Shu ✓

@shuding · joined 07 Mar 2012

@vercel. @v0, @nextjs, @aisdk, Satori, SWR. Don’t talk unless you can improve the silence.

62 141Followers

2 486Following

3 003Posts total

1.2MViews on collected posts

Son gönderiler

Shu ✓@shuding16 Sep

After one week, there are 5 more small models for specific tasks on the web: 1. gpu-time: natural language to JS date and time by @imarikchakma 2. gpu-query: natural language to structured filters by @cheatyyyy 3. neural-flexbox: model to centre a div (!) by @aaronvanston 4. 46.8K views · 682 likes · 34 reposts · 21 replies Open on X →

Shu ✓@shuding11 Sep

Excellent use case, excited to see more gpu-* libs! 58.2K views · 531 likes · 8 reposts · 2 replies Open on X →

Shu ✓@shuding11 Sep

Introducing gpu-time ⏰ I built a small browser-based model to convert from natural language text to JavaScript date and time. It runs on web GPU. It's fast and can extract date and times from any English text. https://t.co/V1o0QDDrdf https://t.co/g9kQVIdfwT

1:31

115.2K views · 832 likes · 43 reposts · 29 replies Open on X →

Shu ✓@shuding9 Sep

Got a lot of attention for this (thank you!), but also there're some criticisms and questions: 1) it's a stupid idea Maybe. Again, this is experimental and not meant to be used in production or as a replacement for anything. It is to explore new possibilities. 2) it's https://

29K views · 273 likes · 6 reposts · 23 replies Open on X →

Shu ✓@shuding8 Sep

@shuding But how fast it vs https://t.co/Q7uWWCHZE7? 53 views · 1 likes · 0 reposts · 0 replies Open on X →

Shu ✓@shuding8 Sep

@shuding using matrix multiplication to turn the word function yellow is truly what alan turing died for 3.7K views · 57 likes · 6 reposts · 1 replies Open on X →

Shu ✓@shuding8 Sep

Why creating this? I’ve been struggling with syntax highlighting for years mostly because: 1) large language grammar bundles; 2) language detection and dynamic loading; 3) bad perf and slow down of main app. And it’s great to explore more possibilities and share! 12.1K views · 123 likes · 0 reposts · 6 replies Open on X →

Shu ✓@shuding8 Sep

Correctness. I’m comparing every span types with Shiki, which is almost the golden standard today. It shows the weighted correctness of top 25 popular languages on GitHub. Shiki uses real AST while others might not, hence the differences. https://t.co/CPHS3nYHrp

14K views · 79 likes · 0 reposts · 2 replies Open on X →

Shu ✓@shuding8 Sep

Size is also impressive considering that it’s trained with 50+ languages (and it behaves okay for languages outside of the training group). The model itself is like 70% of the lib, inlined in the bundle. https://t.co/LWvIY58GDE

17.6K views · 103 likes · 0 reposts · 2 replies Open on X →

Shu ✓@shuding8 Sep

Perf is good thanks to WebGPU, especially when the source is large. Also it doesn’t block the main CPU thread. Keep in mind that all highlighters have different pipelines and use cases. This project can’t yet be used to fully replace any. https://t.co/9bocQRlSGH

25K views · 164 likes · 1 reposts · 5 replies Open on X →

Shu ✓@shuding8 Sep

I trained a small model to do syntax highlighting in the browser with GPU. Meet gpu-lexer from Vercel Labs: Small (27.5KB), fast (runs on WebGPU), and language-agnostic (model guesses the syntax). https://t.co/ELWO2Qtu0J It is experimental and built for learning! https://t.co/

0:49

785.2K views · 4K likes · 285 reposts · 147 replies Open on X →

Shu ✓@shuding6 Sep

13 years ago, Apple asked a question that hits me harder today than ever: If everyone is busy making everything, how can anyone perfect anything? Every day, I see a world flooded with effortless AI slops. I’m realizing that to build something truly great today, you need a level 137.9K views · 3.9K likes · 471 reposts · 104 replies Open on X →

Aynı büyüklükteki hesaplara karşı

Son 90 güne ait 12 gönderi, 10K–100K takipçi aralığıyla yan yana. geniş kitleye gösteriliyor, ama izleyenlerin azı tepki veriyor.

Medyan görüntülenme27 017bu hesap92410K–100K için medyan

Erişim, %43.48%bu hesap3.62%10K–100K için medyan

Etkileşim, %0.99%bu hesap1.52%10K–100K için medyan

Ölçüt	Bu hesap	10K–100K için medyan	Oran
Gönderi başına medyan görüntülenme	27 017	924	29.2×
Erişim (görüntülenme ÷ takipçi)	43.48%	3.62%	12.0×
Etkileşim oranı	0.99%	1.52%	0.65×

Bu aralıktaki diğer hesaplar → Başka bir hesapla karşılaştır → Bu kıyas değerleri nasıl kuruluyor →

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

137.9K6 Sep

785.2K8 Sep

25K

17.6K

14K

12.1K

3.7K

29K9 Sep

115.2K11 Sep

58.2K

46.8K16 Sep

Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

3.31%6 Sep

0.57%8 Sep

0.68%

0.60%

0.58%

1.07%

1.75%

1.89%

1.05%9 Sep

0.80%11 Sep

0.93%

1.59%16 Sep

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

What the audience does

Likes60.1%10 752 in total

Reposts4.8%854 in total

Replies1.9%342 in total

Quotes1.0%182 in total

Bookmarks32.3%5 775 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

Followers by day

17 Sep

Daily snapshots since 17 Sep 2026; the dashed line is the starting count.

Son gönderiler

Aynı büyüklükteki hesaplara karşı

Growth & engagement

Views per post

Engagement rate per post

What the audience does

Followers by day

Benzer hesaplar