it’s surprising to me how many people seem to not understand that great models are built with super high quality curated data finding novel ways to create / get this data is a huge edge↗

Today, we are releasing Rampart: a 14.7MB machine learning model designed to protect citizens’ privacy by redacting personal information directly in your browser before it gets sent to any server↗

Inference Engineering launches today. baseten.com/inference-engi…↗

A curated learning resource focused on building skills inspired by Andrej Karpathy, organizing key topics and practice areas for machine learning.↗

@badlogicgames @stableguvnor @oorusr Knock yourself out. mitsuhiko.github.io/talks/me-and-t…↗

Finally got time to watch this talk by Karpathy. It's just full of good thoughts. Especially wrt keeping the AI on a leash, and scoping your tasks. youtu.be/LCEmiRjPEtQ?si…↗

The End of Cloud Inference docs.google.com/document/d/1Hl…↗

@bentossell @Keshavatearth It's funny how every model ends up needing its own "optimal guidance". I wish there was a job for "just figure out the general meta-heuristics for each provider and model to help everyone else effectively use them". 🤪↗

Even if you’re a great programmer, I’ve found it’s a long climb to becoming a decent AI researcher. The skills around debugging training runs and intuitions around what is happening are really different. It’d be smart to start now though—you’d still be early. Lots of promise.↗

Watching @karpathy presentation from today and taking twitter notes, come along for the ride: If you're like only the practical tips, skip to #32 @karpathy starts with stages: 1 - Pre-training - months x thousands of GPUs 2, 3, 4 - Finetuning stages that take hours or days 1/↗