Channel
@meteorjournal
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
2,658subscribers
-10 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001304502512 |
|---|---|
| Type | Channel |
| Username | @meteorjournal |
| Created | Between 1 March 2018 and 30 June 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/meteorjournal |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 23:03 | 2,658 | -5 |
| 10 Aug 2026, 01:51 | 2,663 | -4 |
| 6 Aug 2026, 21:15 | 2,667 | -1 |
| 6 Aug 2026, 17:16 | 2,668 | no change |
| 6 Aug 2026, 16:59 | 2,668 | first reading |
20 posts held, back to 2 December 2024 — the 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.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 12 December 2025. An engagement rate over an empty window would be a number about nothing.
Measured directly from 3 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.
80 reactions across 19 posts, in 2 distinct kinds. The most used accounts for 97.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 78 | 97.5% | |
| 🤯 | 2 | 2.50% |
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 19 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 80reactions 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 2 December 2024 to 12 December 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
📌 رودمپ تبدیل شدن به متخصص هوش مصنوعی مولد #Artificial_inteligence #ai #machinelearning #Generative_AI #یادگیری_ماشین #هوش_مصنوعی #ماشین_لرنینگ #هوش_مصنوعی_مولد 🙋🏻♀@meteorjournal 👉🏼 کانال 🙋🏻♀@meteorjournalgroup 👉🏼گروه
👍4
Signed Maryam
❇️ سه پرسش حیاتی پیش از ساخت یک محصول مبتنی بر هوش مصنوعی ✍️ بر اساس کتاب The AI Pocket Book اثر Emmanuel Maggiori ⁉️ ۱. آیا من AI را قبل از مشتری قرار میدهم؟ (Am I putting AI before the customer?) ⁉️ ۲. آیا توهمات (Hallucinations) در محصول من قابلقبول هستند؟ (Are hallucinations okay?) ⁉️ ۳. آیا لازم است بدانم خروجی چگونه تولید شده است؟ (Do I need to explain how the output is generated?) 🎯 توضیحات بیشتر دربا…
👍2
Signed Maryam
🤖 یادگیری ۱۱ روشی که ماشین ها یاد می گیرند (به زبان ساده): 🔺 یادگیری تقلیدی (Imitation Learning) 🔺 یادگیری فدراسیون (Federated Learning) 🔺 یادگیری انتقالی (Transfer Learning) 🔺 یادگیری افزایشی (Incremental Learning) 🔺 یادگیری ترکیبی (Ensemble Learning) 🔺 یادگیری هیبرید (Hybrid Learning) 🔺 یادگیری چندوجهی (Multimodal Learning) 🔺 یادگیری کمنمونه (Few-shot Learning) 🔺 فرا یادگیری (Meta Learning) 🔺 یادگیری نمایشی (Repr…
👍1
Signed Maryam
✅ فایل صوتی خلاصه دوره آموزشی شش ساعته Caleb ralston در یوتیوب درباره پرسونال برندینگ (فارسی- تهیه شده توسط هوش مصنوعی) 🎧 نکاتی خوبی در مورد: 🔺 ساختن برند شخصی قوی 🔺 تولید محتوی در این فایل آمده. 📌 به نظرم دوره ایشون برای کسانی که دنبال ایجاد برند شخصی قوی هستند خیلی می تونه مفید باشه: 👈 لینک ویدئو 🙋🏻♀@meteorjournal 👉🏼 کانال 🙋🏻♀@meteorjournalgroup 👉🏼گروه
👍3
Signed Maryam
