Ainun Najib engagement report
@ainunnajib - 157K followers on X
Measured over 15 original posts from a 30-day window, last computed on October 27, 2026.
Engagement
A typical post picks up 873 interactions against 157K followers, an engagement rate of 0.555%. Measured over 15 original posts, its engagement rate beats 83% of 21,185 tracked accounts of a similar size. Comparing inside a size band matters here: engagement rate falls as accounts grow, so a raw rate would mostly just re-measure the follower count. Posts are seen about 49K times each, and 1.80% of those impressions turn into an interaction. That is about 30.9% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.7 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 02:00 UTC, and Sunday is the busiest day of the week. Of the 15 posts sampled, 87% carry an image or video, 7% are part of a thread and 7% link out. The account's strongest tracked post pulled 76K interactions, about 87x its own typical post.
Measured over 15 original posts from a 30-day window, last computed on October 27, 2026. Recurring tag: #salingjagawarga.
Compared with accounts its own size
Ainun Najib's engagement rate beats 83% 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 58% 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 0.555%, Ainun Najib sits above the 50th percentile of the 212,825 accounts in this comparison. That places it in the above the median band, which runs 0.146% to 0.697%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.697% |
| 90th percentile | 2.61% |
| 99th percentile | 83.0% |
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 02:00 UTC, and Sunday 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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 87% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 7% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 87% 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.
- 7% 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 552 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
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
- Oct 25, 202687x their median
Alhamdulillah… Alhamdulillah… Alhamdulillah 😭 Gatau lagi mau tulis apa buat ngungkapin rasa syukur ini dan maaf jika kami norak dan lebay menyambut hujan 😭 Banjar, Kalsel hujan hari ini #SalingJagaWarga https://t.co/kzRp89tmGO
- Sep 20, 202632x their median
金型業界の衰退について書いたら、まったく違う業界の方から返信が来ました。 一つ読むごとに、手が止まりました。 「アパレルの国内生産比率は、もう1.1%です」 「美容業界では、ドライヤーもアイロンもクリッパーも、すでに修理不可能になっています」 「造船は日本にやられて、世界中の造船所が軍艦専門になった。今度は日本が苦境です」 「街の盤屋です。規格がJEM・JECからIECに変えられて、対応が難しくなっています」 「メーカーのサプライヤーですが、この数年でネジ屋、Oリング屋、ベルト屋、刃物屋、研ぎ屋、メッキ屋、精密金型屋が本当に激減した。代わりは、若いエンジニアがPCで注文するカタログ通販です」 同じことが、同じ順番で起きています。 最初に、安いほうへ移ります。 経営判断としては正しい。誰も間違っていません。 次に、国内の作り手が減ります。 ここでもまだ、誰も困りません。物は届くからです。 その次に、直せなくなります。 壊れたら買い替える。修理という選択肢が消える。 最後に、良し悪しがわかる人がいなくなります。 これが一番怖い。 うちの業界は、もうその手前まで来ています。 数字にも出ています。 2025年、日本の金型の輸出はおよそ2,000億円。 輸入はおよそ1,300億円。 そのうち中国からが約770億円です。 5年前と比べると、 中国への輸出は、およそ半分に減りました。 中国からの輸入は、およそ5割増えました。 金型は「物を作るための道具」です。 その道具を、日本は買う側に回りつつある。 何を、いくらで、いつ作れるか。 それを、よその国に決めてもらう側になるということです。 返信でいただいた言葉を、そのまま引きます。 「発注する側に、良し悪しのわかる人間がいなくなる。すでに分からないまま、前と一緒という理由で仕様を出す大手がいます」 「製造業と名ばかりの丸投げ代理店ばかりになり、公差もハメアイも粗さ記号も、鋼材の熱処理も分からない自称エンジニアが増えた」 検収できない人が発注し、検収できない人が受け取る。 そうなると、品質は誰にも見えなくなります。 悪くなったことにすら、気づけない。 そして、ある方がこう書いていました。 「同じことが、AIをめぐる近未来にも起きる。企画も経営も、果ては政治の提案までAIに委ねたとき、検収のできない我々はめくら判を押し続ける機械になる」 金型の話をしているつもりでした。 どうやら、そうではなかったようです。 自分の業界で、最後に「これは違う」と言える人は、あと何人残っていますか。 #金型 #製造業 #町工場の本音
- Sep 20, 202618x their median
https://t.co/sHwWIpclmo
- Oct 27, 20269.3x their median
Guys! ada KABAR BAIK! Alvin Tedjo, generasi kedua orang indonesia di Kanada, terpilih menjadi Mayor (walikota) Mississauga, kota terbesar kedua di Kanada 😭 https://t.co/qkFoPlytB1
- Oct 26, 20268.3x their median
An internal model at OpenAI has produced a claimed refutation of ETH! Russell Impagliazzo, Mohan Paturi, and I have been asked to help organize an open and independent examination of this claim. 1/4
- Sep 14, 20267.8x their median
