Other / unclassifiable — 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 12 September 2026 and assigned it the closest of 31 fixed categories, at 38% 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 the other topics.
Growth
24 measurements spanning 93 days, net +52. 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 6,731–6,809 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 24
Measured (UTC)
Subscribers
Change
8 Nov 2026, 11:19
6,795
-1
2 Nov 2026, 04:56
6,796
+3
30 Oct 2026, 05:31
6,793
-2
27 Oct 2026, 07:06
6,795
+2
24 Oct 2026, 16:40
6,793
-2
22 Oct 2026, 03:20
6,795
-5
15 Oct 2026, 19:58
6,800
+22
12 Oct 2026, 19:09
6,778
+10
6 Oct 2026, 07:15
6,768
+11
29 Sept 2026, 14:37
6,757
+17
19 Sept 2026, 07:55
6,740
-15
14 Sept 2026, 16:57
6,755
-8
11 Sept 2026, 03:38
6,763
-6
6 Sept 2026, 06:01
6,769
-4
2 Sept 2026, 07:57
6,773
-6
30 Aug 2026, 04:15
6,779
+3
27 Aug 2026, 11:57
6,776
-7
24 Aug 2026, 15:08
6,783
-9
20 Aug 2026, 23:37
6,792
+37
17 Aug 2026, 13:23
6,755
first reading
Engagement
131 posts held, back to 5 August 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 20 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
10.8%
avg views ÷ 6,795 subscribers
Avg views / post
736
55 posts measured
Reaction rate
0.318%
reactions ÷ views · ER floor
Posts in window
55
of 131 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. It is computed over the 49 of 55 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 7 November 2026
Posts held
131 (5 August 2026 – 7 November 2026)
Views total
40,478
Reactions total
117
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Nov 2026, 13:59 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
238 reactions across 99 posts, in 4 distinct kinds. The most used accounts for 55.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
131
55.0%
👍
105
44.1%
👀
1
0.42%
🥰
1
0.42%
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 102 of the 131 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 238 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 131 most recent posts we hold, published 5 August 2026 to 7 November 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
Telegram’s own ‘edited’ marker: 0 of 55 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
>>Apollo, AI 칩 구매에 750억달러 투입 계획…어느 Neocloud보다도 큰 규모
•사모펀드 대형사 Apollo가 750억달러에 달하는 대규모 투자를 통해 AI 칩 금융 시장을 재편하고 있음. 350억달러를 투자해 Google TPU를 구매한 뒤 Anthropic에 임대하고, 400억달러 규모로 Nvidia Vera Rubin 칩을 구매해 SpaceX에 임대할 계획임. 이는 최대 Neocloud 업체인 CoreWeave의 270억달러를 크게 웃도는 규모로, Apollo가 Amazon, Microsoft, Google에 이어 글로벌 칩 확보 규모에서 4번째 축으로 올라설 전망
>Apollo拟豪掷750亿美元采购AI芯片,规模远超任何一家Neocloud https://wallstreetcn.com/articles/3785447#from=…
Constellation Software는 2026년 3분기 매출 성장률 19%와 유지보수 및 반복 매출 성장률 23% YoY를 보고했습니다.
유기 매출 성장은 2%였으며, 인수가 대부분의 증가를 주도했습니다.
LTM ROCE는 23.6%로, 2025년의 20.7%에 비해 높습니다.
3월, 6월, 9월로 끝나는 분기 동안 $CSU.TO는 작년 동기 대비 현금 인수 지출을 늘렸으며, 이는 SaaSpocalypse 기간이었습니다.
앞으로 몰락의 단계들을 살펴보면서 여러 가지 형태의 자만을 목격할 것이다. 우선 최고가 될 수 없는 분야에 역량을 갖추지 못한 채 뛰어드는 자만이 있다. 자신이 탁월하게 일할 수 있는 수준 이상으로 성장을 추구하는 자만도 있다. 모순적인 혹은 부정적인 증거를 뻔히 알면서도 과감하게 위험한 결정을 내리는 모습에서도 자만을 발견할 수 있다. 외부 위협이나 내부 침식으로 위기에 몰릴 수 있는 가능성을 부정하는 모습에서도 자만이 드러난다. 그리고 가장 은밀히 퍼지는 자만의 형태가 있는데, 바로 오만하게 기존 사업을 방치하는 것이다.
