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@kala added a new tool Manus AI , 4 days, 15 hours ago.
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Software Developer, RELIANOID

What is Load Balancing in DigitalOcean? ⚖️

A quick 3-minute read on how traffic is distributed across Droplets to improve performance, scalability, and availability—and how RELIANOID enhances it with advanced monitoring, security, and flexibility. 👉 Smart traffic distribution. High availability. Seamless scaling. #LoadBalancing#DigitalOcean#..

Knowledge base Load Balancing in DigitalOcean  RELIANOID
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@viktoriiagolovtseva shared a post, 1 week ago

Jira Action Items Functionality

Jira Action Items are a new feature introduced in Jira to help teams track small tasks inside a work item (issue) without needing to create subtasks. Instead of breaking out additional tickets for quick to-dos or follow-ups, you can now add lightweight checklists directly into rich text fields like ..

Zrzut ekranu 2026-01-02 152946
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@laura_garcia shared a post, 1 week ago
Software Developer, RELIANOID

What is AWS (Amazon Web Services)?

AWS is a global public cloud platform that lets you run compute, storage, networking, databases, and more on demand, pay-as-you-go, and at scale across multiple Regions and Availability Zones. 👉 Build resilient architectures 👉 Automate everything with APIs & IaC 👉 Scale securely and globally in minu..

Knowledge base Why Deploy RELIANOID Load Balancer on AWS
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@viktoriiagolovtseva shared a post, 1 week ago

Vendor Payment Template for Jira

Vendor Payments Take Too Much Time And There Is a Way Out of the Vicious Cycle

Zrzut ekranu 2026-01-02 142203
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.