Channel
@usecasereader
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
25subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002721780710 |
|---|---|
| Type | Channel |
| Username | @usecasereader |
| Created | Between 1 June 2025 and 30 September 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 11 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/usecasereader |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 09:17 | 25 | no change |
| 6 Aug 2026, 20:18 | 25 | first reading |
20 posts held, back to 1 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 1 pageof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 11 August 2026 |
|---|---|
| Posts held | 20 (1 August 2026 – 11 August 2026) |
| Views total | 436 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Aug 2026, 09:17 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.
Lifetime counters from Telegram’s own channel header, read 11 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.
Читатель Use Case: дайджест за 3 дня 5 лучших материалов: 1. От болтливых LLM-агентов к управляемым системам / Хабр Коротко: Разбирает, когда LLM-агент оправдан, а когда лучше workflow или мультиагентная схема. Полезно для выбора архитектуры под production с учетом надежности, безопасности и стоимости поддержки. https://habr.com/ru/articles/1068168/?utm_campaign=1068168&utm_source=habrahabr&utm_medium=rss 2. What y…
Читатель Use Case: статья дня How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC… https://netflixtechblog.com/how-and-why-netflix-built-a-real-time-distributed-graph-part-3-querying-the-graph-with-grpc-0f3468349607?source=rss----2615bd06b42e---4 Разбор: Netflix показал не «ещё один граф», а рабочий способ сделать из графа быстрый API для живых данных. Ключевая идея: в ра…
Читатель Use Case: дайджест за 3 дня 5 лучших материалов: 1. How to Write Acceptance Criteria That Engineers Don’t Quietly Ignore Коротко: Материал о том, как писать acceptance criteria без двусмысленности и лишней воды. Полезно практику, чтобы снизить разночтения между продуктом, разработкой и QA и уменьшить переделки. https://medium.com/analysts-corner/how-to-write-acceptance-criteria-that-engineers-dont-quietly-i…
Читатель Use Case: статья дня From Classification to Autonomy: A Multi-Dimensional Survey of Discriminative, Generative, Agentic… https://medium.com/analysts-corner/from-classification-to-autonomy-a-multi-dimensional-survey-of-discriminative-generative-agentic-efb57b04d390?source=rss----a06b17f08582---4 Разбор: Разбор статьи дня: *From Classification to Autonomy…* Автор пытается собрать в одну схему 4 слоя ИИ для …
Читатель Use Case: дайджест за 3 дня 5 лучших материалов: 1. Best AI Engineering Integrations and Connectors in 2026 - Visure Solutions Коротко: Материал о том, как связать инженерные системы, чтобы AI-ассистент работал не в вакууме, а с требованиями, кодом, тестами и рисками. Полезно для практиков, которым нужно снизить ручную склейку данных и ускорить принятие решений. https://visuresolutions.com/ai-engineering/be…
Читатель Use Case: статья дня From Classification to Autonomy: A Multi-Dimensional Survey of Discriminative, Generative, Agentic… https://medium.com/analysts-corner/from-classification-to-autonomy-a-multi-dimensional-survey-of-discriminative-generative-agentic-efb57b04d390?source=rss----a06b17f08582---4 Разбор: Новая статья про «переход от classification к autonomy» в PLM выглядит как аккуратная витрина всех модных…
Читатель Use Case: дайджест за 3 дня 5 лучших материалов: 1. Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned Коротко: Разбор архитектуры сервиса для построения карты зависимостей в реальном времени: потоковая обработка, backpressure, шардирование и борьба с hot nodes. Полезно практику, который проектирует распределённые системы и хочет понять, как доводят observability до продакшн-у…
Читатель Use Case: статья дня From Classification to Autonomy: A Multi-Dimensional Survey of Discriminative, Generative, Agentic… https://medium.com/analysts-corner/from-classification-to-autonomy-a-multi-dimensional-survey-of-discriminative-generative-agentic-efb57b04d390?source=rss----a06b17f08582---4 Разбор: #РазборДня Статья *From Classification to Autonomy…* продаёт красивую схему: AI в продуктовой разработке…
Читатель Use Case: дайджест за 3 дня 5 лучших материалов: 1. Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned Коротко: Разбор того, как строят real-time карту зависимостей сервисов на большой распределённой системе: потоковая обработка, backpressure, шардирование и time-travel запросы. Полезно практикам, которым нужно проектировать наблюдаемость и устойчивые пайплайны. https://netfli…
Читатель Use Case: статья дня Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned https://netflixtechblog.com/building-service-topology-at-scale-architecture-challenges-and-lessons-learned-f4b792f3f0d8?source=rss----2615bd06b42e---4 Разбор: Netflix выпустил редкий честный разбор: как они строили real-time service topology и почему «просто сделать граф зависимостей» на масштабе Netflix …
Читатель Use Case: дайджест за 3 дня 5 лучших материалов: 1. GenRec: Towards LLM-Native Recommendation at Netflix Коротко: Материал о том, как LLM можно использовать как ранжировщик рекомендаций вместо набора ручных признаков. Практику полезно посмотреть на подход к контексту, дообучению и ограничению выдачи под бизнес-правила. https://netflixtechblog.com/genrec-towards-llm-native-recommendation-at-netflix-f20be6f64…
Читатель Use Case: статья дня Secure every commit to production with Claude and GitLab https://about.gitlab.com/blog/claude-security-and-gitlab/ Разбор: GitLab опубликовал типичный, но полезный enterprise-нарратив: Claude помогает писать код безопаснее, а GitLab должен закрыть всё, что происходит после коммита — approvals, policies, audit trail, compliance. Что в статье сильного: - правильно разделены session-base…
Showing the 12 most recent of 20 posts we hold for @usecasereader. 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.
Citation-graph rank — 522,415 of 1,169,250entries 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.
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.
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 11 August 2026 — this entry's latest reading, not the date you are reading this.
“Читатель Use Case'ов” (@usecasereader), 25 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/usecasereader.
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.