考研的代价
❤2😁2👍1

Channel
@python_fenx
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Cite this entry
32,756subscribers
+6,686 since we began measuring on 9 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001097374184 |
|---|---|
| Type | Channel |
| Username | @python_fenx |
| Description | 本频道为技术分享频道 ,频道中投稿分享的所有漏洞请涉事官方尽快修复!避免不法分子滥用造成危害。 漏洞信息披露、安全预警、技术研究、漏洞分析、风险通报 @pyifenx 防丢失频道。 🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳🇨🇳 |
| Created | Between 1 September 2016 and 30 September 2017 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 13 October 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 13 October 2026 |
| On Telegram | t.me/python_fenx |
Hacking & security — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 50% 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 |
|---|---|---|
| 13 Oct 2026, 11:16 | 32,756 | -96 |
| 9 Oct 2026, 09:15 | 32,852 | -377 |
| 4 Oct 2026, 23:56 | 33,229 | +210 |
| 27 Sept 2026, 17:56 | 33,019 | +9,370 |
| 17 Sept 2026, 15:00 | 23,649 | -231 |
| 15 Sept 2026, 13:15 | 23,880 | -277 |
| 13 Sept 2026, 19:56 | 24,157 | -1,898 |
| 12 Sept 2026, 02:36 | 26,055 | -1,600 |
| 9 Sept 2026, 16:36 | 27,655 | -267 |
| 6 Sept 2026, 07:00 | 27,922 | +24 |
| 4 Sept 2026, 00:39 | 27,898 | -405 |
| 2 Sept 2026, 11:36 | 28,303 | -52 |
| 1 Sept 2026, 09:03 | 28,355 | -10 |
| 31 Aug 2026, 10:28 | 28,365 | -66 |
| 30 Aug 2026, 12:44 | 28,431 | -86 |
| 29 Aug 2026, 09:27 | 28,517 | +9,672 |
| 28 Aug 2026, 08:03 | 18,845 | -899 |
| 27 Aug 2026, 11:04 | 19,744 | -684 |
| 26 Aug 2026, 12:24 | 20,428 | -794 |
| 25 Aug 2026, 11:57 | 21,222 | first reading |
788 posts held, back to 6 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 93 pages of 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. It is computed over the 275 of 313 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 14 October 2026 |
|---|---|
| Posts held | 788 (6 August 2026 – 14 October 2026) |
| Views total | 725,090 |
| Reactions total | 1,487 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 14 Oct 2026, 12:29 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 14 October 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.
Measured directly from 31 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.
4,061 reactions across 589 posts, in 36 distinct kinds. The most used accounts for 27.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 1,121 | 27.6% | |
| ❤ | 780 | 19.2% | |
| 🤣 | 484 | 11.9% | |
| 🔥 | 185 | 4.56% | |
| 🎉 | 182 | 4.48% | |
| 👏 | 177 | 4.36% | |
| 🥰 | 170 | 4.19% | |
| 🤩 | 154 | 3.79% | |
| 🤮 | 116 | 2.86% | |
| 💩 | 115 | 2.83% | |
| 👎 | 101 | 2.49% | |
| 😱 | 70 | 1.72% | |
| 🤡 | 52 | 1.28% | |
| 🤔 | 50 | 1.23% | |
| 😁 | 42 | 1.03% | |
| 😢 | 36 | 0.886% | |
| 🥱 | 29 | 0.714% | |
| 🤬 | 27 | 0.665% | |
| 😡 | 26 | 0.64% | |
| 💔 | 24 | 0.591% | |
| 16 further kinds | 120 | 2.95% |
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 600 of the 788 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 4,061 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 788 most recent posts we hold, published 6 August 2026 to 14 October 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.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @python_fenx. 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 788 most recent posts we hold for this entry, published 6 August 2026 to 14 October 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.
考研的代价
❤2😁2👍1
https://xljk.whebd.cn/ 可查武汉中小学生学籍 账号为sfz,密码为后六位@WH 事例身份证:421123201501090033 小概率出学籍 ——热心网友 📮 投稿通道 ☘️ 交流群组
👍5🤣2❤1
新群,大家进一下,谢谢! @ifengxiang
👍2
i交流 要不开发一段时间的自由贸易?不能打广告,但是各位可以在这里拉客接单,类似春江目前的情况。 你们觉得如何?
