Vendor Payment Template for Jira
Vendor Payments Take Too Much Time And There Is a Way Out of the Vicious Cycle
Updates and recent posts about LangChain.. Story
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@laura_garcia shared a post, 1 week, 4 days ago
Software Developer, RELIANOID
✨ Thank You, 2025 — What a Year for RELIANOID! ✨
As the year comes to a close, we want to take a moment to look back and saythank youto everyone who has been part of RELIANOID’s journey in 2025. This year has been all aboutgrowth, innovation, and community: 🚀Product & Technology - Continued evolution ofRELIANOID Enterprise Edition, delivering high..
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@laura_garcia shared a post, 1 week, 5 days ago
Software Developer, RELIANOID
AI-driven cyberthreats are reshaping industrial security faster than many manufacturers expect.
As we approach 2026, attackers are already leveraging AI to automate reconnaissance, social engineering and intrusion workflows—often at machine speed. For manufacturing environments, where IT and OT increasingly converge, this creates a new risk landscape. In our latest article, we explore: - Why A..
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@viktoriiagolovtseva shared a post, 1 week, 5 days ago
Your Guide to Cloning in JIRA: How to Clone Issues in Different Ways
While cloning in Jira can be done in just a few clicks, it becomes less straightforward when you have special requirements. What if you need to clone an issue to a different project, clone tasks in bulk, or do this automatically on a schedule? In this article, we explore all these scenarios and provide you with examples and step-by-step instructions.
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@ilobe started using tool Weights & Biases , 1 week, 5 days ago.
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@ilobe started using tool Amazon Web Services , 1 week, 5 days ago.
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.
