Percy Liang ✓

@percyliang · Stanford, CA · joined 31 Oct 2009

professor of computer science @Stanford @stanfordnlp, co-founder of @togethercompute, https://t.co/7R5THVnJ6u, @simile_ai, pianist

121 192Followers

424Following

1 334Posts total

606.1KViews on collected posts

Against accounts of the same size

5 posts from the last 90 days, next to the 100K–1M follower range. shown widely, but few of those viewers react.

Median views52 383this account21 891median for 100K–1M

Reach, %43.22%this account6.81%median for 100K–1M

Engagement, %0.81%this account1.14%median for 100K–1M

Metric	This account	Median for 100K–1M	Ratio
Median views per post	52 383	21 891	2.39×
Reach (views ÷ followers)	43.22%	6.81%	6.35×
Engagement rate	0.81%	1.14%	0.71×

Others in this range → Compare with another account → How these benchmarks are built →

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

241.4K19 May

168.2K25 Aug

52.4K

82.1K28 Aug

16.2K2 Sep

45.9K

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.72%19 May

0.36%25 Aug

0.81%

1.75%28 Aug

0.58%2 Sep

1.76%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.14%.

What the audience does

Likes63.1%4 429 in total

Reposts6.3%440 in total

Replies2.0%141 in total

Quotes1.3%93 in total

Bookmarks27.3%1 919 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.

Latest posts

Marin 535B-A23B is 13% through training. This hero run would not be possible without the generous support of the Jen-Hsun and Lori Huang Foundation, which provided the funding for the compute (Coreweave). Thanks @JensenHuang for supporting open models! https://t.co/1qBdgV79S8 45.9K views · 749 likes · 34 reposts · 15 replies 02 Sep 2026 The "What-If Machine" is a vivid articulation of what Simile is building. It's not just about forecasting the future passively. It's about understanding how active interventions on the world change its course. It's the classic difference between causation and correlation. 16.2K views · 85 likes · 5 reposts · 2 replies 02 Sep 2026 Marin 535B-A23B is ~7% done training, and so far things look on track. Next Tuesday (Sept 1 @ 10 PT), we will have a Zoom panel/discussion where the Marin team will talk about the design decisions that went into this run, the tradeoffs made, and our learnings. If you're https:// 82.1K views · 1.3K likes · 80 reposts · 23 replies 28 Aug 2026 The fact that Simile’s first technical blog post is about confidence is notable. Confidence is paramount to simulation. If a coding agent messes up, you can often tell and repair. If a simulation is wrong, you might never know and make a bad consequential decision based on it. 52.4K views · 391 likes · 23 reposts · 8 replies 25 Aug 2026 Anyone can simulate the future. But the simulation only matters if it’s trustworthy. At Simile, we train two types of models: simulation models and confidence models. Our first research blog post explores the origin of our proprietary confidence model, which predicts the 168.2K views · 500 likes · 49 reposts · 21 replies 25 Aug 2026 What would truly open-source AI look like? Not just open weights, open code/data, but *open development*, where the entire research and development process is public *and* anyone can contribute. We built Marin, an open lab, to fulfill this vision: https://t.co/racsvmhyA3 241.4K views · 1.4K likes · 249 reposts · 72 replies 19 May 2025