👍1
⚡️⚡️⚡️⚡️⚡️医疗记录 医疗缴费 报告版本 通通限时⚡️⚡️ ⚡️⚡️⚡️⚡️⚡️医疗记录 医疗缴费 报告版本 通通限时⚡️⚡️ ⚡️⚡️⚡️⚡️⚡️ 医疗记录 医疗缴费 报告版本 通通限时⚡️⚡️ 直接机器人自助报单 @xiligehubot 里面有各种源头秒单业务 例如 婚姻记录5U 假地址个户0.1U 等 直接机器人自助报单 @xiligehubot 里面有各种源头秒单业务 例如 婚姻记录5U 假地址个户0.1U 等 直接机器人自助报单 @xiligehubot 里面有各种源头秒单业务 例如 婚姻记录5U 假地址个户0.1U 等 机器人自动出单 保真不保漏全天秒,因为是每个单子排队出,会出现超时的情况,超时就会回空,出空的可以再报一次看下 也可以找人工出另一个版本 3U 注意:身份证后面的x 需要转换为大写 的X PS:正确推广思路为 婚前调查,男女朋友判断是否有传染病 打胎 分娩等 可配合…
👍1
老牌查档!强势回归 小u业务bot @clzhbot 里面有 免费系列: 免费大头 免费法人 免费加工(10几套板子免费使用图片质量大于市面上免费机器人) 名下卡仅需4.5u 慢线真地址1.9u代理可调优势一天多批 里面有20多套房产几乎都只要9.5u! 邀请一人可得1积分 点击项目后不购买请点击返回菜单以免扣分 机器人内置okpay自动充值其他客服可以私信人工客服 文字全户10u 其他业务报单联系 @xiaoutianyancha 代理谈价格给优势 欢迎代理律所对接 春江8518已上押1600u安全的交易环境让客户放心更多业务正在研发中请加入机器人以备不时之需 #付费广告 11-09 到期
👍3
长期收 文字学历接口 样板: 毕业院校:吉林师范大学 学历层次:本科 专业名称:软件工程 学习形式:全日制 毕业日期:2020-06-30 要求提供姓名身份证 出,专业,毕业时间,学校,接口无限制,支持担保。 全国大头 0️⃣🔤1️⃣🔤 全国户籍地2️⃣🔤 全国批量法人0️⃣🔤4️⃣🔤 有多少吃多少,一万单也是秒。 各地区神父,市场有的全有。 欢迎代理,有量的找我开机器人。 请备注来意, 联系客服 @supet888 联系客服 @supet888_bot 欢迎您得骚扰。期待我们得合作。 问问不要钱,让命运的齿轮从我们认识的这一刻开始转动。 #付费广告 11-08 到期
👍4
🔥 全国常用号回归!全国常用号回归!全国常用号回归!全国常用号回归!全国常用号回归! ✈️ 最强纯公益机器人!免费完整常用号,免费批量三要素,免费单次三要素,免费机主二要素,免费无限制补齐,免费对公账户等等功能功能正在逐步添加..... https://t.me/js96008_Bot?start=QD9RWYDP ✅ 注册就送 1000 积分,无任何套路 🔥 免费常用号回归! #付费广告 10-27 到期
👍3
📢 【智网全国户籍机器人,官方认证,24小时秒开!】 📢 【智网出品,品质保证】 官方认证图标 全自动充值 全天候24小时秒开! 一键生成:律师盖章白底版 / 身份证复印件版 查询升级系统:查得越多,省得越多! 自动升级,等级越高,价格越优惠! 🤝 【团队、代理、合作共赢,福利拉满!】 🤝 10%邀请返利! 欢迎转发、代理、拉群合作,更多专属福利等你! 【智网生态:全方位服务!】 🤖 全国户籍机器人:@zhiwangBOT 📚 免费百亿社工库:@AEONSGKBot 🏦 开户查询请联系:@zhiwangkefu #付费广告 10-16 到期
👍2
长期互换收购各类数据 [📊] 数据要求:未广泛流通、覆盖面广,且数据量需不低于500万。只有个位数的联系我我会直接拉黑! [📌] 推荐数据类型:身份证、手机号 兑换金额将根据数据的质量和数量评估。请勿提供过时的"古董"数据,不要问在么,不要浪费双方时间。 样本频道:https://t.me/Dataleak007 对接联系人: @K114114 双向: @GX_kefu_bot #付费广告 11-14 到期
👍1
全网最牛逼的综合机器人 鼎盛综合机器人 @DnshZhBOT 大头照/假地址0.1U 民政婚姻 5U 带历史无遗漏 抖音出手机号 1U 快手出手机号 1U 小红书出手机号 1U 移动机主名字生日 18U 秒线公网户籍地址 5.5U 慢线公网户籍地址 1.9U 正反/盖章/真地址个户 6.5 U 大头+户籍地+民族 保真保最新 以下24小时秒出↓ 免费法人股东 出单率高 免费加工正反盖章 可过法院诉讼 出入境带名下护照 2U 电信名字/生日 14U 有效期 1U 联通机主 5U 地区个户 1U 名下公司 1U 免费机主模糊位机器人 @DshMhBot 核验权威可靠,百分百准确 支付宝三要素 0.1U 人脸对比 0.1U 携号转网 0.1U 机主二三要素 0.1U 银行卡二三四要素 0.1U 法人四要素 0.1U 姓名身份证二要素 0.1U 机器人: @DnshZhBOT #付费广告 10-18 到期
❤2👍1
⚡️⚡️⚡️⚡️⚡️医疗记录 医疗缴费 报告版本 通通限时⚡️⚡️ ⚡️⚡️⚡️⚡️⚡️医疗记录 医疗缴费 报告版本 通通限时⚡️⚡️ ⚡️⚡️⚡️⚡️⚡️ 医疗记录 医疗缴费 报告版本 通通限时⚡️⚡️ 直接机器人自助报单 @xiligehubot 里面有各种源头秒单业务 例如 婚姻记录5U 假地址个户0.1U 等 直接机器人自助报单 @xiligehubot 里面有各种源头秒单业务 例如 婚姻记录5U 假地址个户0.1U 等 直接机器人自助报单 @xiligehubot 里面有各种源头秒单业务 例如 婚姻记录5U 假地址个户0.1U 等 机器人自动出单 保真不保漏全天秒,因为是每个单子排队出,会出现超时的情况,超时就会回空,出空的可以再报一次看下 也可以找人工出另一个版本 3U 注意:身份证后面的x 需要转换为大写 的X PS:正确推广思路为 婚前调查,男女朋友判断是否有传染病 打胎 分娩等 可配合…
Showing the 12 most recent of 788 posts we hold for @python_fenx. 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.
@python_fenx edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
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.
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.
@python_fenx named 2 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
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 13 October 2026 — this entry's latest reading, not the date you are reading this.
“i分享『软件分享/脚本分享/漏洞分享』” (@python_fenx), 32,756 subscribers as measured 13 October 2026. Telegram Register, tgregister.com/channel/python_fenx.
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.