6 measurements spanning 10 days, net +26. 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 1,777–1,811 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
17 Aug 2026, 22:23
1,807
+5
15 Aug 2026, 00:07
1,802
+17
11 Aug 2026, 13:42
1,785
+1
8 Aug 2026, 10:36
1,784
+3
7 Aug 2026, 20:49
1,781
no change
7 Aug 2026, 18:33
1,781
first reading
Engagement
12 posts held, back to 16 April 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 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
20.3%
avg views ÷ 1,807 subscribers
Avg views / post
366
1 post measured
Reaction rate
2.46%
reactions ÷ views · ER floor
Posts in window
1
of 12 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 5 August 2026
Posts held
12 (16 April 2026 – 5 August 2026)
Views total
366
Reactions total
9
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 20:49 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.
Reaction mix
106 reactions across 10 posts, in 4 distinct kinds. The most used accounts for 88.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
94
88.7%
🔥
9
8.49%
👍
2
1.89%
👎
1
0.943%
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 11 of the 12 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 106reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 12 most recent posts we hold, published 16 April 2026 to 5 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.
📣 NUS Product Club Publicity Department is recruiting!
Love creating content, designing graphics, editing videos, or being in front of the camera? Join the team behind NUS Product Club's brand and help bring our events and community to life.
Open Roles:
🎙 Podcast Host
🎬 Video Editor
🎨 Social Media Designer
What you'll get:
• Build an impressive creative portfolio with real-world projects
• Gain hands-on experience…
📣 NUS Product Club is recruiting!
Want to break into product management, and build with a community that ships high impact events? This is your sign!
What you’ll get:
• Priority access to our exclusive events
• Workshops with industry practitioners
• Networking opportunities and relationships with professionals
• Insights from senior PMs and industry leaders
Who we’re looking for:
• Curious, proactive students (an…
an admin update on “lorong product”
thank you all for supporting our podcast, as well as our club over the past three years! this experience will definitely be a highlight of my life. we hope that our season finale gave you a greater insight on what goes behind the scenes, especially those looking to join us in the near future.
missed episode 25? watch it here!
- admin harry
“Lorong Product”
Episode 25: Building Product Communities
For our final episode, we’ve got a small surprise — Harry and Marcus are stepping into the guest seat!
What does it take to build a student community that lasts? Harry and Marcus reflect on NUS Product Club’s growth over the past three years, from scaling events and partnerships to expanding the podcast itself. In this episode, we explore leadership transiti…
“Lorong Product”
Episode 24: Software Product Management
What does a product internship actually look like beyond the buzzwords? Yi Teng shares her experience as a Software PM Intern at Savant Degrees, where she worked across product documentation, testing, and different stages of the software development lifecycle. In this episode, we explore the realities of day-to-day PM work and what students should expect in th…
“Lorong Product”
Episode 23: AI Product Building
What does building with AI actually look like beyond the hype? Aarav shares how he worked on AI solutions for the Bhutan government, explored agentic AI systems, and later co-founded Evalumate, an AI-powered education startup. In this episode, we explore problem validation, AI product development, and what it takes to build products from zero to one.
Watch Episode 23…
“Lorong Product”
Episode 22: Technical Product Management
Ever wondered what it takes to land PM roles at big-name companies? James shares how he transitioned from software engineering internships into product roles across startups, ByteDance, and now Crypto.com. In this episode, he spills the details behind technical PM interviews, reflects on whether notoriously difficult NUS CS modules actually matter in the work…
“Lorong Product”
Episode 21: Breaking Into Product
Do you need the perfect internship to break into product? Before becoming a Product Manager at Shopee, Jireh built experience across HR, operations, and management roles that didn’t seem directly related to PM at first. In this episode, we explore transferable skills, early career uncertainty, and how students can break into product without following a traditional p…
“Lorong Product”
Episode 20: Product Career Pivots
Product careers are rarely linear. From shaping NUS Product Club’s early branding efforts to exploring product roles before becoming a UX Designer at GovTech, Jing Xuan shares how creativity and product thinking can evolve across different roles. In this episode, we explore transitioning between PM and UX, breaking in as a non-technical student, and why curiosity ma…
Welcome to our series: Startups VS Big Tech 🚀
In this series, we break down the difference between PM roles in Startups vs Big Tech for students — from the skills to build, to interview prep, and the roles you can explore.
Today’s topic: How does each path shape your career?
Where you start as a PM shapes how you think, grow, and the opportunities you unlock later on.
🔶 Startups
You’ll develop a broader product v…
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Showing the 12 most recent of 12 posts we hold for @nuspc. 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 2 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.
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 12 most recent posts we hold, published 16 April 2026 to 5 August 2026. 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.
Citation-graph rank
Citation-graph rank — 588,459 of 1,565,402entries 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.
Mentions
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.
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 17 August 2026 — this
entry's latest reading, not the date you are reading this.
“NUS Product Club” (@nuspc), 1,807 subscribers as measured 17 August 2026. Telegram Register, tgregister.com/channel/nuspc.
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.