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Channel

FastNews | Никита Пастухов

@fastnewsdev

On this record: Growth · Engagement · Reactions · Stars · Posts · Citations · Handles named that no longer answer · Cite this entry

1,552subscribers

+40 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1002405883561
TypeChannel
Username@fastnewsdev
CreatedBetween 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/fastnewsdev

Growth

1,5121,5521,5326 August 2026 — 1,512 subscribers6 August 2026 — 1,512 subscribers6 August 2026 — 1,515 subscribers9 August 2026 — 1,535 subscribers12 August 2026 — 1,552 subscribers6 August 202612 August 2026
5 measurements spanning 7 days, net +40. 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 1,506–1,558 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 23:571,552+17
9 Aug 2026, 19:011,535+20
6 Aug 2026, 18:131,515+3
6 Aug 2026, 07:351,512no change
6 Aug 2026, 03:021,512first reading

Engagement

20 posts held, back to 5 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
92.7%
avg views ÷ 1,552 subscribers
Avg views / post
1,440
13 posts measured
Reaction rate
2.05%
reactions ÷ views · ER floor
Posts in window
13
of 20 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
WindowRolling 30 days · latest post in window 5 August 2026
Posts held20 (5 July 20265 August 2026)
Views total18,706
Reactions total383
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 18:11 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

572 reactions across 19 posts, in 19 distinct kinds. The most used accounts for 38.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥21938.3%
👍13924.3%
12421.7%
😁457.87%
🤡81.40%
61.05%
❤‍🔥40.699%
🥴40.699%
🍓30.524%
👀30.524%
👎30.524%
💅30.524%
💩30.524%
🤔30.524%
🏆10.175%
👏10.175%
😢10.175%
😭10.175%
🤮10.175%

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 19 of the 20 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 572reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 5 July 2026 to 5 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
1
across the posts below
Posts paid on
1
of 19 we hold a reading for · 5%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @fastnewsdev. 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 20 most recent posts we hold for this entry, published 5 July 2026 to 5 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.

Recent posts

5 Aug 2026, 15:40 UTC838 views22 reactionsread 7 August 2026

Matt Pocock буквально 3 часа назад выпустил обновление своих скиллов САМОЕ ВАЖНОЕ: всеми любимый /grilling (я использую его всегда и везде, именно им мы работали на стриме) превратился в /batch-grilling по дефолту Теперь вопросы будут задаваться не по одному, а сразу раундами. Якобы так сессию intent discovery можно пройти гораздо быстрее. Matt оптимизировал этот скилл на своих пректах уже месяца 2, так что хочется

🔥19👀2😭1

4 Aug 2026, 15:18 UTC888 views30 reactionsread 7 August 2026

Я не понимаю - это я тупой или модели? Я уже говорил, что считаю модели достаточно умными, чтобы стать повседневным инструментом программиста. Более того, я считаю, что слишком умные модели не нужны. Opus-4.8 мне нравился больше Fable и Opus-5, а все модели я использую на medium effort (по дефолту стоит high)! Но как же я, сука, ошибался... Волею судьбы мне довелось опробовать на DeepSeek-v4-flash тот пайплайн, чт

😁22👍5🤔3

3 Aug 2026, 04:51 UTC≈1,140 views46 reactionsread 7 August 2026

Пари я проиграл - ваншота не было https://www.youtube.com/watch?v=laJQcNAqxc0 Вчера 4 часа вайбкодили с Никитой Соболевым security-компоненты в django-modern-rest: его фреймворк, его стайлгайды, его ревью. Напомню, что Никита - хейтер AI, и анонсил он это как спор: мол, Пастухов заваншотит всю миграцию dj-rest-auth клодом раньше, чем он успеет выпить бутылку пива. Не заваншотил, но кое-что реально успели сделать:

🔥395💩2

2 Aug 2026, 07:34 UTC≈1,100 views16 reactionsread 7 August 2026

Очередной воскресный дайджест агентского тулинга. Напоминаю, что все срочное падает в чат и там же обсуждаем свой опыт использования. Топ недели - reverse-skill. Не скилл и не библиотека скиллов, а роутер: по задаче сам решает, какой скилл подтянуть, бутстрапит нужный тулчейн на лету и дописывает собственную базу знаний. Тема узкая - реверс и авторизованный пентест, - но интересен он скорее как сигнал: скиллов стало

🔥13👍21

1 Aug 2026, 09:24 UTC≈1,360 views29 reactionsread 7 August 2026

Вайбкодим security компоненты в django-modern-rest (или нет) https://www.youtube.com/watch?v=laJQcNAqxc0 Мой друг Никита Соболев - @opensource_findings сейчас активно разрабатывает django-modern-rest (а заодно CPython и десяток других OpenSource проектов). И он искал помощь, чтобы полностью мигрировать функционал dj-rest-auth в свой фреймворк - https://github.com/wemake-services/django-modern-rest/issues/1193 Чтож

