Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 94% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
34 measurements spanning 51 days, net +349. 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 13,841–14,294 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 34
Measured (UTC)
Subscribers
Change
25 Sept 2026, 23:17
14,242
+61
19 Sept 2026, 04:41
14,181
+33
16 Sept 2026, 16:00
14,148
+23
14 Sept 2026, 20:39
14,125
+50
13 Sept 2026, 04:19
14,075
+7
11 Sept 2026, 05:17
14,068
-19
8 Sept 2026, 07:55
14,087
-2
5 Sept 2026, 05:37
14,089
+2
3 Sept 2026, 09:55
14,087
+8
2 Sept 2026, 01:25
14,079
+12
31 Aug 2026, 22:15
14,067
+9
30 Aug 2026, 23:25
14,058
+16
30 Aug 2026, 00:13
14,042
+15
29 Aug 2026, 02:16
14,027
+6
28 Aug 2026, 00:27
14,021
+13
27 Aug 2026, 01:14
14,008
+2
25 Aug 2026, 22:45
14,006
+6
25 Aug 2026, 01:32
14,000
+13
21 Aug 2026, 23:48
13,987
+8
20 Aug 2026, 16:44
13,979
first reading
Engagement
28 posts held, back to 6 June 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 50 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
26.5%
avg views ÷ 14,242 subscribers
Avg views / post
3,780
2 posts measured
Reaction rate
2.95%
reactions ÷ views · ER floor
Posts in window
2
of 28 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 1 September 2026
Posts held
28 (6 June 2026 – 1 September 2026)
Views total
7,560
Reactions total
223
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 19:12 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.
Reaction mix
2,663 reactions across 27 posts, in 33 distinct kinds. The most used accounts for 32.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
864
32.4%
❤
613
23.0%
😁
496
18.6%
🔥
390
14.6%
🤣
45
1.69%
💯
39
1.46%
💩
36
1.35%
🤔
27
1.01%
👀
21
0.789%
🤡
18
0.676%
🥴
17
0.638%
🌚
13
0.488%
😭
12
0.451%
👎
9
0.338%
👏
9
0.338%
🤮
8
0.3%
🎉
7
0.263%
😐
5
0.188%
🦄
5
0.188%
😱
4
0.15%
13 further kinds
25
0.939%
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 27 of the 28 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 2,663 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 28 most recent posts we hold, published 6 June 2026 to 1 September 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
51
across the posts below
Posts paid on
10
of 27 we hold a reading for · 37%
Most on one post
37
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @orgprog. 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 28 most recent posts we hold for this entry, published 6 June 2026 to 1 September 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.
Адаптация проекта под LLM
Есть два подхода к организации агентного программирования в проекте. Первый это все описывать и заставлять агентов делать все как надо, второй - менять проект под ожидания ии. Обычно в проектах делают и то и то, но сейчас я бы хотел акцентировать внимание на втором.
Чем дольше живет проект, тем больше в нем косяков в именовании (чего угодно), кастомных решений и самопальных либ, разных под…
Прошлый раз с лайвкодингом породил так много вопросов, что пришлось записать еще один выпуск. Он состоит из двух больших тем:
1. Мой сетап. Как устроен мой воркфлоу кодинга: терминалы, комбо, слепая печать, навигация использование специализированных тулов.
2. SDD. В прошлый раз не все заметили, что кроме мелких тикетов, был один, который я делал по spec driven development, поэтому в этот раз мы прямо сетапим воркфло…
Выпуск в сети! В этот раз я лайвкожу с агентами. Пока закрывал тикеты фиганул пару пулреквестов в mantine, которые уже приняли https://youtu.be/Eplxom-e1C4?is=3HME8iWRITMJxhe2
Telegram | Аудио | vk
Зачем ускорять разработку?
Просто через раз вижу этот вопрос, во всех постах про агентов и новую эру автоматического кодинга. Переубедить конечно никого не получится, но написать надо, чтобы получилось структурировано. Погнали.
Нет такого количества задач
Если вы поговорите не с менеджерами, а владельцами бизнесов, то окажется, что количество идей у них такое, что за всю жизнь не сделать. То что из этого не всегда…
Вперед к монорепам
Практика показывает, что эффективнее всего с агентами работать тогда, когда весь контекст есть по рукой и можно просто погрепать, причем речь идет не про один какой-то конкретный сервис/проект, а когда все репозитории проекта, лежат в одной папке, а возможно даже в одном репозитории. В таком случае и дока общая (это важно для спек) и все просвечивается насквозь и пулреквесты можно сразу бахнуть ве…
Открытие дня. Все же знают dependabot, который обновляет зависимости всего и вся на гитхабе? Причем речь не только про пакетные менеджеры всех языков, но и например github actions и даже версии образов в Dockerfile. Так вот есть такое же решение, не привязанное к вендору.
