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@cristiandeluxe started using tool GNU/Linux , 1 month ago.

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@laura_garcia shared a post, 1 month ago

Software Developer, RELIANOID

RELIANOID Live Demo

Behind every successful deployment, there’s a 𝗱𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝘁𝗲𝗮𝗺 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗰𝗹𝗼𝘀𝗲𝗹𝘆 𝘄𝗶𝘁𝗵 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 to 𝙙𝙚𝙡𝙞𝙫𝙚𝙧 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣 𝙖𝙩 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙩𝙞𝙢𝙚. Today’s 𝗹𝗶𝘃𝗲 𝗱𝗲𝗺𝗼 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 is a great example of how our engineers go beyond technology — providing 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲, and a 𝗳𝘂𝗹𝗹𝘆 𝗽𝗲𝗿𝘀𝗼𝗻𝗮..

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@laura_garcia shared a post, 1 month, 1 week ago

Software Developer, RELIANOID

𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗦𝗲𝗰𝘁𝗼𝗿

🚨 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗦𝗲𝗰𝘁𝗼𝗿 🚨 According to recent industry reports, 2025 saw a significant rise in high-severity cyber incidents across critical sectors: 🔹 IT: 23% 🔹 Government: 18% 🔹 Industrial: 18% The industrial and food sectors are becoming increasingly attractive..

high-severity incident per industries graph

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@sanjayaalpha859 started using tool Azure Pipelines , 1 month, 1 week ago.

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@sanjayaalpha859 started using tool AccessTrade , 1 month, 1 week ago.

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@laura_garcia shared a post, 1 month, 1 week ago

Software Developer, RELIANOID

𝗗𝗲𝘃𝗗𝗮𝘆𝘀 • 𝗗𝗲𝘃𝗢𝗽𝘀 𝗣𝗿𝗼 • 𝗖𝘆𝗯𝗲𝗿𝗪𝗶𝘀𝗲𝗖𝗼𝗻 𝗘𝘂𝗿𝗼𝗽𝗲

🚀 𝗗𝗲𝘃𝗗𝗮𝘆𝘀 • 𝗗𝗲𝘃𝗢𝗽𝘀 𝗣𝗿𝗼 • 𝗖𝘆𝗯𝗲𝗿𝗪𝗶𝘀𝗲𝗖𝗼𝗻 𝗘𝘂𝗿𝗼𝗽𝗲 𝘛𝘩𝘳𝘦𝘦 𝘤𝘰𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴. 𝘖𝘯𝘦 𝘦𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮. 𝘜𝘯𝘭𝘪𝘮𝘪𝘵𝘦𝘥 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯. From May 19–22, 2026, 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 will join the unified experience of 𝗗𝗲𝘃𝗗𝗮𝘆𝘀, 𝗗𝗲𝘃𝗢𝗽𝘀 𝗣𝗿𝗼, and 𝗖𝘆𝗯𝗲𝗿𝗪𝗶𝘀𝗲𝗖𝗼𝗻 𝗘𝘂𝗿𝗼𝗽𝗲 in Vilnius. A unique event bringing together developers, DevOps engineers, cybersecu..

devdays_devops_pro_cyberwisecon_europe_vilnius_2026_relianoid

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@harshilmalvi started using tool Microsoft Power Platform , 1 month, 1 week ago.

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@harshilmalvi started using tool CloudSuite , 1 month, 1 week ago.

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.