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@laura_garcia shared a post, 9 hours ago

Software Developer, RELIANOID

We’re heading to Big Data & AI World 2026

We’re heading to Big Data & AI World 2026 📍 4–5 March 2026 | London Part of Tech Show London 2026, this event brings together data and AI leaders focused on responsible, scalable AI and measurable business outcomes. At RELIANOID, we enable secure, high-performance infrastructures ready for AI-driven..

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@yelbur started using tool Python , 1 day, 3 hours ago.

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@yelbur started using tool Node.js , 1 day, 3 hours ago.

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@yelbur started using tool Go , 1 day, 3 hours ago.

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@yelbur started using tool Fedora , 1 day, 3 hours ago.

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@yelbur started using tool Docker , 1 day, 3 hours ago.

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@yelbur started using tool BigQuery , 1 day, 3 hours ago.

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@kala shared a link, 2 days, 8 hours ago

FAUN.dev()

Realtime Prompting Guide

OpenAI shipsgpt-realtimeand declares GA for theRealtime API. It's a speech-to-speech model that tightens instruction-following, steadiestool calling, and lifts voice fidelity. Latency drops. True realtime agents become possible. The release prescribesprompt skeletons,JSON envelopetool outputs,sessio.. read more

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@kala shared a link, 2 days, 8 hours ago

FAUN.dev()

Do you need an MCP to build your native app?

Do you need an MCP to build your native app? Surprisingly, modern agents succeed either way. The real difference is how much time, cost, and context you waste along the way... read more

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@kala shared a link, 2 days, 8 hours ago

FAUN.dev()

The Pentagon is making a mistake by threatening Anthropic

Anthropic's Claude Gov, optimized for national security uses, has fewer restrictions than regular versions. The Pentagon is threatening retaliation if Anthropic does not waive these restrictions by Friday, including invoking the Defense Production Act or declaring Anthropic a supply chain risk. Anth.. read more

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.