#위대한기업은다어디로갔을까
위대한 기업도 몰락할 수 있다. 아무리 많은 것을 이루었더라도, 아무리 멀리 앞서가도, 아무리 많은 힘을 갖고 있더라도 쇠퇴할 가능성이 있다. 가장 강한 것이 끝까지 정상의 자리를 지키는 법은 없다. 누구든 몰락할 수 있으며 대개는 결국 그렇게 된다.
#위대한기업은다어디로갔을까
EL FY1Q27 Earnings Call Key Takeaways
1. "성장 회복"에서 "이익 레버리지" 단계로 진입했다. 오가닉 매출 +6%에 조정 영업이익 +76%(고정환율 +80%)가 나왔다. 판관비가 3%만 늘어나는 동안 매출이 7% 늘어 영업비용률이 68.5%→64.7%로 낮아진 것이 핵심 원인이다. 다만 이 이익 개선은 업계 공통 현상이 아니다. PRGP로 고정비를 미리 줄여 둔 EL 고유의 자구 노력 효과다. 로레알처럼 이미 높은 마진 위에서 매출 성장만으로 이익을 내는 경쟁사와 구분해서 봐야 한다.
2. 향수가 이번 분기의 엔진이며, 일시적 호황이 아니라 업계 구조적 성장 카테고리다. 향수 매출은 +$140M(+19%)으로 전사 증가분의 약 60%, 오가닉 기준 약 2/3를 차지했다. 영업이익은 +93%($86M→$166…
Loreal(OR) Earnings call Key Takeaways
1. 3분기 조정 LFL +7.7%는 시장(약 +5%)의 약 1.5배입니다. 2025년 하반기에 강화한 "뷰티 부양책(Beauty stimulus plan)"과 신제품 계획이 시장점유율 확대로 전환되고 있음을 보여줍니다.
2. 고가 상품의 강세는 시장 성장이 고가 카테고리에 몰려 있기 때문입니다. 향수(중간 한 자릿수)·더마(약 +10%)·프리미엄 헤어가 성장하고 매스는 정체인데, 로레알은 향수·헤어케어 세계 1위이고 더마에서 시장의 1.5배로 성장하는 등 성장 영역에 이미 1등으로 서 있습니다. 그래서 전체 시장이 +5%에 머물러도 구조적으로 더 높게 성장합니다.
3. "접근 가능한 럭셔리" 가격대가 소비 둔화기에 오히려 유리합니다. 경기가 불확실하면 소비자는 초고가…
몇 년 사이에 $PRM이 얼마나 다각화되었는지 정말 미쳤네요.
솔직히 말할게요, 그들의 전략이 서로 다른 산업의 사업체를 인수하는 거라는 소식을 들었을 때 저는 매우 회의적이었어요. 저는 주로 그들의 화재 안전 사업에 관심이 있었는데, 그 사업은 본질적으로 난연제 시장에서 독점적 지위를 가지고 있었죠.
하지만 지금까지 그들의 인수 전략에 대한 제 회의는 근거가 없었네요.
매출 +24% y/y, 조정 EBITDA +20%
@drewcohenmoney
Flock은 프라이버시, 데이터 오용, 그리고 ALPR 네트워크의 취약한 통제에 대한 강한 대중적 반발에 직면해 있습니다. Axon은 이미 많은 동일한 기관들에 바디 캠, 증거 시스템, 실시간 도구를 공급하고 있습니다. Rick Smith는 Q3 통화에서 거의 500개 기관들이 신뢰와 더 나은 보호 장치 때문에 주로 Axon의 ALPR(Outpost)로 전환했거나 전환 중이라고 강조했습니다. Axon은 기존 계약에 Outpost를 간단히 번들링하고 할인하여 총 비용을 낮추며 PR 골칫거리를 해결할 수 있습니다. Microsoft가 Office를 활용해 Teams로 Slack을 제치던 바로 그 방식처럼요.
👍1
Showing the 12 most recent of 131 posts we hold for @QualityInvestingLaboratory. 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
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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
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.
Handles this channel named that no longer answer
Dead references
2
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
2
vacant every time we have ever looked
@QualityInvestingLaboratory named 2 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@nikesharora named in 2 posts, 19 August 2026 – 19 August 2026
@demian_ai named in 1 post, 19 August 2026 – 19 August 2026
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 8 November 2026 — this
entry's latest reading, not the date you are reading this.
“퀄리티기업연구소” (@QualityInvestingLaboratory), 6,795 subscribers as measured 8 November 2026. Telegram Register, tgregister.com/channel/QualityInvestingLaboratory.
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.