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@shurup gave 🐾 to Spotlight on SIG Architecture: API Governance , 2 weeks, 4 days ago.

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@laura_garcia shared a post, 2 weeks, 5 days ago

Software Developer, RELIANOID

🚀 𝗦𝗖𝗔𝗟𝗘 𝟮𝟯𝘅 – 𝗦𝗼𝘂𝘁𝗵𝗲𝗿𝗻 𝗖𝗮𝗹𝗶𝗳𝗼𝗿𝗻𝗶𝗮 𝗟𝗶𝗻𝘂𝘅 𝗘𝘅𝗽𝗼

📅 March 5–8, 2026 | 📍 Pasadena, California SCALE 23x – Southern California Linux Expo is back — North America’s largest community-run open source conference! Four days of: 🔹 Open source innovation 🔹 DevOps & cloud-native deep dives 🔹 Cybersecurity insights 🔹 Hands-on technical workshops 🔹 Real commu..

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@laura_garcia shared a post, 2 weeks, 6 days 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 , 2 weeks, 6 days ago.

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@yelbur started using tool Node.js , 2 weeks, 6 days ago.

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@yelbur started using tool Go , 2 weeks, 6 days ago.

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@yelbur started using tool Fedora , 2 weeks, 6 days ago.

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@yelbur started using tool Docker , 2 weeks, 6 days ago.

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@yelbur started using tool BigQuery , 2 weeks, 6 days ago.

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@kala shared a link, 3 weeks, 1 day 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

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.