Telegram RegisterThe public register of Telegram

Channel

AI Engineers

@LLMEngineers

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

2,411subscribers

+43 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1002600390047
TypeChannel
Username@LLMEngineers
CreatedBetween 1 March 2025 and 31 July 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live12 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/LLMEngineers

Growth

2,3682,4112,389.57 August 2026 — 2,368 subscribers7 August 2026 — 2,372 subscribers10 August 2026 — 2,384 subscribers12 August 2026 — 2,411 subscribers7 August 202612 August 2026
4 measurements spanning 6 days, net +43. 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 2,362–2,417 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 22:232,411+27
10 Aug 2026, 05:582,384+12
7 Aug 2026, 16:282,372+4
7 Aug 2026, 06:462,368first reading

Engagement

18 posts held, back to 22 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 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
43.3%
avg views ÷ 2,411 subscribers
Avg views / post
1,040
18 posts measured
Reaction rate
0.869%
reactions ÷ views · ER floor
Posts in window
18
of 18 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 17 of 18 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 7 August 2026
Posts held18 (22 July 20267 August 2026)
Views total18,771
Reactions total151
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 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.

What this channel posts

Video runtime
8m 32s
Average length
2m 08s

Measured directly from 4 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no mark.

Reaction mix

151 reactions across 17 posts, in 6 distinct kinds. The most used accounts for 31.1% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
4731.1%
🔥4429.1%
👍4328.5%
👌149.27%
💯21.32%
👎10.662%

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 17 of the 18 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 151reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 18 most recent posts we hold, published 22 July 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.

Recent posts

7 Aug 2026, 06:47 UTC515 views8 reactionsread 7 August 2026
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این بنچمارک قشنگ دست مدل‌هایی که موقع بلد نبودن، با اعتمادبه‌نفس دروغ می‌گن رو رو می‌کنه... امتیاز منفی توی این چارت یعنی مدل بیشتر از اینکه حرف درست بزنه، توهم زده و اطلاعات غلط داده. خانواده Claude 5 فعلاً قابل‌اعتمادترین خروجی‌ها رو دارن. اون ته چارت هم وضعیت DeepSeek اصلاً جالب نیست... عملاً داره بیشتر از اطلاعات درست، چرت‌وپرت تحویل می‌ده. https://arxiv.org/abs/2511.13029 🛠 Join @LLMEngineers Community

👍42🔥2

6 Aug 2026, 22:26 UTC551 views4 reactionsread 7 August 2026
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Open Source AI is dominated by China!

💯2👍1👎1

6 Aug 2026, 19:23 UTC610 views7 reactionsread 7 August 2026

امروز داشتم جزئیات Prime Agent رو نگاه می‌کردم؛ سیستم جدید Prime Intellect که ادعا می‌کنه یه ساختار «خود‌-اصلاح‌گر» برای اجنت‌های کدنویسی ساخته. حرف اصلیشون اینه که هارنس‌ها برای مدل‌های قدیمی طراحی شدن و نمی‌ذارن مدل‌های پیشرفته‌ی امروزی از تمام توانشون استفاده کنن. توی این سیستم، دو تا مفهوم اصلی معرفی شده. اولی RLM یا همون مدل زبانی بازگشتیه. اینجا کانتکست رو به چشم یه متغیر می‌بینن و سپردن کار به ساب‌اجنت‌ها مثل

3🔥3👍1

4 Aug 2026, 07:15 UTC802 views8 reactionsread 7 August 2026

داشتم مستندات استاندارد Agent Skills رو بررسی می‌کردم دقیقتر بفهممش، در کل یه ساختار متن‌باز و فایل‌محوره که نشون می‌ده چطور کار مشخص، اسکریپت و دانش عملیاتی رو برای ایجنت‌های هوش مصنوعی بسته‌بندی کنیم. فرض کن دسترسی ایجنت به ابزارها مثل فرستادنش تو یه آشپزخونه‌ست. فایل Skill همون دستور پخت، لیست مواد و روش کنترل کیفیته. نقطه ورودش فایل SKILL.md هست. نکته کلیدی: این‌ها در اصل فقط Prompt هستن. هیچ جادویی نیست. اما پ

4👍3🔥1

3 Aug 2026, 13:25 UTC976 views5 reactionsread 7 August 2026
Photo

مدل Qwen3.8-Max منتشرشد، جدیدترین و قوی‌ترین مدل سری Qwen . چیزایی که خاصش می‌کنه: 🔹 ۲.۴ تریلیون پارامتر داره ولی فقط حدود ۹۵ میلیاردش فعال می‌شه (MoE هوشمند). یعنی هم غوله هم نسبتاً ارزون و سریع کار می‌کنه. 🔹 برای اولین بار یه مدل Max-کلاس رو قراره هفته‌ی بعد وزن‌هاش رو اوپن‌سورس کنن. قبلاً اینا رو بسته نگه می‌داشتن. 🔹 یه تست دیوانه‌وار دادن: از یه فولدر خالی شروع کرد و ۱۶ روز کامل بدون هیچ دخالت انسانی یه فریم‌

