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Yangyi engagement report

@yangyi - 127K followers on X

Measured over 39 original posts from a 30-day window, last computed on October 3, 2026.

Engagement

Bottom quarter for its size
Per follower
0.009%
of 127K followers
Per impression
0.266%
4.5K views on a typical post
Reach
3.57%
of its followers see a post
Typical post
12
interactions (median)
Saved
0.133%
6 bookmarks on a typical post
Posting rate
2.23/day
active 40% of days
Peak time
03:00 UTC
Monday

A typical post picks up 12 interactions against 127K followers, an engagement rate of 0.009%. Measured over 39 original posts, its engagement rate beats 19% of 21,185 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 4.5K times each, and 0.266% of those impressions turn into an interaction. That is about 3.57% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 posts a day over the last 30 days, though only 40% of days saw any activity at all. Most posts go out around 03:00 UTC, and Monday is the busiest day of the week. Of the 39 posts sampled, 51% carry an image or video and 10% are part of a thread. The account's strongest tracked post pulled 952 interactions, about 79x its own typical post.

Measured over 39 original posts from a 30-day window, last computed on October 3, 2026.

Compared with accounts its own size

Yangyi's engagement rate beats 19% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 14% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.009%, Yangyi sits above the 10th percentile of the 212,656 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.

p100.004%
p250.025%
p50 (median)0.146%
p750.697%
p902.61%
p9982.9%
Engagement rate as a share of followers, across the 212,656 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,418 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.025%
50th percentile0.146%
75th percentile0.697%
90th percentile2.61%
99th percentile82.9%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 03:00 UTC, and Monday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 03:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 03:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Monday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Monday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video51% of posts+111%+108% to +115%34K
Outbound link0% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 51% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 0% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
  • Its average post runs 188 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 27, 202679x their median

    ClaudeCode Opus5.5が出来ること多すぎて、仕事が進まない・・・。 賃貸図面→CAD(JWW、DXF)→パース(Blender高画質レンダリング)→Web共有 が一気に出来てしまった https://t.co/3UoxApdlka

    857631319110K viewsView on X
  • Sep 28, 202652x their median

    这次国内节假日期间,最苦批的就是豆包员工了。 多方爆料称,字节跳动的豆包产品线,被紧急下达任务,要求对标 Meta AI 助手 Muse,推出新产品,内部优先级极高,只给予 6 天工期时间。 被对标的产品,是可以一键帮主人,订机票、发邮件、填表格,甚至能接管社交软件,帮主人回消息的 AI 助手。 曾一度登上美区苹果商店免费榜第一。 字节这次紧急,让旗下的豆包,快速迭代重构,推出中国版的 Muse,或许是希望抢在国内对手腾讯、阿里之前,抢占该市场。 即全托管型 AI。

    3841819134394K viewsView on X
  • Sep 21, 202648x their median

    赚钱的一人公司一般都是: 1、做中介的 2、搞自媒体的 3、做SEO的 4、和现有流量渠道合作的 5、卖课做社群的 6、向其他人提供专业服务的 不赚钱的一人公司一般都是: 我有想法,vibe coding做产品的

    5054322955K viewsView on X
  • Sep 26, 202620x their median

    Lately, I’ve been seeing an alarming rise in clients receiving 30+ page "AI Slop" audits generated in seconds with zero critical thinking. These reports look visually impressive at a distance. They’re full of scary red exclamation marks, polished pie charts, and "URGENT" warnings. But when you actually open them up, the claims are laughably out of touch: 🚨 "Missing llms.txt file: CRITICAL SEO ISSUE." 🚨 "Missed blocked directory in robots.txt: HIGH RISK." 🚨 "Missing ALT tag on an icon file: CRITICAL ACTION REQUIRED." Let’s be honest about what’s happening here: 1. Minor noise is being sold as major strategy. An unoptimized alt tag on a decorative footer graphic isn’t killing your rankings. A missing llms.txt file isn't wiping you off Google. Turning low-impact, micro-level best practices into "critical site failures" is just a trick to create fake urgency. 2. Quantity is replacing quality. Clients are being handed 30 pages of generic, auto-generated bullet points instead of 2 pages of actual strategic insight. If an audit doesn't tie its findings directly to revenue, search intent, site architecture, or core technical blockers, it’s not an audit it’s an export script. 3. AI is being used as a substitute for expertise, not a tool. AI is incredible for efficiency, but using it to pump out generic audit templates without human validation destroys trust in the whole industry. If your audit reads like it was generated by a bot that has never looked at a search engine result page, throw it away. What’s the most absurd "Critical SEO Flag" you’ve seen in an automated audit recently?

