Channel
@KubeFM
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
331subscribers
+5 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001563069270 |
|---|---|
| Type | Channel |
| Username | @KubeFM |
| Created | Between 1 August 2021 and 28 February 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 24 August 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 24 August 2026 |
| On Telegram | t.me/KubeFM |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 24 Aug 2026, 23:08 | 331 | -1 |
| 16 Aug 2026, 16:12 | 332 | +5 |
| 8 Aug 2026, 07:18 | 327 | no change |
| 8 Aug 2026, 06:00 | 327 | +1 |
| 7 Aug 2026, 13:50 | 326 | first reading |
20 posts held, back to 4 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 7 August 2026 |
|---|---|
| Posts held | 20 (4 August 2026 – 7 August 2026) |
| Views total | 1,607 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 07:18 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.
Measured directly from 9 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Kubernetes tooling is shifting from deployment pipelines to AI request routing. Joel Vasallo walks through the tools he is watching now: Argo and Kargo for software delivery, then Kagent and Agent Gateway for the next wave of platform work. His point is simple: platform teams now need standards for how AI and agent traffic gets routed, observed, and deployed. Watch the full interview: https://ku.bz/zTPZwj-__
This interview is brought to you by Google Cloud — build on GKE, the open platform for the AI era https://ku.bz/kKZQySLRV
New infrastructure often gets treated like a shortcut to better delivery. It rarely works that way. Nathen Harvey explains why tools are necessary but not sufficient: every infrastructure shift also changes how teams work, collaborate, and approach delivery. Kubernetes, containers, and observability all shape engineering practice, not just the technical stack. Watch the full interview: https://ku.bz/LvXfZXzcN
This interview is brought to you by Google Cloud — build on GKE, the open platform for the AI era https://ku.bz/kKZQySLRV
This episode is brought to you by StormForge — automate Kubernetes rightsizing with machine learning. Smarter limits, less waste, better performance. https://ku.bz/X7ls6SKmr
Oleksii Kolodiazhnyi, Senior Architect @ Mirantis, explains the motivation behind his structured approach to Kubernetes workload assessment and his article "Graphs in Your Head." Oleksii shares how he transformed his real-world assessment work on Mirantis Source Registry 4 (MSR4) into actionable guidance, bridging the gap between academic theory and practical application for architects and engineers who need to asse…
When AI writes Kubernetes, who catches the risk? Qodo explores this in a series on wahy faster infrastructure code needs smarter review, not just more automation → https://ku.bz/wy4kwdFqg
When one pull request spans app code, config, and CI, the weakest point is usually not the app. Artem Lajko explains that ownership is split between developers and platform teams, so config changes lack true end-to-end accountability. That makes breakage harder to spot before merge and easier to discover only when CI or infrastructure behavior changes.
This episode is sponsored by LearnKube - get started on your Kubernetes journey through comprehensive online, in-person or remote training https://learnkube.com/training
David Pech, Staff Cloud Ops Engineer, shares hard-learned lessons about introducing Kubernetes and cloud-native practices to organizations with legacy systems. Drawing from a real-world experience where his technically superior Kubernetes solution was ultimately rejected in favor of FTP deployment, David identifies three critical organizational factors that determine success: 1. Team readiness is fundamental - the d…
Kubernetes quality breaks down fast when code, configuration, and testing all get reviewed the same way. Andrew Block explains that platform teams are dealing with different kinds of artifacts for different purposes. His point is simple: Helm charts should not be treated like application source code, and mixing those concerns makes reviews weaker. Watch the full interview: https://ku.bz/-q_FYPGj8
When AI writes Kubernetes, who catches the risk? Qodo explores this in a series on wahy faster infrastructure code needs smarter review, not just more automation → https://ku.bz/wy4kwdFqg
Showing the 12 most recent of 20 posts we hold for @KubeFM. 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 — 10,708 of 1,620,105entries 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.
Republishes
Channels on the register whose posts this channel has forwarded.
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 24 August 2026 — this entry's latest reading, not the date you are reading this.
“KubeFM” (@KubeFM), 331 subscribers as measured 24 August 2026. Telegram Register, tgregister.com/channel/KubeFM.
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.