🔥32

3 Aug 2026, 07:39 UTC≈1,020 views17 reactionsread 7 August 2026

تو دنیای مدل‌های زبانی یه فرمول ساده داریم: عامل هوشمند (AI Agent) مساویه با مدل به‌علاوه‌ی Harness. مدل زبانی به‌تنهایی فقط یه پیش‌بینی‌کننده‌ی متنه. برای اینکه بتونه کار واقعی انجام بده، به یه لایه‌ی نرم‌افزاری دور خودش نیاز داره که ابزارها، حافظه و منطق اجرا رو بهش بده؛ به این لایه می‌گن Agent Harness. مدل مثل مغز عمل می‌کنه. اون لایه‌ی اطرافش، مثل چشم، دست، محیط کار و ترمز ماشینه. یه مدل خام نهایتاً می‌تونه یه

9👌4👍4

31 Jul 2026, 12:10 UTC≈1,130 views13 reactionsread 7 August 2026
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مدل deepseek v4 flash اپدیت شد همچنین مدل Gpt5.6 luna هم اپدیت شد جفتشون عملکردشون نسبت به هزینشون فوق العادس مدل های محبوب من برای کدنویسی ان

7👍5🔥1

31 Jul 2026, 06:56 UTC≈1,170 views16 reactionsread 7 August 2026

داشتم دسته‌بندی "چیپ هوین" توی کتاب مصاحبه‌های ماشین‌لرنینگ رو نگاه می‌کردم؛ لیست دقیقی از عنوان‌های شغلی این حوزه درآورده. واقعیت اینه که توی شرکت‌های مختلف، این اسم‌ها خیلی سلیقه‌ای استفاده می‌شه و همپوشانی زیادی دارن، اما برای اینکه موقع اپلای کردن گیج نشیم، فهمیدن تفاوت‌هاشون لازمه... AI/ML Research Scientist تمرکزش روی خلق ایده‌ها، معماری‌های جدید و نوشتن مقاله‌های علمیه. موفقیتش با نوآوری و آزمایش‌های تئوری سن

11👍4👌1

30 Jul 2026, 15:11 UTC≈1,140 views27 reactionsread 7 August 2026

سلام دوستان عزیز 👋 بعد از تجربه‌ی خوبی که با Bina OCR و شنوا (ASR فارسی) داشتیم (هنوز تموم نشده)، پروژه‌ی بعدی‌مون شروع شده: ساخت یه مدل TTS فارسی متن‌باز 🎙️ یکی از بزرگ‌ترین چالش‌های TTS فارسی اینه که خط فارسی حرکت‌گذاری نمی‌شه، پس مدل باید حدس بزنه کلمه چطور تلفظ می‌شه. مثلاً «برگزاری» رو می‌شه چند جور خوند، و بدون داده‌ی درست، مدل گاهی اشتباه تلفظ می‌کنه. برای حل این مشکل یه مینی‌گیم ساختیم که توش شما تلفظ درست کل

🔥252

28 Jul 2026, 18:23 UTC≈1,120 views11 reactionsread 7 August 2026

یه مجموعه اسکیل رو به صورت ازمایشی ساختم تا فرآیند Spec-Driven Development رو توی ابزارهای کدنویسی پیاده کنیم... ایده اصلی اینه که جلوی گیج شدن coding agentها و گم شدن تصمیمات معماری رو بگیریم. مشکل اینجاست که وقتی به agent می‌گیم یه قابلیت بزرگ رو بسازه، جزئیات مهم و نیازمندی‌ها ته تاریخچه چت گم می‌شن... معمولاً هم به یک build سبز یا تست‌های ساده بسنده می‌کنن که اصلاً ضامن درست بودن منطق برنامه نیست. توی روش SDD ه

🔥53👍3

28 Jul 2026, 15:11 UTC≈1,110 views6 reactionsread 7 August 2026

مدل جدید کیمی K3 کلاً قضیه مدل‌های متن‌باز رو وارد یه مرحله جدید کرده... یه مدل غول‌پیکر با ۲.۸ تریلیون پارامتر کل که موقع جواب دادن به هر کلمه، ۱۰۴ میلیارد پارامترش فعال می‌شن و تا ۱ میلیون توکن متن یا تصویر رو هم‌زمان پردازش می‌کنه. سازنده‌هاش تونستن بازدهی آموزش مدل رو نسبت به نسل قبل ۲.۵ برابر بهتر کنن. خلاصه تغییرات مهمش ایناست: - ترکیب هوشمندانه توجه (Hybrid Attention): برای اینکه موقع خواندن متن‌های خیلی ط

👍32🔥1

Showing the 12 most recent of 18 posts we hold for @LLMEngineers. 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 — 644,289 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.

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 1 registered channel — 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.

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

“AI Engineers” (@LLMEngineers), 2,411 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/LLMEngineers.

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.