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Channel

Душный NLP

@stuffyNLP

On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

6,718subscribers

+161 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-1001957868781
TypeChannel
Username@stuffyNLP
CreatedBetween 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live9 November 2026
Measurements held25
Confirmed unchanged1 time, most recently 9 November 2026
On Telegramt.me/stuffyNLP

Topic

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 12 September 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 the other topics.

Growth

6,5576,7206,638.56 August 2026 — 6,557 subscribers7 August 2026 — 6,565 subscribers10 August 2026 — 6,572 subscribers13 August 2026 — 6,581 subscribers16 August 2026 — 6,587 subscribers19 August 2026 — 6,628 subscribers22 August 2026 — 6,626 subscribers25 August 2026 — 6,632 subscribers28 August 2026 — 6,635 subscribers1 September 2026 — 6,647 subscribers4 September 2026 — 6,655 subscribers9 September 2026 — 6,653 subscribers13 September 2026 — 6,651 subscribers18 September 2026 — 6,656 subscribers28 September 2026 — 6,694 subscribers5 October 2026 — 6,692 subscribers12 October 2026 — 6,703 subscribers16 October 2026 — 6,701 subscribers22 October 2026 — 6,700 subscribers24 October 2026 — 6,708 subscribers27 October 2026 — 6,703 subscribers31 October 2026 — 6,711 subscribers3 November 2026 — 6,717 subscribers6 November 2026 — 6,720 subscribers9 November 2026 — 6,718 subscribers6,7186 August 20269 November 2026
25 measurements spanning 95 days, net +161. 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 6,533–6,744 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 25
Measured (UTC)SubscribersChange
9 Nov 2026, 17:166,718-2
6 Nov 2026, 05:476,720+3
3 Nov 2026, 11:016,717+6
31 Oct 2026, 02:006,711+8
27 Oct 2026, 19:006,703-5
24 Oct 2026, 21:456,708+8
22 Oct 2026, 00:596,700-1
16 Oct 2026, 00:276,701-2
12 Oct 2026, 17:496,703+11
5 Oct 2026, 22:196,692-2
28 Sept 2026, 13:556,694+38
18 Sept 2026, 16:146,656+5
13 Sept 2026, 21:586,651-2
9 Sept 2026, 18:166,653-2
4 Sept 2026, 12:566,655+8
1 Sept 2026, 00:456,647+12
28 Aug 2026, 18:486,635+3
25 Aug 2026, 17:326,632+6
22 Aug 2026, 23:466,626-2
19 Aug 2026, 13:126,628first reading

Engagement

15 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 19 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
22.3%
avg views ÷ 6,718 subscribers
Avg views / post
1,500
1 post measured
Reaction rate
2.07%
reactions ÷ views · ER floor
Posts in window
1
of 15 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 27 October 2026
Posts held15 (13 July 2026 – 27 October 2026)
Views total1,500
Reactions total31
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken9 Nov 2026, 15:53 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

357 reactions across 15 posts, in 14 distinct kinds. The most used accounts for 35.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤12635.3%
🔥10028.0%
❤‍🔥6117.1%
👍5314.8%
🤩30.84%
🤷‍♂30.84%
🥴30.84%
👾20.56%
👀10.28%
👎10.28%
💯10.28%
🗿10.28%
🤔10.28%
🤮10.28%

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 15 of the 15 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 357 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 15 most recent posts we hold, published 13 July 2026 to 27 October 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

Telegram’s own ‘edited’ marker: 0 of 1 post held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

27 Oct 2026, 10:09 UTC≈1,500 views31 reactionsread 9 November 2026

DFlash — диффузионный метод спекулятивного декодинга Сегодня поговорим о DFlash — методе спекулятивного декодинга, использующем легковесную модель блочной диффузии для параллельной генерации драфтовых токенов. Но для начала о том, почему имеет смысл использовать диффузионные модели в спекулятивном декодинге. Средняя задержка генерации одного токена определяется суммой времени верификации и времени драфтинга, делённ…

❤‍🔥17❤8🔥6

29 Sept 2026, 09:09 UTC≈2,710 views27 reactionsread 9 November 2026
Photo

Технический отчёт DeepSeek-V4.1-Flash — часть 2/2 Продолжаем разбирать техрепорт DeepSeek-V4.1-Flash. Сегодня поговорим об архитектуре, а ещё речь пойдёт об обучении. В SWA прогоняется не весь контекст, а только последние 128 токенов для построения начального SWA KV. Это не сильно влияет на качество, но позволяет уменьшать общий размер кеша в восемь раз по сравнению с тем, что было у DeepSeek-V4-Flash. Также в вер…

