Channel
@SibylSystem
On this record: Topic · Growth · Engagement · Posts · Citations · Cite this entry
278subscribers
-8 since we began measuring on 13 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001416957680 |
|---|---|
| Type | Channel |
| Username | @SibylSystem |
| Created | 25 November 2021 — measured — dated from the channel’s first post |
| First recorded | 13 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/SibylSystem |
Other / unclassifiable — 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 23 September 2026 and assigned it the closest of 31 fixed categories, at 61% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 18 Sept 2026, 17:40 | 278 | +1 |
| 10 Sept 2026, 16:18 | 277 | -3 |
| 2 Sept 2026, 04:03 | 280 | -2 |
| 25 Aug 2026, 20:23 | 282 | -1 |
| 19 Aug 2026, 17:17 | 283 | -3 |
| 13 Aug 2026, 04:15 | 286 | no change |
| 13 Aug 2026, 00:17 | 286 | first reading |
11 posts held, back to 25 November 2021 — 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 11 posts for this entry, the most recent from 25 November 2021. An engagement rate over an empty window would be a number about nothing.
❖ Index of @SibylSystem • Sibyl System • Crime Coefficient • Flags • Hue • Psycho Hazard • Reporting Cheat Sheet • The Dominator • Unbans and Appeals • API, Tokens and Implementation ___________ Powered by ☠️
❖ API, Tokens and Implementation Sibyl allows users to query the database and to gather results of cymatic scans on known criminals and restored users. A detailed documentation and understanding of the API can be gathered by visiting Psycho Pass How do I get my token? PM @SibylRobot All token generations undergo a mandatory Cymatic scan, should your crime coefficient be under 100 you be given a read-access token. …
❖ Unbans and Appeals on @SibylSystem Appeal system on Sibyl is automated, simply visit @SibylRobot and send /start - Sibyl will respond to you should you be worth restoring as a user. Following cases may require a manual appeal • Crime coefficient is above 600 • Crime coefficient is ranging from 251 to 300 and the short ban reason is set to custom • User is being enforced 2nd time _______ • Index
❖ The Dominator The Dominator Portable Psychological Diagnosis and Suppression System (Japanese: 携帯型心理診断鎮圧執行システム Romaji: Keitai-gata shinri shindan chin'atsu shikkō shisutemu)、ドミネーター or 45MW.TRG Dominator is the iconic firearm carried by enlisted Inspection and Enforcement officers of the Public Safety Bureau. This weapon is able to determine the identity of its holder, requiring authentication and wireless connecti…
❖ Sibyl's Cheat-sheet for Reporting and Enforcing The following must be subject to when enforcing a user as Sibyl Keep these in mind when reporting to Sibyl • Report must follow screenshot(s), links and explanation of the issue • Report must be from a non latent criminal, in short you must not be already Sibyl limited • Report by an enforcer must be validated Users can report the following to Reporting Groups i…
❖ What is a Psycho-Hazard? A latent criminal's Crime Coefficient can be contagious. A Psycho-Hazard refers to the poisoning of another's Psycho-Pass following exposure to an already dangerous Psycho-Pass. Users that are admin in spam groups or groups that member scrape and add directly are subject to a psycho hazard event, in this case the admins of the group would be subject to enforcement action along with the ow…
❖ Crime Coefficients - Hue A Hue is a reflection of a person's stress level. Every civilian in Sibyl's database is assigned a specific color depending on the state of their Psycho-Pass. Colors such as light blue or pink indicate a healthy Psycho-Pass, while colors such as medium green or steel blue indicate a deteriorated Psycho-Pass. A clouded hue occurs when stress/spam is consistently present. For example: Spammy…
❖ Crime Coefficients - Flags Basic required reading for Crime coefficient is listed here Civilian ranked coefficients are listed below, these have no enforcement action against them and are free as a bird! +--------------+-------------+ | Label | Coefficient | +==============+=============+ | Civilian | 010-80 | | Restored | 081-100 | | Enforcer | 101-150 | +--------------+-----------…
❖ Crime Coefficient A person's Crime Coefficient (Japanese 犯罪係数 Romaji: Hanzai Keisū) is a numerical measure of said person's probability/propensity to commit a crime. It is one part of a person's overall Psycho-Pass. The Public Safety Bureau uses this measurement to determine if a target is a latent criminal or otherwise. It is calculated and determined by the target's stress level (Hue) and other biological readin…
Sibyl System The Sibyl System is the name given to a law enforcement organization operated in working in cooperation with the weeb government to manage crime in current century Telegram. It is the sole operator and manager of proprietary hardware and software used to analyze the criminal tendencies of the entire telegram's populace. The Sibyl System established the Public Safety Bureau and rules over its personnel, …
Channel created
Showing the 11 most recent of 11 posts we hold for @SibylSystem. 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.
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“Sibyl System” (@SibylSystem), 278 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/SibylSystem.
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.