Marketing & affiliate — 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 9 September 2026 and assigned it the closest of 31 fixed categories, at 86% 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
35 measurements spanning 57 days, net -1,549. 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 69,933–71,946 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 35
Measured (UTC)
Subscribers
Change
3 Oct 2026, 17:41
70,165
-353
25 Sept 2026, 02:02
70,518
-238
18 Sept 2026, 18:58
70,756
-89
16 Sept 2026, 13:00
70,845
-87
14 Sept 2026, 15:01
70,932
-83
13 Sept 2026, 00:18
71,015
-86
10 Sept 2026, 22:00
71,101
+865
7 Sept 2026, 20:56
70,236
-144
4 Sept 2026, 17:55
70,380
-96
3 Sept 2026, 01:38
70,476
-39
2 Sept 2026, 00:56
70,515
-35
1 Sept 2026, 03:58
70,550
-63
31 Aug 2026, 00:06
70,613
-29
29 Aug 2026, 22:45
70,642
-39
28 Aug 2026, 21:48
70,681
-43
28 Aug 2026, 01:16
70,724
-55
26 Aug 2026, 22:32
70,779
-59
25 Aug 2026, 19:13
70,838
-39
24 Aug 2026, 20:13
70,877
-84
23 Aug 2026, 03:14
70,961
first reading
Engagement
61 posts held, back to 10 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 123 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
13.5%
avg views ÷ 70,165 subscribers
Avg views / post
9,440
21 posts measured
Reaction rate
0.677%
reactions ÷ views · ER floor
Posts in window
21
of 61 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
Window
Rolling 30 days · latest post in window 6 October 2026
Posts held
61 (10 July 2026 – 6 October 2026)
Views total
198,232
Reactions total
1,342
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
6 Oct 2026, 15:42 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
Photos
640
Videos
4
Links
160
Lifetime counters from Telegram’s own channel header, read 6 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
5,230 reactions across 61 posts, in 13 distinct kinds. The most used accounts for 16.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
862
16.5%
👍
721
13.8%
🎉
667
12.8%
🤩
628
12.0%
🔥
626
12.0%
💯
378
7.23%
😍
378
7.23%
🥰
366
7.00%
❤🔥
358
6.85%
😁
235
4.49%
👏
7
0.134%
🏆
3
0.057%
👎
1
0.019%
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 61 of the 61 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 5,230 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 61 most recent posts we hold, published 10 July 2026 to 6 October 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.
🧑💻 Why “user hesitation” is a growth signal
Most users don’t leave instantly. They slow down first. Hesitation is often the first visible sign that something in the flow feels unclear, risky, or too heavy.
💬 Pauses reveal confusion: Users stop when the next step isn’t obvious.
💬 Doubt reduces momentum: Every second of uncertainty weakens intent.
💬 Risk slows action: People avoid choices that feel easy to get wrong…
🧑💻 Why “shareable wins” drive organic growth
People don’t share products because a referral button exists. They share moments that make them look smart, successful or early.
💬 Wins create emotion: Users share when they feel proud.
💬 Outputs explain value: A result tells the story faster than a pitch.
💬 Identity drives distribution: People post what reflects well on them.
💬 One-tap sharing preserves impulse: Fricti…
🧑💻 How “decision shortcuts” increase conversion
Users don’t always need more options. Most of the time, they need help choosing faster.
💬 Too many choices create doubt: Users pause when every path looks similar.
💬 Defaults reduce anxiety: A recommended option feels safer.
💬 Clear comparison speeds action: People convert when differences are obvious.
💬 One primary CTA protects focus: Attention should not be split.
…
🧑💻 Why stock trading is becoming the next attention shift
Crypto trading has become noisy, overcrowded and exhausting for many people. The same narratives, the same pumps, the same promises — and less clarity on where the real edge is.
💬 Attention is moving: Traders are starting to look for markets with stronger structure.
