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Zhengyao Jiang ✓

@zhengyaojiang · London · joined 31 Oct 2015

Cofounder & CEO @WecoAI - automated hill climbing with LLMs. Prev: PhD in ML @UCL_DARK. (Zheng=j-uhng, j as in job; yao=y-aoww)

13 400Followers
741Following
885Posts total
4MViews on collected posts

Latest posts

As people delegate more research and engineering tasks directly to agents, I think the gap between achieving a goal and understanding how it was achieved will become a growing problem. I’m a bit torn about this, as someone working on autoresearch who was also trained as a 1K views · 11 likes · 2 reposts · 7 replies Open on X →
A great read. I have similar feelings about how AI labs approach progress directly and without nurturing of scientific communities & intuition. The math research community went through the transition the fastest, so it was felt most. Other fields next. https://t.co/M2uCXIERyC 39.4K views · 551 likes · 66 reposts · 26 replies Open on X →
With autonomous research, it probably won't take a decade before most frontier research is beyond the understanding of the best human experts. This feels like sci-fi. We'll live in a super fast progressing world shaped by discoveries that only AI can fully understand 604 views · 5 likes · 2 reposts · 2 replies Open on X →
I'm always a bit surprised this thread of work keeps making progress. How come completely different forms of time series can be modeled by a single model, and there's even positive transfer? Almost feels like a deeper mystery than LLMs 103.5K views · 1.1K likes · 54 reposts · 33 replies Open on X →
Introducing TimesFM-3, a state-of-the-art time series foundation model that enables accurate multivariate time series forecasting in a single forward pass, significantly outperforming other forecasting models across major benchmarks. More on the blog →https://t.co/uSlnIdUJ4Q htt
TimesFM-3 architecture.
2M views · 11.6K likes · 1.3K reposts · 185 replies Open on X →
Had a look at Cordis, the theory behind DeepSeek Harness's plugin architecture. Quick thoughts: It's basically a theory that, as long as you follow a set of contracts for the components of a harness, there are theoretical guarantees that your harness can safely remove a 36K views · 407 likes · 25 reposts · 22 replies Open on X →
The first experimental evidence of recursive self-improvement (RSI). Autoresearching the autoresearch agent for eight days. The result beats the harness we hand-tuned for two years, on held-out benchmarks: 🧵(1/7) https://t.co/8kfmR2AlT7
0:40
1.8M views · 9.6K likes · 1.2K reposts · 266 replies Open on X →

Against accounts of the same size

7 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.

Median views39 389this account924median for 10K–100K
Reach, %293.95%this account3.62%median for 10K–100K
Engagement, %1.26%this account1.52%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post39 38992442.6×
Reach (views ÷ followers)2.9× audience3.62%81.2×
Engagement rate1.26%1.52%0.83×

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

1.8M14 Jul
36K14 Aug
2M31 Aug
103.5K
6048 Sep
39.4K11 Sep
1K12 Sep

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.61%14 Jul
1.26%14 Aug
0.65%31 Aug
1.10%
1.49%8 Sep
1.63%11 Sep
1.97%12 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes47.9%23 223 in total
Reposts5.4%2 625 in total
Replies1.1%541 in total
Quotes0.4%211 in total
Bookmarks45.2%21 900 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.

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