Vivek Raghunathan

Wild Moose is now consistently hitting >80% accuracy in identifying root causes - within just 3 weeks of deployment. The idea of an AI SRE has been floating around for years now. Investors put their money on it. But customers remained (rightfully) skeptical. Why? Because when something breaks in production, every minute counts. And an AI agent that points you in the wrong direction isn’t just useless - it's harmful. The truth is: building a generative AI agent that understands the intimate dynamics of your system is incredibly hard. That’s why despite all the hype, most tools in this space never made it past demo stage. For us, something shifted earlier this year. Technical bets we made early on - around how to express tribal knowledge, and how to gradually increase the Moose’s autonomy through real-time reasoning - started paying off. Everything started clicking. And with that, something else happened: Our customers became our biggest advocates. It’s hard to describe how energizing that feels as a founder. We’re feeling unstoppable. More to come.

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Is your team running inference at scale using open weights models like Llama, Gemma, Qwen or Deepseek? The combination of vLLM + LMCache + Redis enables faster inference at large context sizes by allowing models to skip recomputation for overlapping content. LMCache reduces redundant computation by caching and reusing key-value (KV) pairs for repeated token chunks. Redis provides the real-time infrastructure to store and retrieve those chunks at scale. Excited to partner with the amazing LMCache team. Read more here: https://lnkd.in/gWCgVPrF

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We're introducing Stax, an experimental developer tool to streamline LLM evaluation with custom and pre-built autoraters. Stax is an AI evaluation platform that allows you to assess and benchmark generative AI models and applications against your specific needs and criteria. In contrast to general leaderboards, Stax helps you understand how an LLM or prompt will perform for your use cases. Testing AI is challenging due to the absence of a single "right" answer and it's non-deterministic nature. Stax provides a platform to measure the quality of AI-generated content, ensuring it aligns with your unique standards and criteria. With Stax, you can: ⚡ Experiment Rapidly: Use the "Quick Compare" feature to test different prompts across various models. 🌐 Evaluate at Scale: Utilize "Projects & Datasets" to run extensive evaluations on large sets of data. ✅ Define Your Own Criteria: Create custom evaluators tailored to your specific use case or use pre-built system evaluators for common criteria like fluency, groundedness, and safety. 📈 Analyze Performance: Visualize evaluation results and performance metrics on the Analytics page to gain insights into model behavior. Go try it now: https://lnkd.in/e_YvmrHC

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"Context engineering" is an advance on "prompt engineering". But there's still something missing. New blog on how domain modeling is key to optimizing Gen AI, proposing Domain-Integrated Context Engineering (DICE): https://lnkd.in/gWCnWT68 James Ward Josh Long John Davies Eric Evans

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