Search Latent Space

Find your next rabbit hole.

Articles, conversations, profiles, and grounded writeups.

?
Search tips

Keyword matches words; semantic matches meaning and exact titles or names; hybrid combines both. In keyword mode, use quotation marks for exact phrases. Author matches published credits. Year applies to posts; wiki entries have no author or publication year. You can also use author:"Ada" or type:podcast in non-regex searches.

Reset filters

Select Apply filters to update results.

2093 results

Lilian Weng summarizes 35 papers on Harness Engineering for RSIAI News · 2026-07-08AI News—2026-07-08
Ben Holmes

Software developer and educator building AI-assisted tools for development, conversation, and personal knowledge.

Person · 2026-09-17
Person—2026-09-17
The Field Guide to FableAI News · 2026-07-07AI News—2026-07-07
Ben Poole

Machine-learning researcher whose collaborative work spans diffusion models, text-to-3D generation, dynamic scene reconstruction and neural-network learning.

Person · 2026-09-20
Person—2026-09-20
AIEWF Daily Dispatch: The great loops debate and the state of AI engineeringArticle · Richard MacManus · 2026-07-03ArticleRichard MacManus2026-07-03
Benchmarks 101: What a Model’s Score Actually Tells You

A benchmark tests particular capabilities under particular conditions. Understanding those choices helps explain both model progress and the gap between a leaderboard score and a useful product.

Writeup · Date unavailable
Writeup——
Vercel's Andrew Qu on why agents are a new kind of softwareArticle · Richard MacManus · 2026-07-03ArticleRichard MacManus2026-07-03
Benchmarks 201: What a Leaderboard Score Actually Measures

Clémentine Fourrier on reproducible comparisons, the limits of preference rankings, and the engineering choices behind model scores, from the July 2024 conversation.

Writeup · Date unavailable
Writeup——
The website of the future may assemble itself for every visitorArticle · Richard MacManus · 2026-07-02ArticleRichard MacManus2026-07-02
Benchmarks, Self-RAG, and Step-by-Step Verification — Part 3 of 5

Part 3 follows an agent’s failed travel plan into the science of benchmarks, then examines how Self-RAG and process supervision use intermediate judgments to select better outputs.

Writeup · Date unavailable
Writeup——
Skill engineering and the case against one-shot AI designArticle · Richard MacManus · 2026-07-02ArticleRichard MacManus2026-07-02
Better training data starts with what the model still needs to learn

Datology’s case for optimizing filtering, rewriting and training order together—and measuring their value in learning efficiency, model capability and inference cost.

Writeup · Date unavailable
Writeup——
Your place in Latent Space

Join Latent Space.

Sign in with Google, or get the newsletter by email.

Loading Google sign-in…

or subscribe by email

Free. Change your preferences or unsubscribe any time.