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Channel

Python Community

@Python_Community_ru

On this record: Topic · Observations · Also posting the same content · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

11,671subscribers

-44 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001387967118
TypeChannel
Username@Python_Community_ru
DescriptionPython Community RU - СНГ сообщество Python-разработчиков Чат канала: @python_community_chat Сотрудничество: @cyberJohnny и @Sergey_bzd РКН реестр: https://knd.gov.ru/license?id=67847dd98e552d6b54a511ed&registryType=bloggersPermission
CreatedBetween 1 March 2018 and 31 July 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live21 August 2026
Measurements held14
Confirmed unchanged1 time, most recently 21 August 2026
On Telegramt.me/Python_Community_ru

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 100% 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.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Content that also appears on other registered channels

Posts published here appear word for word on 1 other registered channel. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

Matching posts — open both and compare (6 of the pairs behind the counts below)
Posted firstThenOverlapGap
@pythonl/56487 Aug 2026, 10:15 UTC@Python_Community_ru/3234 · this entry7 Aug 2026, 15:57 UTC0.995.7 hours
@pythonl/56455 Aug 2026, 20:53 UTC@Python_Community_ru/3230 · this entry5 Aug 2026, 22:03 UTC0.9970 minutes
@pythonl/56435 Aug 2026, 11:09 UTC@Python_Community_ru/3229 · this entry5 Aug 2026, 16:04 UTC0.994.9 hours
@pythonl/56341 Aug 2026, 10:24 UTC@Python_Community_ru/3220 · this entry1 Aug 2026, 16:20 UTC0.995.9 hours
@pythonl/562729 Jul 2026, 11:01 UTC@Python_Community_ru/3216 · this entry29 Jul 2026, 16:32 UTC0.995.5 hours
@pythonl/562528 Jul 2026, 12:40 UTC@Python_Community_ru/3214 · this entry28 Jul 2026, 16:36 UTC0.993.9 hours
Every channel this entry shares post bodies with
ChannelMatching postsText overlapTypical gapPublished first
@pythonl10 (8/8 hand-verifiable sample passed)0.994.0 hours@pythonl (100)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 2 of the 10 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.

“Published first” means first in this corpus. We hold 16 comparable posts for this entry, running 27 July 2026 to 7 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Across the whole group of 2, the earliest publisher we hold is @pythonl. That is a statement about our reading window, not a claim of authorship.

Recorded under the key clone_copy, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Also posting the same content

This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.

Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

11,67111,71511,6937 August 2026 — 11,715 subscribers7 August 2026 — 11,715 subscribers8 August 2026 — 11,714 subscribers10 August 2026 — 11,713 subscribers11 August 2026 — 11,711 subscribers12 August 2026 — 11,708 subscribers13 August 2026 — 11,704 subscribers14 August 2026 — 11,692 subscribers16 August 2026 — 11,690 subscribers17 August 2026 — 11,687 subscribers18 August 2026 — 11,685 subscribers19 August 2026 — 11,683 subscribers20 August 2026 — 11,678 subscribers21 August 2026 — 11,671 subscribers7 August 202621 August 2026
14 measurements spanning 14 days, net -44. 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 11,664–11,722 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
21 Aug 2026, 15:5811,671-7
20 Aug 2026, 15:3611,678-5
19 Aug 2026, 15:1611,683-2
18 Aug 2026, 16:0311,685-2
17 Aug 2026, 13:0311,687-3
16 Aug 2026, 06:1211,690-2
14 Aug 2026, 19:4711,692-12
13 Aug 2026, 12:1611,704-4
12 Aug 2026, 13:2611,708-3
11 Aug 2026, 10:0311,711-2
10 Aug 2026, 10:5011,713-1
8 Aug 2026, 13:3911,714-1
7 Aug 2026, 05:1511,715no change
7 Aug 2026, 05:0511,715first reading

