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@devopslinks shared a link, 1 week, 6 days ago
FAUN.dev()

Nanoservices: Why Serverless Got Architecture Right

A fresh take onAWS Lambdaand serverless: thinknanoservices- tiny, isolated functions instead of chunky microservices. No shared state or shared runtime but clean separation, lean logic, and fewer ways to screw up scaling. Where microservices can spiral into spaghetti, nanoservices stay crisp. Each f.. read more  

Nanoservices: Why Serverless Got Architecture Right
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@devopslinks shared a link, 1 week, 6 days ago
FAUN.dev()

Supply-chain risk of agentic AI - infecting infrastructures via skill worms

AI assistants with shell, network, or filesystem "skills" don't just help, they expose. These hooks can run commands before any human checks the model’s output. That means a bigger attack surface. More room for lateral movement. Easier persistence. In setups where tools like Claude Code run often, i.. read more  

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@kala shared an update, 1 week, 6 days ago
FAUN.dev()

OpenClaw - Former Moltbot, Former Clawdbot - Went Viral Overnight. Then Security Reality Hit.

OpenClaw

OpenClaw, an open-source AI assistant platform, has been launched, evolving from Clawdbot and Moltbot. It features new plugins, enhanced security, and support for new models, while addressing a major security vulnerability. The platform emphasizes community involvement and invites contributions for its development.

OpenClaw - Former Moltbot, Former Clawdbot - Went Viral Overnight. Then Security Reality Hit.
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@kala added a new tool OpenClaw , 1 week, 6 days ago.
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@kaptain shared an update, 1 week, 6 days ago
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Cluster API v1.12 Released: In-Place Updates and Chained Upgrades

Kubernetes

Cluster API v1.12 introduces in-place updates and chained upgrades to enhance Kubernetes cluster management. In-place updates modify existing machines without deletion, while chained upgrades streamline multi-version upgrades. The release also includes improvements to immutable rollouts and various bug fixes.

Cluster API v1.12 Released: In-Place Updates and Chained Upgrades
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@laura_garcia shared a post, 2 weeks ago
Software Developer, RELIANOID

Shield Your Core 🛡️

Cybersec Asia is coming to Bangkok on February 4–5, 2026 — the key APAC event for cybersecurity, cloud, and data protection. 🌏 Bringing together global and regional leaders to tackle evolving threats and unlock new opportunities across CLMVT & APAC. 🤝 Meet RELIANOID and discover how we deliver secur..

cybersec asia 2026 RELIANOID
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@dejones923 started using tool Python , 2 weeks, 2 days ago.
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@sancharini shared a post, 2 weeks, 2 days ago

Verification vs Validation Explained for Beginners in QA

Learn the difference between verification vs validation in QA. This beginner-friendly guide explains how both ensure software is built correctly and meets user expectations.

Verification vs Validation
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@laura_garcia shared a post, 2 weeks, 3 days ago
Software Developer, RELIANOID

🚗🔐 Automotive Cybersecurity: Connected Cars and a Vulnerable Supply Chain

We originally published this article back in November, but it remains highly relevant today. Sharing it again in case you missed it 👇 Connected cars are no longer just mechanical machines — they are computers on wheels, embedded in complex digital ecosystems. As shown in the “Supply Chain in the aut..

Supply-Chain-in-the-Automotive-Industry_RELIANOID
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.