Telegram RegisterThe public register of Telegram

Channel

DL in NLP

@dlinnlp

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

11,680subscribers

-10 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
DescriptionНовости и обзоры статей на тему обработки естественного языка, нейросетей и всего такого. Связь: @dropout05 (рекламы нет)
Created24 September 2018measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held8
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/dlinnlp

Growth

11,68011,69011,6856 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 subscribers6 August 202612 August 2026
8 measurements spanning 7 days, net -10. 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 11,679–11,692 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 22:3311,680-1
11 Aug 2026, 21:4111,681-3
10 Aug 2026, 18:2511,684+1
9 Aug 2026, 19:5111,683+2
8 Aug 2026, 16:1511,681-4
7 Aug 2026, 12:4111,685-4
6 Aug 2026, 11:2111,689-1
6 Aug 2026, 04:1511,690first 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 16 pagesof 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

Photos
547
Videos
13
Links
1,100

Lifetime counters from Telegram’s own channel header, read 13 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
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

1,444 reactions across 17 posts, in 23 distinct kinds. The most used accounts for 39.1% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥56439.1%
29820.6%
👍28920.0%
😁1037.13%
❤‍🔥745.12%
👏332.29%
🤡231.59%
🥰201.39%
😱151.04%
🥴40.277%
🆒30.208%
🤔30.208%
🤯30.208%
👎20.139%
🙏20.139%
10.069%
🍾10.069%
🎉10.069%
👌10.069%
🙈10.069%
3 further kinds30.208%

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 1,444reactions 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≈13,000 views48 reactionsread 13 August 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≈13,300 views84 reactionsread 13 August 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≈18,100 views102 reactionsread 13 August 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👍2014🫡1🙈1

Signed Vlad Lialin

23 Nov 2024, 23:16 UTC≈14,100 views60 reactionsread 13 August 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 views236 reactionsread 13 August 2026

Но дадут ли нобелевку по литературе за Deep Learning Book

🔥115😁97🤡17🥴42👏1

Signed Vlad Lialin

8 Oct 2024, 16:08 UTC≈20,600 views121 reactionsread 13 August 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≈13,600 views67 reactionsread 13 August 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 13 August 2026

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

Signed Vlad Lialin

25 Sept 2024, 08:09 UTC≈13,200 views32 reactionsread 13 August 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 13 August 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≈9,500 views117 reactionsread 13 August 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 13 August 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

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.

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

“DL in NLP” (@dlinnlp), 11,680 subscribers as measured 12 August 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.