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What I Really Mean When I Say “Good Communication” in Incident Response

In the world of incidents,communication is key. Tailor messages for different audiences: be clear for business stakeholders, factual for IT management, and detailed for fellow responders. Don't let vagueness derail incident response - keep stakeholders informed with precise updates and clear expecta.. read more  

What I Really Mean When I Say “Good Communication” in Incident Response
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Monitoring & Observability: Using Logs, Metrics, Traces, and Alerts to Understand System Failures

Railway just leveled up its observability game. Now logs, metrics, and alerts all live in one tidy dashboard - clean and connected. Structured logs flow straight from stdout/stderr. Metrics pulse in real time. Alerts plug into monitors or deployment webhooks so teams catch firesbeforethey rage... read more  

Monitoring & Observability: Using Logs, Metrics, Traces, and Alerts to Understand System Failures
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Year in Review: Lessons From 12 Projects Patreon Shipped in 2025

Patreon engineers made massive bets in 2025, shipping code across all areas of the system and enabling impactful features like Autopilot's growth tools suite. Expanding Autopilot's scope, reach, and effectiveness was a challenge, especially guaranteeing recipient redemption after email delivery in a.. read more  

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Making a micro Linux distro

A dev dives into building a barebones Linux distro for RISC-V using QEMU. Starts at the metal: compiles the kernel, wires up a no-frills init process, packs it all into an initramfs. Then levels up, drops inu-rootto swap out raw shell scripts for Go-powered userland tools. Adds network. Now it’s a f.. read more  

Making a micro Linux distro
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@devopslinks shared an update, 4 weeks, 1 day ago
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Canonical Introduces Minimal Ubuntu Pro: Smaller Images and Secure Cloud Workloads at Scale

Ubuntu GNU/Linux

Canonical has launched Minimal Ubuntu Pro, enhancing cloud security with lightweight images and robust features. Available on AWS, Azure, and Google Cloud, it offers minimized attack surfaces and long-term support.

Canonical Introduces Minimal Ubuntu Pro: Smaller Images and Secure Cloud Workloads at Scale
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AI's Dependence on Python Deepens as Anthropic Funds Core Ecosystem Work

Python

Anthropic invests $1.5 million in the Python Software Foundation to boost Python ecosystem security. The funding targets improvements in CPython and PyPI, including new tools for package review and malware datasets. It also supports the PSF's core activities and community initiatives.

AI's Dependence on Python Deepens as Anthropic Funds Core Ecosystem Work
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@kala shared an update, 4 weeks, 1 day ago
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Anthropic’s New "Economic Primitives" Reveal Who Uses Claude, for What, and How Well It Works

Anthropic's new Economic Index report introduces five "economic primitives" to measure *how* Claude is used: task complexity, user and AI skill level, use case (work, coursework, personal), autonomy, and task success - built from privacy-preserving classification of anonymized Claude.ai and first-party API transcripts from **November 2025**.

Anthropic’s New "Economic Primitives" Reveal Who Uses Claude, for What, and How Well It Works
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@varbear shared an update, 4 weeks, 1 day ago
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Tailwind CSS Lays Off 75% of Its Engineering Team as AI Cuts Documentation Traffic by 40%

tailwindcss Vercel

Tailwind CSS laid off roughly **75% of its engineering team** after a **~40% drop in documentation traffic** and an estimated **~80% decline in revenue**, even as usage of the framework continues to grow. According to its creator, AI-driven access to documentation has broken the link between adoption and sustainability.

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@devopslinks shared an update, 4 weeks, 1 day ago
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Pulumi Expands IaC Platform to Support Terraform, OpenTofu, and Native HCL

Pulumi Terraform

Pulumi added support for managing Terraform and OpenTofu state in Pulumi Cloud and introduced native HCL support in its infrastructure as code engine. These changes allow teams to use Terraform, OpenTofu, Pulumi languages, and HCL side by side, with shared state visibility, governance features, and AI-assisted operations available across tools.

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@thomas-byern started using tool Visual Studio Code , 1 month ago.
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.