Angela Jiang ✓

@angjiang · 🌎 · joined 20 Jul 2009

interdisciplinarian. head of product @anthropicai platform.

35 485Followers

79Following

1 300Posts total

2.4MViews on collected posts

Against accounts of the same size

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

Median views5 188this account2 896median for 10K–100K

Reach, %14.62%this account9.51%median for 10K–100K

Engagement, %0.69%this account1.56%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	5 188	2 896	1.79×
Reach (views ÷ followers)	14.62%	9.51%	1.54×
Engagement rate	0.69%	1.56%	0.45×

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

7.5K23 Jul

1.2M29 Jul

2.9K30 Jul

2K16 Aug

1.1M1 Sep

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

0.44%23 Jul

0.86%29 Jul

0.41%30 Jul

0.86%16 Aug

0.58%1 Sep

0.81%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.56%.

What the audience does

Likes84.1%14 958 in total

Reposts5.8%1 040 in total

Replies2.9%522 in total

Quotes3.5%616 in total

Bookmarks3.6%646 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

Fable gets a glow up. SOTA on the benchmarks, very excellent on low reasoning, and with a 75% drop on api cache reads for those long running agentic workloads 2K views · 14 likes · 1 reposts · 1 replies 01 Sep 2026 Across our benchmarks, the model sets a new standard. It scores 52.6% on Terminal-Bench-Science 0.1, more than double Fable 5. On Terminal-Bench 4.0, it scores 55.8% against 42.0% for Fable 5. https://t.co/aSb72LSxee 1.1M views · 5.5K likes · 448 reposts · 127 replies 01 Sep 2026 “Harness” does not mean application. An application can have many harnesses. A harness is a loop that runs the model. Its purpose is to get the model to give you a good answer. The application uses the harness(es) to give you a good experience. 2K views · 16 likes · 0 reposts · 0 replies 16 Aug 2026 This is why it’s better to use the API directly from the model provider. Yes, harness matters. The API itself is the lowest level harness and different models have different expressions. 2.9K views · 11 likes · 0 reposts · 1 replies 30 Jul 2026 GPT-5.6 Sol has been used to solve open problems in mathematics. So why was it struggling with ARC-AGI-3, a benchmark of 2D puzzle games? We investigated. The harness was not letting it remember what it had learned. We found that enabling two API settings tripled our scores ht 1.2M views · 9.4K likes · 591 reposts · 391 replies 29 Jul 2026 A lot of moats are moving from first order to second order. A first order moat tends to defend a product and is mostly about technology. A second order moat tends to surround the thing that makes the product and defends the rate of adaptation. 7.5K views · 30 likes · 0 reposts · 2 replies 23 Jul 2026 If you're perfectly qualified to do something, you've already outgrown it 0 views · 3.2K likes · 482 reposts · 22 replies 10 Feb 2021