It’s not Friday…
Posts by Ian
This is huge. Amazing work by @mmccue.bsky.social and the Surf team, and also to Vox for making some really bold moves embracing the open social Internet. And, look how good it looks!
teaser image for Dune 3, except it's called Doug, and Paul and Chani are replaced with Doug and Patti
Wikipedia screenshot of https://en.wikipedia.org/wiki/Dust_II
Happy 25 years of dust2
Cyberspaces you could navigate as well as any place you’ve ever lived.
Posted here as well. www.linkedin.com/posts/gaurav...
I hope this gets the visibility to the the right audience
Also thanks to @recsysml.bsky.social for proofreading and providing me a platform to post. Follow them for more great recsys content.
A picture of the bluesky discover feed from the Apple App Store, showing a post by Jerry, and more importantly, a very cute corgi.
you and my dog are equally famous
It’s impossible to watch any political thriller now because whatever incident is generating dramatic tension pales in comparison to reality.
Sign in PRO for presumed liability. app.leg.wa.gov/csi/House?se...
its on the roadmap!
A picture of house minority leader Hakeem Jeffries.
Every time I get in a Lyft they cal me Lan. Serifs could fix this very specific me problem.
If we want to create a more affordable, livable Seattle, we need to design for *reducing* car volumes. This is the only way to meet our climate, public health, vision zero, accessibility and housing goals. Our fight to design highway on-ramps with LESS capacity epitomizes why it's still hard.
In this three part series I compare #LLM and #RecommenderSystems to show the gaps and opportunities. They are surprisingly fewer than one would think.
Part 1 open.substack.com/pub/recsysml...
This project is absolutely nuts. You can take the vector embeddings for text and convert them back to the text that generated them.
github.com/vec2text/vec...
hah maybe. I feel like I would start with TransAct, and maybe PinSage to cover the world of recs at Pintrest
also a bunch of practical stuff on how they serve these long user histories. Clearly a lot of work went into this, and a lot of goodies in this paper.
- ranking model that tried to predict the next user action in a sequence. Use a cross-entropy loss to predict labels/actions on the sample and a next action loss to predict the next action the user will take.
- impression based negative sampling doubled some offline metrics
Read Xia et al., “TransAct V2.” earlier today, some notes:
- long and short term user histories (long term [10k items], short term [100], impressions [100])
- sampled soft max loss function seems useful in environments with very weak negatives
#recsys #ml
arxiv.org/abs/2506.02267
A corgi lying on the ground facing the camera. A red ball is sitting next to his snoot.
Atticus wants to play
but some more than others
you most of all
watching Alien: Earth and I think I'm rooting for the alien
todays discover outage was brought to you by the letter <SPACE>
anyone got opinions on data orchestration tools? Specifically Dagster vs Temporal?
This person has clearly never used protobufs, especially in python.