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Use Python for Scripting!

Shell scripts love to break across macOS and Linux. Blame all the GNU vs BSD quirks;sed,date,readlink, take your pick. The mess adds up fast, especially in build pipelines and CI systems. This post makes the case for a cleaner way:Python 3. Standard library. Predictable behavior. Same results whethe.. read more  

Use Python for Scripting!
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How Reddit Migrated Comments Functionality from Python to Go

Reddit successfully migrated its monolithic, high-traffic Comments service from legacy Python to modern Go microservices with zero user disruption. This was achieved by using a "tap compare" for reads and isolated "sister datastores" for writes, ensuring safe verification of the new code against pro.. read more  

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Why Kubernetes Won: Perfect Timing & Developer Culture

Kubernetes won big because the stars aligned, DevOps took off, Docker exploded, and enterprises finally stopped side-eyeing open source. Then came the institutional tailwind: CNCF pushed hard, GCP bet big, and the rest followed. Kubernetes isn't just tech. It's a new operating model, built in the op.. read more  

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An In-Depth Look at Istio Ambient Mode with Calico

Tigera just wiredIstio Ambient Modeinto Calico. That means you getsidecarless service mesh, think mTLS, L4/L7 policy, and observability, without stuffing every pod with a sidecar. It’s all handled by lean zTunnel and Waypoint proxies. Ports stay visible, soCalico and Istio policiesplay nice. No rewr.. read more  

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Kubernetes Made Simple: A Guide for JVM Developers

A sharp walkthrough for JVM devs shipping aKotlin Spring Boot app on Kubernetes. It covers the full deployment arc, packaging with Docker, wiring upDeploymentandServicemanifests, and managing config withConfigMapsandSecrets. There's a cleanPostgreSQLintegration baked in. It even gets intoheader-base.. read more  

Kubernetes Made Simple: A Guide for JVM Developers
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Compose to Kubernetes to Cloud With Kanvas

Docker just droppedKanvas, a new visual toy for building multi-cloud Kubernetes setups, without drowning in YAML. It bolts onto Docker Desktop and runs onMeshery. Drag and drop services into a topology, then bring them to life across AWS, GCP, or Azure. Mix inpolicy-driven validationandreal-time mut.. read more  

Compose to Kubernetes to Cloud With Kanvas
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How to Add MCP Servers to ChatGPT

ChatGPT leveled up with fullModel Context Protocol (MCP)support. It can now run real developer tasks, scraping, writing to a database, even making GitHub commits, through secure, containerized tools in Docker. TheDocker MCP Toolkitconnects ChatGPT’s language smarts to production-safe tools like Stri.. read more  

How to Add MCP Servers to ChatGPT
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A Deep Dive into Kubernetes Headless Service

Headless Serviceis a powerfulKubernetesfeature enabling direct pod-to-pod communication forstateful applicationsand preciseservice discoverywithout traditional load balancing.No automatic load balancing, pod IP changes, andspecial use casesmake it ideal for specific scenarios, not general workloads... read more  

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The “Inception” of Kubernetes: A Deep Dive into vCluster Architecture and Benefits

vCluster, a CNCF sandbox project, spins up real-deal Kubernetes control planes inside pods. Each lives in its own namespace but behaves like a full cluster, admin access, CRDs, Helm, the works. It reuses the host’s worker nodes using a syncer that routes vCluster workloads onto the real thing... read more  

The “Inception” of Kubernetes: A Deep Dive into vCluster Architecture and Benefits
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Kubernetes 1.35 - New security features

Kubernetes 1.35 is done with legacy baggage. cgroups v1? Deprecated. Image pull credentials? Now re-verified by default—no more freeloading. kubectl SPDY API upgrades? Locked down. You’ll needcreatepermissions just to speak the protocol. Expect breakage if your workflows leaned on old assumptions. U.. read more  

Kubernetes 1.35 - New security features
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).