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Channel

Tensorflow(@CVision)

@cvision

On this record: Topic · Observations · Also posting the same content · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

15,111subscribers

+103 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001072608535
TypeChannel
Username@cvision
Created4 November 2016measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live4 September 2026
Measurements held24
Confirmed unchanged1 time, most recently 4 September 2026
On Telegramt.me/cvision

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Content that also appears on other registered channels

Posts published here appear word for word on 1 other registered channel. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

No sample pairs were kept for this entry. The counts below were measured, but without the pairs there is nothing here for you to open and check, so treat the relationship as recorded rather than demonstrated.

Every channel this entry shares post bodies with
ChannelMatching postsText overlapTypical gapPublished first
@llm_huggingface7 (7/7 hand-verifiable sample passed)1.00under a minutethis entry (43)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 1 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.

“Published first” means first in this corpus. We hold 13 comparable posts for this entry, running 16 July 2026 to 7 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Across the whole group of 2, the earliest publisher we hold is @llm_huggingface. That is a statement about our reading window, not a claim of authorship.

Recorded under the key clone_mutual, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Also posting the same content

This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.

Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

15,00715,11815,062.56 August 2026 — 15,008 subscribers6 August 2026 — 15,012 subscribers7 August 2026 — 15,007 subscribers8 August 2026 — 15,016 subscribers9 August 2026 — 15,034 subscribers10 August 2026 — 15,042 subscribers11 August 2026 — 15,053 subscribers12 August 2026 — 15,067 subscribers13 August 2026 — 15,075 subscribers15 August 2026 — 15,082 subscribers16 August 2026 — 15,093 subscribers19 August 2026 — 15,096 subscribers20 August 2026 — 15,099 subscribers21 August 2026 — 15,105 subscribers22 August 2026 — 15,113 subscribers24 August 2026 — 15,118 subscribers25 August 2026 — 15,114 subscribers26 August 2026 — 15,113 subscribers27 August 2026 — 15,112 subscribers28 August 2026 — 15,109 subscribers29 August 2026 — 15,100 subscribers30 August 2026 — 15,103 subscribers31 August 2026 — 15,104 subscribers4 September 2026 — 15,111 subscribers15,1116 August 20264 September 2026
24 measurements spanning 29 days, net +103. 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 14,990–15,135 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 24
Measured (UTC)SubscribersChange
4 Sept 2026, 00:1915,111+7
31 Aug 2026, 13:4315,104+1
30 Aug 2026, 11:3815,103+3
29 Aug 2026, 14:1315,100-9
28 Aug 2026, 16:5815,109-3
27 Aug 2026, 13:3415,112-1
26 Aug 2026, 10:4515,113-1
25 Aug 2026, 11:4215,114-4
24 Aug 2026, 13:2415,118+5
22 Aug 2026, 18:0915,113+8
21 Aug 2026, 05:0415,105+6
20 Aug 2026, 02:3415,099+3
19 Aug 2026, 00:2215,096+3
16 Aug 2026, 17:3715,093+11
15 Aug 2026, 01:5515,082+7
13 Aug 2026, 18:0915,075+8
12 Aug 2026, 14:3415,067+14
11 Aug 2026, 14:1215,053+11
10 Aug 2026, 12:5215,042+8
9 Aug 2026, 14:1715,034first reading

Engagement

25 posts held, back to 16 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 54 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
19.3%
avg views ÷ 15,111 subscribers
Avg views / post
2,920
11 posts measured
Reaction rate
0.672%
reactions ÷ views · ER floor
Posts in window
11
of 25 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 29 August 2026
Posts held25 (16 July 202629 August 2026)
Views total32,160
Reactions total216
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken3 Sept 2026, 10:58 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
4m 30s
Average length
2m 15s

Measured directly from 2 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

410 reactions across 23 posts, in 11 distinct kinds. The most used accounts for 54.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
22354.4%
🔥6916.8%
👍4711.5%
👏184.39%
👌143.41%
❤‍🔥133.17%
😱81.95%
🙏61.46%
40.976%
👀40.976%
🤔40.976%

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 25 of the 25 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 434 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 25 most recent posts we hold, published 16 July 2026 to 29 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

