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How to Actually Evaluate Fine-Tuned LLMs (3-Tier Framework)
AI Agent Inference Performance Optimizations + vLLM vs. SGLang vs. TensorRT w/ Charles Frye (Modal)
Beyond SWE-Bench Pro - Where do Agents go from Here
SWE-Agent Team Interview - Agents, Programming, and Benchmarks!
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Last Updated: September 26, 2026
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Summary
John Yang is a PhD student at Stanford and the creator of the For more information about Stanford's graduate programs, visit: online.stanford.edu/graduate-education November 21,Ā ... In this talk, Ernst Haagsman, Product Leader at JetBrains, shares his expertise on scaling developer tools from his early days onĀ ... HeyGen to create your own free avatar: tinyurl.com/6y9b4nkk For HyperFrames, visit:Ā ... Hash maps are computing's most fundamental data structure. Google found they burn roughly 1% of all server CPUĀ ... Open-source LLMs are great for conversational applications, but they can be difficult to scale in production and deliver latencyĀ ... Finding and fixing weaknesses and vulnerabilities in source We're going to be a bit all over the map here, first some new "It looks good" is not a deployment strategy. Most fine-tuned models fail silently in production because their evaluation stack lacksĀ ... Zoom link: us02web.zoom.us/j/82308186562 Talk Introductions and Meetup Updates by Chris Fregly and Antje BarthĀ ...
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