Machine learning engineer working on recommender systems, personalization and information retrieval; previously at Mozilla.ai, Duo, Tumblr and Automattic, and author of the 'What are embeddings?' text.
Our Share of Night by Mariana Enríquez - I was struck to the bone by this book. I thought about it for many weeks after I read it and even had several dreams about it.
Her 'Favorite books of 2024' post, where she tries to balance technical reading with fiction.
The Tombs of Atuan - My only regret is that I came to Ursula K. LeGuin so late in life, and this might be my favorite of all of her books I've read so far.
Her 'Favorite books of 2024' post, where she tries to balance technical reading with fiction.
The Outlaws by Javier Cercas - This book, translated from Spanish, covers the years of Spain after the Franco dictatorship but only obliquely hints at them.
Olga Dies Dreaming by Xochitl Gonzalez - Olga grows up in the Puerto Rican community in New York and becomes enormously successful as a wedding planner, but cannot manage her own relationships
The Rattle Bag edited by Seamus Heaney and Ted Hughes - In an age of LLM-generated poetry and machine learning curation (even coming from yours truly sometimes), it's refreshing to have real people who love poetry curate it and show you what you need to read to touch grass.
Her 'Favorite books of 2024' post, where she tries to balance technical reading with fiction.
I also read a ton of technical books last year, but the most impactful one for me was Neural Network Methods for Natural Language processing by Yoav Goldberg
Every entry is a verbatim quote and a link to the public post it came from. Nothing is paraphrased. If it is not on record somewhere public, it is not here.
The verdict is what they actually said: loved and liked are explicit; recommended means they told others to get it; uses means they only say they use it; read is a book on their public shelf with no verdict; mixed and disliked are kept too.
A dashed affiliate link, sponsored or their own label means the post disclosed a relationship. It is shown, never hidden.
The shading behind a date shows how dated a recommendation is for its kind of thing.
The shading behind a date shows how dated a recommendation is, for its kind of thing. Fully green is recent. It drains a quarter at a time as the post gets older, and is empty once it reads as dated.
What counts as old depends on the thing. A laptop praised four years ago has been replaced twice; a book loved twenty years ago is probably still loved. The shading is gone after about:
Hardware, services4 years
Software5 years
Podcasts, courses, other8 years
Books25 years
Stripes mean the post carries no date and the date shown is an estimate. No date at all, no shading.