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@maydali28 started using tool Terraform , 2 weeks ago.

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@maydali28 started using tool Spring , 2 weeks ago.

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@maydali28 started using tool Kubernetes , 2 weeks ago.

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@maydali28 started using tool Java , 2 weeks ago.

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@maydali28 started using tool Azure Kubernetes Service (AKS) , 2 weeks ago.

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@sancharini shared a post, 2 weeks ago

Why Understanding Software Testing Basics Is Essential for Every Developer?

Understand why software testing basics is essential for every developer. Learn key testing types, levels, techniques, and best practices to write reliable, maintainable, and high-quality code.

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@eon01 published a course, 2 weeks ago

Founder, FAUN.dev

Practical MCP with FastMCP & LangChain

#LangCha... #Model C... #FastMCP #AI agen...

Engineering the Agentic Experience

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@kala added a new tool FastMCP , 2 weeks ago.

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@kala shared an update, 2 weeks ago

FAUN.dev()

FastMCP 3.0 Released: Community-Driven Enhancements Unveiled

#FastMCP #FastMCP... #Prefect... #AI #MCP

FastMCP 3.0 is now generally available. It keeps the @mcp.tool() API but rebuilds the internals around components + providers + transforms, adds a CLI, and ships production features like component versioning, per-component auth + OAuth additions, OpenTelemetry tracing, background tasks, pagination, tool timeouts, and hot reload. The project moved from jlowin/fastmcp to PrefectHQ/fastmcp on GitHub, and upgrading is supported via dedicated guides for FastMCP 2 and MCP SDK users.

FastMCP 3.0 Released: Community-Driven Enhancements Unveiled

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.