Updates and recent posts about Flask..

Posts
Description

Link

@faun shared a link, 4 days, 18 hours ago

Paused Kubernetes project finds path forward

TheExternal Secrets Operator (ESO)is moving again. After hitting pause from maintainer burnout, it’s back under CNCF incubation—with a rebooted structure in place. New governance, clear contributor paths, and support tracks for CI, core dev, and testing are all in. But don’t expect fresh releases ju..

Link

@faun shared a link, 4 days, 18 hours ago

Scaling Prometheus: Managing 80M Metrics Smoothly

Flipkart ditched its creakyStatsD + InfluxDBstack for afederated Prometheussetup—built to handle 80M+ time-series metrics without choking. The move leaned intopull-based collection,PromQL's firepower, andhierarchical federationfor smarter aggregation and long-haul queries. Why it matters:Prometheus..

Link

@faun shared a link, 4 days, 18 hours ago

Why I Ditched Docker for Podman (And You Should Too)

Older container technologies like Docker have been prone to security vulnerabilities, such as CVE-2019-5736 and CVE-2022-0847, which allowed for potential host system compromise. Podman changes the game by eliminating the need for a persistent background service like the Docker daemon, enhancing sec..

Link

@faun shared a link, 4 days, 18 hours ago

Dynamic Kubernetes request right sizing with Kubecost

Kubecost’s Amazon EKS add-on now handlesautomated container request right-sizing. That means teams can tweak CPU and memory requests based on actual usage—once or on a recurring schedule. Optimization profiles are customizable, and resizing can be baked into cluster setup using Helm. Yes, that mean..

Link

@faun shared a link, 4 days, 18 hours ago

Kubernetes right-sizing with metrics-driven GitOps automation

AWS just dropped a GitOps-native pattern for tuning EKS resources—built to runoutsidethe cluster. It’s wired up withAmazon Managed Service for Prometheus,Argo CD, andBedrockto automate resource recommendations straight into Git. Here’s the play: it maps usage metrics to templated manifests, then sp..

Link

@faun shared a link, 4 days, 18 hours ago

Lucidity turns spotlight onto Kubernetes storage costs

Lucidity has upgraded itsAutoScaler. It now handles persistent volumes on AWS-hosted Kubernetes, automatically scaling storage and reducing waste. The upgrade bringspod-level isolation,fault tolerance, andbulk Linux onboarding. Azure and GCP are next on the list...

Link

@faun shared a link, 4 days, 18 hours ago

Amazon EKS Enables Ultra-Scale AI/ML Workloads with Support for 100K Nodes per Cluster

Amazon EKS just cranked its Kubernetes cluster limit to100,000 nodes—a 10x jump. The secret sauce? A reworkedetcdwith an internaljournalsystem andin-memorystorage. Toss in tightAPI server tuningand network tweaks, and the result is wild: 500 pods per second, 900K pods, 10M+ objects, no sweat—even un..

Link

@faun shared a link, 4 days, 18 hours ago

Kubernetes VPA: Limitations, Best Practices, and the Future of Pod Rightsizing

Kubernetes'Vertical Pod Autoscaler (VPA)tries to be helpful by tweaking CPU and memory requests on the fly. Problem is, it needs to bounce your pods to do it. And if you're also runningHorizontal Pod Autoscaler (HPA)on the same metrics? Now they're fighting over control. VPA sees a narrow slice of ..

Link

@faun shared a link, 4 days, 18 hours ago

The Quiet Revolution in Kubernetes Security

Nigel Douglas discusses the challenges of security in Kubernetes, particularly with traditional base operating systems. Talos Linux offers a different approach with a secure-by-default, API-driven model specifically for Kubernetes. CISOs play a critical role in guiding organizations through the shif..

Link

@faun shared a link, 4 days, 18 hours ago

Kubernetes Primer: Dynamic Resource Allocation (DRA) for GPU Workloads

Kubernetes 1.34 brings serious heat for anyone juggling GPUs or accelerators. MeetDynamic Resource Allocation (DRA)—a new way to schedule hardware like you mean it. DRA addsResourceClaims,DeviceClasses, andResourceSlices, slicing device management away from pod specs. It replaces the old device plu..

Flask is an open-source web framework written in Python and created by Armin Ronacher in 2010. It is known as a microframework, not because it is weak or incomplete, but because it provides only the essential building blocks for developing web applications. Its core focuses on handling HTTP requests, defining routes, and rendering templates, while leaving decisions about databases, authentication, form handling, and other components to the developer. This minimalistic design makes Flask lightweight, flexible, and easy to learn, but also powerful enough to support complex systems when extended with the right tools.

At the heart of Flask are two libraries: Werkzeug, which is a WSGI utility library that handles the low-level details of communication between web servers and applications, and Jinja2, a templating engine that allows developers to write dynamic HTML pages with embedded Python logic. By combining these two, Flask provides a clean and pythonic way to create web applications without imposing strict architectural patterns.

One of the defining characteristics of Flask is its explicitness. Unlike larger frameworks such as Django, Flask does not try to hide complexity behind layers of abstraction or dictate how a project should be structured. Instead, it gives developers complete control over how they organize their code and which tools they integrate. This explicit nature makes applications easier to reason about and gives teams the freedom to design solutions that match their exact needs. At the same time, Flask benefits from a vast ecosystem of extensions contributed by the community. These extensions cover areas such as database integration through SQLAlchemy, user session and authentication management, form validation with CSRF protection, and database migration handling. This modular approach means a developer can start with a very simple application and gradually add only the pieces they require, avoiding the overhead of unused components.

Flask is also widely appreciated for its simplicity and approachability. Many developers write their first web application in Flask because the learning curve is gentle, the documentation is clear, and the framework itself avoids unnecessary complexity. It is particularly well suited for building prototypes, REST APIs, microservices, or small to medium-sized web applications. At the same time, production-grade deployments are supported by running Flask applications on WSGI servers such as Gunicorn or uWSGI, since the development server included with Flask is intended only for testing and debugging.

The strengths of Flask lie in its minimalism, flexibility, and extensibility. It gives developers the freedom to assemble their application architecture, choose their own libraries, and maintain tight control over how things work under the hood. This is attractive to experienced engineers who dislike being boxed in by heavy frameworks. However, the same freedom can become a limitation. Flask does not include features like an ORM, admin interface, or built-in authentication system, which means teams working on very large applications must take on more responsibility for enforcing patterns and maintaining consistency. In situations where a project requires an opinionated, all-in-one solution, Django or another full-stack framework may be a better fit.

In practice, Flask has grown far beyond its initial positioning as a lightweight tool. It has been used by startups for rapid prototypes and by large companies for production systems. Its design philosophy—keep the core simple, make extensions easy, and let developers decide—continues to attract both beginners and professionals. This balance between simplicity and power has made Flask one of the most enduring and widely used Python web frameworks.