29 Aug 2026, 16:03 UTC≈1,600 views17 reactionsread 3 September 2026
Video

🔥 رقابت غول‌های تولید ویدئو با هوش مصنوعی! چند روز پیش یک رقابت جذاب بین تعدادی از مطرح‌ترین مدل‌های تولید ویدئوی AI برگزار شد: 🎬 Seedance 2.5 🎬 WAN 3 🎬 FLUX 3 🎬 Minimax H3 🎬 Seedance 2.0 🎬 Seedance 2.0 Mini 🎬 Kling 3 Omni 🎬 Grok Imagine 1.5 اما نکته جذاب اینجاست که شرایط برای همه کاملاً یکسان بود: ✅ یک پرامپت مشترک ✅ ۷ تصویر مرجع یکسان ✅ انتخاب اولین ویدئوی تولیدشده توسط هر مدل ❌ بدون گزینش دستی ❌ بدون تدوین و و

👏10👍4👀3

Signed Ablfzl

28 Aug 2026, 02:00 UTC≈1,830 views18 reactionsread 3 September 2026

from google import genai client = genai.Client() # Upload audio file via the Files API audio_file = client.files.upload(file="path/to/audio.wav") # Transcribe with speaker diarization and word timestamps interaction = client.interactions.create( model="gemini-3.5-transcribe", input=[{ "type": "audio", "uri": audio_file.uri, "mime_type": audio_file.mime_type, }], generation_c

16👍2

27 Aug 2026, 16:12 UTC≈1,930 views9 reactionsread 3 September 2026
Photo

� مدعی جدید دنیای تولید ویدیو از راه رسید! 🎬🔥 هوش مصنوعی جدید علی‌بابا، Wan 3.0، با قابلیت تولید ویدیوهای سینمایی و واقع‌گرایانه وارد رقابت شده؛ مدلی که می‌تونه از متن و تصویر، ویدیوهای باکیفیت و جذاب تولید کنه و کنترل بیشتری روی حرکت، دوربین و جزئیات صحنه در اختیار کاربر بذاره. اگه دنبال ساخت ویدیوهای حرفه‌ای با هوش مصنوعی هستی، Wan 3.0 یکی از مدل‌هاییه که ارزش امتحان کردن داره. � @cvision

8👍1

Signed Ablfzl

27 Aug 2026, 13:58 UTC≈2,190 views11 reactionsread 3 September 2026

مدل جدید در مکالمه‌های واقعی می‌تواند مکث‌ها و کلمات اضافه مثل «اِمم» را حذف کند، اصلاح جمله توسط گوینده را متوجه شود و متن را به‌صورت خودکار فرمت کند. همچنین امکان تعریف Custom Vocabulary برای اصطلاحات تخصصی و نام‌های خاص هم وجود دارد؛ قابلیتی که برای کاربردهای سازمانی و فنی اهمیت زیادی دارد. 📝⚡️ یکی از جذاب‌ترین بخش‌ها، پشتیبانی از بیش از ۸۵ زبان، تشخیص لهجه‌ها و جابه‌جایی بین زبان‌هاست. برای فایل‌های ضبط‌شده هم ق

8🔥3

Signed 𝘴𝘪𝘯𝘢

27 Aug 2026, 13:58 UTC≈2,050 views15 reactionsread 3 September 2026
Photo

گوگل یک قدم جدی‌تر وارد رقابت تبدیل صدا به متن شد 🎙🧠 گوگل مدل جدید Gemini 3.5 Transcribe را معرفی کرده؛ مدلی تخصصی برای Speech-to-Text که به گفته‌ی گوگل، دقیق‌ترین مدل تبدیل گفتار به متن این شرکت تا امروز است. نکته مهم اینجاست که هدف فقط تبدیل کلمه‌به‌کلمه‌ی صدا نیست؛ مدل تلاش می‌کند خروجی را به شکل تمیز، ساختاریافته و قابل استفاده تحویل دهد. 🌀 @cvision 🌀

