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@mashka shared a link, 1 year ago

Paid Acquisition and Growth Marketing, xygeni

AI-Powered DevSecOps. Orchestrating Security at Cloud Scale

SafeDev Talk: AI-Powered DevSecOps – Orchestrating Security at Cloud Scale

Join us for an insightful discussion on how AI is revolutionizing DevSecOps, enhancing security across the Software Development Life Cycle (SDLC).

Don't miss this opportunity to understand how AI is reshaping the future of DevSecOps https://www.linkedin.com/events/7335954209948819457 SafeDev Talk on AI-Powered DevSecOps

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@anjali shared a link, 1 year ago

Customer Marketing Manager, Last9

How to Log Into a Docker Container

Understand how to quickly log into a Docker container using simple commands to troubleshoot and manage your apps effectively.

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@carlos_devops shared a post, 1 year ago

Consultant, Independent

What is a recommended as a good alternative from JFrog for artifact management as an entrpise grade solution?

When thinking about enterprise-grade artifact management beyond JFrog Artifactory, how do other solutions measure up in terms of universal package support, scalability, security, and seamless DevOps integration?

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@laura_garcia shared a post, 1 year ago

Software Developer, RELIANOID

📣 We're thrilled to see our solutions featured on TechBullion!

A big thank you to the TechBullion team for highlighting our work and helping spread the word about what we do. 🙌 🔗 https://www.relianoid.com/about-us/relianoid-related-articles/ #TechBullion#RELIANOID#CyberSecurity#LoadBalancing#Networking#Innovation#TechNews..

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@laura_garcia shared a post, 1 year ago

Software Developer, RELIANOID

🚀 Our first time in Taiwan! DevOpsDays Taipei

📍 June 5–6 | Taipei, Taiwan We’re excited to join DevOpsDays Taipei 2025, Taiwan’s biggest DevOps event! Over 700 IT pros, engineers, and tech leaders will gather to dive into automation, CI/CD, observability, SRE, and DevOps culture. 👥 Meet the RELIANOID team on-site! Discover how we help DevOps te..

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@anjali shared a link, 1 year ago

Customer Marketing Manager, Last9

Prometheus Alerting Examples for Developers

Know how to set up smarter Prometheus alerts from basic CPU checks to app-aware rules that reduce noise and catch real issues early.

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@anjali shared a link, 1 year ago

Customer Marketing Manager, Last9

Jaeger vs Zipkin: Which is Right for Your Distributed Tracing

Compare Jaeger and Zipkin to find the best fit for your distributed tracing needs, infrastructure, and observability goals.

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@laura_garcia shared a post, 1 year ago

Software Developer, RELIANOID

🔐 RELIANOID at Cyber Security Congress 2025 – Enabling a Secure Future

📍 June 4–5 | Santa Clara, California | Part of TechEx North America The future of cybersecurity demands smart, scalable solutions — and we’re ready to deliver. Join us at#CyberSecurityCongress, where RELIANOID will showcase advanced application delivery and threat protection technologies built for h..

Cyber Security Congress North America 2025

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@readdive shared a post, 1 year ago

Founder, Read Dive

Snapchat and Generative AI: The Next Phase of Augmented Reality

Explore how Snapchat combines generative AI and augmented reality to transform digital creativity, user interaction, and storytelling in exciting new ways.

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@readdive shared a post, 1 year ago

Founder, Read Dive

Ensuring Performance and Security: Testing Solutions for Crypto Mobile Apps

Ensure secure, high-performing crypto apps with expert solutions from mobile app testing companies. Learn key strategies and testing essentials.

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.