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Channel

LLM Jounal Club

@LLM_JC

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

18subscribers

+2 since we began measuring on 7 August 2026

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

Register entry

Telegram ID-1002260359594
TypeChannel
Username@LLM_JC
Created19 February 2025measured — dated from the channel’s first post
First recorded11 August 2026
Last confirmed live30 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 30 August 2026
On Telegramt.me/LLM_JC

Growth

161917.57 August 2026 — 16 subscribers8 August 2026 — 17 subscribers11 August 2026 — 17 subscribers16 August 2026 — 19 subscribers30 August 2026 — 18 subscribers187 August 202630 August 2026
5 measurements spanning 23 days, net +2. 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 16–19 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
30 Aug 2026, 12:0718-1
16 Aug 2026, 06:2619+2
11 Aug 2026, 20:4617no change
8 Aug 2026, 14:4417+1
7 Aug 2026, 15:3216first reading

Engagement

16 posts held, back to 19 February 2025the 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
122.2%
avg views ÷ 18 subscribers
Avg views / post
22.0
1 post measured
Reaction rate
this channel exposes no reaction counts
Posts in window
1
of 16 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 8 August 2026
Posts held16 (19 February 20258 August 2026)
Views total22
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Aug 2026, 20:46 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

6 reactions across 4 posts, in 3 distinct kinds. The most used accounts for 50.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
350.0%
👌233.3%
🔥116.7%

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 5 of the 16 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 6reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 16 most recent posts we hold, published 19 February 2025 to 8 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

8 Aug 2026, 11:43 UTC22 viewsread 11 August 2026
Photo

آلفاکایو یه نقشه به اسم researcher map داره، توش دانشمندان معروفی که مقالات‌شون رو پوشش میده آورده، دایره‌هه هم هر چقدر بزرگ‌تر باشه احتمالا اون فرد معروف تره. بیشتر اسم‌های آشنا برام تو قسمت deep learning بود مثل Li Fei-Fei و Christopher Manning و Yann LeCun و Ilya Sutskever کوفاندر openAI. @LLM_JC

17 Jul 2026, 16:23 UTC88 views1 reactionsread 11 August 2026
File

توضیح صوتی notebookLM باز هم مثل بالایی @LLM_JC

1

17 Jul 2026, 16:22 UTC80 views1 reactionsread 11 August 2026
File

توضیح صوتی notebookLM در دو نسخه انگلیسی بلند و فارسی (معمولی) @LLM_JC

1

17 Jul 2026, 16:12 UTC77 viewsread 11 August 2026
Photo

یکی دیگه از سایت‌های فوق العاده برای ریسرچ alphaxiv هست. حالا نمیدونم ربطی به arxiv هم داره یا نه. همونطور که تو عکس مشخصه شما میتونید مقاله رو شکل یه بلاگ در بیارید (به هر زبونی ولی قاعدتا انگلیسیش چیز تمیزتریه) یه قسمت خاص رو که نفهمیدید هایلایت کنید و از دستیار هوش مصنوعیش سوال بپرسید. https://www.alphaxiv.org/overview/2306.00890

17 Jul 2026, 16:00 UTC68 viewsread 11 August 2026
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برای اینکه یه لینک به مقاله بدم بنظرم semantic scholar اون مقاله بهترین گزینه است. البته فکر کنم تحریم/فیلتری چیزی باشه ولی یه خیلی اطلاعات زیادی از مقاله رو میتونید اینجا پیدا کنید. از دیگر لینک‌های مقاله گرفته تا رفرنس‌هاش و مقالاتی که به این مقاله سایتیشن زدن و مقالات مرتبط و این که کجا و کی چاپ شده و ... حالا خود سایتش رو هم ببینید شاید جذب شدید:

17 Jul 2026, 15:56 UTC65 viewsread 11 August 2026

این عبارت مخفف Too Long, Didn't Read هست که سایت semantic scholar ارائه داده اش (به معنی خیلی زیاده نخوندم 🗿). یه خلاصه خیلی کوتاه و مفید برای اینکه بدونید قراره تقریبا تو مقاله چی ببینید.

17 Jul 2026, 15:54 UTC66 viewsread 11 August 2026
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📄 Towards Generalist Biomedical AI TLDR: Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights and reaches performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. semantic scholar @LLM_JC

17 Jul 2026, 15:52 UTC58 viewsread 11 August 2026
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📄 LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day TLDR: This paper proposes a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images, and releases instruction-following data and the LLaVA-Med model, which exhibits excellent multimodal conversational capability. semantic scholar @LLM_JC

5 Jul 2026, 20:22 UTC108 viewsread 11 August 2026
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توضیح صوتی notebookLM در دو نسخه انگلیسی و فارسی @LLM_JC

5 Jul 2026, 20:18 UTC106 viewsread 11 August 2026

❕اسم‌ها رو به آیدی (بالاتر گفتم چیه) تغییر دادم، به همین خاطر اینطوری اند

5 Jul 2026, 20:17 UTC99 viewsread 11 August 2026
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📄 A SURVEY OF LLM-BASED MULTI-AGENT SYSTEMS IN MEDICINE authors: Yanna Lin, Shaojie Xu, et al. keywords: Survey, LLM-based Multi-agent Systems, Medical domain paper link @LLM_JC

5 Jul 2026, 20:13 UTC92 views1 reactionsread 11 August 2026

انگیزه ام از ایجاد این کانال این بود که همزمان با سمینارم بیام اون مسیر خودم تا دفاع و بعدش رو اینجا هم بزارم تا انگیزه‌ای بشه برای ادامه. کارهایی که تا اینجا کردم (البته چون همزمان بود با امتحانات و حتی الان که همزمانه با پروژه‌ها) زیاد نبوده. فعلا شیت پایین رو درست کردم و با یه دیپ ریسرچ جمینای چند تا مقاله رو اضافه کردم به شیت. برای دادن نظم به کارهام یه گوگل شیت درست کردم که چهار تا تب داره: - schedule (یه رودمپ

🔥1

Showing the 12 most recent of 16 posts we hold for @LLM_JC. 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.

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

“LLM Jounal Club” (@LLM_JC), 18 subscribers as measured 30 August 2026. Telegram Register, tgregister.com/channel/LLM_JC.

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.