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Channel

Библиотека собеса по Python | вопросы с собеседований

@py_interview_lib

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

5,947subscribers

-2 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001901762391
TypeChannel
Username@py_interview_lib
CreatedBetween 1 March 2023 and 30 November 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/py_interview_lib

Growth

5,9475,9505,948.56 August 2026 — 5,949 subscribers7 August 2026 — 5,948 subscribers9 August 2026 — 5,950 subscribers12 August 2026 — 5,947 subscribers6 August 202612 August 2026
4 measurements spanning 7 days, net -2. 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 5,947–5,950 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 23:465,947-3
9 Aug 2026, 18:115,950+2
7 Aug 2026, 02:425,948-1
6 Aug 2026, 09:175,949first reading

Engagement

20 posts held, back to 15 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 9 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
6.38%
avg views ÷ 5,947 subscribers
Avg views / post
380
20 posts measured
Reaction rate
0.455%
reactions ÷ views · ER floor
Posts in window
20
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. It is computed over the 6 of 20 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 5 August 2026
Posts held20 (15 July 20265 August 2026)
Views total7,591
Reactions total11
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 03:41 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

11 reactions across 6 posts, in 5 distinct kinds. The most used accounts for 27.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
327.3%
👍327.3%
👾327.3%
🔥19.09%
🥰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 6 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 11reactions 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 15 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.

Recent posts

5 Aug 2026, 18:02 UTC307 views1 reactionsread 12 August 2026

У вас есть Python-сервис, который обрабатывает асинхронные запросы через asyncio. При нагрузочном тесте задержки начинают расти, хотя CPU и память почти не используются. В чём может быть причина и как её решать? Скорее всего, внутри event loop есть блокирующие операции (синхронные вызовы к БД, файлам или тяжёлые вычисления). Их нужно вынести в отдельный процесс или поток (ProcessPoolExecutor/ThreadPoolExecutor) либо

1

5 Aug 2026, 17:15 UTC207 viewsread 12 August 2026
Forwarded from @proglib_academyPhoto

👅 Самое сложное — выбрать не курс, а направление Сегодня хочется разобраться в AI-агентах. Через пару недель появляется задача, где нужны алгоритмы. Потом понимаешь, что неплохо бы освежить Python или математику для Data Science. 🔜 Именно поэтому подписка на обучение многим оказывается удобнее отдельных курсов. Можно спокойно переключаться между темами, когда меняются задачи, а не покупать новую программу каждый ра

5 Aug 2026, 17:15 UTC294 viewsread 12 August 2026

Покупать новый курс каждый раз, когда меняется рабочая задача, — довольно странная механика. Нашли альтернативу 👇

30 Jul 2026, 20:44 UTC475 viewsread 12 August 2026

Как спроектировать CLI-утилиту с подкомандами, автокомплитом и хорошей тестируемостью? Постройте команды как чистые функции и свяжите их через Typer/Click; типизируйте параметры (Typer читает type hints), включите генерацию completion-скриптов, не делайте тяжёлую работу на уровне импорта. Пакуйте через pyproject.toml и console_scripts, логируйте в stderr. Тестируйте CliRunner/subprocess с фикстурами и золотыми этало

30 Jul 2026, 18:12 UTC340 viewsread 12 August 2026
Forwarded from @proglib_academyPhoto

🎯 У разработчика, DevOps-инженера и техлида разные причины задуматься о системной работе с AI-агентами Но цель одна: перейти от случайных удачных запусков к управляемому процессу на реальном проекте — с понятными правилами, ревью, контролем расходов и безопасности ✔️ На курсе «ИИ для разработчиков» участники выстраивают такой процесс на практике: подключают инструменты, задают границы для агента и учатся понимать,

30 Jul 2026, 18:12 UTC373 viewsread 12 August 2026

Разработчику нужен стабильный результат, DevOps — контроль инфраструктуры, техлиду — правила для всей команды. Собрали это в одном посте 👇

29 Jul 2026, 19:43 UTC415 viewsread 12 August 2026

Асинхронный сервис под нагрузкой «раздувается» по памяти: создаются тысячи задач через asyncio.create_task, но нагрузка на CPU низкая. Как ограничить параллелизм и удержать память? Ввести backpressure: использовать ограниченную очередь/семафор (asyncio.Semaphore, asyncio.Queue) или TaskGroup с лимитом; батчировать работу, await-ить завершение перед запуском новых задач, таймауты/отмена подвисших тасков, и не хранить

28 Jul 2026, 11:25 UTC347 views4 reactionsread 12 August 2026
Forwarded from @proglib_academyPhoto

У вас в проекте есть файл вроде utils_v2_final_FIX.py, который все боятся трогать ❓ Что делаете с таким легаси? ❤️ Не трогаю: работает — и ладно 👍 Переписываю с нуля и надеюсь на лучшее 🔥 Сначала покрываю тестами, потом рефакторю по частям 👾 У нас такого нет — честно-честно На курсе «ИИ для разработчиков» разберём третий подход: как с помощью агента зафиксировать текущее поведение легаси-кода тестами, а затем меня

👾21🔥1

28 Jul 2026, 11:25 UTC372 viewsread 12 August 2026

Самый страшный файл в проекте — тот, который «работает, не трогай» 👇

27 Jul 2026, 20:02 UTC436 viewsread 12 August 2026

Объясните, как устроен импорт в Python: роль sys.modules (кэш), цепочка sys.meta_path/finders/ModuleSpec/loaders, что происходит при циклических зависимостях, и почему importlib.reload() не «обновляет» ссылки в других модулях. При import сначала проверяется sys.modules; если модуля нет, по sys.meta_path ищут spec, создают объект модуля, сразу кладут его в sys.modules (для разрыва циклов) и затем loader.exec_module()

25 Jul 2026, 20:43 UTC444 views1 reactionsread 12 August 2026

В высоконагруженном Python-сервисе вы замечаете, что CPU загружен слабо, но задержки обработки запросов постоянно растут. При анализе видно, что большая часть времени тратится на сетевые операции. Что может быть причиной и как это исправить? Причина в том, что сервис выполняет блокирующие I/O-операции в потоках или синхронно. Для исправления нужно перейти на асинхронную модель (asyncio, uvloop), использовать асинхро

👾1

25 Jul 2026, 13:10 UTC327 viewsread 12 August 2026
Forwarded from @proglib_academyPhoto

📈 Скоро фраза «умею пользоваться AI» в резюме будет звучать так же странно, как сегодня «умею пользоваться Git» Это просто станет частью профессии. А вот умение встроить AI в процесс разработки так, чтобы он действительно ускорял работу команды, останется конкурентным преимуществом 💡 🔜 На курсе «ИИ для разработчиков» участники работают со своими проектами и учатся делать AI частью ежедневной разработки: планировать

Showing the 12 most recent of 20 posts we hold for @py_interview_lib. 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 — 703,449 of 1,151,006entries 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

Republishes

Channels on the register whose posts this channel has forwarded.

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 6 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.

“Библиотека собеса по Python | вопросы с собеседований” (@py_interview_lib), 5,947 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/py_interview_lib.

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.