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@laura_garcia shared a post, 3 months, 1 week ago

Software Developer, RELIANOID

🔐 RELIANOID Load Balancer – Security Contributions

At RELIANOID, we actively and selflessly contribute to improving global cybersecurity, staying true to our open-source spirit. 🤝 We maintain close collaborations with security platforms, forums, and threat-intelligence communities, sharing our expertise to help strengthen protection across the Inter..

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@laura_garcia shared a post, 3 months, 1 week ago

Software Developer, RELIANOID

📍 RELIANOID at Bett UK 2026

We’re excited to take part in Bett UK 2026, the world’s leading EdTech event, bringing together educators, innovators, and decision-makers shaping the future of education. 🗓 January 21–23, 2026 📍 London, United Kingdom Join us to discover how RELIANOID enables secure, scalable, and highly available ..

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@laura_garcia shared a post, 3 months, 1 week ago

Software Developer, RELIANOID

🚀 If you’re building AI systems, reliability is no longer optional

Many teams are rushing to adopt AI, but few are asking the most critical question: 👉 What happens when AI fails? Back in December, we published an article that remains more relevant than ever: AI is redefining Site Reliability Engineering (SRE). Why? Because AI inference workloads introduce new reli..

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@aleonrangel gave 🐾 to Difference between Agile and Scrum , 3 months, 1 week ago.

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@aleonrangel gave 🐾 to Difference between Agile and Scrum , 3 months, 1 week ago.

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@laura_garcia shared a post, 3 months, 1 week ago

Software Developer, RELIANOID

🔐 Reminder: Azure MFA Enforcement Is Now in Place

Some time ago, Microsoft announced and enforced mandatory multifactor authentication (MFA) for all Azure tenants performing resource management actions. 👉 This marked a clear turning point: MFA is no longer optional — it’s a requirement. At RELIANOID, we shared how this change reinforces the need to..

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@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

How to build internal developer tools with a small team

A fresh way to think about internal dev tooling: three axes,width(new features),depth(polish and stability), andpreparation(future-ready architecture). Instead of treating tradeoffs as binary, the model maps them as vectors in a shared space. Less tug-of-war. More informed roadmap moves... read more

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@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

How Browsers Work

An interactive open-source guide breaks down browser internals with slick, step-through models coveringDNS resolution,TCP handshakes, andHTML parsing. It walks through the browser'ssequential pipeline- from URL to DOM - blending protocol deep-dives with hands-on visuals you can poke at... read more

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@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

The Mac Malware of 2025 👾

The 2025 macOS malware scene leveled up hard. Thinkmodular infostealers, built for stealth, slipping in with staged loaders, encrypted configs, and slick social engineering - fake updates, bogus job interviews, even sketchy terminal promos like “ClickFix.” Attackers leaned onAppleScript,JXA, andGo-b.. read more

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@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

Web development is fun again

A seasoned dev takes a hard look at today’s messy full-stack reality: scattered tools, niche deep-dives, and burnout baked into the job. ButAI coding assistantsflipped the script. They help offload overhead, mimic pro-level workflows, and sanity-check the code. Now this dev moves across frontend and.. read more

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.