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Announcing FAUN.sensei() โ€” Self-paced guides to grow fast โ€” even when tech moves faster.

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After months of hard work, FAUN.sensei() is finally alive!

FAUN.sensei()
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๐ŸŒŸ ๐—ช๐—ฒโ€™๐—ฟ๐—ฒ ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด! ๐—๐—ผ๐—ถ๐—ป ๐˜๐—ต๐—ฒ ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐—— ๐—ง๐—ฒ๐—ฎ๐—บ ๐ŸŒŸ

Are you passionate about technology, networking, and innovation? At RELIANOID, weโ€™re building cutting-edge solutions that power secure, scalable, and reliable infrastructures โ€” and weโ€™re looking for talented people to join us on this journey! ๐Ÿš€ Whether youโ€™re an experienced professional or just star..

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Helm Cheat Sheet: Everything You Need to Know to Start Using Helm

Helm Kubernetes

Helm is the package manager Kubernetes was missing. It lets you package applications and their dependencies into charts, deploy them as versioned releases, and manage installs, upgrades, and rollbacks in a consistent and repeatable way. This post walks through what Helm is, how to install it, and the core commands you will use day to day.

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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.