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Manage Secrets of your Kubernetes Platform at Scale with GitOps

Learn how to manage secrets with the External Secrets Operator and plug it into Argo CD to power your Internal Developer Platform without manual management, enabling self-service secrets management and secure connections between workload clusters and the control plane. With a chain of trust between .. read more  

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Kubernetes with Buildkite: faster, simpler, and ready for scale

Buildkite just added a major revamp of its Kubernetes Agent Stack. Highlights:REST-based config,leaner K8s objects, andhardened security defaults. It handlestens of thousands of concurrent jobswithout breaking a sweat. Shared environment vars cut down pod config noise. Error messages come with full .. read more  

Kubernetes with Buildkite: faster, simpler, and ready for scale
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How Airbnb Runs Distributed Databases on Kubernetes at Scale

Airbnb runs distributed databases across multiple Kubernetes clusters - each tied to its own AWS Availability Zone. That setup isolates failures down to individual pods and keeps the whole system highly available. They built a custom Kubernetes operator and leaned on EBS volumes with PVCs to smooth .. read more  

How Airbnb Runs Distributed Databases on Kubernetes at Scale
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Azure Developer CLI: Azure Container Apps Dev-to-Prod Deployment with Layered Infrastructure

Azure Developer CLI v1.20.0 leveled up Container Apps. Build and push are now split from deploy, so you can finally "build once, deploy everywhere" and mean it. It adds layered infrastructure support, lets you share anAzure Container Registryacross environments, and handles resource dependency seque.. read more  

Azure Developer CLI: Azure Container Apps Dev-to-Prod Deployment with Layered Infrastructure
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Zero-Trust Kubernetes: Enforcing Security & Multi-Tenancy with Custom Admission Webhooks

Tools likeOPA Gatekeeper,Kyverno, and custom webhooks slam the brakes on sketchy workloadsbeforethey ever spin up. These controllers aren’t just gatekeepers - they’re enforcers. They check pod configs, block unverified images, and apply live, scoped policies like tenant-awarenetwork isolationandreso.. read more  

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@kala shared a link, 1 month, 1 week ago
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You Should Write An Agent

Building LLM agents - essentially looping stateless models through tools - looks simple. Until it isn't. Peel back the layers, and you hit real architectural puzzles:context engineering, agent loops, sub-agent choreography, execution constraints... read more  

You Should Write An Agent
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@kala shared a link, 1 month, 1 week ago
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AI's Dial-Up Era

AI's reshaping jobs - but not evenly. Some industries will feel the squeeze faster than others. It all comes down to a race: productivity vs. demand. History's playbook? Think textiles, steel, autos. Automation boosted output. Jobs stuck around - as long as demand kept growing. Once markets topped o.. read more  

AI's Dial-Up Era
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How I Use Every Claude Code Feature

Claude Code isn't just generating responses anymore - it's gearing up to run projects. The new direction turns it into a programmable, auditable agent runtime. Think custom hooks, restart logic, planning workflows, GitHub Actions, and subagent delegation tricks like the “Master-Clone” pattern. At th.. read more  

How I Use Every Claude Code Feature
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AI Broke Interviews

AI has revolutionized technical interviews, blurring the line between genuine skill and cheating with perfect solutions and polished answers. In response, companies are shifting back to in-person interviews for real-time cognitive transparency, authenticity constraints, realistic collaboration signa.. read more  

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Why I Like Using Docker Compose in Production

A decade in, and this dev still rides with Docker Compose for production. Why? It just works. Clean deployments, solid uptime, same setup everywhere. No yak-shaving. It shines when you pair it with Git hooks for hands-off, zero-downtime deploys. No need to drag in Kubernetes unless you’re actually w.. read more  

Why I Like Using Docker Compose in Production
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).