language.
Ten critical questions for CEO/CTO to evaluate AI-native infrastructure readiness.
A curated collection of AI learning resources we removed from the AI Resources list: awesome lists, courses, tutorials, and cookbooks. These educational materials deserve their own spotlight.
Before ChatGPT and TensorFlow, there was Hadoop, Kafka, and Kubernetes. This post honors the traditional open source infrastructure that became the foundation of today's AI revolution.
Observations from my first month at Dynamia: From cloud native to AI Native Infra, why this direction is worth investing in, and the key issues and opportunities in compute governance.
Exploring how Spec becomes the governable core asset in Agent-Driven Development (ADD) and the trend toward control-plane engineering systems.
Comparing Miaoyan, Zhipu, and Shandianshuo voice input methods for developers: speed, stability, command capabilities, and cost models.
How technical standards and data sovereignty shape AI open source paths and infrastructure competition in the global AI era.
Joining Dynamia as Open Source Ecosystem VP to drive AI-native infrastructure ecosystem development, transforming compute from hardware consumption to core asset.
A hands-on experience with Verdent's standalone Mac app, exploring how parallel AI agents, isolated workspaces, and task-oriented workflows change real-world development.
A look back at the major changes in 2025: shifting from Cloud Native to AI Native Infrastructure, AI tool ecosystem, and major website improvements.
Manus's acquisition by Meta sparked polarized opinions. This article explores the butterfly effect in AI applications and key lessons for entrepreneurs on growth strategies.
Beijing and Shanghai's open source plans reveal opportunities and challenges for China's AI infrastructure, balancing technology and governance.
In 2025, software engineering shifts from code-centric to runtime and cost governance. AI and Agents move complexity to runtime, compute, and budget layers, reshaping engineering value.
Explores why AI Agents need Kubernetes infrastructure and how Agent orchestration, MCP services, and AI gateways enable production-ready AI architectures.
Comprehensive introduction to the AI Open Source Landscape's positioning, interface, scoring model, and data mechanisms to help developers efficiently discover quality AI projects.
2026 AI's turning point: not models, but infrastructure, agentic runtimes, GPU efficiency, and new organizational forms.
From an engineering and organizer's perspective, real changes at COSCon'25: AI as the default backdrop, discussions returning to engineering issues, and Chinese open source entering a long-term phase.
An analysis of Block's Goose project, why it became one of the first Agentic AI Foundation (AAIF) projects, and what this means for Agentic Runtime and the evolution of AI-Native infrastructure.
How ARK uses cloud-native architecture and declarative runtime to drive engineering adoption of multi-agent systems and shape the Agentic Runtime ecosystem.
Lunary, an open-source project in the AI DevTool space, suddenly deleted its GitHub repo, exposing the instability of commercial open source projects.
An analysis of the background, strategic urgency, differences and division of labor between Agentic AI Foundation (AAIF) and CNCF/CNAI, and its significance for the AI Native era.
KCD Beijing 2026: Exploring the future of Cloud Native and AI along the technology axis.
Bun's acquisition by Anthropic marks the first time a general-purpose language runtime is integrated into a large model engineering system, revealing a structural trend for AI-native runtimes.
Analyzing Ark from architecture, semantics, community activity, and engineering paradigms to reveal its impact on 2026 AI Infra trends and the ArkSphere community.
Analysis of McKinsey's Ark project: architecture, CRDs, control plane, design paradigms, production readiness, and implications for ArkSphere and AI infrastructure.
ArkSphere Community launches for developers building AI Infrastructure, runtimes, and agent systems. Focused on open-source, verifiable, and evolvable solutions.
AI's real turning point is moving from using AI tools to building AI systems. Why the era of AI engineering hasn't begun, and the developer opportunity in the next three years.
How to configure the extension marketplace, install AMP and CodeX plugins, and adjust editor settings to make Antigravity behave like VS Code for AI development.
An analysis of the Cloudflare global outage on November 18, 2025, exploring implicit assumptions, automated configuration pipelines, and systemic risks in modern infrastructure.
A decade of cloud native evolution, a look ahead to AI-Native Platform engineering, technical layers, and key changes. KubeCon NA 2025 signals a new era.
Based on months of deep usage, this article analyzes how NotebookLM helps me learn new technologies, read complex documents, generate teaching outlines, and shares future improvement expectations.
