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Channel

DL in NLP

@dlinnlp

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Referenced elsewhere · Cite this entry

11,620subscribers

-70 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1001188069510
TypeChannel
Username@dlinnlp
Created24 September 2018measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live6 September 2026
Measurements held27
Confirmed unchanged1 time, most recently 6 September 2026
On Telegramt.me/dlinnlp

Growth

11,62011,69011,6556 August 2026 — 11,690 subscribers6 August 2026 — 11,689 subscribers7 August 2026 — 11,685 subscribers8 August 2026 — 11,681 subscribers9 August 2026 — 11,683 subscribers10 August 2026 — 11,684 subscribers11 August 2026 — 11,681 subscribers12 August 2026 — 11,680 subscribers14 August 2026 — 11,679 subscribers15 August 2026 — 11,676 subscribers17 August 2026 — 11,668 subscribers19 August 2026 — 11,665 subscribers20 August 2026 — 11,657 subscribers21 August 2026 — 11,658 subscribers22 August 2026 — 11,652 subscribers24 August 2026 — 11,650 subscribers25 August 2026 — 11,651 subscribers26 August 2026 — 11,650 subscribers27 August 2026 — 11,649 subscribers28 August 2026 — 11,645 subscribers29 August 2026 — 11,641 subscribers30 August 2026 — 11,638 subscribers31 August 2026 — 11,634 subscribers1 September 2026 — 11,633 subscribers2 September 2026 — 11,632 subscribers4 September 2026 — 11,627 subscribers6 September 2026 — 11,620 subscribers6 August 20266 September 2026
27 measurements spanning 32 days, net -70. 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 11,610–11,701 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 27
Measured (UTC)SubscribersChange
6 Sept 2026, 17:0111,620-7
4 Sept 2026, 09:1911,627-5
2 Sept 2026, 19:0811,632-1
1 Sept 2026, 15:3611,633-1
31 Aug 2026, 13:1311,634-4
30 Aug 2026, 10:1511,638-3
29 Aug 2026, 07:1711,641-4
28 Aug 2026, 06:3311,645-4
27 Aug 2026, 10:0311,649-1
26 Aug 2026, 13:0611,650-1
25 Aug 2026, 09:5611,651+1
24 Aug 2026, 07:0711,650-2
22 Aug 2026, 17:3711,652-6
21 Aug 2026, 07:1211,658+1
20 Aug 2026, 08:2411,657-8
19 Aug 2026, 10:4911,665-3
17 Aug 2026, 11:2211,668-8
15 Aug 2026, 19:2811,676-3
14 Aug 2026, 07:5811,679-1
12 Aug 2026, 22:3311,680first reading

Engagement

18 posts held, back to 18 July 2024the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 46 pages of Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 18 posts for this entry, the most recent from 21 February 2025. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime
2m 36s
Average length
52s

Measured directly from 3 videos 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

1,446 reactions across 17 posts, in 23 distinct kinds. The most used accounts for 39.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥56439.0%
29920.7%
👍28920.0%
😁1037.12%
❤‍🔥745.12%
👏332.28%
🤡231.59%
🥰201.38%
😱151.04%
🥴50.346%
🆒30.207%
🤔30.207%
🤯30.207%
👎20.138%
🙏20.138%
10.069%
🍾10.069%
🎉10.069%
👌10.069%
🙈10.069%
3 further kinds30.207%

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 17 of the 18 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 1,446 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 18 most recent posts we hold, published 18 July 2024 to 21 February 2025, 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

21 Feb 2025, 19:52 UTC≈13,200 views48 reactionsread 3 September 2026

https://www.youtube.com/watch?v=uVcBa6NXAbk https://www.1x.tech/discover/introducing-neo-gamma

👏3013👍5

Signed Vlad Lialin

27 Jan 2025, 21:38 UTC≈13,400 views84 reactionsread 3 September 2026
Forwarded from @gonzo_ML

В продолжение темы, Jay Alammar, у которого были прекрасные визуальные объяснения про работу трансформера, в сто раз лучшие оригинальной статьи, выпустил только что иллюстрированный DeepSeek-R1 https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r1

84

Signed Vlad Lialin

23 Jan 2025, 21:40 UTC≈18,200 views103 reactionsread 3 September 2026
Photo

Всем приветики. Давно не было постов, тк становится всё сложнее вести канал. Не буду обещать что исправлюсь, но буду постить когда есть что-то о чём другие каналы не говорят достаточно. И сегодня будут не новости (о ChatGPT Operator можете прочитать где угодно), а открытая позиция на PhD студента в моей старой лабе в UMass Lowell - Text Machine Lab. Это NLPшная позиция с довольно широким спектром того чем можно зан

🔥66👍2015🫡1🙈1

Signed Vlad Lialin

23 Nov 2024, 23:16 UTC≈14,200 views60 reactionsread 3 September 2026

Programming Massively Parallel Processors https://a.co/d/6QEiuCq Наткнулся на книгу которая кажется весьма известна в мире GPU-программирования. Она довольно детально погружается в Nvidia GPU и CUDA. В четвертом издании (2022 года) ещё и добавили современные архитектуры: Ampere (A100) и Hopper (H100). Это важно тк архитектуры довольно сильно изменились с 2016 года. Очень надеюсь просмотреть хотя бы по-диагонали и н

