Active channel: 3 posts in the index in 30 days, reach ≈3.2% of the audience, audience is stable.
51,880subscribers?±0 in a day
Subscribers from the latest crawl of the venue. Growth is the difference with daily measurements collected since 29.09.2026. How we count →
3posts in 30 days?
Channel posts in the search index over the last 30 days. Posts published as the channel are not indexed — these are author-signed ones. How we count →
1,674average post reach?
Average views of a channel post in 30 days, counting posts for which Telegram shows views. How we count →
3.2%ER — reach to subscribers?
ER = average post reach ÷ subscribers × 100%. Channels with 100+ subscribers and at least 3 posts in 30 days. How we count →
13post forwards in 30 days?
How many times the channel's posts were forwarded in 30 days, by Telegram's counter. How we count →
0venues cited it in 30 days?0 all time
How many other venues forwarded its posts in 30 days; below — all time. Self-forwards are not counted. How we count →
959posts in the index?1,371 total in Telegram
The venue's posts in our index over its whole history; below — the number of the latest message in Telegram. How we count →
2023-07-22first post?
Date of the venue's earliest post in the index. How we count →
#102 938catalog rank?#5 185 in topic · #6 243 in language
Rank by the combined score: subscribers, activity, reach and citations. Below — rank in its topic and language. How we count →
Subscribers 51,880
history since 2026-10-03
2026-10-032026-10-06
Audience checkhow we check
Not enough data to checkToo few posts with views in 30 days to compare
Posts per day
max 2
2026-07-092026-10-06
Average post reach by day
max 4,698
2026-07-092026-10-06
When it posts (UTC, 90 days)
036912151821MoTuWeThFrSaSu
What it posts (90 days)
Top posts in 30 days
GigaChat 3.5 Reasoning is a new open-source LLM designed to reason before generating responses. The model breaks problems into stages, builds execution plans, checks intermediate results, and self-corrects when needed.
2,067 views · 2 forwards · 2026-09-24 19:09 UTCUnderstanding Popular ML Algorithms:
1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.
2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likeli2,048 views · 6 forwards · 2026-09-23 19:19 UTCSkills for data analyst907 views · 5 forwards · 2026-10-02 08:45 UTC
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5,221+1subscribers150posts in 30 d672reach12.9%ER1cited bycatalog data as of 06.10.2026 21:58 UTC · Updated: 2026-10-06 22:42 UTC · How we count →