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@varbear shared a link, 3 months, 3 weeks ago
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14x Faster Faceted Search in PostgreSQL with ParadeDB

ParadeDB brings Elasticsearch-stylefacetingtoPostgreSQL, ranked search results and filter counts, all in one shot. No extra passes. It pulls this off with a customwindow function, planner hooks, andTantivy's columnar index under the hood. That's how they’re squeezing out10×+ speedupson hefty dataset.. read more  

14x Faster Faceted Search in PostgreSQL with ParadeDB
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@varbear shared a link, 3 months, 3 weeks ago
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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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@kaptain shared a link, 3 months, 3 weeks ago
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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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@kaptain shared a link, 3 months, 3 weeks ago
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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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@kaptain shared a link, 3 months, 3 weeks ago
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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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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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@kaptain shared a link, 3 months, 3 weeks ago
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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
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@kaptain shared a link, 3 months, 3 weeks ago
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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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@kaptain shared a link, 3 months, 3 weeks ago
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How to Troubleshoot Common Kubernetes Errors

A fresh Kubernetes troubleshooting guide lays out real-world tactics for tracking down 12 common cluster headaches. Think:kubectlsleuthing, poking through system logs, scraping observability metrics, and jumping intodebug containers. The guide breaks down howAIOpsis stepping in, digesting event data.. read more  

How to Troubleshoot Common Kubernetes Errors
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.