Oxford researchers argue that LLMs can never invent anything. It is mathematically impossible. They published a paper called “Theory Is All You Need" and it argues against the claim that computational models can generate genuine novelty or new knowledge. They analyzed the limits of generative ai, and the results are a brutal reality check for the idea that ai will replace human decision making under uncertainty. Here is why AI is stuck and human cognition wins: backward-looking vs forward-looking.. llms are probability machines that look backward at existing data. human cognition is forward-looking and capable of generating genuine novelty. human cognition operates theoretically "top-down" rather than "bottom-up" from data. the "data-belief asymmetry".. the researchers use the invention of "heavier-than-air flight" to illustrate this concept. an ai relies on data-based prediction, which is largely imitative. humans, however, use theory-based causal logic that allows them to hold beliefs that go beyond existing data. the intervention gap.. humans don't just process information; we use theory to practically "intervene" in the world. we engage in directed experimentation to generate entirely new data. ai-based models are theory-free and place primacy on existing data and prediction. tldr? AI uses a probability-based approach to knowledge and ia largely imitative. It can process data and make predictions, but human cognition relies on theory-based causal reasoning. The decades-old analogy comparing human minds and computers to mere "input-output" devices is fundamentally flawed.
- Nov 25, 202522x their median
My take on the jagged frontier debate: https://t.co/7TaKLbEZeM
- Sep 17, 20266.8x their median
Setiap Tersangkut Kasus Korupsi Di Rumahnya Mengadakan Doa bersama? Agama Di Jadikan Alat Untuk Membenarkan Perbuatan Korup? https://t.co/MdBphCDq6f
- Sep 13, 20266.1x their median
An important project I think everyone should do: I got a 16TB hard drive and I'm downloading as many open source models as I can This is insurance for the future. In case they are banned If you have them already downloaded, they can't be taken away from you And even if they aren't banned, it will be incredible to look back in 30 years and load up Qwen 3 just to see what life was like back then If you have a smaller hard drive, I still think this is worth doing to stay independent and free, and having intelligence whenever need it. Download any models you can get your hands on from HuggingFace It's preserving history and preserving your sovereignty
- Oct 25, 20265.6x their median
Geoffrey Hinton once noticed that the tellers at his bank kept changing. When he asked one of them why, the teller didn't know, only that they got moved around a lot. Hinton guessed the reason... if staff never work with the same people for long, it's much harder for a few of them to team up and cheat the bank. That thought became one of the most useful tricks in deep learning. While a neural network trains, you randomly switch off about half its neurons at every step. No neuron can lean on a particular partner, so each one has to learn something useful on its own. The trick is called dropout, and it helped AlexNet win the 2012 ImageNet contest, which set off the modern AI boom. It's still built into many of today's networks.
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 873 interactions against 157K followers, an engagement rate of 0.555%. Measured over 15 original posts, its engagement rate beats 83% of 21,185 tracked accounts of a similar size. Comparing inside a size band matters here: engagement rate falls as accounts grow, so a raw rate would mostly just re-measure the follower count. Posts are seen about 49K times each, and 1.80% of those impressions turn into an interaction. That is about 30.9% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.7 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 02:00 UTC, and Sunday is the busiest day of the week. Of the 15 posts sampled, 87% carry an image or video, 7% are part of a thread and 7% link out. The account's strongest tracked post pulled 76K interactions, about 87x its own typical post.
- What is Ainun Najib's engagement rate on X?
- Ainun Najib (@ainunnajib) has an engagement rate of 0.555%, based on the median interactions across 15 original posts from the last 30 days against 157,346 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.555%, Ainun Najib sits above the 50th percentile of the 212,825 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 @ainunnajib have real engagement?
- Its engagement rate beats 83% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the top 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 @ainunnajib post?
- Most posts go out around 02:00 UTC, and Sunday is its busiest day, at roughly 2.7 posts per day across the measured window.