Vicki

@vickiboykis.com

Founding list[float] engineer. Recsys. Personalization. Infra. Systems. Normcore code. Nutella. Vectors. Words. Vibes. Bad puns (soon). https://vickiboykis.com/what_are_embeddings/

Blog posts we used to have: “how I reduced latency across several hundred machines by 200 ms by removing two O(n)^2 hot paths” Blog posts we have now: “Here’s how to get the most out of /ultrathink in the latest release of Claudex 3.0.5”

Curious about people’s ML dependency management strategies in the face of supply chain risks. Often pinning dependencies particularly in the torch/HuggingFace ecosystem is the only way to create reproducible training and inference code but that means dependabot isn’t happy.

There are like only three design patterns you need to use 80% to make stuff good and fast and elegant Make it a lookup (hashmap) Split it up into smaller pieces (sharding) Combine it into one large piece (map reduce)

Looking to read an in-depth examination of the project history and code evolution of Bun in Zig, along with a technical evaluation of its current Rust re-implementation. I want to check out links to PRs, sidebars on Zig and Rust, the works. Who’s doing this?

Everyone working on recsys/embeddings/information retrieval/search is sleeping on this newsletter if you're not subscribed. I can't believe I didn't mention it before but it just summarizes the best content in the field every week recsys.substack.com

Top Information Retrieval Papers of the Week | Sumit | Substack

A weekly curated newsletter about the latest research papers in the Information Retrieval domain, including Recommender Systems, Search, Retrieval, and Ranking. Click to read Top Information Retrieval...

recsys.substack.com

the actual most revolutionary thing the ai labs have actually done is make it look like it's insanely easy to duct tape together python microservices with all their concurrency issues and trained models with all their transformers issues and have them run as one api call

this is so cool, continuing my local experiments and using pi and lm studio and gemma to introspect how i use agents locally. On the left, you can literally see the streaming tokens and the context window filling up.

screenshot of lm studio and pi running in a ghostty terminal

It’s not “do good work and be successful.” It’s doing good work, talking about the work, figuring out how to get your work to the right people, accepting that good work is slower than bad work and finding a path through that anyways

Very strongly recommend breaking out of the token discourse and touching grass from time to time to maintain a grip on reality

What Is Art For?
BOWIE'S BOOKSHELF
THE HUNDRED BOOKS THAT CHANGED DAVID BOWIE'S LIFE
JOHN O'CONNELL
The Beauty of Everyday Things
Soetsu Yanagi
THE POCKET POETS SERIES
LUNCH POEMS
by
Frank O'Hara
SECOND EDITION
DICTIONARY OF SUBJECTS AND SYMBOLS IN ART
JAMES HALL Introduction by Kenneth Clark
ABER NINETEEN

Something happens to my brain after agentic coding that I can’t describe. It’s like cognitive offloading which folks have already written about, but even more. It feels like I can’t think through problems anymore. Like a fog. Using agentic but losing my hard-won agency.

Legitimately feels like an unquantifiable vibe shift the last few weeks where the pendulum is swinging back to reasonable takes and people experimenting with model choice 🙏

Top HN headlines over the past 24 with reasonable takes like “Local AI needs to be the norm” and “I’m going back to writing code by hand”

I find that I can’t concretely commit to an implementation until I touch the code a bit and one thing that it has gotten easier than ever to do is to get to a draft PR where you share your proposed approach and easier see the trade offs of certain design decisions