
Everything you wanted to know about data, ML, and AI, but were too afraid to ask.
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Recent posts by this newsletter. Browse the email archive.
In this post, we’ll build two AI agents from scratch in Python. One will be a coding agent, the other a search agent.
Our final Building with AI course cohort kicks off in two weeks. Below you’ll find the guest lineup (I’m excited!), plus lessons on durable agents, mistakes at Google, and synthetic consumers, including:
As part of our course on Building AI Applications for Data Scientists and Software Engineers, I hosted Doug Turnbull (Led Search at Reddit & Shopify) to discuss the emerging field of agentic search. Doug explored why traditional search stac...
Ravin Kumar worked on NotebookLM, Project Mariner, and now the Gemini and Gemma family of models at Google DeepMind. Here’s his practical advice on why most AI products fail, and how to build ones that last.
Other publications recommended by the authors of this newsletter.
The writers behind this newsletter.
Hugo is a data scientist, educator, evangelist, content marketer, data strategy consultant, and host of the industry podcast Vanishing Gradients.
ML Engineer @ ZenML
There's a chasm between data science and engineering. Let me show you how to bridge it.
MLOps + Data + LLMOps; creator of https://github.com/dagworks-inc/hamilton. Formerly of: Stitch Fix, Idibon, Nextdoor, LinkedIn, HRI, IBM. CEO @ DAGWorks - dagworks.io
Co-founder Delphina. Past: Uber, Gopuff, Wealthfront. PhD Harvard.
Isaac is an independent consultant who has helped many companies incorporate AI into their products. I've taught AI efficiency to companies for years and worked for tech startups and cutting edge labs.
I was an early product engineer on GitHub Copilot. And I wrote a couple of books! One on Prompt Engineering (https://amzn.to/4gChsFf) and one on Relevant Search (https://amzn.to/3TXmDHk)
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