🔥11👍9❤5❤‍🔥2

24 Sept 2026, 08:48 UTC≈3,060 views30 reactionsread 9 November 2026
Photo

Технический отчёт DeepSeek-V4.1-Flash — часть 1/2 Разберём технический отчёт DeepSeek-V4.1-Flash. В первой части обсудим архитектуру модели. Главное достижение авторов — экстремальное сжатие KV-кеша. При стандартном для frontier-моделей максимальном контексте в миллион токенов глобальный KV-кеш на один токен составляет 890 байт, что делает инференс даже максимального числа токенов почти бесплатным (менее одного гига…

❤18🔥6👍4👾2

11 Sept 2026, 12:15 UTC≈3,580 views31 reactionsread 9 November 2026
Photo

Дистилляция DeepSeek-R1 Сегодня разберём, как DeepSeek-R1 на 671B параметров дистиллировали в модель на 32 миллиарда параметров. Зачем вообще это нужно? Вышедшая в сентябре 2024 года o1 доказала, что test-time scaling работает — если дать модели подумать, то она даст более качественный ответ. Однако тогда пользователю не показывали сырую цепочку рассуждений, а особенности метода не раскрывали. Было непонятно, как эт…

❤16👍7🤷‍♂3🥴3🔥2

3 Sept 2026, 11:15 UTC≈3,600 views28 reactionsread 9 November 2026
Photo

Loop the Loopies! Сегодня обсудим статью, которая посвящена моделям Loopies, построенным на шеринге параметров между разными слоями. Обычно у трансформерных моделей и свёрточных нейросетей отдельные параметры на каждом блоке, благодаря чему объём вычислений и число параметров масштабируются с глубиной. Это даёт гибкость — каждый блок может выполнять специфические задачи. Но при этом с ростом числа слоёв растёт и ко…

🔥19❤4❤‍🔥3💯1🤔1

20 Aug 2026, 12:20 UTC≈3,400 views15 reactionsread 9 November 2026
Photo

Context-1 — поисковый агент, который умеет избавляться от лишнего. Часть 2/2 Продолжаем изучать агент Context-1. В первой части мы рассмотрели, что это вообще такое и какие задачи перед собой ставили авторы. А в этой части поговорим о метриках и обучении. Для оценки качества ответа авторы предлагают использовать четыре метрики: Final answer found (смогла ли модель найти документ с правильным ответом в выдаче), Reca…

🔥8❤4❤‍🔥2🗿1

13 Aug 2026, 11:41 UTC≈3,420 views16 reactionsread 9 November 2026

Context-1 — поисковый агент, который умеет избавляться от лишнего. Часть 1/2 Авторы сегодняшней статьи заявляют следующую проблему. Когда агент ищет что-то в поиске, то нерелевантные страницы, выданные браузером, всё равно остаются в контексте агента. Чтобы справиться с этим, контекст можно обрезать или суммировать. Ещё одно весьма перспективное направление в этой области — self-editing context, при котором модель …

❤7🔥5❤‍🔥3🤮1

6 Aug 2026, 12:28 UTC≈3,650 views21 reactionsread 9 November 2026
Photo

Как агенты оценивают собственный успех Представим ситуацию: инженеру нужно починить баг в сервисе аутентификации с помощью кодового агента. До начала работ агент сообщает, что вероятность успеха — 72%. По ходу работы меняет мнение на 78%, после всех изменений — 92%, а какой-нибудь AI-ревьюер пророчит положительный результат с вероятностью 85%. Однако по итогу патч, сделанный агентом, не работает — то есть прогнозы о…

❤9🔥8❤‍🔥3👍1

31 Jul 2026, 08:42 UTC≈3,330 views24 reactionsread 9 November 2026
Photo

AgentFold — метод управления контекстом для агентов на длинных задачах На длинных сценариях поиска и анализа веб-агенты либо хранят слишком много сырой информации, либо слишком часто её суммаризируют, что забивает контекст. Авторы сегодняшней статьи предлагают метод AgentFold (GitHub), призванный решить эту проблему, сделав память агента динамической и управляемой самим агентом. Сейчас в веб-поиске используются два…

❤‍🔥10👍9❤3🔥2

21 Jul 2026, 09:12 UTC≈3,220 views18 reactionsread 9 November 2026
File

Asynchronous Reasoning: Training-Free Interactive Thinking LLMs Сегодня поговорим о статье, в написании которой принимали участие инженеры Яндекса. Публикация посвящена асинхронному ризонингу, а в её основе лежит метод, описанный в работе Hogwild! Inference: Parallel LLM Generation via Concurrent Attention, поэтому сперва — кратко о ней. Это тоже статья от Yandex Research, а также от HSE и IST Austria. Авторы поста…