Небольшая предыстория. Помимо гитхаба у нас есть свой гитлаб с большим количеством реп, в котором так же находится инфраструктура запуска практик …
Как я провел лето
Ну все, прилетел, два с половиной месяца в рф пролетели как один день. Документы привел в порядок, кого надо прописал, кому надо сделал паспорт, оформил гражданство мелкому. В процессе оформление многодетства, но это уже можно закончить онлайн. Выступил на 6 конфах, провел пару мастерклассов и один двухдневный воркшоп, даже был фасилитатором на одном мероприятии, где разбирался вопрос внедрения ии …
Программирование с явно выделенным состоянием
Одна из моих любимых тем, про которую не устаю говорить. В модели данных часто бывает ситуация, когда состояние выражено не прямым образом, а косвенно. Буквально вчера я реализовывал кастомные тарифы под конкретных пользователей. Отличие такого тарифа от обычного сводится к тому, заполнено ли поле user_id в тарифе или нет. Если не заполнено, то значит это общий тариф, ес…
Новый выпуск подкаста про Agile, Scrum и ИИ трансформацию уже доступен https://youtu.be/FhthTCoR3uw?is=X5GCG6XrrFds65ws В этот раз с Асхатом Уразбаевым мы вспоминаем как это было и куда пришло. Взлеты и падения, культы карго и трансформации в компаниях. А что в конце? Дейли не нужен, вот такие пироги
Telegram | Аудио | vk
Иммутабельная денормализация
Изучение баз данных всегда сопровождается понятием нормализации, а конкретно первыми тремя формами, которые задают нам ограничения, помогающие правильно разложить все по таблицам. И это действительно база, без которой нормально работать не получится. Но есть, как обычно, нюансы.
В реальных проектах выясняется, что нормализованные данные сложно соединять и выбирать. При глубоком уровне з…
👍37❤15😐5🔥3👏1🤪1
Showing the 12 most recent of 28 posts we hold for @orgprog. 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.
Posts edited after publishing
@orgprog edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
11 August 2026
Most recent edit
30 August 2026
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
Named by 5 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
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.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Диджитализируй! @t0digital · 28,475 Telegram ranks this channel #7 of 88 here — alongside 87 others — read 9 September 2026
Digital Ниндзя @digital_ninjaa · 40,313 Telegram ranks this channel #7 of 91 here — alongside 90 others — read 30 August 2026
FEDOR BORSHEV @pmdaily · 23,698 Telegram ranks this channel #9 of 97 here — alongside 96 others — read 17 September 2026
Осознанная Меркантильность | Антон Назаров @m0rtymerr_channel · 80,400 Telegram ranks this channel #11 of 87 here — alongside 86 others — read 18 August 2026
Alek OS @Alek_OS · 32,888 Telegram ranks this channel #12 of 95 here — alongside 94 others — read 4 September 2026
запуск завтра @ctodaily · 33,699 Telegram ranks this channel #16 of 96 here — alongside 95 others — read 4 September 2026
Библиотека Go-разработчика | Golang @goproglib · 24,076 Telegram ranks this channel #27 of 91 here — alongside 90 others — read 17 September 2026
Golang @Golang_google · 40,461 Telegram ranks this channel #29 of 90 here — alongside 89 others — read 30 August 2026
Профсоюз работников IT @ruitunion · 35,942 Telegram ranks this channel #30 of 94 here — alongside 93 others — read 2 September 2026
Timeweb Cloud Alerts @timewebcloud_alerts · 27,753 Telegram ranks this channel #33 of 87 here — alongside 86 others — read 9 September 2026
Рефералки в IT | Вакансии и резюме @refer_me_it · 30,925 Telegram ranks this channel #35 of 89 here — alongside 88 others — read 7 September 2026
The ExtremeCode Times @extremecode · 35,596 Telegram ranks this channel #35 of 93 here — alongside 92 others — read 2 September 2026
Golden Borodutch @golden_borodutch · 65,429 Telegram ranks this channel #36 of 68 here — alongside 67 others — read 29 August 2026
Владилен: IT в эпоху AI @vladm · 39,286 Telegram ranks this channel #43 of 96 here — alongside 95 others — read 30 August 2026
AvitoTech @avitotech · 25,625 Telegram ranks this channel #45 of 96 here — alongside 95 others — read 14 September 2026
Максим Дорофеев: Прокрастинация и джедайские техники @mnogosdelal · 26,254 Telegram ranks this channel #50 of 97 here — alongside 96 others — read 14 September 2026
Teamlead Good Reads – ежедневные советы про менеджмент людей и команд @leadgr · 28,120 Telegram ranks this channel #52 of 96 here — alongside 95 others — read 10 September 2026
Stepik – онлайн-курсы @stepik_courses · 24,454 Telegram ranks this channel #53 of 93 here — alongside 92 others — read 16 September 2026
Dev & ML Connectable Jobs @dev_connectablejobs · 27,959 Telegram ranks this channel #62 of 94 here — alongside 93 others — read 10 September 2026
addmeto @addmeto · 71,835 Telegram ranks this channel #65 of 95 here — alongside 94 others — read 20 August 2026
Ozon Tech @ozon_tech · 29,768 Telegram ranks this channel #74 of 98 here — alongside 97 others — read 8 September 2026
Библиотека фронтендера | Frontend, JS, JavaScript, React.js, Angular.js, Vue.js @frontendproglib · 20,930 Telegram ranks this channel #89 of 93 here — alongside 92 others — read 25 September 2026
This channel appears in 22 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 25 September 2026 — this
entry's latest reading, not the date you are reading this.
“Организованное программирование | Кирилл Мокевнин” (@orgprog), 14,242 subscribers as measured 25 September 2026. Telegram Register, tgregister.com/channel/orgprog.
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.