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Ethan Carter

@ethancarterhub · San Francisco, CA · joined 10 May 2023

Exploring AI, Tech, and Productivity Hacks | Daily insights on AI-powered tools and trends for creators & entrepreneurs | DM for collaboration 📩

16 750Followers
534Following
82 808Posts total
772.9KViews on collected posts

โพสต์ล่าสุด

The AI tool stack is getting harder to manage, not easier. New models keep arriving, and every release seems to bring another account, dashboard, billing setup, and workflow to maintain. Having Claude, GPT, Gemini, Grok, GLM, DeepSeek, and MiniMax accessible through one balance 26.3K views · 29 likes · 29 reposts · 0 replies Open on X →
Financial agents need to work across messy information, not just answer a tidy question. Ling-3.0-flash-Fin is open sourced for those workflows, and FinFIRST adds a public way to examine the quality of the search process around them. 32.8K views · 54 likes · 61 reposts · 0 replies Open on X →
“Open” is an easy word to use. Intermediate checkpoints are harder to ship. K2 Horizon gives researchers a way to see how a model changed during training, instead of only judging the polished final weights. That is the kind of release I want more of. 22.2K views · 28 likes · 29 reposts · 0 replies Open on X →
What gets me about Hachikō is that nobody could explain to him what had happened. So he just kept going back. Same place. Same hope. That is probably why this story has stayed with people for so long. 16.9K views · 39 likes · 39 reposts · 2 replies Open on X →
The benchmark list here is broad, from search and research through spreadsheets and finance agents. That does not settle the question of usefulness, but it is the right range of tasks to demand from a finance focused model. Ling-3.0-flash-Fin is free for one month through 18.2K views · 29 likes · 29 reposts · 0 replies Open on X →
Financial work depends on trustworthy sources, consistent definitions, accurate calculations and auditable outputs. Introducing Ling-3.0-flash-Fin, a finance-enhanced version of Ling-3.0-flash, developed with financial institutions and domain experts. With 124B total and 5.1B htt 656.6K views · 430 likes · 51 reposts · 28 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

6 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.

ยอดดูมัธยฐาน24 214บัญชีนี้927ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %144.56%บัญชีนี้3.40%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %0.29%บัญชีนี้1.54%ค่ามัธยฐานของ 10K–100K
ตัวชี้วัดบัญชีนี้ค่ามัธยฐานของ 10K–100Kอัตราส่วน
ยอดดูมัธยฐานต่อโพสต์24 21492726.1×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)144.56%3.40%42.5×
อัตราการมีส่วนร่วม0.29%1.54%0.19×

บัญชีอื่นในช่วงนี้ →   เปรียบเทียบกับบัญชีอื่น →   ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →

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

656.6K27 Aug
18.2K28 Aug
16.9K1 Sep
22.2K3 Sep
32.8K
26.3K4 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

0.09%27 Aug
0.32%28 Aug
0.47%1 Sep
0.26%3 Sep
0.35%
0.22%4 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.54%.

What the audience does

Likes65.3%609 in total
Reposts25.5%238 in total
Replies3.2%30 in total
Quotes5.6%52 in total
Bookmarks0.4%4 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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