Engagement

35 posts held, back to 27 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 27 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
4.41%
avg views ÷ 11,671 subscribers
Avg views / post
515
35 posts measured
Reaction rate
0.702%
reactions ÷ views · ER floor
Posts in window
35
of 35 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. It is computed over the 3 of 35 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 21 August 2026
Posts held35 (27 July 202621 August 2026)
Views total18,011
Reactions total11
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Aug 2026, 14:43 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

Photos
1,460
Videos
118
Links
987

Lifetime counters from Telegram’s own channel header, read 22 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
9s
Average length
9s

Measured directly from 1 video 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

11 reactions across 3 posts, in 3 distinct kinds. The most used accounts for 54.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥654.5%
👎436.4%
👍19.09%

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 3 of the 35 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 11reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 35 most recent posts we hold, published 27 July 2026 to 21 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.

Advertising

Ad load
2.86%
1 of 35 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
331
over 1 measured post
Median views · rest
526
over 34 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor.A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2VtzqwpQLgT120 August 202620 August 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.

Measured over the 35 most recent posts we hold, published 27 July 2026 to 21 August 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

21 Aug 2026, 15:00 UTC233 viewsread 22 August 2026
Photo

Python Game Server: простой сервер для пошаговых мультиплеерных игр python-game-server — лёгкий open source-фреймворк на Python для создания пошаговых мультиплеерных игр. Он даёт готовую серверную основу, чтобы не писать с нуля комнаты, игровые сессии и сетевое взаимодействие. Из коробки есть: — единый API для разных игр — несколько параллельных игровых сессий — подключение к конкретной комнате — автоматический в

20 Aug 2026, 21:03 UTC341 viewsread 22 August 2026
Photo

Для тех, кто работает с ACP-агентами из терминала, появился простой REPL acp-repl запускает любой stdio-сервер с поддержкой Agent Client Protocol и позволяет общаться с агентом прямо из терминала: отправлять промпты, создавать сессии, переключать модель и режим, обрабатывать permission-запросы и смотреть debug-логи. Работает не с одним конкретным агентом. В README есть примеры для OpenCode, Codex, Claude Code и Pi,

20 Aug 2026, 15:04 UTC331 viewsread 22 August 2026
Advertisementerid 2VtzqwpQLgT

‍25 августа встречаемся на Agentic Coding Meetup (https://developers.sber.ru/portal/sreda/agentic-coding-meetup?utm_source=telegram&utm_medium=fix&utm_campaign=agentic_meetup_august_2026_post&utm_content=&utm_term=pythonl&erid=2VtzqwpQLgT) в Сбер.Среде и в онлайне⚡️ AI-агенты уже умеют писать код. Но самое интересное начинается дальше — когда вместе с инструментами меняются роли инженеров, команды и сам процесс разр

19 Aug 2026, 15:08 UTC372 viewsread 22 August 2026
Photo

🖥 Для Python появился Interlock - современный circuit breaker без зоопарка отдельных решений для sync и async. Один CircuitBreaker умеет работать и с обычными функциями, и с async, считать процент ошибок по скользящему окну и отдельно реагировать на слишком медленные вызовы. Можно собрать полноценный pipeline отказоустойчивости: pipeline = ( Pipeline.builder() .fallback(...) .retry(attempts=4) .ci

18 Aug 2026, 21:10 UTC499 views9 reactionsread 22 August 2026
Video

Разница между C++ и Python @Python_Community_ru

👎4🔥4👍1

18 Aug 2026, 15:11 UTC467 viewsread 22 August 2026

‍🔥 DeepSeek Harness - пример того, почему сегодня важна не только модель, но и всё окружение вокруг неё Harness - это слой между LLM и реальной работой: инструменты, terminal, permissions, память, tool loop и правила обработки ответов. У deepseek-harness есть Python-библиотека, CLI dsh, MCP-сервер и интеграция в формате Anthropic Skill. Проект документирует особенности протокола DeepSeek V4-Pro/V4-Flash и автоматич

17 Aug 2026, 15:15 UTC486 viewsread 22 August 2026
Photo

Полный инженерный курс по AI-агентам на русском: от tool calling до production Курс инженерный, а не обзорный. Здесь почти нет рассуждений о том, «изменит ли ИИ мир», зато есть: минимальные работающие реализации каждого механизма, лабораторные с критериями приёмки, шаблоны для копирования, чек-листы перед релизом, каталог антипаттернов и набор бенчмарков, по которым можно честно сравнить две версии своего агента.