🔥22👍4❤‍🔥2💩1

1 Aug 2026, 06:13 UTC≈1,320 views33 reactionsread 7 August 2026

Ребят, я очень надеюсь, что не вызываю ни у кого чувство FOMO своими постами про AI и агентов. Наоборот, тут я пытаюсь вам рассказать, насколько все просто и понятно, если копнуть в механику происходящего Серьезно, все, что я вам рассказывал тут последний месяц - это дело пары дней на "потыкать" и пары недель на адаптацию под себя Но если вы хотите, чтобы я рассказал про что-то другое - самое время написать мне об

28🔥5

31 Jul 2026, 04:14 UTC≈1,830 views37 reactionsread 7 August 2026

Мой полный сетап скиллов для разработки Две недели назад переехал на сетап скиллов от Matt Pocock, докрутил пилот - показываю весь конвейер: от "хочу фичу X" до открытого PR. Идея под ним ровно та, что разбирал во вторник: агент должен получать контекст порциями, а не тонуть в одной бесконечной сессии. Каждый этап - своя сессия, в которую влезает меньше 100-200к. Ставится все это одной командой: npx skills@latest

👍267🔥3🥴1

28 Jul 2026, 15:11 UTC≈1,540 views30 reactionsread 7 August 2026

Поздравляю, вы уже пользуетесь SDD Если вы хоть раз гоняли агента через plan-mode - вы уже в SDD-клубе, просто об этом не знали. Я обещал вам рассказать про свой опыт с сетапом скиллов Мэтта, но без разговора об SDD ничего не получится Так что заходить буду издалека, с боли. Модели с заявленным контекстом в 1_000_000 начинают тупить уже на 100_000. А на 200_000 - тупить безбожно. Виновата Lost in the middle: модел

216🥴2👍1

27 Jul 2026, 17:39 UTC≈1,390 views17 reactionsread 7 August 2026

Всю прошлую неделю я был в отпуске, поэтому не смог выпустить воскресный дайджест в воскресенье. Ловите его в понедельник. Топ недели - OmniRoute. Один эндпоинт, за которым 39 пулов провайдеров и 460+ моделей: агрегирует все документированные бесплатные тарифы в общий живой счетчик (обещают ~1.4 млрд бесплатных токенов в месяц) и сам перекидывает тебя на следующую квоту, когда уперся в лимит. Прикручивается к Claude

🔥8👍63

23 Jul 2026, 15:42 UTC≈1,800 views48 reactionsread 7 August 2026

Я сейчас в отпуске и для души делаю проект с полным вайбкодингом по 10 часов в день (развлечения разрабов🥲) - даже в код не заглядываю. И наконец-то понял, почему все вайбкодеры подсаживаются на этот КАЙФ😁 Сидишь, 30-40 минут дизайнишь фичу, а потом даешь отмашку агенту - и он 2 часа пыхтит не переставая. Сам себя ревьюит, правит, бьет себя по рукам за нарушение стайлгайда. Тебе остается только подойти, когда все го

🔥29👍86👎2🤡2🤮1

19 Jul 2026, 09:42 UTC≈1,960 views30 reactionsread 7 August 2026

За AI-тулингом невозможно следить руками, поэтому поднял себе радар: агент раз в день прочесывает GitHub Trending, Reddit, X и HN в поисках нового тулинга для кодинг-агентов - скиллы, MCP-серверы, CLI. Если это что-то стоящее - кидаю в канал. Но стоящего в последнее время много, поэтому предлагаю новый формат: • по воскресеньям закидываю в канал дайджест за неделю • если что-то стоящее появляется день в день - я кид

👍203🔥3👎1🤡1🥴1😁1

17 Jul 2026, 05:04 UTC≈1,840 views21 reactionsread 7 August 2026

Периодически я закидываю в канал новые skills для агентов - для дизайна, SDD, свои собственные. Большую часть из них я пробую сам, но результаты этого тестирования обсуждаем в основном в чатике. Исправляю это недоразумение - поделюсь личным сетапом скиллов, которым пользуюсь каждый день: grill-with-docs - 10/10, мой топ ​ Заставляет агента мучать тебя вопросами, пока не выудит все нюансы задачи, - и только потом

14👍4🍓1👀1😢1

Showing the 12 most recent of 20 posts we hold for @fastnewsdev. 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.

Citation-graph rank

Citation-graph rank — 38,415 of 1,160,990entries 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

Named by 4 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.

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 12 August 2026 — this entry's latest reading, not the date you are reading this.

“FastNews | Никита Пастухов” (@fastnewsdev), 1,552 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/fastnewsdev.

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.