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@laura_garcia shared a post, 5Β months ago
Software Developer, RELIANOID

πŸŽ‰ π—ͺ𝗲 𝗗𝗢𝗱 π—œπ˜ β€” 𝟭,𝟳𝟬𝟬+ π—™π—Όπ—Ήπ—Ήπ—Όπ˜„π—²π—Ώπ˜€ 𝗼𝗻 π—Ÿπ—Άπ—»π—Έπ—²π—±π—œπ—»! πŸš€

Big news from the RELIANOID team! We’ve just crossed 𝟭,𝟳𝟬𝟬 π—³π—Όπ—Ήπ—Ήπ—Όπ˜„π—²π—Ώπ˜€ on LinkedIn, and we couldn’t be more grateful πŸ™Œ This community is growing thanks to you β€” our partners, customers, tech enthusiasts, and cybersecurity professionals who follow our journey in application delivery, security, and high..

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Link Xygeni Team
@mashka shared a link, 5Β months ago
Paid Acquisition and Growth Marketing, xygeni

When AI Became Part of the Attack Surface

AI is now a core execution layer in software delivery. In 2025, attackers exploited automation, trusted pipelines, and AI-generated code instead of vulnerabilities. This report explains why traditional AppSec signals failed and what must change in 2026.

New AppSec Attack Trends for 2026 - Promo Redes (3)
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@viktoriiagolovtseva shared a post, 5Β months ago

How to Plan a Product Release in Jira

From the Release Hub and backlog management to automated release notes, Jira has plenty of tools to help you plan your next release. In this blog post, we explain how to use these tools effectively for different release types. You will also get practical tips for extending the native Jira release planning capabilities with additional apps. Let’s dive in!

Zrzut ekranu 2026-01-20 123937
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@varbear shared a link, 5Β months ago
FAUN.dev()

A better way to limit Claude Code (and other coding agents!) access to Secrets

A new workflow dropsClaude Codeinto aBubblewrap-based sandbox, cutting Anthropic's client-side code out of the trust loop. Compared to spinning up Docker or juggling user accounts, Bubblewrap locks things down tighter - with less setup and cleaner OS-level walls around files, network access, and sec.. read more Β 

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@varbear shared a link, 5Β months ago
FAUN.dev()

Reversing YouTube's "Most Replayed" Graph

An engineer cracked open YouTube’s β€œmost replayed” heatmap. Turns out it runs onsampled view frequency arrays, client-sidenormalization, andSVG renderingstitched together withCubic BΓ©zier splinesfor that smooth, snappy curve. Behind the scenes, playback gets logged with adifference array + prefix su.. read more Β 

Reversing YouTube's "Most Replayed" Graph
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@varbear shared a link, 5Β months ago
FAUN.dev()

An Honest Review of Go

Go gets big props for its built-in concurrency model withgoroutinesandchannels, which make lightweight, scalable parallelism easy and ergonomic. The author criticizes Go's type system for lacking things likeenums, closed type sets, and tuples, making certain patterns awkward compared with Rust's ric.. read more Β 

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@varbear shared a link, 5Β months ago
FAUN.dev()

How Github monopoly is destroying the open source ecosystem

Out of 238 student open source contributions over seven years, 237 landed onGitHub- even though they were told to look elsewhere. One short-lived GitHub IP block brought everything to a standstill. No commits. No reviews. Just silence. Turns out, a single platform holds the keys to a whole ecosystem.. read more Β 

How Github monopoly is destroying the open source ecosystem
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@laura_garcia shared a post, 5Β months ago
Software Developer, RELIANOID

🚨 Join RELIANOID at the Dallas Cybersecurity Conference 2026! 🚨

πŸ“ Dallas, Texas | πŸ—“ January 22, 2026 Securing the Future starts here. We’re excited to be part of FutureCon Dallas, a high-impact event bringing together CISOs, C-suite leaders, and senior security professionals to tackle today’s most pressing cyber threats. πŸ”Ή Why attend? Gain actionable insights in..

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@kaptain shared a link, 5Β months ago
FAUN.dev()

v1.35: Restricting executables invoked by kubeconfigs via exec plugin allowList added to kuberc

Kubernetes v1.35 lands with acredential plugin allowlist, now in beta, no feature gate needed. It lets you lock down whichexecplugins your kubeconfigs can run. Tighter leash, lower risk. Especially when the credential pipeline gets sketchy... read more Β 

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@kaptain shared a link, 5Β months ago
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A Brief Deep-Dive into Attacking and Defending Kubernetes

A sharp teardown of Kubernetes’ attack surface maps out where things go sideways: pods, the control plane, RBAC, admission controllers, and etcd. Misconfigurations like anonymous API access, wildcard roles, and hostPath mounts aren't just sloppy- they're attack vectors. Fixes? ThinkFalco,RBAC lockdo.. read more Β 

A Brief Deep-Dive into Attacking and Defending Kubernetes
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 β€” from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets β€” within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.