What people recommend, in their own words, with the receipt.
Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.
4 things
A Safe Path to Open WeightsOther Thinking Machines Lab
A clear articulation on how to balance releasing powerful open-weight models while taking safety seriously — A Safe Path to Open Weights , Thinking Machines Lab (Jul. 2026).
Nathan Lambert's curated reading list of the best writing on open models, shared as research materials for policy-facing writing
interconnects.ai ↗·2026-09-11
Are Open Models Catching Up?Other SemiAnalysis
SemiAnalysis article which ran independent evaluations, concluding that open models have been getting closer to the closer frontier of performance over time — Are Open Models Catching Up? , SemiAnalysis (Aug. 2026)
Nathan Lambert's curated reading list of the best writing on open models, shared as research materials for policy-facing writing
interconnects.ai ↗·2026-09-11
How far behind are open models?Other Håvard Tveit Ihle
An independent analysis of the open-closed gap across a mix of public and private evaluations — How far behind are open models? , Håvard Tveit Ihle (May 2026)
Nathan Lambert's curated reading list of the best writing on open models, shared as research materials for policy-facing writing
interconnects.ai ↗·2026-09-11
On the Societal Impact of Open Foundation ModelsOther Sayash Kapoor, Rishi Bommasani et al.
Early paper on marginal risks that showed text-focused LLMs very marginally increased documented potential risks of models — On the Societal Impact of Open Foundation Models , Sayash Kapoor, Rishi Bommasani et al. (Feb. 2024).
Nathan Lambert's curated reading list of the best writing on open models, shared as research materials for policy-facing writing
interconnects.ai ↗·2026-09-11
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 small bar beside a date shows how dated a recommendation is for its kind of thing.
The bar shows how dated a recommendation is, for its kind of thing. Full and green is recent. It drains, and turns amber and then grey, as the post gets older.
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 bar is empty after about:
A striped bar means the post carries no date and the date shown is an estimate. No date at all, no bar.