Optimize Your Dev Setup For Evals w/ Cursor Rules & MCP
Hosted by Isaac Flath, Hamel Husain, and Shreya Shankar
Mon, Jul 14, 2025
6:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course


Mon, Jul 14, 2025
6:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
484 students
Go deeper with a course


What you'll learn
How to use MCP context for AI evaluation frameworks
Cursor rules for Phoenix, Braintrust, and Inspect
Use AI for evaluation development and debugging
Why this topic matters
You'll learn from
Isaac Flath
AI Engineer & Fullstack Developer
Isaac is a data scientist focused on AI applications. While this often means machine learning and deep learning it often means web app development and other things. AI is only a component of a successful AI application.
I am currently building out Gallery.FastHT.ML](https://gallery.fastht.ml/) and generally developing the FastHTML ecosystem.
My primary hobby is dance. I used to teach ballroom dance full time, which is where I met my partner. My partner runs her own dance instruction business here in D.C.
Hamel Husain
ML Engineer with 20 years of experience
Hamel is a machine learning engineer with over 20 years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI, for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools. Hamel is currently an independent consultant helping companies build AI products.
Shreya Shankar
ML Systems Researcher Making AI Evaluation Work in Practice
Shreya is an experienced ML Engineer who is currently a PhD candidate in computer science at UC Berkeley, where she builds systems that help people use AI to work with data effectively. Her research focuses on developing practical tools and frameworks for building reliable ML systems, with recent groundbreaking work on LLM evaluation and data quality. She has published influential papers on evaluating and aligning LLM systems, including "Who Validates the Validators?" which explores how to systematically align LLM evaluations with human preferences.
Prior to her PhD, Shreya worked as an ML engineer in industry and completed her BS and MS in computer science at Stanford. Her work appears in top data management and HCI venues including SIGMOD, VLDB, and UIST. She is currently supported by the NDSEG Fellowship and has collaborated extensively with major tech companies and startups to deploy her research in production environments. Her recent projects like DocETL and SPADE demonstrate her ability to bridge theoretical frameworks with practical implementations that help developers build more reliable AI systems.
Learn directly from Isaac Flath, Hamel Husain, and Shreya Shankar
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