Fotos y Fondos
@fotosyfondos · Post #9728 · 11/23/2018, 04:39 PM
📸🖼📸🖼📸🖼📸🖼📸🖼📸🖼 ➡️ Fantasmas #Fantasmas#Terror#Luigi#FondosDePantalla @fotosyfondos 📸🖼📸🖼📸🖼📸🖼📸🖼📸🖼
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Source channel @githubtrending · Post #15521 · Feb 25
#rust#ai_gateway#ai_gateway_support#envoy#envoyproxy#gateway#generative_ai#llm_gateway#llm_inference#llm_proxy#llm_routing#llmops#llms#openai#prompt#proxy#proxy_server#routing Plano is an AI-native proxy server that handles key tasks for agentic apps like routing between agents, smart LLM model selection, safety guardrails, and automatic traces for observability. Define agents in simple YAML, write basic HTTP code in any language, and start Plano to run multi-agent systems without custom plumbing or framework lock-in. You benefit by building and shipping reliable agents to production much faster, focusing on core logic while gaining safety, low latency, and easy scaling. https://github.com/katanemo/plano
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djangoproject
@djangoproject · Post #275 · 03/18/2017, 01:51 AM
https://github.com/spotify/luigi Writing batch jobs is generally only one part of processing heaps of data; you also have to string all the jobs together into something resembling a #workflow or a #pipeline. #Luigi, created by Spotify and named for the other plucky plumber made famous by Nintendo, was built to "address all the plumbing typically associated with long-running batch processes." With Luigi, a developer can take several different unrelated data processing tasks — "a Hive query, a Hadoop job in Java, a Spark job in Scala, dumping a table from a database" — and create a workflow that runs them, end to end. The entire description of a job and its dependencies are created as Python modules, not as XML config files or another data format, so it can be integrated into other Python-centric projects. #Machine_learning