✅ توهمات هوش مصنوعی؛ وقتی ماشین با اطمینان دروغ میگوید! هوش مصنوعی در چند سال اخیر وارد همهجای زندگی ما شده؛ از نوشتن و ترجمه گرفته تا طراحی و حتی مشاوره حقوقی. اما در کنار تمام این قابلیتها، یک مشکل جدی وجود دارد: توهمات (Hallucinations)؛ یعنی زمانی که مدل با اعتماد کامل، اطلاعات کاملاً غلط تولید میکند! در ادامه، نگاهی کوتاه و کاربردی به این پدیده و پیامدهایش میاندازیم 👇 🔍 توهم در هوش مصنوعی یعنی چه؟ وقتی م…
👍4
Signed Maryam
🙋🏻♂ سلام دوستان ✅ قسمتهای جدید از کتاب صوتی چنین گفت زرتشت اثر فریدریش نیچه، ترجمهی داریوش آشوری در کانال یوتیوب آپلود شد 👈🏼 برای شنیدن کلیک کنید 👉🏼 لطفاً برای حمایت از ما دوست داشتید لایک و سابسکرایب کنید ❤️ #کتاب #کتاب_صوتی #نیچه #فریدریش_نیچه #چنین_گفت_زرتشت 🙋🏻♂@meteorjournal 👉🏼 کانال 🙋🏻♂@meteorjournalgroup 👉🏼 گروه
👍3
Signed Shahab
📊 تفاوت بین Interpretability (تفسیر پذیری) و Explainability (توضیح پذیری) در یادگیری ماشین 🤔 در دنیای #داده_ساینس و #هوش_مصنوعی زیاد میشنویم که مدل باید قابل تفسیر (Interpretable) یا قابل توضیح (Explainable) باشد. اما این دو دقیقاً چه فرقی با هم دارند؟ 👇 🔹 Interpretability (قابلیت تفسیر) یعنی خودِ مدل بهطور مستقیم برای انسان قابل درک باشد. مدلهای ساده مثل رگرسیون خطی یا درخت تصمیم نمونههای خوبیاند، چون دقیقا…
👍4
Signed Maryam
📚 فایل صوتی خلاصه فارسی کتاب AI Agent in Practice 📌 (تهیه شده توسط هوش مصنوعی) لینک دانلود کتاب اصلی #AI_Agent #Artificial_inteligence #ai #large_language_model #machinelearning #یادگیری_ماشین #هوش_مصنوعی #ماشین_لرنینگ #عامل_هوش_مصنوعی 🙋🏻♀@meteorjournal 👉🏼 کانال 🙋🏻♀@meteorjournalgroup 👉🏼گروه
👍2
Signed Maryam
📗 کتاب: AI Agent in Practice 🖌 نوشته: Valentina Alto 📚 انتشارات Packt ⏰ سال 2025 ✅ لینک دریافت خلاصه صوتی فارسی #AI_Agent #Artificial_inteligence #ai #large_language_model #machinelearning #یادگیری_ماشین #هوش_مصنوعی #ماشین_لرنینگ #عامل_هوش_مصنوعی 🙋🏻♀@meteorjournal 👉🏼 کانال 🙋🏻♀@meteorjournalgroup 👉🏼گروه
👍1
Signed Maryam
📌 کتاب Beyond ChatGPT نوشته A.Yommi 🔺 این کتاب به دسته بندی ابزارهای هوش مصنوعی (AI Tools) درحوزه های مختلف میپردازد. 🔺 نشان میدهد چطور میتوان با این ابزارها کسب درآمد دلاری و آنلاین داشت. 🔺 استراتژی هایی هایی برای کسب درآمد از AI مطرح می کند. 🔺 در آخر پیشنهاداتی به عنوان فرصت های شغلی با هوش مصنوعی ارائه می کند. 🎯 اگر به دنبال درآمد با هوش مصنوعی هستید یا میخواهید آینده شغلها و ابزارهای AI را بشناسید، این کت…
👍1
Signed Maryam
✅ مقدمه ای بر یادگیری تقویتی در این ویدئو با یادگیری تقویتی، مولفه های اصلی آن، انواع یادگیری تقویتی و کاربرد آن در بهبود مدل های زبانی بزرگ (RLHF) آشنا می شوید 👈🏼 برای مشاهده کلیک کنید 👉🏼 https://youtu.be/sG7Hi0hpACI?si=pb2-d1lMzoAvAKXc #Reinforcement_Learning #Reinforcement_Learning_from_Human_Feedback #Machine_Learning #Artificial_Intelligence 🙋🏻♀@meteorjournal 👉🏼 کانال 🙋🏻♀@meteorjournalgroup 👉🏼گروه
👍2
Signed Shahab
Channel photo updated
Showing the 12 most recent of 20 posts we hold for @meteorjournal. 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 — 1,126,738 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.
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.
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.
“Meteor journal ☄” (@meteorjournal), 2,658 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/meteorjournal.
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.