Technology — 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 22 September 2026 and assigned it the closest of 31 fixed categories, at 64% 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
8 measurements spanning 51 days, net -6. 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 295–304 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
27 Sept 2026, 23:17
296
-6
14 Sept 2026, 00:38
302
-1
6 Sept 2026, 20:18
303
+3
30 Aug 2026, 05:26
300
-2
22 Aug 2026, 12:08
302
+3
15 Aug 2026, 00:25
299
-3
8 Aug 2026, 08:01
302
no change
7 Aug 2026, 19:07
302
first reading
Engagement
5 posts held, back to 23 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 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 5 posts for this entry, the most recent from 6 August 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
6 reactions across 3 posts, in 3 distinct kinds. The most used accounts for 50.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
3
50.0%
👍
2
33.3%
👀
1
16.7%
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 3 of the 5 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 6 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 5 most recent posts we hold, published 23 July 2026 to 6 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.
Вам говорят: ≪Это нужно автоматизировать≫.
Какие первые мысли?🤔
У многих руководителей возникают ассоциации с:
— месяцами работы,
— сложными интеграциями,
— долгим обучением сотрудников
— постоянным участием IT-специалистов.
Это причины, по которым многие компании откладывают автоматизацию “на потом”, хотя процессы уже требуют изменений.
⚡️️ В случае с программным комплексом FIT FLEET все устроено иначе. Мы специ…
Автоматизация — это не цель. Это инструмент, который помогает устранить конкретные ограничения в работе компании.
Чтобы процесс был эффективнее после автоматизации, нужно понять, где компания реально теряет время, деньги или управляемость. Только после этого выбирать решение.
🔵 Провести первичную оценку поможет наш экспресс-аудит. За 10–15 минут вы сможете определить точки роста и понять, какие направления автомати…
Чтобы принимать сильные управленческие решения, важно быть в курсе того, как меняется отрасль.
📆 Мы собрали крупнейшие выставки и конференции для руководителей автопарков, транспортных компаний и специалистов по логистике.
На этих мероприятиях обсудят:
✅ цифровизацию управления автопарками;
✅ автоматизацию сервисных процессов;
✅ изменения транспортного законодательства;
✅ коммерческий транспорт и сервис;
✅ логисти…
🚘🚘 Кажется, что самые большие расходы автопарка — это ремонты, запчасти и топливо.
❗️ Но есть менее заметная статья расходов, которая каждый месяц незаметно "съедает" деньги компании.
📑 Бумажные документы, ожидание подписей, пересылка оригиналов, потерянные акты, задержки оплат, ручная обработка в бухгалтерии — все это превращается в реальные финансовые потери.
⚡️️ При этом эксперты прогнозируют, что в ближайшие…
🔥3
Showing the 5 most recent of 5 posts we hold for @fit_fleet_autopark. 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.
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 27 September 2026 — this
entry's latest reading, not the date you are reading this.
“АВТО ПУЛЬС | Про автопарки и управление” (@fit_fleet_autopark), 296 subscribers as measured 27 September 2026. Telegram Register, tgregister.com/channel/fit_fleet_autopark.
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.