🔥13❤3👍2

17 Jul 2026, 09:40 UTC≈4,590 views27 reactionsread 9 November 2026
Photo

Тренды из мира бенчмарков на ICML 2026 [2/2] SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale Ребята из Nebius расширили свой SWE-rebench на новые языки: сфокусировались на масштабируемости и пригодности для сбора RL-лёрна. Две ключевые части пайплайна: сборка окружения под каждый репозиторий и отбор задач для тестов. Окружение собирают собственным интерактивным агентом. Он читает README или конфиги …

❤‍🔥10🔥8❤7👎1🤩1

17 Jul 2026, 09:40 UTC≈2,410 views15 reactionsread 9 November 2026
Photo

Тренды из мира бенчмарков на ICML 2026 [1/2] Работ о бенчмарках на ICML традиционно много. По сравнению с прошлым годом, в 2026 стало заметно больше бенчей для агентов. А ещё начали чаще встречаться работы из академии. Объяснение простое. Корпорации вкладывают много сил во внутренние бенчи: делают сами, покупают их у data-labeling-компаний (например, Surge, Mercor, Handshake AI, Toloka) — и, как следствие, такие бе…

👍7❤5❤‍🔥2🔥1

Showing the 12 most recent of 15 posts we hold for @stuffyNLP. 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.

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 3 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.

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.

Yandex for ML
@yandexforml · 19,136
Telegram ranks this channel #5 of 96 here — alongside 95 others — read 4 October 2026
gonzo-обзоры ML статей
@gonzo_ML · 24,695
Telegram ranks this channel #20 of 93 here — alongside 92 others — read 16 September 2026
Сиолошная
@seeallochnaya · 81,254
Telegram ranks this channel #43 of 97 here — alongside 96 others — read 19 August 2026
Machinelearning
@ai_machinelearning_big_data · 275,659
Telegram ranks this channel #45 of 95 here — alongside 94 others — read 10 August 2026
Data Secrets
@data_secrets · 96,035
Telegram ranks this channel #49 of 98 here — alongside 97 others — read 17 August 2026
Machine learning Interview
@machinelearning_interview · 30,560
Telegram ranks this channel #52 of 98 here — alongside 97 others — read 7 September 2026
Take Friends to Luna Park
@hrlunapark · 21,014
Telegram ranks this channel #61 of 88 here — alongside 87 others — read 28 September 2026
Kali Novskaya
@rybolos_channel · 19,136
Telegram ranks this channel #68 of 93 here — alongside 92 others — read 4 October 2026
Yandex for Developers
@Yandex4Developers · 28,656
Telegram ranks this channel #73 of 94 here — alongside 93 others — read 9 September 2026
LLM под капотом
@llm_under_hood · 30,219
Telegram ranks this channel #73 of 96 here — alongside 95 others — read 9 September 2026
эйай ньюз
@ai_newz · 98,339
Telegram ranks this channel #75 of 94 here — alongside 93 others — read 16 August 2026
Dev & ML Connectable Jobs
@dev_connectablejobs · 29,105
Telegram ranks this channel #81 of 94 here — alongside 93 others — read 10 September 2026
Data Science | Machinelearning [ru]
@devsp · 19,659
Telegram ranks this channel #82 of 91 here — alongside 90 others — read 30 September 2026
Время Валеры
@cryptovalerii · 32,082
Telegram ranks this channel #85 of 94 here — alongside 93 others — read 6 September 2026
Яндекс Образование
@Education_Yandex · 38,032
Telegram ranks this channel #87 of 96 here — alongside 95 others — read 31 August 2026
Data Science Jobs
@datasciencejobs · 22,587
Telegram ranks this channel #92 of 93 here — alongside 92 others — read 21 September 2026
8БИТ. Журнал Яндекс Образования
@journal_yandex_education · 18,835
Telegram ranks this channel #93 of 97 here — alongside 96 others — read 4 October 2026
Data Science Jobs
@datascienceml_jobs · 21,642
Telegram ranks this channel #97 of 97 here — alongside 96 others — read 24 September 2026

This channel appears in 18 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 9 November 2026 — this entry's latest reading, not the date you are reading this.

“Душный NLP” (@stuffyNLP), 6,718 subscribers as measured 9 November 2026. Telegram Register, tgregister.com/channel/stuffyNLP.

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.