Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 26 September 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.
Growth
6 measurements spanning 33 days, net -15. 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 1,786–1,805 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
11 Oct 2026, 08:39
1,788
-5
5 Oct 2026, 08:00
1,793
-3
26 Sept 2026, 20:57
1,796
-4
18 Sept 2026, 03:56
1,800
-3
10 Sept 2026, 10:16
1,803
no change
7 Sept 2026, 22:22
1,803
first reading
Engagement
16 posts held, back to 9 July 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 1 page of 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 16 posts for this entry, the most recent from 17 July 2024. An engagement rate over an empty window would be a number about nothing.
Reaction mix
289 reactions across 13 posts, in 9 distinct kinds. The most used accounts for 50.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
145
50.2%
👍
69
23.9%
👎
38
13.1%
🤔
16
5.54%
🔥
10
3.46%
👌
4
1.38%
👀
3
1.04%
👏
2
0.692%
🤣
2
0.692%
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 13 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 289 reactions 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 9 July 2024 to 17 July 2024, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
For Better user experience,
Use our Android App
App link : https://play.google.com/store/apps/details?id=com.learning.akraas
For complete Super notes Contents - check out our Android App
❤️❤️Super Notes plan details
Offer price only for 2 hours after that price revised to original price = 299rs (1 year validity)
Original price = 599 (1 year validity)
Marrow Schema Topics based Super Mcqs (like custom model)
Mcq Link : https://is.gd/Marrow_schema_custom_supermcqs
Step 1 = Click and open the link in the browser
Step 2 = Search any word or click the topic, Related mcqs will come below, practice the mcqs
MCQs with proper optionwise explanation
MCQs focused only on high yield exam oriented points only (no unwanted stuff)
Super Notes Link: https://is.gd/Marrow_schema_supenotes_sample
Step 1 = Click and open the link in Browser
Step 2 = Search any word, Scroll to bottom for results or Click the "Select subject", then click any topic, Scroll to the bottom to see the notes or click the select tag, click any tag, scroll to the bottom to see the Notes
Search and REVISE all high yield important points (REVISE IN SMART WAY)
🏅🏅Start : 7 days Revision program started
Sorry for the delay ( Delay due to refining the revision contents , to make it simplified for quick effective revision)
NEET PG EXAM is MCQ exam, not university exam ,so no need to spend too much time on vast theoretical points
Just focus on clinical-oriented, exam oriented Mcq points (MCQs keyword points)
⭐ i focused only on MCQ keywords points of all 1947 MARROW SCHEMA …
🏅🏅7 days Revision Program (Starts from tomorrow 11 am)
⭐ By Filtering Marrow Schema Topics (only 902 Topics are Real topics - These topics only have potential to ask in Clinical oriented questions)
I Going to share Only High yield key points only of these 902 topics
3 days is more than enough for Revising (Reading) those 902 Topics High Yield Key Points
Remaining 4 days for Recalling those High Yield key points…
7 days Revision Program (Covers all high yield topics points in 7 days) - COMING SOON
I WILL SHARE - More details - Tomorrow morning - 11 am
Out of 1947 Marrow Schema Topics - Only 1000+ Topics Real topics, other topics (remaining 900+ topics) come under those 1000+ topics
Revising those 1000 topic's high yield points in 7 days (Easy and Effective way to score high in NEET PG EXAM)
@NeetpgMnemonics
All topics given in the app (Quick Decoding Topics, Key Differentiating Points) are Marrow schema topics only,
Given in Alphabetical order, you can compare the topics in alphabetic order with marrow schema Topic (100% Match)
Topics are based on Marrow Schema Topics (1943 topics) =High Yield, Repeatedly asking Topics,
Go through the High Yield Topics oriented Clinical points for Tackling clinical oriented questions
To Refresh the App, Close the App completely (completely exit the App), Then > Again open the App
It Refresh the contents, (newly added contents also visible to you)
Refresh the App for more contents uploaded just now
How many like it, How many need it, (Quick Clinical One-liners for easy quick revision) Tell me, based on your feedback only, i try to create and upload
I like it, I need it for all 19 subjects91%
No need (I am not interested in Easy, Quick, Smart Revision) (I am interested in Difficult Revision)9%
Shares as published. No per-option vote count is published by Telegram, so none is shown.
👍4❤1
Showing the 12 most recent of 16 posts we hold for @NeetpgMnemonics. 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.
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
How many like it, How many need it, (Quick Clinical One-liners for easy quick revision) Tell me, based on your feedback only, i try to create and upload
I like it, I need it for all 19 subjects91%
No need (I am not interested in Easy, Quick, Smart Revision) (I am interested in Difficult Revision)9%
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 16 most recent posts we hold, published 9 July 2024 to 17 July 2024. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
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.
𝑩𝑳𝑨𝑪𝑲𝑩𝑶𝑶𝑲 𝑩𝑬𝑻𝑻𝑰𝑵𝑮 @fdshjnf · 28,070 Telegram ranks this channel #38 of 44 here — alongside 43 others — read 7 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list 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 11 October 2026 — this
entry's latest reading, not the date you are reading this.
“NEET PG Mnemonics - LAST MINUTE FinishingTouch” (@NeetpgMnemonics), 1,788 subscribers as measured 11 October 2026. Telegram Register, tgregister.com/channel/NeetpgMnemonics.
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.