It’s been a month and a half since I decided to pursue independent living, and 43 days since becoming a content creator (UP主uploader). So far, there hasn’t been any significant progress in terms of my existing skills, the digital nomad lifestyle, or content creation. Regardless, it’s important to reflect and summarize at each stage.

Recently, Cursor has garnered widespread attention after an 8-year-old girl developed a chatbot in just 45 minutes, sparking renewed discussions on the potential of generative AI. Meanwhile, in an interview with The New Stack and during a “fireside chat” on September 11th with Civo co-founder and CEO Mark Boost, Kelsey Hightower delved into how generative AI is reshaping the future of developers.

The previous article discussed the principles of containerd’s image storage, where images are stored in two forms in containerd: one as the original image files (manifest, config, blob), and the other as snapshots of the decompressed layers. When running containers, only the decompressed layer snapshots are used, which leads to wasted space for the original image blobs in scenarios where image pushing is not needed. In environments with limited disk space, it is easy for images to consume excessive disk space. So how can we save space?

When we execute nerdctl pull, crictl pull, or nerdctl load, have you ever wondered how the images are stored in the system?

This article will introduce how containerd stores images to help you understand the image storage mechanism in containerd.

After deciding to embark on an independent survival mode, I initially tried fishing record videos, but found that I wasn’t very suited to this direction, which can be considered as a learning experience.

Cloud-native infrastructure, once a hot topic in the tech world, introduced advanced technologies such as containerization, microservices, and automated deployment, significantly improving the scalability and resilience of applications. However, in recent years, we seem to hear less about technologies like Kubernetes, Service Mesh, and Serverless, with AI, GPT, large models, and generative AI taking the spotlight instead. Does this mean the cloud-native infrastructure trend has passed? Has cloud-native infrastructure lost its appeal? This article explores the glorious history, current state, challenges, and future trends of cloud-native infrastructure from multiple angles.