💬 Stocks feel more grounded: Price moves are often tied to businesses, reports and real cata…
🧑💻 Why “user momentum” is your real retention asset
Retention doesn’t start when users build a habit. It starts when they feel they are already moving toward something.
💬 Momentum creates return intent: Users come back when progress feels unfinished.
💬 Small wins protect attention: Progress makes the product feel alive.
💬 Clear next steps prevent drop-off: Users don’t need to plan alone.
💬 Visible movement builds …
🧑💻 How “friction mapping” unlocks hidden growth
Most growth problems are not caused by missing features. They come from small moments where users slow down, hesitate or quit.
💬 Tiny blockers compound fast: One extra step can reduce completion.
💬 Confusion creates silent churn: Users rarely complain before leaving.
💬 Effort has a cost: Every click should earn its place.
💬 Removal creates speed: Less friction means …
🧑💻 Why “early proof” beats long explanations
Most users don’t need to understand everything before they act. They need one clear proof that the product is worth their attention.
💬 Proof reduces doubt: Users trust what they experience faster than what they read.
💬 Long explanations drain energy: Too much context creates hesitation.
💬 One result builds belief: A quick win makes the next step easier.
💬 Action creates…
🧑💻 Why “shareable outcomes” beat referral links
Most referral programs feel forced because users are asked to share the product. Stronger growth happens when users share their own result.
💬 Outcomes carry trust: A real result explains value faster than a pitch.
💬 Identity drives sharing: People post what makes them look smart or successful.
💬 Friction kills distribution: One-tap sharing keeps the loop alive.
💬 Rec…
🧑💻 Why “user confusion” looks like low intent
Many users don’t leave because they are uninterested. They leave because they don’t understand what to do next.
💬 Confusion slows action: Every pause weakens momentum.
💬 Unclear paths create doubt: Users fear making the wrong move.
💬 Simple sequencing builds confidence: One step should naturally lead to another.
💬 Guidance protects attention: Users stay when the produc…
🧑💻 How “clear limits” can increase upgrades
Users don’t upgrade just because a feature is locked. They upgrade when the limit appears exactly when they need more.
💬 Limits reveal demand: Hitting a wall proves intent.
💬 Context reduces frustration: The right limit feels logical, not random.
💬 Specific unlocks convert better: “Get 10 more exports” beats “Go Pro.”
💬 Timing shapes willingness: Users pay when the need …
🧑💻 Why “first value” should happen before setup
Most products lose users because setup arrives before proof. Users don’t want to configure something they don’t trust yet.
💬 Setup feels like work: Effort before value creates resistance.
💬 Proof creates patience: Users accept complexity after a first win.
💬 Defaults reduce friction: A smart starting point beats empty choices.
💬 Fast output builds belief: Seeing valu…
😁12🎉11❤9🔥9🤩9👍5👏1
Showing the 12 most recent of 61 posts we hold for @gr0wth_hack. 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.
Mentions
Names
Channels on the register whose handles appear in this channel's posts.
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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 22 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
Sin Unicorn 🦄 @sinunicorn · 99,919 Telegram ranks this channel #19 of 72 here — alongside 71 others — read 22 August 2026
Ecom: The Future. AI, Innovation @ecommerce_AI · 54,788 Telegram ranks this channel #35 of 64 here — alongside 63 others — read 22 August 2026
Crypto Aunt @milfz_crypto · 108,090 Telegram ranks this channel #37 of 67 here — alongside 66 others — read 22 August 2026
Tech Paper @TechPaper · 28,790 Telegram ranks this channel #49 of 65 here — alongside 64 others — read 8 September 2026
This channel appears in 4 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 3 October 2026 — this
entry's latest reading, not the date you are reading this.
“Growth Hacker” (@gr0wth_hack), 70,165 subscribers as measured 3 October 2026. Telegram Register, tgregister.com/channel/gr0wth_hack.
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.
