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Products that do what InfraSim does

Zero-risk chaos engineering — prove your infra's ceiling

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  9. 9UD

    Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…

    2024

  10. 10OS

    Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…

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  17. 17MI

    I've been working on a platform that uses LLMs to build maintain and manage k8s clusters on any cloud. The system writes infra as code to your Github repos and automatically containerizes and scales any services (public or private). The goal is to give your average engineer a vercel-like deployment experience for any service in any language at minimal cost. We have humans involved at the moment auditing LLM outputs and keeping an eye on clusters. We are looking for folks who may be thinking about their first infra/devops hire. Just connect your github and your cloud provider. The system…

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  18. 18OS

    Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…

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  19. 19CA

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  23. 23OS
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