An analysis of Helm 4's core changes, including Server-Side Apply, WASM plugin system, kstatus status model, reproducible builds, and content hash caching, with a timeline review of Helm's history.
Kimi K2 Thinking's open source marks China's entry into thinking models. This article reviews its technical approach and compares it with Claude and Gemini.
A comparison of TRAE SOLO and VS Code (Copilot, Agent HQ) via the AI Engineering Entity framework, focusing on automation, collaboration, model transparency, and engineering roles.
Analysis of closed-source model acceleration and open-source ecosystem response, exploring core engineering contradictions and infrastructure evolution.
The retirement of Ingress NGINX reveals technical debt, migration paths, and the trend toward standardized traffic management in cloud native infrastructure.
Discover what defines a truly Kubernetes-native AI platform, key criteria for conformance, and how standardization drives interoperability and growth in cloud-native AI infrastructure.
A developer's perspective on why ChatGPT Atlas is massive, architecturally complex, fundamentally different from Chrome, and a deep dive into its Agent runtime mechanism and limitations.
After two weeks using Atlas as my main browser, I break down its architecture, workflow boosts, pain points, and future directions from a developer's view.
CNCF is standardizing AI inference infrastructure for scalable deployment in multi-cluster Kubernetes environments through KAITO and KubeFleet.
Essential reading for cloud native and AI-native architects: how KServe, vLLM, llm-d, and WG Serving form the cloud native 'quartet' for large model inference, their roles, synergy, and ecosystem trends.
Explore why Kubernetes is the ideal runtime for AI inference — delivering elastic, cost-efficient, low-latency model serving with GPU-aware autoscaling, versioning, and observability.
In the AI era, why are materials like glass getting cheaper, but installation and labor costs keep rising? The answer lies in the interplay of Jevons Paradox and the Baumol Effect.
Recent content in Jimmy Song 的博客 on Jimmy Song
Core definition, boundaries, and evaluation criteria for AI-native infrastructure, focusing on model behavior, compute scarcity, and uncertainty governance.
Three planes (Intent, Execution, Governance) + closed-loop feedback for AI-native infrastructure architecture alignment.
Discussing Intent vs Consequence, why compute and cost are the first-order constraints of AI-native infrastructure.
Analyzing the closed-loop governance of metrics, budgets, isolation, and sharing in AI-native infrastructure, and explaining how SLO maps to cost and risk.
Redrawing boundaries across platform, infra, ML, and security, and transforming accountability and collaboration in the AI era.
An actionable roadmap for AI-native migration, covering bypass pilot, domain isolation, AI-first refactoring, and anti-patterns, with focus on governance loops and organizational contracts.
Bilingual glossary of core AI-native infrastructure terminology for aligning organizational language.
Ten critical questions for CEO/CTO to evaluate AI-native infrastructure readiness.
A curated collection of AI learning resources we removed from the AI Resources list: awesome lists, courses, tutorials, and cookbooks. These educational materials deserve their own spotlight.
Before ChatGPT and TensorFlow, there was Hadoop, Kafka, and Kubernetes. This post honors the traditional open source infrastructure that became the foundation of today's AI revolution.
Observations from my first month at Dynamia: From cloud native to AI Native Infra, why this direction is worth investing in, and the key issues and opportunities in compute governance.
Exploring how Spec becomes the governable core asset in Agent-Driven Development (ADD) and the trend toward control-plane engineering systems.
Comparing Miaoyan, Zhipu, and Shandianshuo voice input methods for developers: speed, stability, command capabilities, and cost models.
How technical standards and data sovereignty shape AI open source paths and infrastructure competition in the global AI era.
Joining Dynamia as Open Source Ecosystem VP to drive AI-native infrastructure ecosystem development, transforming compute from hardware consumption to core asset.
A hands-on experience with Verdent's standalone Mac app, exploring how parallel AI agents, isolated workspaces, and task-oriented workflows change real-world development.
A look back at the major changes in 2025: shifting from Cloud Native to AI Native Infrastructure, AI tool ecosystem, and major website improvements.
Manus's acquisition by Meta sparked polarized opinions. This article explores the butterfly effect in AI applications and key lessons for entrepreneurs on growth strategies.
Beijing and Shanghai's open source plans reveal opportunities and challenges for China's AI infrastructure, balancing technology and governance.
In 2025, software engineering shifts from code-centric to runtime and cost governance. AI and Agents move complexity to runtime, compute, and budget layers, reshaping engineering value.
