Channel
@mlprior
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
666subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001135238826 |
|---|---|
| Type | Channel |
| Username | @mlprior |
| Description | Author: Vladislav Ishimtsev @ishvlad |
| Created | 15 August 2017 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/mlprior |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 6 Aug 2026, 16:40 | 666 | no change |
| 6 Aug 2026, 04:20 | 666 | first reading |
20 posts held, back to 12 July 2019 — 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 pageof 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 20 posts for this entry, the most recent from 11 June 2025. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 6 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
32 reactions across 5 posts, in 3 distinct kinds. The most used accounts for 71.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 23 | 71.9% | |
| ❤ | 6 | 18.8% | |
| 👍 | 3 | 9.38% |
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 32reactions 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 12 July 2019 to 11 June 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.
Продолжение предыдущего поста про систему трекинга точек TAPNext: Tracking Any Point (TAP) as Next Token Prediction. В прошлой части мы разобрали архитектуру сети, в этой части сфокусируемся на лоссах и данных для обучения. Обзор: https://ishvlad.notion.site/TAPNext-Tracking-Any-Point-TAP-as-Next-Token-Prediction-Training-Losses-209f3c393a9280e291ebe58b771c1ef2
🔥4
Signed Vladislav Ishimtsev
Video, posted without a caption
🔥4
Signed Vladislav Ishimtsev
We are so back! Итак, следующая статья будет про трекинг точек на видео. На вход — видео с чем угодно, на выходе — радужные траектории любых точек (как на видео ниже). Совместная коллаба двух групп из DeepMind: одна работает над задачей трекинга точек, а другая — над видео трансформерами: TAPNext: Tracking Any Point (TAP) as Next Token Prediction Research group: Google DeepMind https://tap-next.github.io/ TL;DR: У…
❤4🔥2
Signed Vladislav Ishimtsev
Стенфордский курс по внутреннему устройству LLM CS336, Language Modeling from Scratch, показывает, как сделать полноценную LLM с нуля: от сбора и очистки датасета до тренировки, профайлинга и развёртывания модели. Все конспекты, ноутбуки и код сразу публикуют в открытой репе, так что можно повторять эксперименты дома хоть на одной-двух карточках или в колабе. Курс сделан с большим упором на практику — в качестве пя…
🔥8
Signed Vladislav Ishimtsev
Video, posted without a caption
Я долго думал, какую статью взять для первого поста, но кажется это не так уж и важно. Главное — начать писать. Вот я и пишу, а предметом сегодняшнего топика будет история, как ресерч группа из Тайланда пыталась запечь view dependency в MPI в период, когда все угорали по NERF’ам. Ребята стабильно залетают на A* конференции, часто получают oral’ы, но после 2021 года стали больше ориентироваться на диффузионки. Вообще…
🔥5👍3❤2
Искусственный интеллект в борьбе с короновирусом CoRover, стартап в области искусственного интеллекта, создал “видео-бота”, сотрудничая с врачом из Fortis Healthcare. На этой платформе настоящий врач, будет отвечать на вопросы людей о Covid-19. Прямо сейчас вы можете задать вопрос доктору о вашем самочувствии, лечении и других беспокоящих вопросах про COVID–19 на английском языке. https://corover.ai/ C19check.com …
Signed Lubov Ishimtseva
Как AI делает Теслу одним из гигантов автомобильной промышленности - Уже как 6 лет Тесла собирает данные для устранения проблем в автомобилях. Действие во время автопилота: 360 градусов видимости вокруг автомобиля, AI помогает следовать дорожным знакам, правилам, находить пустые места для парковки и парковаться автоматически без водителя -С помощью AI Tesla анализирует форумы своих покупателей, чтобы улучшать свой …
Signed Lubov Ishimtseva
Google Research Football Environment, a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. Details: http://mlprior.com/articles/details/169519
How Python became the dominant language thanks to ML.
Integration between @mlprior and paperswithcode.com is started! For now there are 20893 links between articles and GitHubs. Check this out on the example http://mlprior.com/articles/details/22990
A cyber war against Chinese artificial intelligence. Here Hong Kong protestors are using lasers to avoid facial recognition cameras.
Showing the 12 most recent of 20 posts we hold for @mlprior. 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.
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 6 August 2026 — this entry's latest reading, not the date you are reading this.
“ML p(r)ior” (@mlprior), 666 subscribers as measured 6 August 2026. Telegram Register, tgregister.com/channel/mlprior.
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.