16 Aug 2026, 15:20 UTC460 viewsread 22 August 2026

‍🐍 Python-разработчики часто тестируют код с зависимостью от времени. Но обычный mock для дат — это намного сложнее, чем кажется. Библиотека time-machine решает эту проблему: она позволяет буквально «перемещать время» внутри тестов. Например: import datetime as dt import time_machine @time_machine.travel("2030-01-01") def test_future(): assert dt.date.today() == dt.date(2030, 1, 1) В отличие от популярных

15 Aug 2026, 15:24 UTC514 viewsread 22 August 2026

🐍 Опытный Python-разработчик рассказал, для каких проектов в 2026 году он уже не выбирает Python первым. Автор десятилетиями использовал Python практически для всего, но со временем пришёл к простой мысли: хороший универсальный язык не обязан быть лучшим инструментом для каждой задачи. В презентации разбирается, где ограничения Python начинают перевешивать его удобство и почему для некоторых проектов сегодня разумн

14 Aug 2026, 13:57 UTC519 viewsread 22 August 2026
Photo

Модель показывает отличную точность в ноутбуке, но так и не становится рабочим сервисом. Причина часто не в качестве самой модели, а в архитектуре системы вокруг неё. 07 сентября в 20:00 на открытом уроке курса «ML System Design» вы разберёте, как превратить модель на Python в полноценный ML-сервис. Увидите, как связаны хранение данных, обучение, реестр моделей, сервис инференса и мониторинг. На занятии рассмотрите

13 Aug 2026, 15:32 UTC605 viewsread 22 August 2026

🔥 Kimi K3 на 2,78 ТРИЛЛИОНА параметров запустили на CPU с 8 ГБ RAM. Без GPU, BLAS и фреймворков Проект kimi-k3-in-c реализует inference Kimi K3 практически с нуля на portable C99. Чекпоинт модели занимает 1,56 ТБ, но пик RAM при запуске составил всего 8,24 ГБ. Фокус в MoE и стриминге весов. Из 896 экспертов на токен выбираются только 16, а остальные веса не обязаны постоянно лежать в оперативной памяти. Огромная ча

12 Aug 2026, 21:35 UTC507 viewsread 22 August 2026

🤖 Agency Agents - готовая команда специализированных AI-агентов для разработки, маркетинга, дизайна и бизнеса Вместо одного универсального «сделай всё» агента проект предлагает десятки узких специалистов: Backend Architect, AI Engineer, DevOps Automator, Code Reviewer, Database Optimizer, SRE, Prompt Engineer, RAG Engineer и многих других. Каждый агент - это не просто короткий prompt. Внутри описаны: * роль и стил

Showing the 12 most recent of 35 posts we hold for @Python_Community_ru. 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.

Posts edited after publishing

@Python_Community_ru edited 1 post 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
17 August 2026
Most recent edit
17 August 2026

Citation-graph rank

Citation-graph rank — 749,715 of 1,584,142entries 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 1 registered channel — 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.

Однажды в трендах
@trendo · 68,975
Telegram ranks this channel #14 of 90 here — alongside 89 others — read 20 August 2026
Abituriyent | Axborot portali
@Abituriyent_DTMuzb_Dtmuz_Dtm_uz · 67,002
Telegram ranks this channel #63 of 64 here — alongside 63 others — read 21 August 2026
Скиллгет | Онлайн образование
@skillget · 81,480
Telegram ranks this channel #63 of 88 here — alongside 87 others — read 18 August 2026

This channel appears in 3 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 21 August 2026 — this entry's latest reading, not the date you are reading this.

“Python Community” (@Python_Community_ru), 11,671 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/Python_Community_ru.

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.