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ContentUpdates and recent posts about BigQuery..
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@laura_garcia shared a post, 7 months, 2 weeks ago
Software Developer, RELIANOID

🔄 In case you missed it last month…

🔒 Incident Response in 2025: Lessons Learned From food supply disruptions and airline data breaches to sector-wide attacks on insurers, June–August 2025 highlighted how critical rapid and prepared responses are in cybersecurity. At the same time, advances like AI-powered detection and resilience fea..

Incident Response june, july, august 2025
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@laura_garcia shared a post, 7 months, 2 weeks ago
Software Developer, RELIANOID

⚡ Windows Server Load Balancing Explained

Windows Server’s built-in Network Load Balancing (NLB) feature helps organizations distribute traffic, ensure redundancy, and keep mission-critical applications running without downtime. But while NLB is effective, modern workloads demand more. In our latest article, we cover: ✔️ What Windows Server..

Knowledge base Windows Server Load Balancing
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@laura_garcia shared a post, 7 months, 2 weeks ago
Software Developer, RELIANOID

DevOps Days Cairo is coming!

- RELIANOID at DevOpsDays Cairo 2025 On September 27th, DevOpsDays returns to Giza, Egypt, bringing its 8th edition with a strong focus on the intersection of AI × DevOps — from MLOps and AIOps to infrastructure automation and AI-powered security. We’re excited to join this flagship DevOps event in ..

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@mashka shared a link, 7 months, 2 weeks ago
Paid Acquisition and Growth Marketing, xygeni

Upcoming 𝐖𝐞𝐛𝐢𝐧𝐚𝐫: 𝐀𝐈 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧!

Join Xygeni for a hands-on webinar exploring how AI can automate application security and streamline developer workflows. Learn how to move beyond noisy alerts and manual triage with intelligent, real-time remediation workflows that secure your CI/CD pipeline, without slowing developers down.

What you'll learn:

- How to auto-fix secrets, OSS vulnerabilities, and code flaws directly from alerts
- Ways to reduce false positives and focus on what really matters
- How to set up developer-friendly guardrails across your SDLC
- Practical steps to protect every commit and pull request

- and much more!
Date: October 8
Time: 17:00 CEST / 11:00 EDT
Platform: LinkedIn

The session includes live demos and real-world examples. Replay available for all registrants.

👉 Register here: https://www.linkedin.com/events/7375842799042248704/

See you there!

Webinar - 𝐀𝐈 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧
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@laura_garcia shared a post, 7 months, 2 weeks ago
Software Developer, RELIANOID

🔎 Understanding VRF (Virtual Routing and Forwarding)

VRF enables secure traffic isolation, scalability, and multi-tenant networking on a single infrastructure. In our latest article, we explain how it works, key benefits, and how RELIANOID implements per-NIC VRF to enhance security and flexibility 🚀 👉 Read more in the full article! https://www.reliano..

kb VRF Virtual routing and forwarding
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@faun shared a link, 7 months, 2 weeks ago
FAUN.dev()

Self-replicating worm hits 180+ npm packages in (largely) automated supply chain attack

A supply chain worm called **Shai-hulud** is loose in the npm wild. It's not just lurking—it’s replicating through npm packages, lifting developer tokens, and injecting tainted versions of real, maintained libraries. Once in, it grabs GitHub secrets, flips private repos public, and piggybacks on Gi.. read more  

Self-replicating worm hits 180+ npm packages in (largely) automated supply chain attack
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@faun shared a link, 7 months, 2 weeks ago
FAUN.dev()

MCP vulnerability case study: SQL injection in the Postgres MCP server

A nasty SQL injection bug in Anthropic’s now-retiredPostgres MCP serverlet attackers blow past read-only mode and run whatever SQL they wanted. The repo got archived back in May 2025—but it’s far from dead. The unpatched package still racks up21,000 NPM installsand1,000 Docker pullsevery week... read more  

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@faun shared a link, 7 months, 2 weeks ago
FAUN.dev()

GitHub Copilot Custom Chat Modes: AI Personas that Match Your Needs

GitHub Copilot Chat just jot better in **VS Code 1.101** with **Custom Chat Modes**. Devs can now drop Markdown files into their workspace to shape Copilot’s persona—tone, tools, constraints, the works. Want an AI buddy for security audits? Or a test-writing machine with zero patience for flaky cod.. read more  

GitHub Copilot Custom Chat Modes: AI Personas that Match Your Needs
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@faun shared a link, 7 months, 2 weeks ago
FAUN.dev()

How In-Memory Caching Works in Redis

Redis isn’t just a cache anymore. Sure, it still owns the in-memory speed game—with **key expiration**, **data persistence**, and **horizontal scaling** via **replication** and **clustering**. But if you're only using it to stash a few keys, you're missing the point. This thing handles **streams**,.. read more  

How In-Memory Caching Works in Redis
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@faun shared a link, 7 months, 2 weeks ago
FAUN.dev()

Experimenting with local LLMs on macOS

Running **open-weight LLMs locally on macOS**? This post breaks it down clean. It compares **llama.cpp**—great for tweaking things—to **LM Studio**, which trades control for simplicity. Covers what fits in memory, which quantized models to grab (hint: 4-bit GGUF), and what’s coming down the pipe: *.. read more  

Experimenting with local LLMs on macOS
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).