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

Googleโ€™s Cloud APIs Become Agent-Ready with Official MCP Support

#Model C...ย  #Apigeeย  #Google ...ย  #AIย  #MCPย 
Apigee Google Cloud Platform Google Kubernetes Engine (GKE) BigQuery

Google supports the Model Context Protocol to enhance AI interactions across its services, introducing managed servers and enterprise capabilities through Apigee.

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@devopslinks added a new tool BigQuery , 3ย months, 2ย weeks ago.
News FAUN.dev() Team Trending
@devopslinks shared an update, 3ย months, 2ย weeks ago
FAUN.dev()

AWS Previews DevOps Agent to Automate Incident Investigation Across Cloud Environments

#DevOpsย  #inciden...ย  #GenAIย  #DevOps ...ย  #awsย 
Datadog Amazon CloudWatch Dynatrace New Relic Amazon Web Services

AWS introduces an autonomous AI DevOps Agent to enhance incident response and system reliability, integrating with tools like Amazon CloudWatch and ServiceNow for proactive recommendations.

AWS Previews DevOps Agent to Automate Incident Investigation Across Cloud Environments
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@devopslinks added a new tool ServiceNow , 3ย months, 2ย weeks ago.
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@laura_garcia shared a post, 3ย months, 2ย weeks ago
Software Developer, RELIANOID

The UK raises the bar on digital security

With cyberattacks on the rise, the Product Security and Telecommunications Infrastructure (PSTI) Act marks a major step toward making connected technology secure by design. In our latest article, we explain: What the PSTI Act requires Why it matters beyond consumer IoT How it signals a global sh..

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@shurup shared a post, 3ย months, 2ย weeks ago
@palark

New CNCF Sandbox projects in 2025: From Podman to CloudNativePG

Kubernetes

Each year, 25-30 new Open Source projects related to the Cloud Native ecosystem are accepted to the CNCF Sandbox. In January 2025, there were 13 additions, with four of them donated by Red Hat. Here's the list of these newly added CNCF projects: - Podman Container Tools (security-focused Docker alte..

CNCF Sandbox projects in January 2025
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@sancharini shared a post, 3ย months, 2ย weeks ago

CI Testing Best Practices for Reliable and Fast Builds

As software teams adopt continuous integration, build speed and reliability become critical success factors. CI testing plays a central role in ensuring that every code change is validated quickly and consistently before it moves further down the delivery pipeline. Without clear practices, however, ..

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@eon01 shared a post, 3ย months, 2ย weeks ago
Founder, FAUN.dev

Announcing FAUN.sensei() โ€” Self-paced guides to grow fast โ€” even when tech moves faster.

Docker GitLab CI/CD Helm Kubernetes GitHub Copilot

After months of hard work, FAUN.sensei() is finally alive!

FAUN.sensei()
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@laura_garcia shared a post, 3ย months, 2ย weeks ago
Software Developer, RELIANOID

๐ŸŒŸ ๐—ช๐—ฒโ€™๐—ฟ๐—ฒ ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด! ๐—๐—ผ๐—ถ๐—ป ๐˜๐—ต๐—ฒ ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐—— ๐—ง๐—ฒ๐—ฎ๐—บ ๐ŸŒŸ

Are you passionate about technology, networking, and innovation? At RELIANOID, weโ€™re building cutting-edge solutions that power secure, scalable, and reliable infrastructures โ€” and weโ€™re looking for talented people to join us on this journey! ๐Ÿš€ Whether youโ€™re an experienced professional or just star..

careers RELIANOID hiring
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 AIStorโ€™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 โ€” from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets โ€” within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.