Explores why AI Agents need Kubernetes infrastructure and how Agent orchestration, MCP services, and AI gateways enable production-ready AI architectures.
Comprehensive introduction to the AI Open Source Landscape's positioning, interface, scoring model, and data mechanisms to help developers efficiently discover quality AI projects.
2026 AI's turning point: not models, but infrastructure, agentic runtimes, GPU efficiency, and new organizational forms.
From an engineering and organizer's perspective, real changes at COSCon'25: AI as the default backdrop, discussions returning to engineering issues, and Chinese open source entering a long-term phase.
An analysis of Block's Goose project, why it became one of the first Agentic AI Foundation (AAIF) projects, and what this means for Agentic Runtime and the evolution of AI-Native infrastructure.
How ARK uses cloud-native architecture and declarative runtime to drive engineering adoption of multi-agent systems and shape the Agentic Runtime ecosystem.
Lunary, an open-source project in the AI DevTool space, suddenly deleted its GitHub repo, exposing the instability of commercial open source projects.
An analysis of the background, strategic urgency, differences and division of labor between Agentic AI Foundation (AAIF) and CNCF/CNAI, and its significance for the AI Native era.
KCD Beijing 2026: Exploring the future of Cloud Native and AI along the technology axis.
Bun's acquisition by Anthropic marks the first time a general-purpose language runtime is integrated into a large model engineering system, revealing a structural trend for AI-native runtimes.
Analyzing Ark from architecture, semantics, community activity, and engineering paradigms to reveal its impact on 2026 AI Infra trends and the ArkSphere community.
Analysis of McKinsey's Ark project: architecture, CRDs, control plane, design paradigms, production readiness, and implications for ArkSphere and AI infrastructure.
ArkSphere Community launches for developers building AI Infrastructure, runtimes, and agent systems. Focused on open-source, verifiable, and evolvable solutions.
AI's real turning point is moving from using AI tools to building AI systems. Why the era of AI engineering hasn't begun, and the developer opportunity in the next three years.
How to configure the extension marketplace, install AMP and CodeX plugins, and adjust editor settings to make Antigravity behave like VS Code for AI development.
An analysis of the Cloudflare global outage on November 18, 2025, exploring implicit assumptions, automated configuration pipelines, and systemic risks in modern infrastructure.
A decade of cloud native evolution, a look ahead to AI-Native Platform engineering, technical layers, and key changes. KubeCon NA 2025 signals a new era.
Based on months of deep usage, this article analyzes how NotebookLM helps me learn new technologies, read complex documents, generate teaching outlines, and shares future improvement expectations.
An analysis of Helm 4's core changes, including Server-Side Apply, WASM plugin system, kstatus status model, reproducible builds, and content hash caching, with a timeline review of Helm's history.
Kimi K2 Thinking's open source marks China's entry into thinking models. This article reviews its technical approach and compares it with Claude and Gemini.
A comparison of TRAE SOLO and VS Code (Copilot, Agent HQ) via the AI Engineering Entity framework, focusing on automation, collaboration, model transparency, and engineering roles.
Analysis of closed-source model acceleration and open-source ecosystem response, exploring core engineering contradictions and infrastructure evolution.
The retirement of Ingress NGINX reveals technical debt, migration paths, and the trend toward standardized traffic management in cloud native infrastructure.
Discover what defines a truly Kubernetes-native AI platform, key criteria for conformance, and how standardization drives interoperability and growth in cloud-native AI infrastructure.
A developer's perspective on why ChatGPT Atlas is massive, architecturally complex, fundamentally different from Chrome, and a deep dive into its Agent runtime mechanism and limitations.
After two weeks using Atlas as my main browser, I break down its architecture, workflow boosts, pain points, and future directions from a developer's view.
CNCF is standardizing AI inference infrastructure for scalable deployment in multi-cluster Kubernetes environments through KAITO and KubeFleet.
Essential reading for cloud native and AI-native architects: how KServe, vLLM, llm-d, and WG Serving form the cloud native 'quartet' for large model inference, their roles, synergy, and ecosystem trends.
Explore why Kubernetes is the ideal runtime for AI inference — delivering elastic, cost-efficient, low-latency model serving with GPU-aware autoscaling, versioning, and observability.
In the AI era, why are materials like glass getting cheaper, but installation and labor costs keep rising? The answer lies in the interplay of Jevons Paradox and the Baumol Effect.