#no-root-optimization

#no-root-optimization

Both work with Linux's existing swapping mechanism. Swapping (called paging in Windows) is a way for the kernel to handle running low on available RAM. It chooses pages of memory that aren't in use right now and copies them to disk, then those blocks can be marked as free and reused for something else.

He stormed up to my desk, leaned over my partition, and began his rant before I could so much as say hello. He screamed about the rubbish laptops and IT systems we had, nothing ever worked, all the usual stuff. The user's rant ended with a thundered 'Just FIX IT!'

I began by creating a soft link locally from my blog's repo of posts to the src/pages/posts of a new Astro site. My blog currently has 6742 posts (all high quality I assure you). Each one looks like so: --- layout: post title: "Creating Reddit Summaries with URL Context and Gemini" date: "2026-02-09T18:00:00" categories: ["development"] tags: ["python","generative ai"] banner_image: /images/banners/cat_on_papers2.jpg permalink: /2026/02/09/creating-reddit-summaries-with-gemini description: Using Gemini APIs to create a summary of a subreddit. --- Interesting content no one will probably read here...

At that point, backpressure and load shedding are the only things that retain a system that can still operate. If you have ever been in a Starbucks overwhelmed by mobile orders, you know the feeling. The in-store experience breaks down. You no longer know how many orders are ahead of you. There is no clear line, no reliable wait estimate, and often no real cancellation path unless you escalate and make noise.

Docker builds images in layers, caching each one.When you rebuild, Docker reuses unchanged layers to avoid re-executing steps - this is build caching. So the order of your instructions and the size of your build context have huge impact on speed and image size. Here are the quick tips to optimize and achieve 2 times faster speed building images: 1. Place least-changing instructions at the top

Kubernetes Horizontal Pod Autoscaler (HPA)'s delayed reactions might impact edge performance, while creating a custom autoscaler could achieve more stable scale-up and scale-down behavior based on domain-specific metrics and multiple signal evaluations. Startup time of pods should be included in the autoscaling logic because reacting only when CPU spiking occurs delays the increase in scale and reduces performance. Safe scale-down policies and a cooldown window are necessary to prevent replica oscillations, especially when high-frequency metric signals are being used.

NotebookLM is quietly becoming one of the most powerful tools for serious thinking work; yet most people use only a fraction of its potential. If you work with research, strategy, product thinking, or complex data research & analysis, NotebookLM can dramatically improve the quality of your decisions. I've demonstrated what NotebookLM is capable of in the article NotebookLM for Product Designers.