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@yelbur started using tool Python , 14 hours, 2 minutes ago.

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@yelbur started using tool Node.js , 14 hours, 2 minutes ago.

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@yelbur started using tool Go , 14 hours, 2 minutes ago.

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@yelbur started using tool Fedora , 14 hours, 2 minutes ago.

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@yelbur started using tool Docker , 14 hours, 2 minutes ago.

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@yelbur started using tool BigQuery , 14 hours, 2 minutes ago.

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@kala shared a link, 1 day, 19 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, 1 day, 19 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, 1 day, 19 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

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@kala shared a link, 1 day, 19 hours ago

FAUN.dev()

Introducing helm

helm usesTypeScripttypes to registerskillsas typed functions with structured I/O. Permissions follow a clear precedence: exact→wildcard→skill→global. Agents get a keywordsearchtool and a code-execution tool that runs JS inside anSESsandbox. A recursiveproxyforwards calls overIPCto the parent, which .. read more

vLLM is an advanced open-source framework for serving and running large language models efficiently at scale. Developed by researchers and engineers from UC Berkeley and adopted widely across the AI industry, vLLM focuses on optimizing inference performance through its innovative PagedAttention mechanism — a memory management system that enables near-zero waste in GPU memory utilization. It supports model parallelism, continuous batching, tensor parallelism, and dynamic batching across GPUs, making it ideal for real-world deployment of foundation models. vLLM integrates seamlessly with Hugging Face Transformers, OpenAI-compatible APIs, and popular orchestration tools like Ray Serve and Kubernetes. Its design allows developers and enterprises to host LLMs with reduced latency, lower hardware costs, and increased throughput, powering everything from chatbots to enterprise-scale AI services.