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@laura_garcia shared a post, 3ย months, 2ย weeks ago
Software Developer, RELIANOID

AI-driven cyberthreats previsions

At the end of last year, we warned that AI-driven cyberthreats would accelerate industrial risk in 2026. Just a few months into the year, that prediction is already proving accurate. Weโ€™re seeing clear signals that attackers are increasingly leveraging Artificial Intelligence to: - Automate reconnai..

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@laura_garcia shared a post, 3ย months, 2ย weeks ago
Software Developer, RELIANOID

๐—ก๐—œ๐—ฆ๐Ÿฎ ๐—–๐—ผ๐—บ๐—ฝ๐—น๐—ถ๐—ฎ๐—ป๐—ฐ๐—ฒ ๐—ฆ๐˜๐—ฎ๐˜๐—ฒ๐—บ๐—ฒ๐—ป๐˜

Alignment for RELIANOID Load Balancer & Organizational Operations At RELIANOID, we recognize the strategic importance of the EU NIS2 Directive in strengthening cybersecurity resilience across Europe. As an advanced load balancing and application delivery provider, RELIANOID qualifies as an Important..

nis2 compliance RELIANOID
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@craitrix created an organization Craitrix , 3ย months, 2ย weeks ago.
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@laura_garcia shared a post, 3ย months, 2ย weeks ago
Software Developer, RELIANOID

๐—–๐—น๐—ผ๐˜‚๐—ฑ ๐—ฃ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜ ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—ฒ๐—ฟ (๐—™๐˜‚๐—น๐—น-๐˜๐—ถ๐—บ๐—ฒ | ๐—ฅ๐—ฒ๐—บ๐—ผ๐˜๐—ฒ / ๐—›๐˜†๐—ฏ๐—ฟ๐—ถ๐—ฑ)

๐Ÿš€ ๐—ช๐—ฒโ€™๐—ฟ๐—ฒ ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด: ๐—–๐—น๐—ผ๐˜‚๐—ฑ ๐—ฃ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜ ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—ฒ๐—ฟ (๐—™๐˜‚๐—น๐—น-๐˜๐—ถ๐—บ๐—ฒ | ๐—ฅ๐—ฒ๐—บ๐—ผ๐˜๐—ฒ / ๐—›๐˜†๐—ฏ๐—ฟ๐—ถ๐—ฑ) At RELIANOID, weโ€™re accelerating the evolution of our cloud-native solutions โ€” and we need talented engineers to join us. Weโ€™re looking for ๐Ÿฎ ๐—–๐—น๐—ผ๐˜‚๐—ฑ ๐—ฃ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜ ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—ฒ๐—ฟ๐˜€ to join our Product Engineering team and help shape the future ..

careers RELIANOID hiring
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@devopslinks shared a link, 3ย months, 2ย weeks ago
FAUN.dev()

The Only Claude Skill Every DevOps Engineer Needs

TheTerraform Claude SkillturnsClaude Codeinto a Terraform expert. It enforces modularity, strict naming, and consistent tagging. It treats thestate fileas the single source of truth. It wires in external tools: linting, security checks, and cost estimates. It forces real-doc validation and blocks in.. read more ย 

The Only Claude Skill Every DevOps Engineer Needs
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@devopslinks shared a link, 3ย months, 2ย weeks ago
FAUN.dev()

Please stop externalizing your costs directly into my face

SourceHut spent20โ€“100%of weekly time mitigating hyperโ€‘aggressive LLM crawlers. That work caused dozens of short outages and delayed core projects. The crawlers ignorerobots.txt. They hit costly endpoints likegit blame. They scan full git logs and commits. They rotate randomUserโ€‘Agentsand thousands o.. read more ย 

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@devopslinks shared a link, 3ย months, 2ย weeks ago
FAUN.dev()

The hunt for truly zero-CVE container images

Chainguard's Factory 2.0 andDriftlessAFrebuild images from source on upstream changes. They produce 2,000+ minimalzeroโ€‘CVEimages. Each image includes anSBOMand a cryptographicsignature. Docker'sDHIbuilds onDebianandAlpine. It mirrors Debian'snoโ€‘DSAtriage intoVEX. It also suppresses real CVEs until D.. read more ย 

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@devopslinks shared a link, 3ย months, 2ย weeks ago
FAUN.dev()

Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners

Introduces anAI Agent Gateway. It mediates agent requests, validates intent, enforcespolicy-as-code, and isolates execution inephemeral runners. Agents discover tools viaMCP. They submitJSON-RPCcalls and receiveOPAdecisions. Jobs queue and run in short-lived namespaces. Each run carries plan hashes,.. read more ย 

Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners
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@devopslinks shared a link, 3ย months, 2ย weeks ago
FAUN.dev()

Chinese Vulnerability Database: CNVD vs CNNVD Analysis

Investigation profilesCNNVDandCNVDechoCVE. They reveal manual errors and poor machine-readability. Chinaโ€™s July 2021RMSVmandates 48-hour reporting and bans pre-patch disclosure. Mapping gaps exist. The databases published about1.4kentries ahead ofCVE, with lead times measured in months... read more ย 

Chinese Vulnerability Database: CNVD vs CNNVD Analysis
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@secuodsoft created an organization Secuodsoft , 3ย months, 3ย 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 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.