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Linux Detection Engineering - A Sequel on Persistence Mechanisms

PANIX turns the tangled web of Linux persistence and Process Capabilities on its head. It makes them as easy to test as flipping a light switch—and sharpens your detection game along the way... read more  

Linux Detection Engineering - A Sequel on Persistence Mechanisms
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Serverless Is a Lie (But It’s Still Useful)

ECS Fargatedominates 24/7 stateless APIs, dodging those peskyLambdacold starts. Meanwhile,Lambdathrives in event-driven bursts but hits a 15-minute ceiling. For lean, mean APIs with built-in auth, lean towardsAPI Gateway. But if speed matters, marryFargatewith anApp Load Balancer.Step Functionsstrea.. read more  

Serverless Is a Lie (But It’s Still Useful)
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Sending Emails with MCP and Azure Communication Services

MCPstruts onto the scene as the new AI-comms rockstar. Now featured in GitHub Copilot, it turns email automation withAzure Communication Servicesinto a walk in the park... read more  

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Scaling Nextdoor’s Datastores

Nextdoor took on database scalability like a pro. Theydynamically routed queriesto read replicas and keptcache consistencytight, even while yanking the carpet out with schema changes. Multi JOINs blocked their move todistributed SQLlike annoying roadblocks. But Nextdoor, the sly foxes, extended thei.. read more  

Scaling Nextdoor’s Datastores
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AWS Well-Architected Framework: Performance Efficiency Pillar

The AWS Well-Architected Framework's Performance Pillar champions nimble, cloud-native and serverless-first approaches. These tactics help systems pivot like a ballerina, dodge vendor lock-in, and slash costs. WithWardley Mapping, gain clarity. Prioritize flexibility. Ditch the shackles of hard-code.. read more  

AWS Well-Architected Framework: Performance Efficiency Pillar
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The Post-Developer Era

AI crafts 25% of Google's code while human developers orchestrate the symphony, molding and refining the AI's raw outputs.These AI tools "boost," they don't "replace," the seasoned artisans of code.Startups peddling the AI-only coding pipe dream tend to implode, tripped up by unanticipated hurdles.W.. read more  

The Post-Developer Era
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@laura_garcia shared a post, 1 year, 1 month ago
Software Developer, RELIANOID

📍 VI Cybersecurity & Data Innovation Summit

🛡️ Madrid: The Hub of Cybersecurity Innovation in 2025 🗓️ April 29th | Madrid, Spain Over 300 cybersecurity professionals will gather to explore the future of digital protection — and RELIANOID will be there! What to expect: 🔹 Inspiring keynotes & expert panels on cybersecurity trends. 🔹 Live demos ..

VI CYBERSECURITY & DATA INNOVATION SUMMIT 2025 RELIANOID
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Customer Marketing Manager, Last9

Distributed Network Monitoring: Guide to Getting Started & Troubleshooting

A practical guide to getting started with distributed network monitoring and solving common issues across modern, complex systems.

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@laura_garcia shared a post, 1 year, 1 month ago
Software Developer, RELIANOID

🌍💡 World Creativity and Innovation Day — April 21 💡🌍

At RELIANOID, creativity isn’t just a value — it’s the foundation of everything we do. In a world where technology evolves at lightning speed, standing still is not an option. That’s why our team constantly challenges the status quo, reimagining howApplication Delivery, Security, and High-Performanc..

World-Creativity-and-Innovation-Day RELIANOID
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Customer Marketing Manager, Last9

A Comprehensive Guide to Monitoring Disk I/O on Linux

Learn how to monitor and optimize disk I/O performance on Linux with this comprehensive guide to better manage system resources.

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Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.