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Software Developer, RELIANOID

The UK raises the bar on digital security

With cyberattacks on the rise, the Product Security and Telecommunications Infrastructure (PSTI) Act marks a major step toward making connected technology secure by design. In our latest article, we explain: What the PSTI Act requires Why it matters beyond consumer IoT How it signals a global sh..

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New CNCF Sandbox projects in 2025: From Podman to CloudNativePG

Kubernetes

Each year, 25-30 new Open Source projects related to the Cloud Native ecosystem are accepted to the CNCF Sandbox. In January 2025, there were 13 additions, with four of them donated by Red Hat. Here's the list of these newly added CNCF projects: - Podman Container Tools (security-focused Docker alte..

CNCF Sandbox projects in January 2025
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CI Testing Best Practices for Reliable and Fast Builds

As software teams adopt continuous integration, build speed and reliability become critical success factors. CI testing plays a central role in ensuring that every code change is validated quickly and consistently before it moves further down the delivery pipeline. Without clear practices, however, ..

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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.