Lilian Weng ✓

@lilianweng · San Francisco, CA · joined 15 Dec 2009

MTS @OpenAI; Author of Lil'Log Prev: Ex Co-founder of Thinking Machines Lab; Ex-VP, AI Safety & robotics, applied research OpenAI

292 958Followers

195Following

220Posts total

4.7MViews on collected posts

Derniers posts

Lilian Weng ✓@lilianweng28 Jul

I've always been a curiosity-driven person. Leaning new knowledge or figuring out how to solve problems that are not well defined often gives me simple joy. The fun part of being a cofounder is how it "forced" me to develop new perspectives on company strategies, team building 126.1K views · 602 likes · 20 reposts · 13 replies Open on X →

Lilian Weng ✓@lilianweng27 Jul

@lilianweng We’ll miss you, it’s been wonderful building Thinky together. I’m glad that you’re putting your health first. Thank you for everything ❤️ 75.4K views · 996 likes · 1 reposts · 16 replies Open on X →

Lilian Weng ✓@lilianweng27 Jul

It is a hard and sad decision. I shared this message with folks at Thinky. Thank you all for the time together♥️ Just as the last sentence in my message: The future worth building is human. https://t.co/VGb5fM5chX

2.2M views · 9.1K likes · 226 reposts · 376 replies Open on X →

Lilian Weng ✓@lilianweng15 Jul

Inkling is our open weights model. It aims to serve as a foundation with solid performance across a broad categories of capabilities, for use in practice and customization. Play it on Tinker! 😄 164.6K views · 1.4K likes · 64 reposts · 41 replies Open on X →

Lilian Weng ✓@lilianweng7 Jul

new post on harness engineering for AI self-improvement: https://t.co/XNCycrAZbM It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter 884K views · 5.4K likes · 793 reposts · 122 replies Open on X →

Lilian Weng ✓@lilianweng25 Jun

A super long overdue (3+ years?) post on scaling laws. Compute is expensive. Scaling laws are a way to help us reason about the optimal compute allocation between data and model size before committing to a large run. The post covers what scaling laws predict, how 470.3K views · 4.7K likes · 593 reposts · 70 replies Open on X →

Lilian Weng ✓@lilianweng19 May

We would love to see more collaboration and research in the field of human-AI interactivity. Check it out! 135.6K views · 450 likes · 28 reposts · 29 replies Open on X →

Lilian Weng ✓@lilianweng19 May

We are offering grants of $100,000 + Tinker credits to researchers advancing the field of human-AI interactivity. Submit your proposals by June 19th! https://t.co/907HfBy7g3 726.3K views · 1.7K likes · 203 reposts · 61 replies Open on X →

Face aux comptes de taille comparable

6 posts des 90 derniers jours, à côté de la tranche de 100K–1M abonnés. diffusé à plus de monde que les comptes de taille comparable.

Vues médianes317 458ce compte5 881médiane pour 100K–1M

Portée, %108.36%ce compte1.56%médiane pour 100K–1M

Engagement, %0.84%ce compte0.98%médiane pour 100K–1M

Indicateur	Ce compte	Médiane pour 100K–1M	Rapport
Vues médianes par post	317 458	5 881	54.0×
Portée (vues ÷ abonnés)	108.36%	1.56%	69.5×
Taux d'engagement	0.84%	0.98%	0.85×

Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

726.3K19 May

135.6K

470.3K25 Jun

884K7 Jul

164.6K15 Jul

2.2M27 Jul

75.4K

126.1K28 Jul

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.28%19 May

0.38%

1.15%25 Jun

0.73%7 Jul

0.94%15 Jul

0.46%27 Jul

1.35%

0.51%28 Jul

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.98%.

What the audience does

Likes56.8%24 395 in total

Reposts4.5%1 928 in total

Replies1.7%728 in total

Quotes1.0%431 in total

Bookmarks36.0%15 458 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.