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@laura_garcia shared a post, 11 months, 3 weeks ago
Software Developer, RELIANOID

💸 The Cost of Cyber Insecurity

The average data breach in 2024 costs $4.45M — over $10M in finance and healthcare. Cyber incidents = market value loss, sales drop, and reputation damage. But there’s good news: 💡 Invest $500K in security → avoid $2M in losses = 300% ROI 🧠 Microsegmentation users saw 152% ROI, saved $2.9M, cut staf..

Blog Cybersecurity ROI RELIANOID
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@anjali shared a link, 11 months, 4 weeks ago
Customer Marketing Manager, Last9

Prometheus Logging Explained for Developers

Understand how Prometheus logging captures structured metrics, improves query performance, and scales observability in production systems.

Prometheus_logging
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@anjali shared a link, 11 months, 4 weeks ago
Customer Marketing Manager, Last9

Docker Stop vs Kill: When to Use Each Command

docker stop gives containers time to shut down cleanly. docker kill doesn't—use it only when you need an immediate shutdown.

docker
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@ambertalavera shared a post, 11 months, 4 weeks ago
Abto Software

Optimizing Research Efficiency with Custom Lab Inventory Management Software Development

Discover how custom lab inventory management software enhances research efficiency with real-time tracking, RFID, cloud access, and AI-powered analytics. Learn from industry use cases and expert insights.

laboratory inventory management software
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@laura_garcia shared a post, 1 year ago
Software Developer, RELIANOID

🚀 What an incredible few days at VIVA Technology!

We had the chance to connect withso many inspiring people, from innovative startups to global tech leaders. The energy, ideas, and conversations were truly next level. 📸 We’re excited to share some real moments from the event — because it’s not just about technology, it’s about the people behind it...

Viva Technology post evento
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@anjali shared a link, 1 year ago
Customer Marketing Manager, Last9

Network Latency: Types, Causes, and Fixes

Learn what network latency means, what causes it, and how to fix slowdowns before they start affecting your users.

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

🔐 CISOs are ramping up crisis simulations in 2025!

A recent study shows 74% of CISOs plan to increase their budgets for cyber crisis exercises. Why? The rise in sophisticated attacks and high-profile breaches like those affecting 23andMe, NHS, and Cencora highlight the urgent need for proactive defense strategies. At RELIANOID, we help organizations..

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@eon01 shared a post, 1 year ago
Founder, FAUN.dev

🚀 Meet This Week’s Human: A New Way to Celebrate Builders

Every week, thousands of developers read FAUN to stay sharp, discover tools, and learn what’s trending in Software Engineering.

Now, we’re adding a human touch.

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

✈️ Understanding Airport Software Systems

From check-in to takeoff, modern airports rely on a complex network of integrated IT systems to ensure efficiency, safety, and smooth operations. We’ve visualized this in a new diagram, highlighting key components like: ✅ AODB (Airport Operational Database) ✅ Passenger & baggage handling systems ✅ A..

Airport Software Systems
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@faun shared a link, 1 year ago
FAUN.dev()

Why Go is a good fit for agents

Gorules the realm of long-lived, concurrent agent tasks. Its lightning-fast goroutines and petite memory use make Node.js and Python look like clunky dinosaurs trudging through thick mud. And don't get started on itscancellation mechanism—seamless cancelation, zero drama... read more  

Why Go is a good fit for agents
LangChain is a modular framework designed to help developers build complex, production-grade applications that leverage large language models. It abstracts the underlying complexity of prompt management, context retrieval, and model orchestration into reusable components. At its core, LangChain introduces primitives like Chains, Agents, and Tools, allowing developers to sequence model calls, make decisions dynamically, and integrate real-world data or APIs into LLM workflows.

LangChain supports retrieval-augmented generation (RAG) pipelines through integrations with vector databases, enabling models to access and reason over large external knowledge bases efficiently. It also provides utilities for handling long-term context via memory management and supports multiple backends like OpenAI, Anthropic, and local models.

Technically, LangChain simplifies building LLM-driven architectures such as chatbots, document Q&A systems, and autonomous agents. Its ecosystem includes components for caching, tracing, evaluation, and deployment, allowing seamless movement from prototype to production. It serves as a foundational layer for developers who need tight control over how language models interact with data and external systems.