10🔥4👍1

Signed 𝘴𝘪𝘯𝘢

25 Aug 2026, 06:29 UTC≈2,330 views25 reactionsread 3 September 2026
Forwarded from @class_visionPhoto

یک خبر خوب برای اونایی که مدت‌هاست می‌خوان یه مهارت جدید یاد بگیرن 👇 با همکاری مکتب‌خونه، ۵۰۰ دوره آموزشی به‌صورت رایگان در طرح «ایران‌ماهر» در دسترس قرار گرفته. 🎓 چند تا از دوره‌های #کلاس_ویژن هم در این طرح قرار دادیم تا اگر تا امروز فرصت یا امکان تهیه‌شون رو نداشتید، بتونید با یکی از اون‌ها شروع کنید. 🌱 🔹 آموزش جامع یادگیری عمیق 🔹 ساخت هوش مصنوعی شخصی در مرورگر 🔹 آموزش مدل‌های زبانی-تصویری 🔹 آموزش پردازش تصویر

22❤‍🔥2👏1

10 Aug 2026, 19:16 UTC≈4,120 views21 reactionsread 3 September 2026
Photo

🎙 ـTypeless؛ ابزاری که تایپ کردن رو خیلی کمتر می‌کنه ـTypeless یک ابزار مبتنی بر هوش مصنوعیه که صحبت شما رو به متن تبدیل می‌کنه، اما نه به شکل ساده‌ی Voice to Text. هنگام صحبت، تپق‌ها، تکرارها و اصلاحاتی که وسط جمله انجام می‌دید رو تشخیص می‌ده و در نهایت یک متن مرتب و قابل استفاده تحویل می‌ده. مثلاً اگر بگید: «جلسه رو بذاریم فردا... نه، بهتره پس‌فردا ساعت ۱۰ باشه» خروجی نهایی می‌تونه این باشه: «جلسه را برای پس‌فردا س

🔥183

Signed 𝘴𝘪𝘯𝘢

8 Aug 2026, 05:03 UTC≈4,120 views19 reactionsread 3 September 2026
Photo

🚀 معرفی مدل ویدیوساز قدرتمند و جدید MiniMax H3 مدل هوش مصنوعی MiniMax H3 (که به عنوان Hailuo 3.0 هم شناخته می‌شود) به تازگی به صورت Open-Weights منتشر شده و امکانات بی‌نظیری را برای تولید محتوای ویدیویی در اختیار کاربران قرار داده است. این مدل توانایی رقابت با بهترین مدل‌های بسته بازار را دارد و حتی روی سیستم‌های لوکال (مثل ComfyUI) هم قابل اجراست. *خبرخوب : به زودی براتون بصورت اختصاصی ورک فلو ها و مدل های خوبش رو ق

16👌2👍1

Signed Ablfzl

7 Aug 2026, 18:13 UTC≈4,190 views15 reactionsread 3 September 2026
File

👁 درک عمیق و هوشمند ویدیوها با اسکیل /watch با اضافه کردن این اسکیل قدرتمند، Agentهای هوش مصنوعی شما (مثل Claude Code و Codex) قابلیت تماشا و تحلیل جامع ویدیوها رو پیدا می‌کنن! ویژگی‌های کلیدی: 🎞 آنالیز فریم‌به‌فریم: درک دقیق و بصری از اتفاقات داخل تصویر. 🗣 تشخیص هوشمند گفتار: فهم کامل دیالوگ‌ها و صدای روی ویدیو. ⚡️ راه‌اندازی تمام‌خودکار: بدون درگیری با مراحل نصب؛ فقط فایل رو به Agent خودتون بدید تا خودش پیش‌نیازها

15

Signed Ablfzl

7 Aug 2026, 14:09 UTC≈4,320 views50 reactionsread 3 September 2026

هز چه قدر پول بدی آش میخوری :) چند روز داشتم یه پروژه‌ی RAG با Neo4j و LangChain کار می‌کردم. یه جای کار retriever همیشه نتیجه‌ی خالی برمی‌گردوند — امبدینگ‌ها سالم، ایندکس سالم، ولی هرچی می‌گشتم هیچی پیدا نمی‌شد. توی یه دیسکاشن طولانی با Sonnet، کلی فرضیه و راه‌حل امتحان کردیم: نسخه‌ی پکیج، نوع مدل امبدینگ، search_type، حتی رفتیم سراغ ساخت retriever سفارشی و full-text index دستی — هرکدوم یه گوشه از مشکل رو حل می‌کر

