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@secuodsoft started using tool Docker , 1 week, 5 days ago.

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@secuodsoft started using tool Python , 1 week, 5 days ago.

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@secuodsoft started using tool PHP , 1 week, 5 days ago.

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@secuodsoft started using tool Node.js , 1 week, 5 days ago.

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@secuodsoft started using tool MongoDB , 1 week, 5 days ago.

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@secuodsoft started using tool Java , 1 week, 5 days ago.

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@secuodsoft created an organization Secuodsoft , 1 week, 5 days ago.

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@laura_garcia shared a post, 1 week, 6 days ago

Software Developer, RELIANOID

𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 𝟴.𝟱 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗘𝗱𝗶𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘄 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲!

- 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 𝟴.𝟱 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗘𝗱𝗶𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘄 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲! This release represents a major step forward in 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝘀𝘆𝘀𝘁𝗲𝗺 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆. - 𝗨𝗘𝗙𝗜 𝗦𝗲𝗰𝘂𝗿𝗲 𝗕𝗼𝗼𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 – cryptographic verification of the boot chain - 𝗗𝗲𝗯𝗶𝗮𝗻 𝗕𝗼𝗼𝗸𝘄𝗼𝗿𝗺 𝟭𝟮.𝟭𝟯 𝗯𝗮𝘀𝗲 + 𝗞𝗲𝗿𝗻𝗲𝗹 𝟲.𝟭.𝟭𝟱𝟵-𝟭-𝗻𝗼𝗶𝗱𝟬 - 𝗛𝗦𝗧𝗦..

relianoid_enterprise_edition_8.5_release

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@viktoriiagolovtseva shared a post, 1 week, 6 days ago

A Git and Jira Integration Guide: How to Connect GitHub, GitLab, and Bitbucket to Jira Cloud

If you ask a developer, product manager, and QA “why integrate your Git repository with Jira,” they will all give different answers. Some like it for reducing context switching and providing automation options, while others value the transparency and improved traceability. But no matter who you ask, everyone is unanimous: this integration is immensely useful for the teams.

In this article, we focus on the most popular Git applications: GitHub, GitLab, and Bitbucket. We explain how to connect them to Jira Cloud and provide you with practical tips on making the most out of this integration. You will learn how to use smart commits, leverage automation, and gain extra value from third-party apps.

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@ashwinisdave shared a post, 1 week, 6 days ago

Developer advocate, Middleware

From Metrics to Meaning: Building Context-Aware Dashboards That Actually Help Debug Production Issues

#DevOps #AI Dash... #observa... #log mon...

Most dashboards show what's happening but not why it matters. Learn how to build context-aware dashboards that actually help engineers debug production issues faster.

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.