👍59🙏1

Signed Vlad Lialin

10 Oct 2024, 18:51 UTC≈16,900 views237 reactionsread 3 September 2026

Но дадут ли нобелевку по литературе за Deep Learning Book

🔥115😁97🤡17🥴52👏1

Signed Vlad Lialin

8 Oct 2024, 16:08 UTC≈20,600 views121 reactionsread 3 September 2026

Почему не стоит верить nvidia-smi “GPU utilization” arthurchiao.github.io/blog/understanding-gpu-performance/ Nvidia использует очень особый способ определения утилизации GPU. 100% означают не что девайс загружен на 100%, а что хотя бы одно ядро было использовано хотя бы чуть-чуть 100% времени за последние N (мили)секунд Очень яркий пример это примитивы синхронизации: когда вы вызываете torch.barrier GPU Utilizatio

🔥83👍22😱1131👏1

Signed Vlad Lialin

2 Oct 2024, 16:46 UTC≈13,700 views67 reactionsread 3 September 2026

Soumith Chintala (создатель pytorch) выдаёт базу о том как тренироваться на 10К GPU x.com/soumithchintala/status/1841498799652708712 Оч короткий TL;DR (всем рекомендую прочитать оригинал, он не длинный) 1. Maximize batch size and GPU utilization: 3D parallelism + gradient checkpointing 1. Overlap communication, e.g. while N-1th layer is computing backward, all GPUs with an Nth layer can all-reduce 1. Optimize for y

🔥3720👍9👏1

Signed Vlad Lialin

25 Sept 2024, 08:09 UTC≈11,800 viewsread 3 September 2026

https://x.com/hughbzhang/status/1838288923656941860?s=12&t=QgBLS4SmhE8cqdYBmhrqJA

Signed Vlad Lialin

25 Sept 2024, 08:09 UTC≈13,300 views32 reactionsread 3 September 2026

O1 mini inference scaling experiments Прикольное саммари экспериментов одного чела. Коротко: если убедить модель дольше думать (что пока что непросто) pass@1 реально будет расти лог-линейно. При этом это скорее всего не majority voting или self consistency тк эти методы упираются в потолок

🔥282🤔2

Signed Vlad Lialin

17 Sept 2024, 03:42 UTC≈13,900 views26 reactionsread 3 September 2026

OpenDuck - очень классный проект по опенсорсной (хард+софт) реимплементации диснеевского робота https://github.com/apirrone/Open_Duck_Mini Очень мило. Буду следить за ними. А вот тут они уже умеют стоять: https://x.com/antoinepirrone/status/1835679313506562502

🥰176👍3

Signed Vlad Lialin

14 Sept 2024, 18:21 UTC≈9,510 views117 reactionsread 3 September 2026
Forwarded from @ai_newzVideo

Наткнулся в Твиттере на шикарную визуализацию LLM. Как выяснилось, ей уже целый год, но для новичков это все ещё полезная штука. Кроме красивой 3D-модельки, здесь еще подробный гайд по работе каждого элемента, как говорит автор, до каждого "сложить и умножить". По архитектурам там есть GPT-2, nanoGPT, GPT-2 XL, ну и GPT-3. Ссылочка на визуализацию @ai_newz

❤‍🔥74🔥23👍143🆒3

Signed Vlad Lialin

12 Sept 2024, 17:29 UTC≈10,200 views91 reactionsread 3 September 2026
Photo

🍓 openai.com/index/learning-to-reason-with-llms 1. GPT-o1 это затюненая с помощью RL модель на улучшение reasoning (деталей как это сделано, конечно же нет) 1. Scaling c train-time compute (как долго делать RL) и test-time compute (как долго генерировать ответ) -- на текущих графиках никакого намёка на то чтобы модель выходила на плато 🔥 1. По сравнению с 4o на codeforces o1 получает 89 перцентиль вместо 11 1. В Ph

🔥72👍126🙏1

Signed Vlad Lialin

Showing the 12 most recent of 18 posts we hold for @dlinnlp. 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

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 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.

Names

Channels on the register whose handles appear in this channel's posts.

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.

LLM под капотом
@llm_under_hood · 29,013
Telegram ranks this channel #36 of 96 here — alongside 95 others — read 9 September 2026
эйай ньюз
@ai_newz · 96,728
Telegram ranks this channel #41 of 94 here — alongside 93 others — read 16 August 2026
Сиолошная
@seeallochnaya · 78,788
Telegram ranks this channel #47 of 97 here — alongside 96 others — read 19 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.

Referenced elsewhere

This handle named by sources this register does not control and did not measure — each shown exactly as found, attributed by name, dated to when it was read.

Hacker News

This handle was named once in a Hacker News comment or story, via the public Algolia search API. HN comment and story text has no confirmed reuse licence, so nothing quoted from either is reproduced here — only that a mention exists, when, and by whom, with a link to read it at the source.

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 6 September 2026 — this entry's latest reading, not the date you are reading this.

“DL in NLP” (@dlinnlp), 11,620 subscribers as measured 6 September 2026. Telegram Register, tgregister.com/channel/dlinnlp.

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.