32👍104🤔3👀1

7 Aug 2026, 07:41 UTC≈3,480 views16 reactionsread 3 September 2026
Photo

https://go.neo4j.com/rs/710-RRC-335/images/Essential-GraphRAG.pdf

13👍2🔥1

6 Aug 2026, 15:12 UTC≈3,270 views11 reactionsread 3 September 2026

یادداشت شماره ۱: درباره «مدلهای جهانی به عنوان بنیانهای شناختی برای AGI» من یک مجموعه جدید درباره #WorldModels به عنوان زیربنای هوش_مصنوعی_عمومی (#AGI) شروع میکنم. هوش مصنوعی فراتر از یادگیری ماشین سنتی و حتی #مدلهای_زبانی_بزرگ (#LLMs) امروزی به سرعت در حال تکامل است. یکی از هیجانانگیزترین پرسشها این است: چگونه سیستمهای هوش_مصنوعی میتوانند یک درک درونی از جهان بسازند، درباره موقعیتهای جدید استدلال کنند، و پیش از اق

👍63👌2

Signed Ali B

Showing the 12 most recent of 25 posts we hold for @cvision. 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.

Forward network

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 3 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.

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.

Appears in Telegram’s recommendations for other channels

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.

مجله هوش مصنوعی
@HomeAI · 32,769
Telegram ranks this channel #6 of 91 here — alongside 90 others — read 5 September 2026
Machine Learning | یادگیری ماشین
@MachineLearning_ir · 33,936
Telegram ranks this channel #12 of 90 here — alongside 89 others — read 3 September 2026
هوش مصنوعی در پژوهش
@AI_in_Research · 241,277
Telegram ranks this channel #24 of 92 here — alongside 91 others — read 19 August 2026
عصر گویش | هوش مصنوعی
@asrgooyeshpardaz · 100,360
Telegram ranks this channel #36 of 89 here — alongside 88 others — read 24 August 2026
ApplyKite
@ApplyIR2UK · 37,816
Telegram ranks this channel #55 of 88 here — alongside 87 others — read 31 August 2026
Dr Ahmadian
@drmjahmadian · 90,984
Telegram ranks this channel #56 of 93 here — alongside 92 others — read 17 August 2026
پیشنهادهایی برای #اپلای #applyabroad
@safarnamee7 · 33,598
Telegram ranks this channel #60 of 88 here — alongside 87 others — read 4 September 2026
FaraDars | فرادرس‌‎
@faradars · 61,315
Telegram ranks this channel #73 of 87 here — alongside 86 others — read 22 August 2026
Data Science | علم داده
@DataScience_ir · 49,625
Telegram ranks this channel #75 of 92 here — alongside 91 others — read 25 August 2026
Canada Dream
@Canada_channel · 38,910
Telegram ranks this channel #78 of 79 here — alongside 78 others — read 31 August 2026
مکتب‌خونه | Maktabkhooneh
@maktabkhooneh · 34,529
Telegram ranks this channel #81 of 90 here — alongside 89 others — read 3 September 2026
رویدادهای دانشجویی تهران
@roydad_daneshjoo · 56,138
Telegram ranks this channel #82 of 91 here — alongside 90 others — read 23 August 2026
ApplyAbroad | هلدینگ پرگار
@pargarwiki · 131,814
Telegram ranks this channel #83 of 90 here — alongside 89 others — read 19 August 2026
دانشگاه صنعتی شریف
@Sharif_prm · 38,380
Telegram ranks this channel #85 of 93 here — alongside 92 others — read 31 August 2026
جاب‌ویژن | Jobvision | استخدام | کاریابی
@jobvision · 59,223
Telegram ranks this channel #97 of 98 here — alongside 97 others — read 22 August 2026

This channel appears in 15 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

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 4 September 2026 — this entry's latest reading, not the date you are reading this.

“Tensorflow(@CVision)” (@cvision), 15,111 subscribers as measured 4 September 2026. Telegram Register, tgregister.com/channel/cvision.

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.