nathan chen ✓

@nathancgy4 · Shenzhen · joined 24 Apr 2022

learning, entropy-maximizing, opinions

20 779Followers

741Following

433Posts total

321.7KViews on collected posts

Neueste Beiträge

nathan chen ✓@nathancgy410 Sep

For people who aren’t sure whether these research innovations actually matter, here’s a simple explanation of why labs like DeepSeek and Kimi keep producing them: Within a year, once LLMs can handle most everyday tasks, no one will choose an expensive model when a comparable one 34.1K views · 495 likes · 30 reposts · 23 replies Open on X →

nathan chen ✓@nathancgy410 Sep

fortunately, taste matters much more than raw gpu compute when it comes to model architecture & optimizer research 32.5K views · 201 likes · 17 reposts · 4 replies Open on X →

nathan chen ✓@nathancgy410 Sep

woa... deepseek really pushed research on model efficiency to an insane extent... it is a pleasure to read such an interesting work. kudos to deepseek🫡 https://t.co/OJG8q58Xk8

49.5K views · 415 likes · 16 reposts · 5 replies Open on X →

nathan chen ✓@nathancgy43 Sep

many unsaturated evals are not relevant to real frontier intelligence "AA Daily Intelligence Index" is a better name (still useful though) 6.7K views · 48 likes · 2 reposts · 5 replies Open on X →

nathan chen ✓@nathancgy416 Jul

tho open science in comparison to open model is not that crucial, there will still be k3-related research shared! ;) 12.8K views · 229 likes · 1 reposts · 3 replies Open on X →

nathan chen ✓@nathancgy416 Jul

@nathancgy4 huge congrats man 7.4K views · 64 likes · 0 reposts · 1 replies Open on X →

nathan chen ✓@nathancgy416 Jul

man ignore the benchmarks and the race i just want there to always remain a frontier level intelligence model that is open and safe, available to everyone regardless of region it is truly incredible to have built such an open (and strong) model with a group of genuine people htt

178.7K views · 3.2K likes · 196 reposts · 152 replies Open on X →

Im Vergleich zu Konten gleicher Größe

7 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird weit gezeigt, aber nur wenige dieser Zuschauer reagieren.

Medianaufrufe32 482dieses Konto924Median für 10K–100K

Reichweite, %156.32%dieses Konto3.62%Median für 10K–100K

Interaktion, %0.88%dieses Konto1.52%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	32 482	924	35.1×
Reichweite (Aufrufe ÷ Follower)	156.32%	3.62%	43.2×
Interaktionsrate	0.88%	1.52%	0.58×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

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

178.7K16 Jul

7.4K

12.8K

6.7K3 Sep

49.5K10 Sep

32.5K

34.1K

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

1.99%16 Jul

0.88%

1.82%

0.82%3 Sep

0.88%10 Sep

0.68%

1.61%

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

What the audience does

Likes81.9%4 663 in total

Reposts4.6%262 in total

Replies3.4%193 in total

Bookmarks10.1%575 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.

Neueste Beiträge

Im Vergleich zu Konten gleicher Größe

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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