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News FAUN.dev() Team
@kala shared an update, 2 months, 3 weeks ago
FAUN.dev()

Guido van Rossum: “AI Should Adapt to Python - Not the Other Way Around”

Python TypeScript

Guido van Rossum discussed Python's enduring relevance in AI and education at GitHub's Octoverse, emphasizing its clarity, accessibility, and community-driven growth despite TypeScript's rise.

Guido van Rossum: “AI Should Adapt to Python - Not the Other Way Around”
Story Palark Team
@shurup shared a post, 2 months, 3 weeks ago
@palark

Kubernetes 1.35 new alpha features

Kubernetes

The next Kubernetes release, v1.35, is scheduled for December 17th. It should bring 15 new Alpha features, including the following ones: - Gang scheduling support - Mutable PersistentVolume node affinity - Restart all containers on container exits - Consider terminating Pods in Deployments - CSI vol..

Kubernetes v1.35 release
News FAUN.dev() Team
@varbear shared an update, 2 months, 3 weeks ago
FAUN.dev()

NordPass: Worst Passwords of 2025 and How Each Generation Compares

NordPass's latest research reveals the ongoing global reliance on weak passwords like "123456" and "password," despite slight improvements in security practices.

NordPass: Worst Passwords of 2025 and How Each Generation Compares
News FAUN.dev() Team Trending
@kaptain shared an update, 2 months, 3 weeks ago
FAUN.dev()

Kubernetes v1.35: A Deep Dive Into the Biggest Changes Before the December 17 Release

Kubernetes containerd

Kubernetes v1.35 release removes cgroup v1 and containerd v1.X support, urging admins to migrate to newer versions and adopt enhancements like in-place Pod updates and OCI image volume support.

Kubernetes v1.35: A Deep Dive Into the Biggest Changes Before the December 17 Release
News FAUN.dev() Team
@devopslinks shared an update, 2 months, 3 weeks ago
FAUN.dev()

Researcher Scans 5.6M GitLab Repositories, Uncovers 17,000 Live Secrets and a Decade of Exposed Credentials

TruffleHog AWS Lambda GitLab GitLab CI/CD Atlassian Bitbucket

A security research project led by Luke Marshall scanned 5.6 million GitLab repositories, uncovering over 17,000 live secrets and earning $9,000 in bounties, highlighting GitLab's larger scale and higher exposure risk compared to Bitbucket.

Researcher Scans 5.6M GitLab Repositories, Uncovers 17,000 Live Secrets and a Decade of Exposed Credentials
 Activity
@devopslinks added a new tool TruffleHog , 2 months, 3 weeks ago.
News FAUN.dev() Team
@devopslinks shared an update, 2 months, 3 weeks ago
FAUN.dev()

AWS Optimizer Targets Unused NAT Gateways for Cost Savings

Amazon CloudWatch Amazon Web Services

AWS Compute Optimizer now helps identify unused NAT Gateways to boost cost savings by analyzing traffic activity and route table associations.

AWS Optimizer Targets Unused NAT Gateways for Cost Savings
News FAUN.dev() Team
@devopslinks shared an update, 2 months, 3 weeks ago
FAUN.dev()

GitLab Uncovers Massive npm Attack - Developers on High Alert

npm Amazon Web Services GitLab GitHub

GitLab's team discovers a large-scale npm supply chain attack with malware that spreads through npm packages, threatening data destruction if disrupted.

GitLab Uncovers Massive npm Attack - Developers on High Alert
 Activity
@varbear added a new tool npm , 2 months, 3 weeks ago.
 Activity
@devopslinks added a new tool GitHub , 2 months, 3 weeks 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.