15 measurements spanning 14 days, net -711. 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 70,896–71,821 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
21 Aug 2026, 13:58
71,003
-57
20 Aug 2026, 13:17
71,060
-47
19 Aug 2026, 11:07
71,107
-47
18 Aug 2026, 12:02
71,154
-59
17 Aug 2026, 14:05
71,213
-73
16 Aug 2026, 08:15
71,286
-81
14 Aug 2026, 18:15
71,367
-78
13 Aug 2026, 11:23
71,445
-31
12 Aug 2026, 14:24
71,476
-61
11 Aug 2026, 12:47
71,537
-61
10 Aug 2026, 09:28
71,598
-38
9 Aug 2026, 11:32
71,636
-32
8 Aug 2026, 10:34
71,668
-46
7 Aug 2026, 13:15
71,714
no change
7 Aug 2026, 13:08
71,714
first reading
Engagement
30 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 34 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
10.6%
avg views ÷ 71,003 subscribers
Avg views / post
7,490
20 posts measured
Reaction rate
1.06%
reactions ÷ views · ER floor
Posts in window
20
of 30 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 21 August 2026
Posts held
30 (10 July 2026 – 21 August 2026)
Views total
149,850
Reactions total
1,593
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
22 Aug 2026, 12:40 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
609
Videos
4
Links
129
Lifetime counters from Telegram’s own channel header, read 22 August 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
2,898 reactions across 30 posts, in 13 distinct kinds. The most used accounts for 14.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
420
14.5%
👍
340
11.7%
🎉
335
11.6%
💯
316
10.9%
🤩
306
10.6%
🥰
299
10.3%
😍
298
10.3%
❤🔥
290
10.0%
🔥
280
9.66%
👏
6
0.207%
😁
4
0.138%
🏆
3
0.104%
👎
1
0.035%
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 30 of the 30 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 2,898reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 30 most recent posts we hold, published 10 July 2026 to 21 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.
🧑💻 How “emotional triggers” improve sharing
Users don’t share because a button exists — they share because the moment feels worth showing. Emotion turns usage into distribution.
💬 Pride drives sharing: People post what makes them look capable.
💬 Surprise creates momentum: Unexpected wins travel faster.
💬 Identity makes sharing natural: Users share what reflects who they are.
💬 Timing beats placement: Ask when the …
🧑💻 Why “hidden complexity” increases conversion
Simplicity doesn’t mean the product is basic — it means complexity appears only when users are ready. Timing makes depth feel manageable.
💬 Too much depth scares beginners: Advanced options create early doubt.
💬 Simple starts create confidence: Users move faster when choices are limited.
💬 Complexity should be earned: Show more after the first win.
💬 Power features c…
🧑💻 How “behavior-based onboarding” beats user surveys
Users don’t always know what they need before trying the product. Behavior reveals intent better than forms.
💬 Questions create friction: Long setup feels like work before value.
💬 Actions show real goals: Clicks expose what users actually care about.
💬 Adaptive flows feel smarter: The product responds instead of interrogating.
💬 Less asking builds speed: Users…
🧑💻 Why “activation debt” kills growth later
Most products don’t lose users immediately — they lose them after building confusion into the first experience. Activation debt compounds quietly.
💬 Skipped clarity becomes churn: Users carry early confusion into later steps.
💬 Weak first wins reduce trust: People stop believing the product will pay off.
💬 Later nudges can’t fix bad starts: Re-engagement is harder after …
🧑💻 Why “user control” makes automation convert
Automation doesn’t win because it does everything — it wins when users feel safe letting it help. Control is what turns automation into trust.
💬 Invisible actions create fear: Users need to know what changed.
💬 Edit options reduce resistance: Control makes automation feel safer.
💬 Explanations build confidence: “Why this happened” matters.
💬 Manual override protects a…
🧑💻 How “shareable identity” creates organic growth
People don’t share products because they like features — they share things that say something about them. Identity makes distribution natural.
💬 Status drives sharing: Users post what makes them look smart.
💬 Personal wins travel faster: “Look what I did” beats “Try this app.”
💬 Outputs carry the message: A good result explains the product by itself.
💬 One-tap sha…
🧑💻 Why “first result quality” drives retention
Retention doesn’t start after weeks of usage — it starts with the first result. If the first output feels weak, users stop believing.
💬 First results set expectations: Users judge the whole product early.
💬 Quality builds trust fast: A strong output makes the next action easier.
💬 Weak wins create doubt: “Is this worth it?” becomes the churn trigger.
💬 Improvement sho…
🧑💻 How “loss framing” increases upgrade intent
Users don’t always upgrade because they want more — they upgrade when they understand what they’re losing by staying free. Lost potential creates urgency.
💬 Missing value creates tension: Users notice what they can’t do yet.
💬 Limits reveal demand: A wall matters only after real usage.
💬 Specific loss beats vague premium: “Unlock 3 more reports” is clearer than “Go Pr…
🧑💻 Why “fast onboarding” still fails without direction
Most onboarding doesn’t fail because it’s slow — it fails because speed without direction creates confusion. Users need momentum, but they also need a clear path.
💬 Fast isn’t enough: Speed only helps when the next step is obvious.
💬 Direction reduces anxiety: Users act when they know what happens next.
💬 One path beats shortcuts: Too many routes still create …
🧑💻 Why “upgrade timing” matters more than the paywall
Most users don’t reject upgrades because they hate paying — they reject them because the ask appears before the need is clear. Timing makes monetization feel logical.
💬 Early paywalls feel pushy: Users haven’t experienced enough value yet.
💬 Limits reveal demand: Friction proves the user wants more.
💬 Context reduces resistance: A relevant upgrade feels like he…
🧑💻 How “unfinished loops” drive reactivation
Most winback campaigns fail because they remind users to return without giving them a reason. Unfinished progress creates that reason.
💬 Open loops create tension: People want to complete what they started.
💬 Context restores intent: “Your setup is 80% done” beats “Come back.”
💬 Deep links remove effort: Users should land exactly where action continues.
💬 One next step …
🧑💻 Why “generic social proof” doesn’t convert
Most social proof fails not because users ignore trust signals — but because the proof feels irrelevant. Trust needs similarity.
💬 Random logos create distance: Users need proof from people like them.
💬 Specific outcomes feel credible: Numbers beat vague praise.
💬 Context makes proof stronger: Show it near the decision point.
💬 Relevance reduces doubt: The right exampl…
❤🔥18🎉15🥰15❤10👍10🔥10😍10🤩8
Showing the 12 most recent of 30 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.
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 · 102,473 Telegram ranks this channel #19 of 72 here — alongside 71 others — read 22 August 2026
Ecom: The Future. AI, Innovation @ecommerce_AI · 59,070 Telegram ranks this channel #35 of 64 here — alongside 63 others — read 22 August 2026
Crypto Aunt @milfz_crypto · 111,393 Telegram ranks this channel #37 of 67 here — alongside 66 others — read 22 August 2026
This channel appears in 3 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 21 August 2026 — this
entry's latest reading, not the date you are reading this.
“Growth Hacker” (@gr0wth_hack), 71,003 subscribers as measured 21 August 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.