4 measurements spanning 7 days, net +10. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 1,934–1,952 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
15 Aug 2026, 00:22
1,946
-4
11 Aug 2026, 11:53
1,950
+14
8 Aug 2026, 05:37
1,936
no change
7 Aug 2026, 16:48
1,936
first reading
Engagement
18 posts held, back to 13 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
54.6%
avg views ÷ 1,946 subscribers
Avg views / post
1,060
14 posts measured
Reaction rate
1.96%
reactions ÷ views · ER floor
Posts in window
14
of 18 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 7 August 2026
Posts held
18 (13 July 2026 – 7 August 2026)
Views total
14,879
Reactions total
292
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 16:48 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
11s
Average length
6s
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
417 reactions across 18 posts, in 9 distinct kinds. The most used accounts for 25.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
💯
106
25.4%
❤🔥
93
22.3%
custom 5467899285167157308
68
16.3%
custom 5280546416509340552
47
11.3%
custom 5408880430155836385
30
7.19%
custom 5416039998904345140
29
6.95%
custom 5249166827099011082
26
6.24%
custom 5375135722514685501
17
4.08%
custom 5471979757501439582
1
0.24%
Custom emoji. 7 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them areTelegram’s.
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 18 of the 18 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 417reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 18 most recent posts we hold, published 13 July 2026 to 7 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
2
across the posts below
Posts paid on
2
of 18 we hold a reading for · 11%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ux_huix. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 18 most recent posts we hold for this entry, published 13 July 2026 to 7 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
В якості експерименту, якщо знайдуться бажаючі — можемо зробити spotlight:
Ви — підписники, можете мені в особисті (або в коментарі до цього поста, якщо я все правильно зробив) скинути свій пет-проєкт, своє портфоліо, кейс, статтю якою хочете поділитися і т.д.
Можете закинути тему для обговорення. Або можемо підтримати ваш лонч на продактхант 🤗
Я не проти, якщо це будуть автори інших каналів — ви можете розказати …
Шановні хуїксята,
автор трошки втомився і вирішив відпочити від екранів (і навіть більше ніж один тиждень), тож найближчим часом не буде нічого крім мемів (вони налаштовані через відкладені повідомлення на пів року вперед)...
І не тікайте: як відпочину — завершу і релізну дещо цікаве😈
Моя дружина — психолог. Звучить як діагноз (так і є), але зараз не про це 😂️️️️
Ми іноді спілкуємось про те, як люди вирішують свої психологічні питання за допомогою ШІ і зокрема говорили, що якщо буде повстання машин — хто з нас буде вище в рейтингу на ліквідацію за те що ми грубо відповідаємо чатуЖПТ та все навколо цього... І врешти зійшлися на тому, що емоційно "відриватися" на чатботах — це продуктивна модель по…
Сьогодні не про дизайн, АІшку та ІТшку, а про смачну каву 🤗
Я полюбляю влітку пити колдбрю, експресо-толік і оце все. Нещодавно приятель скинув в наш кавовий чатік (ага, і такий є) ось цей рецепт і останні тижні я повністю перейшов з колдбрю на оцейво 👇🏽
https://youtu.be/PApBycDrPo0?si=COhGbcyXDRq6rO87
Чим воно краще за колдбрю:
1. Швидше. Не треба починати готувати каву ввечері, щоб попити її зранку.
2. За рахунок…
Аналогічно можна купу різних правил прописати контрактами для агентів і на рівні алгоритму для лінтера і він це все зробить.
Наприклад: НІКОЛИ БЛЯХА НЕ РОБИ СІРИЙ ТЕКСТ НА КОЛЬОРОВОМУ ФОНІ!!11111
Чому це не можна було реалізувати раніше?
Ну бо ручками це все програмувати я проходив ще у 2012-2016 роках і я тоді заї6@вся. Зараз простіше😊
Go по аргументам:
Крім очевидного «прибрати зайву ланку між наміром та реалізацією»
В фігмі є чудові автолеяути, змінні, слоти (із запізненням на кілька років)
АЛЕ нема жодного алгоритму чи «контракту».
Наприклад:
Я в фігмі створив примітиви на рівні дизайн-токенів, зробив семантичний маппінг, потім component-specific mapping. АЛЕ я не можу зробити "відносний" маппінг. У мене є 4 рівня вкладеності: section (produ…
👇🏽 Продовжую історичний екскурс перед тим, як навести парочку прикладів, чому повертаюся в код і майже відмовляюся від фігми
3. +/- в 2016 я відкрив для себе скетч і перейшов туди, бо вже було щось типу autolayouts та і загальна структура вже ок для інтерфейсів + стилі, а змінні можна було реалізувати плагінами. Тобто вже працюють правила, а не треба ручками кожен піксель вирівнювати... Ну а потім перейшов на фігму…
Відходжу від використання Фігми як джерела правди в своїх проєктах і воно остаточно переїжджає в код... Але перед аргументацію — трошки історії 🤗
Приблизно з 2009 до 2016 я робив макети одразу в коді. Чому?
1. Тому що скетч та фігма тоді ще не існували, а фотошоп... він не для цього.
2. Наявність змінних, вирівнювання, відступів і т.д. — це все дозволяло робити макети одразу прямо в коді набагато швидше та комфор…
І ще важливе доповнення👇🏽
Fable має найбільше сенсу юзати в Cowork сесіях як радника, дослідника і того хто специфікацію формує та дистилює і займається "гігієною контексту" (легасі = слоп).
Подальшу реалізацію вже можна віддавати Опусу, але якщо прогавати момент де він плутається в беклогу — буде бідося. І Фейбл допомагає цю бідосю розгрібати займаючись "гігієною контексту"🤗
З АІшкою як і з людьми — чим краще сфо…
Showing the 12 most recent of 18 posts we hold for @ux_huix. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Citation-graph rank
Citation-graph rank — 824,143 of 1,481,217entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 15 August 2026 — this
entry's latest reading, not the date you are reading this.
“ХУЇКС” (@ux_huix), 1,946 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/ux_huix.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.