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Channel

DevSecOps Talks

@devsecops_weekly

On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Telegram's recommendations · Cite this entry

7,995subscribers

+33 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001336968975
TypeChannel
Username@devsecops_weekly
DescriptionРассказываем об актуальном в мире DevSecOps. Канал DevSecOps-команды "Инфосистемы Джет"
Created13 May 2020measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live20 August 2026
Measurements held6
Confirmed unchanged1 time, most recently 20 August 2026
On Telegramt.me/devsecops_weekly

Growth

7,9627,9957,978.56 August 2026 — 7,962 subscribers7 August 2026 — 7,966 subscribers10 August 2026 — 7,971 subscribers13 August 2026 — 7,977 subscribers16 August 2026 — 7,989 subscribers20 August 2026 — 7,995 subscribers6 August 202620 August 2026
6 measurements spanning 14 days, net +33. 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 7,957–8,000 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
20 Aug 2026, 02:327,995+6
16 Aug 2026, 21:287,989+12
13 Aug 2026, 23:377,977+6
10 Aug 2026, 15:317,971+5
7 Aug 2026, 06:437,966+4
6 Aug 2026, 13:177,962first reading

Engagement

32 posts held, back to 10 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 16 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
21.7%
avg views ÷ 7,995 subscribers
Avg views / post
1,730
22 posts measured
Reaction rate
0.244%
reactions ÷ views · ER floor
Posts in window
22
of 32 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 20 of 22 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 21 August 2026
Posts held32 (10 July 202621 August 2026)
Views total38,149
Reactions total84
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Aug 2026, 20:12 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.

What this channel posts

Photos
96
Videos
1
Links
1,380

Lifetime counters from Telegram’s own channel header, read 22 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Reaction mix

118 reactions across 27 posts, in 12 distinct kinds. The most used accounts for 49.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
5849.2%
👍3529.7%
🔥108.47%
🤔32.54%
🤡32.54%
🖕21.69%
😁21.69%
👎10.847%
👏10.847%
💯10.847%
🗿10.847%
🤝10.847%

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 28 of the 32 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 118reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 32 most recent posts we hold, published 10 July 2026 to 21 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.

Telegram Stars

Stars received
21
across the posts below
Posts paid on
1
of 32 we hold a reading for · 3%
Most on one post
21
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @devsecops_weekly. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 32 most recent posts we hold for this entry, published 10 July 2026 to 21 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Recent posts

21 Aug 2026, 04:23 UTC789 views3 reactionsread 22 August 2026

Kubesplaining: анализ безопасности Kubernetes Всем привет! Зачастую информации о некорректной конфигурации может быть недостаточно. Хочется понять, «к каким последствиям это может привести? Можно ли этим воспользоваться?». Именно на эти вопросы может ответить Kubesplaining. Он позволяет построить «карту передвижения» злоумышленника с указанием возможных способов его реализации. Это достигается за счёт анализа: 🍭

3

20 Aug 2026, 10:28 UTC≈1,050 views11 reactions21 Starsread 22 August 2026

Исследования рынков DevSecOps и MLSecOps Привет, друзья! Предлагаем вашему вниманию целых три (!) исследования, которые мы запустили: 🍗Исследование рынка безопасной разработки и DevSecOps 🍗Исследование рынка средств контейнеризации 🍗Исследование рынка безопасности ИИ-систем (MLSecOps) Они направлены на выявление реального положения дел в DevSecOps и MLSecOps, как наиболее хайповых и быстрорастущих направлениях в рос

👍53🔥3

20 Aug 2026, 04:22 UTC≈1,370 views6 reactionsread 22 August 2026
File

OWASP Agentic Skills: Top 10 Всем привет! В приложении можно найти материал от OWASP (~ 66 страниц), посвящённый вопросам обеспечения ИБ при работе с агентами. «По классике» представлены 10 наиболее значимых угроз: 🍭 Malicious Skills 🍭 Supply Chain Compromise 🍭 Over-Privileged Skills 🍭 Insecure Metadata 🍭 Untrusted External Instructions и не только Для каждой из них приводится описание, подтверждение актуальности

👍5🤝1

19 Aug 2026, 04:25 UTC≈1,190 views2 reactionsread 22 August 2026

Насколько хорошо LLM генерируют рекомендации по устранению ИБ-дефектов? Всем привет! Практика создания исправлений для ИБ-дефектов с использованием LLM становится всё более и более распространённой. Но можно ли им полностью доверять? Изменяют ли предлагаемые ими обновления поведение приложения? Могут ли они, исправляя одни ИБ-дефекты, добавлять другие? Ответам на эти вопросы посвящена статья от Off-by-1 Labs (ИБ

👍2

18 Aug 2026, 04:23 UTC≈1,270 views2 reactionsread 22 August 2026

VulnReach: композиционный анализ с учётом достижимости Всем привет! VulnReach – open-source проект, который комбинирует практики композиционного анализа, taint-анализа и анализ данных во время эксплуатации ПО. На основе этих данных формируется RBoM – Runtime Bill Of Materials. Всё это необходимо для того, чтобы понять, какая именно уязвимость достижима и может быть эксплуатируема, а какая – нет. Для подтверждени

