Поздравляю Ваню с принятием статьи на RecSys! 🔥 А у него в канале уже есть разбор Barlow Twins - подписываемся и ставим лайки, в ожидании новых разборов: https://t.me/razvor_channel/6
🔥6

Channel
@Vladik_repostnul
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
152subscribers
+2 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001094811268 |
|---|---|
| Type | Channel |
| Username | @Vladik_repostnul |
| Created | 8 January 2017 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 8 August 2026 |
| Last confirmed live | 16 September 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 16 September 2026 |
| On Telegram | t.me/Vladik_repostnul |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Sept 2026, 23:19 | 152 | +2 |
| 9 Sept 2026, 09:17 | 150 | +1 |
| 17 Aug 2026, 09:58 | 149 | -1 |
| 8 Aug 2026, 20:00 | 150 | no change |
| 7 Aug 2026, 17:45 | 150 | first reading |
19 posts held, back to 28 March 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 1 page 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 19 posts for this entry, the most recent from 5 August 2026. An engagement rate over an empty window would be a number about nothing.
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.
109 reactions across 17 posts, in 6 distinct kinds. The most used accounts for 34.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 😁 | 38 | 34.9% | |
| 💅 | 30 | 27.5% | |
| ❤ | 19 | 17.4% | |
| 🔥 | 10 | 9.17% | |
| 👍 | 9 | 8.26% | |
| 👀 | 3 | 2.75% |
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 19 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 109 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 19 most recent posts we hold, published 28 March 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.
Поздравляю Ваню с принятием статьи на RecSys! 🔥 А у него в канале уже есть разбор Barlow Twins - подписываемся и ставим лайки, в ожидании новых разборов: https://t.me/razvor_channel/6
🔥6
RecSys опубликовали список принятых статей! Посмотреть можно тут: recsys.acm.org/recsys26/contributions/ Уже успел пробежаться по авторам и увидел несколько знакомых имён, в том числе ребят из нашего чата! 🎉 Примите искренние поздравления, отличный результат! Жаль, конечно, что в этом году не указали affiliations — отбирать статьи по топовым компаниям и лабам теперь чуть сложнее. 👇 Делитесь в комментариях: что уж…
Да, было
😁15
Поговорил с Jiangxia Cao из Kuaishou OneRec Team на SIGIR’26 Признаю, я давний фанат Kuaishou. Это люди, которые внедряют много всего в прод и с невероятной скоростью. При этом еще и успевают писать про это статьи. На конференции SIGIR’26 мне удалось пообщаться с Jiangxia Cao, одним из ключевых разработчиков в OneRec Team из Kuaishou. У него более 1800 цитирований, он является соавтором таких работ, как OneReason, …
❤7
Autoresearch на autoresearch AIDE² за 8 дней без участия человека нашёл 7 последовательных улучшений собственного research agent и обогнал версию, которую тюнили два года AIDE2 has two autoresearch loops: - An inner loop, just like a normal autoresearch agent, optimizing code against an eval. - An outer loop, optimizing the inner-loop agent's harness code. Улучшения перенеслись на новые задачи, а reward hacking сн…
💅2👍1
Measuring the Impact of Personalized Recommendations Netflix предлагает раскладывать эффект рекомендаций на три части: • Exposure - тайтл посмотрели, потому что его вообще показали. • Selection - его показали тем, кто и без рекомендации был склонен посмотреть. • Targeting - показ изменил решение именно этого пользователя. Наблюдаемый просмотр смешивает все три эффекта. Поэтому высокий CTR рекомендации ещё ничего н…
💅6👍3
NVIDIA Kaggle Plugin gives agents end-to-end Kaggle competition workflows through a single skill 👉 Top Solution Writeups 👉 Competition Overview And Dataset 👉 Public Kernel Research 👉 Kernel Setup Quick Install codex plugin marketplace add https://github.com/NVIDIA/ https://github.com/NVIDIA/nvidia-kaggle
💅1
Организаторы recsys challenge после окончания соревнования такие типа: а давайте дадим ещё сабмитов читерам Думаю что в топ проберется обфусцированный лик, проверить все в сжатые сроки будет не тривиально
😁5
Мой мем зафорсили и превратили в сайт huggingbay.xyz
👍5
Photo, posted without a caption
❤8
🛠️ NextLat trains the transformer to predict its own next latent state given the current latent state and next token. Key benefits: 1) 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: NextLat encourages transformers to compress history into compact belief states. 2) 𝗕𝗲𝘁𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: predicting in latent space provides denser supervision than predicting one-hot tokens. 3) 𝗙𝗮𝘀𝘁𝗲𝗿 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: via recursive multi-step lookahead. ✍️ Blog…
💅3
Toward Generalist Autonomous Research via Hypothesis-Tree Refinement Arbor grows a hypothesis tree: every idea becomes a branch — pruned if it fails, harvested if it works — and insights propagate back so later ideas start smarter. Paper page: paperswithcode.co/paper/2606.119 CLI, code & docs: github.com/RUC-NLPIR/Arbor Project page: RUC-NLPIR.github.io/Arbor/
Showing the 12 most recent of 19 posts we hold for @Vladik_repostnul. 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
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.
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 16 September 2026 — this entry's latest reading, not the date you are reading this.
“candidate generation” (@Vladik_repostnul), 152 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/Vladik_repostnul.
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.