Telegram RegisterThe public register of Telegram

Channel

CoreInfra

@coreinfra

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

466subscribers

+3 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1002357083807
TypeChannel
Username@coreinfra
CreatedBetween 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live6 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 6 August 2026
On Telegramt.me/coreinfra

Growth

463466464.56 Aug 2026, 02:20 — 463 subscribers6 Aug 2026, 19:03 — 463 subscribers6 Aug 2026, 19:10 — 466 subscribers6 Aug 2026, 02:206 Aug 2026, 19:10
3 measurements taken within a single day, net +3. 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 463–466 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
6 Aug 2026, 19:10466+3
6 Aug 2026, 19:03463no change
6 Aug 2026, 02:20463first reading

Engagement

20 posts held, back to 9 July 2026the 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 · 30 days
101.8%
avg views ÷ 466 subscribers
Avg views / post
474
16 posts measured
Reaction rate
2.59%
reactions ÷ views · ER floor
Posts in window
16
of 20 held

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. It is computed over the 15 of 16 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 6 August 2026
Posts held20 (9 July 20266 August 2026)
Views total7,591
Reactions total183
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Aug 2026, 19:03 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.

Reaction mix

225 reactions across 18 posts, in 18 distinct kinds. The most used accounts for 32.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥7332.4%
👍3917.3%
3816.9%
😁2812.4%
🎉125.33%
🤯73.11%
👏52.22%
🤩52.22%
👀41.78%
🎅31.33%
💯20.889%
😭20.889%
🫡20.889%
🌚10.444%
😈10.444%
🤔10.444%
🤨10.444%
🤷‍♂10.444%

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 18 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 225reactions 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 9 July 2026 to 6 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.

Recent posts

6 Aug 2026, 15:50 UTC133 views14 reactionsread 6 August 2026
Photo

Мне спокойнее использовать Codex, потому что там есть нормальный sandbox в отличие от тех же OpenCode и Pi. К чему я это. Мы поддержали Responses API для deepseek-v4-flash. И теперь вы можете использовать его прямо из Codex. Для простоты настройки мы сделали скрипт который одной командой настроит codex для работы с CoreInfra AI Hub uvx --from git+https://github.com/CoreInfraAI/setup-codex setup-codex --api-key <yo

🤩54🔥4👍1

5 Aug 2026, 09:22 UTC245 views11 reactionsread 6 August 2026
Photo

Две забавные мысли: Программа : циклы процессора ~= агент : токены. LLM-ки похожи на фаззеры тем, что и там, и там – мы понимаем механизм, но не понимаем, почему это работает, и в обоих случаях в сердце процесса compute go brrrr. LLM-ки – это фаззеры мыслительного процесса. Да-да, за таблетками уже иду 🙂 – @pgregory

😁7👍4

4 Aug 2026, 15:53 UTC289 views11 reactionsread 6 August 2026

Объяснить я и сам могу. Мне очень нравится этот анекдот. Он буквально про агентов, которые готовы объяснить, что угодно, но не только про них. Хороший маркетолог расскажет, как строится рынок, разработает стратегию продвижения и объяснит, почему одно сработало, а другое нет. Но он не сможет что-то построить, строить и создавать могут только авторы продукта. Никто за основателя не придумает бренд, не сможет его напол

😁4🔥3🎅1👍1💯1🤨1

3 Aug 2026, 15:09 UTC353 views34 reactionsread 6 August 2026
Photo

Сегодня – лучшее время, чтобы вернуться к своим пет проектам. Есть у меня проект: "улицы Санкт-Петербурга", где я хочу обойти все улицы этого прекрасного города. Но как понять, что я действительно это сделал? Очевидно, нужно записывать GPS треки и сделать софтину, которая подскажет что осталось. В до-LLM времена, я как начинал думать, что нужно ресечить OpenStreetMap API, считать геометрию в WGS84, учиться маппить т

🔥21👍53👏3😈1😭1

30 Jul 2026, 08:36 UTC649 views9 reactionsread 6 August 2026
Photo

Когда программируешь с агентами обычно нужно читать много кода и ревьюить коммиты. (ну если только ты не школьник-вайбкодер, хи-хи). Интересным вопросом является вопрос структурирования больших PR, об этом я думаю как-нибудь расскажет @pgregory, а @razmser мне подсказал классный тул, который рекомендую к использованию. revdiff супер прост в использовании, согласно документации буквально: Usage: revdiff [base] [ag

👍6🔥3

29 Jul 2026, 10:01 UTC580 views8 reactionsread 6 August 2026
Photo

Самое загадочное в больших языковых моделях – это то, что они все работают, несмотря на огромную разницу во всем. Dense, MoE, LatentMoE, все варианты attention (в т.ч. например Kimi Linear который вообще не совсем attention), все варианты функций активации, все варианты обучения – классический pretrain, JEPA-like цели, RL, все модальности – текст, сырое (!) аудио и картинки, видео, квантизация и дистилляция – все ра

4👍3🤷‍♂1

27 Jul 2026, 17:11 UTC520 views10 reactionsread 6 August 2026
Forwarded from @profunctor_ioPhoto

Photo, posted without a caption

😁6👍3🔥1

25 Jul 2026, 15:20 UTC602 views6 reactionsread 6 August 2026

Свежее выступление моего любимого поляка Фила Пизло про его проект, скромно названный Fil-C – самое интересное, что случилось в системном программировании за последний год. https://www.youtube.com/watch?v=5F-2Y1LPRek Ну и легкий троллинг Раста это всегда хорошо :-) – @pgregory

😁4👍2

24 Jul 2026, 15:55 UTC556 views15 reactionsread 6 August 2026

Открытые китайские модели всё ближе подбираются к фронтиру. Сначала появился DeepSeek R1, удививший всех своей доступностью. Затем вышел GLM-5.2 — он лишь на полшага отстаёт от фронтирных моделей и сейчас особенно популярен для ревью кода. А недавно Moonshot AI представила Kimi K3 — уже полноценную фронтирную модель. В веб-разработке она показывает одни из лучших результатов. С сегодняшнего дня вы можете попробова

🔥12👍2👏1

23 Jul 2026, 22:58 UTC678 views9 reactionsread 6 August 2026
Photo

И опять контрэкземпл, но не там где ждали и не от AI. На этот раз с инвариантом свойства логарифма для Lua и PHP (кто бы сомневался). Утверждается, что если a>b>1, то логарифмы от x, где x>1 по основанию a и b будут строго меньше соответвующе. Но PHP и Lua получают обратный результат. Очень забавная находка и спойлер такой, что дело не в арифметике с плавающей точкой, это было бы достаточно скучно, дело в том, что

😁4🔥21👍1😭1

23 Jul 2026, 15:15 UTC478 views11 reactionsread 6 August 2026
Photo

Работаю над CoreInfra AT1 и переодически сталкиваюсь с тем, что OpenAI считает это cybersecurity professional tool и отказывается работать. С одной стороны это приятно, поскольку действительно мы считаем AT1 лучшим и уникальным продуктом в сфере полносистемного семантического фаззинга и поиска ошибок, с другой доставляет некоторые неудобства. Сейчас спасает, что под боком в хабе есть китайские модельки, которые не

🔥8🫡2👍1

Showing the 12 most recent of 20 posts we hold for @coreinfra. 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

Citation-graph rank — 37,233 of 1,160,990entries 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.

Forward network

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.

Mentions

Named by 5 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.

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.

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

“CoreInfra” (@coreinfra), 466 subscribers as measured 6 August 2026. Telegram Register, tgregister.com/channel/coreinfra.

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.