2

17 Aug 2026, 04:21 UTC≈1,290 views2 reactionsread 22 August 2026

JavaScript Analysis for Pentesters Всем привет! В статье можно найти достаточно объёмное и подробное руководство о том, на что обращать внимание при анализе JavaScript-приложений. Статья написана с точки зрения «атакующего», но может быть полезна и тем, кто «защищает». Материал разбит на части: 🍭 Static Analysis 🍭 Dynamic Analysis 🍭 (De) obfustation 🍭 Bypass Code Protection и не только В каждом разделе приводитс

2

14 Aug 2026, 04:23 UTC≈1,650 viewsread 22 August 2026

KubeShark: использование LLM в Kubernetes Всем привет! KubeShark – skill для работы с Kubernetes. Основная его задача – генерировать и/или исправлять конфигурации в ресурсах. Основная проблема, которую пытался решить Автор – галлюцинации, которые зачастую случаются при работе LLM с Kubernetes. Для этого он реализовал следующий подход: 🍭 Изучение контекста. Версия кластера, используемый namespace, окружение, тип р

13 Aug 2026, 04:23 UTC≈1,780 views2 reactionsread 22 August 2026

Поиск ИБ-дефектов с LLM: идемпотентность Всем привет! «Может ли LLM найти один и тот же ИБ- дефект дважды?» - именно этот вопрос задала себе команда Snyk. Для того, чтобы ответить на него ребята запустили 300 сканирований. Один и тот же исходный код. Один и тот же prompt. Один и тот же harness. Несколько раз. Что получилось? Ответ можно найти в достаточно объемной статье (~ 29 минут на прочтение). tl;dr – резул

2

12 Aug 2026, 04:23 UTC≈1,510 views4 reactionsread 22 August 2026

Drogonsec: комплексный анализ ПО Всем привет! Drogonsec – open-source сканер, который объединяет в себе сразу несколько типов анализа: Secrets, SAST и SCA. В результате работы формируется единый отчёт, в котором всё структурировано по типам практик. При желании их можно «включать» и «отключать». Secrets анализирует как текущую директорию, так и историю (при необходимости можно отключить) на наличие чувствительных

👍31

11 Aug 2026, 04:23 UTC≈1,850 views4 reactionsread 22 August 2026

XSS Laboratories Всем привет! XSS Laboratories – open-source проект, в котором неожиданно! собраны лабораторные работы, обучающие тому, что такое XSS и какие они бывают. Всего доступно 9 лабораторных: 🍭 Introduction to XSS Basics 🍭 Stored XSS Attacks 🍭 DOM-based XSS 🍭 Advanced XSS Techniques 🍭 Edit/View Functionality XSS и не только Итого – 5 Reflected, 3 Stored и 1 DOM XSS. Если нет желания запускать локально,

4

10 Aug 2026, 04:23 UTC≈1,830 views5 reactionsread 22 August 2026

Awesome: LLM4Cybersecurity Всем привет! Сегодня хотим рассказать вам про ещё одну Awesome-подборку. В ней собрана информация о возможных способах применения и возможностях LLM, применительно к информационной безопасности. Awesome разбит на разделы: 🍭 LLM Assisted Defense 🍭 Vulnerability Detection 🍭 Program/Vulnerability Repair 🍭 FUZZ 🍭 Insecure Code Generation и не только Для каждого раздела собраны ссылки на ре

👍32

7 Aug 2026, 04:23 UTC≈1,970 views4 reactionsread 22 August 2026

Deployah: «быстрый» deploy в Kubernetes Всем привет! Deployah – open-source утилита, которая упрощает процесс разворачивания приложений в кластере Kubernetes. «Внутри» она содержит всё необходимое: helm, kubectl, kind. За счёт этого требуется всего лишь создать небольшой конфигурационный файл – deployah.yaml, а дальше она сама сделает всё необходимое. Работает это примерно так: 🍭 Анализ конфигурации. Поиск ошибок

4

Showing the 12 most recent of 32 posts we hold for @devsecops_weekly. 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.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

Citation-graph rank

Citation-graph rank — 393,550 of 1,584,142entries 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.

Forward network

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.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Selectel
@Selectel · 63,648
Telegram ranks this channel #38 of 96 here — alongside 95 others — read 21 August 2026
infosec
@it_secur · 61,139
Telegram ranks this channel #46 of 81 here — alongside 80 others — read 22 August 2026
SecurityLab.ru
@SecLabNews · 83,697
Telegram ranks this channel #86 of 90 here — alongside 89 others — read 18 August 2026

This channel appears in 3 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

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 20 August 2026 — this entry's latest reading, not the date you are reading this.

“DevSecOps Talks” (@devsecops_weekly), 7,995 subscribers as measured 20 August 2026. Telegram Register, tgregister.com/channel/devsecops_weekly.

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.