Channel
@aimalysheva
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
699subscribers
+2 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1004298907398 |
|---|---|
| Type | Channel |
| Username | @aimalysheva |
| Created | Between 1 May 2026 and 17 July 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 6 August 2026 |
| Measurements held | 3 |
| On Telegram | t.me/aimalysheva |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 6 Aug 2026, 16:37 | 699 | no change |
| 6 Aug 2026, 11:40 | 699 | +2 |
| 6 Aug 2026, 05:05 | 697 | first reading |
20 posts held, back to 17 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 1 pageof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 5 August 2026 |
|---|---|
| Posts held | 20 (17 July 2026 – 5 August 2026) |
| Views total | 31,663 |
| Reactions total | 618 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Aug 2026, 16:37 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.
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.
618 reactions across 20 posts, in 18 distinct kinds. The most used accounts for 33.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 206 | 33.3% | |
| 🔥 | 131 | 21.2% | |
| 😁 | 79 | 12.8% | |
| 👍 | 54 | 8.74% | |
| 💅 | 34 | 5.50% | |
| 🤔 | 22 | 3.56% | |
| ❤🔥 | 18 | 2.91% | |
| 🥰 | 17 | 2.75% | |
| 💯 | 14 | 2.27% | |
| 👏 | 13 | 2.10% | |
| 😱 | 8 | 1.29% | |
| 🤣 | 6 | 0.971% | |
| 😍 | 5 | 0.809% | |
| ⚡ | 4 | 0.647% | |
| 🎉 | 3 | 0.485% | |
| ✍ | 2 | 0.324% | |
| 👀 | 1 | 0.162% | |
| 👌 | 1 | 0.162% |
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 20 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 618reactions 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 17 July 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.
компании уже пару лет как файнтюнят модели под конкретные задачи, и вся эта работа становится ненужной при смене базовой модели: с каждым новым фронтир-релизом приходится либо оставаться на старой базовой модели, либо апгрейдиться и терять всё, что было пост-трейнено раньше поэтому за рисерчем про трансляцию моделей между друг другом сейчас особенно интересно следить: например, vec2vec научились переводить эмбеддинг…
❤15👍7🔥5
за последние годы инференс моделей очень подешевел: в конце 2022 модели уровня GPT-3.5 стоили $20 за миллион токенов, а к концу 2024 уже $0.07 (по данным Stanford's AI Index), то есть за два года разница 280x! так что сейчас поставить пять моделей на один запрос стоит примерно как одна модель совсем недавно, что сильно повышает привлекательность ансамблей для решения сложных задач поэтому я думаю что следующий bott…
🔥12⚡4💯3
весь computing stack раньше был монолитным и потом разделялся на части, как только для этого появлялся интерфейс AI при этом всё ещё на mainframe-стадии, то есть одна гигантская модель мне кажется это все еще не декомпозировалось потому что интерфейс между моделями сейчас это текст, а текст это последнее, что производит модель, то есть summary всего, что она посчитала и поэтому при каждой передаче от одной модели к…
❤16🔥9❤🔥6
витамины и био-добавки на кухнях в офисах были бы куда лучше чем снек-бары, м? 👀
😁13❤5🤔5🔥2🤣1
и если мы думаем про кривые на манифолдах, то следующий вопрос, который сразу возникает это "какая у этих манифолдов топология?" понимание формы (дыры, петли, может ещё какие-то сохраняющиеся структуры) говорит очень много о том, какие вообще кривые на них возможны релевантная статья: Persistent Topological Features in LLMs (arxiv.org/pdf/2410.11042), где авторы используют zigzag persistence из topological data ana…
❤9🔥4❤🔥2👀1😁1
я довольно сильно уверена, что существует какой-то universal language manifold (= поверхность, образованная meaning-векторами), который может даже примерно общий и для людей, и для LLMок но мы не тренируем LLM явно репрезентировать этот manifold, мы скорее тренируем их его аппроксимировать и двигаться по нему, строя на нём кривые и эти кривые, это reasoning в геометрических терминах, типа reasoning trace — это крив…
❤21❤🔥10👏5
AI уже прямо повсюду, конечно
😁19💯4🔥4👌1
многие продукты сегодня рассчитаны на AI pricing, который вероятно не будет таким же низким всегда и у части стартапов вообще нет плана Б, если экономика поменяется вот основное, что может помочь в таких случаях: (1) opensource модели, (2) модели поменьше и подешевле, (3) более умный model routing на практике это значит: • начинать использовать OSS уже сейчас, даже если он похуже • для простых задач использовать м…
👍14🔥3❤1
researcher mode: off
💅34🔥18❤5😍5👍1
Photo, posted without a caption
😁19
сравнивала для себя STAR и PARLA как интервью-фреймворки, и PARLA мне нравится намного больше STAR всегда ощущался как corporate ops чек-лист, потому что "опиши задачу, которую ты выполнил" отлично работает, если у компании стабильные процессы и чётко определённые роли но вот "Learned" и "Applied" в PARLA еще дают проверить, развивается ли человек и переносит ли заработанные инсайты дальше, и это гораздо лучше подх…
❤10🤔4👍3
мы тренируем модели в изоляции, совсем не так, как учатся люди, а потом удивляемся, почему они не генерализируют знания так же хорошо, как мы но люди учатся сравнивая ответы, видя reasoning друг друга прямо посреди процесса, а еще соревнуясь и адаптируясь, и этого всего нет в стандартном training run так что может следующий качественный переход в тренировке моделей будет какая-то AI-версия "социального" обучения, т…
❤20🤔11🔥7😁1
Showing the 12 most recent of 20 posts we hold for @aimalysheva. 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.
Citation-graph rank — 7,580 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
Named by 9 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.
Named by
Channels on the register whose posts name this channel's handle.
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.
“Саша делает стартап” (@aimalysheva), 699 subscribers as measured 6 August 2026. Telegram Register, tgregister.com/channel/aimalysheva.
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.