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@kala shared a link, 4 months, 1 week ago
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Claude Skills are awesome, maybe a bigger deal than MCP

Anthropic releasedClaude Skills—a lean way to snap specialized instructions and scripts into Claude without bloating the prompt. Each “skill” lives in a folder with Markdown and optional code. Frontmatter tags tell Claude when to load what. No need to cram everything into the context window—Claude g.. read more  

Claude Skills are awesome, maybe a bigger deal than MCP
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@kala shared a link, 4 months, 1 week ago
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OpenAI Needs $400 Billion In The Next 12 Months

OpenAI, Broadcom, NVIDIA, and AMD say they’ll deploy10GWof AI compute by end of 2026. That includes custom chips and slews of 1GW data centers. What they didn’t say: where, when, or how. No sites named. No shovels in dirt. OpenAI alone aims for250GW by 2033—a moonshot that needs$400Bin the next 12 m.. read more  

OpenAI Needs $400 Billion In The Next 12 Months
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@devopslinks shared a link, 4 months, 1 week ago
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How AI can help your DevSecOps pipeline

AI is sliding into DevSecOps and turning security into less of a slog. Tools likeDarktrace PREVENT,CrowdStrike Falcon, andMicrosoft Security Copilotaren't just watching—they're flagging weird behavior, proposing fixes, and unclogging patch pipelines inside CI/CD. The shift:DevSecOps is on its way to.. read more  

How AI can help your DevSecOps pipeline
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@devopslinks shared a link, 4 months, 1 week ago
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How Shopify Handles 30TB of Data Every Minute with a Monolithic Architecture

Shopify handles billions of Black Friday requests on amodular monolith, built with Ruby on Rails and kept in check byPackwerk. Domain boundaries are enforced. Chaos averted. Inside, it blendsHexagonal Architecture, isolatedPods, and real-time Kafka pipes. The system scales without fracturing into mi.. read more  

How Shopify Handles 30TB of Data Every Minute with a Monolithic Architecture
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@devopslinks shared a link, 4 months, 1 week ago
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How I Block All 26 Million Of Your Curl Requests

A developer built a razor-sharp TLS fingerprinting and blocking tool—all in kernel space—witheBPFandXDP. It hooks into incoming packets, scrapes TLS Client Hello messages, and cranks out simplified JA4-style hashes from their cipher suite lists. The fun part? It's running under tight stack limits, s.. read more  

How I Block All 26 Million Of Your Curl Requests
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@devopslinks shared a link, 4 months, 1 week ago
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Migrating to Hetzner - We saved 76% on our cloud bills

DigitalSociety ditched AWS and DigitalOcean. Swapped the comfort of cloud for full control onHetzner, built onTalos Linux. PostgreSQL? Now running onCloudNativePG. Traffic flows throughIngress NGINXwithExternalDNShandling the names. The payoff: monthly costs dropped from $449.50 to under $100. ARM v.. read more  

Migrating to Hetzner - We saved 76% on our cloud bills
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@devopslinks shared a link, 4 months, 1 week ago
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CVE-2025-49844 - The Redis CVSS 10.0 vulnerability and how we responded

Report URI closed the door on Redis CVE-2025-49844 fast. They rolled out ACL-based command blocks and jumped to Redis8.2.2, now running on a freshRedis Sentinel-based HA setup. To prove the fix stuck, they ran command counter checks and layered in enforced blocking rules—then pushed it all out fleet.. read more  

CVE-2025-49844 - The Redis CVSS 10.0 vulnerability and how we responded
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@devopslinks shared a link, 4 months, 1 week ago
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Hosting Remote MCP Server on Azure Container Apps (ACA) using Streamable HTTP transport mechanism

A fresh setup shows how to runModel Context Protocol (MCP) servers over HTTPinsideAzure Container Apps—stateless, serverless, and ready for real-time jobs like live forex conversion. It pipes in a live API fallback, adds caching, and speaksJSON-RPC 2.0overPOST. You can spin it up withBicep templates.. read more  

Hosting Remote MCP Server on Azure Container Apps (ACA) using Streamable HTTP transport mechanism
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@kaptain shared a link, 4 months, 1 week ago
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A fully functional Kubernetes cluster with 1 million active nodes.

Pushing Kubernetes to 1M nodes isn’t just hardware—it's architectural judo. Networking flips to exclusive IPv6.Less chatter, more breathing room. etcd hits a wall.Write throughput stalls at scale, so they swap it out. Entermem_etcd, a Rust-built replacement pushing over 1M buffered writes per second.. read more  

A fully functional Kubernetes cluster with 1 million active nodes.
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@kaptain shared a link, 4 months, 1 week ago
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Debug Builds with Visual Studio Code

Docker droppedBuildx debuggingfor VS Code. Set breakpoints in your Dockerfiles. Peek into image layers. Even jump into an interactive shell mid-build. It runs on theDebug Adapter Protocol, so editors likeNeovimandJetBrains IDEscan join the party too... read more  

Debug Builds with Visual Studio Code
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.