New research on personal agents:
How does @Muse find and recommend products? Analysis based on running 50 prompts from a single consumer retail category.
Findings from Researcher, @allisoncyhuang 🧵
The model understands the scene in three dimensions, not just as a flat layout, so objects settle into the space instead of overlapping or spilling out of frame.
I like this description of model behavior, and I'm excited about further thinking on what makes a model a good collaborator (as opposed to simply a good worker)
People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way.
We share our approach, early results, and a quick look at our model in action.
https://t.co/AFJZ5kH7Ku
⭐ New talk! https://t.co/dhDLxakvrC
Coding agents might help us finally break out of two cages: the app model, which traps computing in one-size-fits-all silos; and programming as a specialization, which has crowded out cultures of imagination and domain insight.
Our digital advent calendar builder is here at https://t.co/FYkteL17h1
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FAQ: https://t.co/q7t3KTltP2
Discord for updates & support: https://t.co/MSJpYFRItz
Here's my enormous round-up of everything we learned about LLMs in 2025 - the third in my annual series of reviews of the past twelve months
https://t.co/HD9Zf85SG2
This year it's divided into 26 sections! This is the table of contents:
Mapping LLMs with Sparse Autoencoders
https://t.co/FIcY91YkS4
An interactive introduction to Sparse Autoencoders and their use cases
with Nada Hussein, @shivamravalxai, Jimbo Wilson, Ari Alberich, @NeelNanda5, @iislucas, and @Nithum