Shengjia Zhao ✓

@shengjia_zhao · joined 01 Nov 2016

Chief Scientist @ Meta MSL. Formerly MTS @ OpenAI, PhD @ Stanford. I train models. All opinions my own.

53 284Followers

234Following

327Posts total

183KViews on collected posts

Against accounts of the same size

5 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views34 602this account964median for 10K–100K

Reach, %64.94%this account3.25%median for 10K–100K

Engagement, %1.21%this account2.01%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	34 602	964	35.9×
Reach (views ÷ followers)	64.94%	3.25%	20.0×
Engagement rate	1.21%	2.01%	0.60×

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

34.6K7 Jul

55.5K9 Jul

22K14 Jul

34.6K5 Aug

36.3K2 Sep

Last 5 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.35%7 Jul

1.11%9 Jul

0.85%14 Jul

1.21%5 Aug

1.39%2 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 2.01%.

What the audience does

Likes79.1%1 851 in total

Reposts9.8%229 in total

Replies4.0%93 in total

Quotes0.9%20 in total

Bookmarks6.3%148 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

Today we're releasing Muse Spark 1.3, our strongest model for agentic and coding tasks in the spark model line. It’s designed to support longer-horizon work, excel in agentic tasks, and follow complex instructions more reliably than previous models. Muse Spark 1.3 is available ht 36.3K views · 441 likes · 49 reposts · 12 replies 02 Sep 2026 · Open on X →

Today we're releasing Muse Code (beta) and Muse Spark 1.2. Muse Code is our first coding agent — it coordinates multiple persistent subagents to complete complex engineering tasks faster, more accurately, and with less intervention. Muse Spark 1.2 is our newest model, co-trained 34.6K views · 355 likes · 41 reposts · 22 replies 05 Aug 2026 · Open on X →

An internal muse spark achieved a perfect score on APhO. Big congrats to the team, and thank you to the APhO committee! 22K views · 158 likes · 17 reposts · 11 replies 14 Jul 2026 · Open on X →

Today we are launching Muse Spark 1.1, an upgrade to muse spark 1 that greatly improves agentic, coding, multimodal, and computer use capabilities. We're also launching the Meta Model API in public preview. https://t.co/Th9DzacJaW https://t.co/2dPrm5b0U2 55.5K views · 502 likes · 75 reposts · 31 replies 09 Jul 2026 · Open on X →

We are excited to launch muse Image, the first media generation model built by MSL. Muse image can use reasoning, refinement, and tools to improve precision and quality, with very clear test time scaling trends. Also sharing Muse Video preview today. https://t.co/GOHEd6GWIJ http 34.6K views · 395 likes · 47 reposts · 17 replies 07 Jul 2026 · Open on X →