    183182710449K viewsView on X
  • Sep 21, 202614x their median

    AI内容流量密码,排名不分先后 1、AI搞钱(电商,外贸,FDE) 2、AI个人知识库(AI打通obsidian,AI整理书签) 3、AI搞线索(挖外贸客户,找达人) 4、AI销售(AI企业微信,AI Whatsapp) 5、AI整理微信(微信CLI) 6、AI算命 7、AI炒股 8、AI剪辑 以上话题,写多烂都有人看

    141219211K viewsView on X
  • Sep 29, 202613x their median

    大家都说Claude封号 我用NewMax客户端跑Claude跑了半年了 都没被封过号 是不是因为Claude客户端给你们传标记啊 要不都授权到NewMax跑任务吧 稳定还不需要命令行 https://t.co/IkGwVLr7c8

    135793109K viewsView on X
  • Sep 28, 20267.4x their median

    我有个感觉,开源模型的私有化部署将成为新的热点趋势。 1,隐私,数据安全的风险比较凸显,特别是anthropic这段时间的主动曝光。很多合规问题也可以规避。 2,开源模型目前能力超过阈值,也就是可用性已经足够好,我最近测试了智谱编程,不算完美,但可用,而且未来一年会更可期待。 3,成本可控。特别是一些企业的token开销越发失控。 当然,这里还是有不少问题的。 第一是版本迭代更新需要持续投入; 第二是如果企业使用频率不够,优化能力不足,综合token成本会显著高于云厂商。 第三是私有化部署的稳定性和可靠性不一定能保障。 另外最近还有一个词,所谓主权AI是个方向,这世界基本上除了中美,基本上都没有独立研发大模型的能力,但从安全和信仰,文化考虑,很多都需要做自己的AI大模型(包括自己的安全围栏设计),其实就是国家级的开源私有化,拿开源大模型训练本地语料,并增加本地合规的安全限制策略。 而开源大模型,目前中国明显处于领先位置。 这是个机会,但这不是官方的机会,是民间,第三方的机会。

    59917414K viewsView on X
  • Sep 28, 20267.1x their median

    我给一家专门做营销的上市公司做Reddit服务执行 他们找的懂英语的内容营销写手 一个产品一个月20多条内容 平均展示量只有1-3k 甚至好几条只有7,800 views 偶有一条一万的客户都觉得很不错 这个月我让AI跑了20条 70%在5-8k 5条大几万 1条十来万 这是第一个执行月,AI还在早期探索学习期,但成绩已经比人类强了,可效果其实都没那么关键 最关键的是,处理效率是以前的几十倍 这意味着他们可以在有限的人力情况下大量扩展业务 以前需要写手来写稿子 现在可能只需要写手校对一下 一个写手以前一个月只能服务4-5个客户 现在同步能跟进十几个甚至20个 效果还比以前好 AI真的在全方位碾压人类 从这个角度上看 那些被淘汰的写手工资,将被掌握如何设计AI,使用AI,构建AI Loop的人所占有 另外的写手也会溢价,他们会因为懂得如何使用现成的AI,如何审阅,如何给客户提供情绪价值而涨工资 接下来,工资可能倾向于两类模式支付了 一类是强化&替代人类,为AI提供智能 一类是连接人类&AI,提供真实的人感价值,或者交互物理世界,做AI做不了的那部分工作

    756408.3K viewsView on X
  • Aug 10, 20265.9x their median

    https://t.co/irk1HKXJqM

    6134321K viewsView on X
  • Sep 20, 20265.3x their median

    有太多 Vibe Coding 想法,但身体告诉我必须去钓鱼了! 发完这条就出发。 本周计划开源一个Obsidian Agent插件。 还有个一句话部署网站的系统,支持Vercel、Cloudflare。 只需复制Prompt就能开始用。

    21041116K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 12 interactions against 127K followers, an engagement rate of 0.009%. Measured over 39 original posts, its engagement rate beats 19% of 21,185 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 4.5K times each, and 0.266% of those impressions turn into an interaction. That is about 3.57% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 posts a day over the last 30 days, though only 40% of days saw any activity at all. Most posts go out around 03:00 UTC, and Monday is the busiest day of the week. Of the 39 posts sampled, 51% carry an image or video and 10% are part of a thread. The account's strongest tracked post pulled 952 interactions, about 79x its own typical post.

What is Yangyi's engagement rate on X?
Yangyi (@yangyi) has an engagement rate of 0.009%, based on the median interactions across 39 original posts from the last 30 days against 126,649 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.009%, Yangyi sits above the 10th percentile of the 212,656 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @yangyi have real engagement?
Its engagement rate beats 19% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the bottom quarter for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @yangyi post?
Most posts go out around 03:00 UTC, and Monday is its busiest day, at roughly 2.23 posts per day across the measured window.

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