Channel
@quant_prune_distill
On this record: Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
3,449subscribers
+39 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001916228977 |
|---|---|
| Type | Channel |
| Username | @quant_prune_distill |
| Created | Between 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 5 September 2026 |
| Measurements held | 12 |
| Confirmed unchanged | 1 time, most recently 5 September 2026 |
| On Telegram | t.me/quant_prune_distill |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 5 Sept 2026, 13:00 | 3,449 | +11 |
| 1 Sept 2026, 20:05 | 3,438 | +3 |
| 29 Aug 2026, 13:45 | 3,435 | +3 |
| 26 Aug 2026, 04:33 | 3,432 | +10 |
| 23 Aug 2026, 05:03 | 3,422 | -3 |
| 19 Aug 2026, 12:15 | 3,425 | -12 |
| 16 Aug 2026, 14:55 | 3,437 | +9 |
| 12 Aug 2026, 13:45 | 3,428 | +5 |
| 9 Aug 2026, 21:22 | 3,423 | +9 |
| 6 Aug 2026, 21:49 | 3,414 | +4 |
| 6 Aug 2026, 04:45 | 3,410 | no change |
| 6 Aug 2026, 02:19 | 3,410 | first reading |
22 posts held, back to 29 June 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 3 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 22 posts for this entry, the most recent from 7 August 2026. An engagement rate over an empty window would be a number about nothing.
354 reactions across 20 posts, in 14 distinct kinds. The most used accounts for 30.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 😁 | 107 | 30.2% | |
| ❤ | 80 | 22.6% | |
| 🔥 | 68 | 19.2% | |
| 👍 | 33 | 9.32% | |
| 😭 | 16 | 4.52% | |
| 👏 | 15 | 4.24% | |
| 🏆 | 12 | 3.39% | |
| 🤣 | 7 | 1.98% | |
| 👎 | 5 | 1.41% | |
| 💯 | 5 | 1.41% | |
| 😱 | 2 | 0.565% | |
| 🥰 | 2 | 0.565% | |
| ❤🔥 | 1 | 0.282% | |
| 🥴 | 1 | 0.282% |
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 22 of the 22 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 423 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 29 June 2026 to 7 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.
99 usage limit resets in Codex, 99 usage limit resets Take one down and pass it around, 98 usage limit resets in Codex 98 usage limit resets in Codex, 98 usage limit resets Take one down and pass it around, 97 usage limit resets in Codex ... No usage limit resets in Codex, no more usage limit resets Go to chatgpt.com and buy a new subscription, 99 usage limit resets in Codex
👏12
Лайфак по использованию агентов. Если не запускать GPT Sol и Claude Opus на любой чих, то подписка расходуется не так быстро.
😁48👎4
Когда месяц еще только начался, а ты израсходовал на вайбкодинг почти всю месячную квоту. https://youtu.be/W7rnkwrgEm0?si=Ug5Ah5GYNy_Eqh5d
😁21❤1🔥1
🧪 Метод и эксперименты Основными задачи при разработке кернела были: • Уменьшить обьем CPU работы и CPU-GPU синхронизации • Максимально эффективно перекрывать вычисления и коммуникации Рассматриваются два типа dispatching Push-based dispatch (традиционный подход) 1. Router для каждого токена определяет его экспертов. 2. Для каждого токена выполняется: Отправить этот токен эксперту №7. То есть именно токен "толк…
❤14
Mixture-of-Kittens: our open-source MoE megakernel for NVL72s 📄 Блогпост 💻 Код Ребята из курсора реализовали MXFP8 мегакернел под названием Mixture-of-Kittens, где пофьюжены все операции для слоя смеси экспертов, специализированный под GB300 NVL72s. В отличие от MegaMoE от команды Дипсика 🐋, этот кернел не только под инференс, а еще и под обучение.
🏆12👍5🥰2
📚 Презентация про квантизацию с моего выступления на нашей Hardware Efficiency Reading Group, где мы обсуждаем статьи, идеи и практические аспекты эффективного глубокого обучения. 🎥 Записи и 📋 программу прошлых семинаров можно найти здесь. 🗓️ Семинар проходит (почти) каждый понедельник с 13:00 до 14:00. 📢 Группа с анонсами и материалами — тут. Будем рады всем, кто интересуется эффективным глубоким обучением, GPU,…
👍17❤11🔥5
🚀 Лаба Song Han (одного из апостолов EfficientDL) из MIT выпустила репозиторий со скиллом для агентов, предназначенным для оптимизации кернелов под Hopper и Blackwell. 📚 Репозиторий содержит набор скриптов, которые позволяют агенту обращаться к различным базам знаний, блогам, PR'ам и другим артефактам, посвященным тензорным ядрам и архитектурным особенностям Hopper и Blackwell. 🛠️ В частности, в него заложены знани…
🔥11❤4👍2
step: 11 loss: 9.8672 grad_norm: 2.8441 tps: 5,471 time/step: 2.99s tflops: 198,319.19 mfu: 63563.84% memory: 243.85GiB MFU > 60000% Такое даже Уроборосу не снилось)
😁17🤣7🔥5
Серёжа сьел токены сына Зачем? Не объяснив причину Серёжа запускает клод, рой агентов И сьедает квоту его Зачем так ждать чего-то? Так сильно хотеть чего-то? Так сильно обливать сердце кровью? И в снег, и в метель, и в грозы Писать Деду Морозу Чтоб выслал новые токены А не какой-нибудь сраный пенал…
😭16👏3👎1🥴1
Бывает и такое, что Reviewer #2 ставит тебе Accept. Но ревью настолько короткое и несодержательное, что Area Chair не примет во внимание...
😁14💯5
Интересная по описанию либа Humming от InclusionAI. Позиционируется как легковесный, высокопроизводительный фреймворк с JIT-компилированными GEMM-операциями (под NVIDIA GPU). ⚙️ Он предлагает широкий ассортимент кернелов под разные конфигурации квантованных весов и активаций: • 🧮 Веса можно квантовать почти в любую целочисленную битность от 1 до 8, а также в разные варианты FP. • ⚡ Активации можно квантовать в FP1…
❤10
🧪 Метод и эксперименты В данной работе фокусируются на 2-битном weight-only-сжатии ризонящих моделей. В аппендиксе есть эксперименты с FP4, со сжатием активаций и KV-кэшей. Квантизуют модели Qwen3-8B / Qwen3-32B через GPTQ. Оказывается, что квантизация сильно меняет поведение трейсов ризонинга: 🔄 Число циклов резко возрастает, особенно для меньшей модели. 📏 Многие трейсы не вписываются в заданный лимит токенов. ⚠️…
❤7
Showing the 12 most recent of 22 posts we hold for @quant_prune_distill. 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.
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 2 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.
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.
This channel appears in 7 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 5 September 2026 — this entry's latest reading, not the date you are reading this.
“КПД” (@quant_prune_distill), 3,449 subscribers as measured 5 September 2026. Telegram Register, tgregister.com/channel/quant_prune_distill.
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.