🌟 【一人一节点】日本/韩国/美国专线 (300 G) 还在忍受共享机场 IP 乱跳导致的账号风控、异地登录警告吗? 独立原生 IP,每次连接稳定如一 完美支持主流交易所与 DeFi 深度交互 极速低延迟,告别卡顿与断连 👇 立即体验独享稳定网络 🚀 网站: https://tglink.io/9160ea193c88b9 7折优惠码 ✈️ 群组: https://t.me/happyvless
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Channel
@NeuralZone
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Domains linked from posts · Cite this entry
389,956subscribers
-6,018 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 316,228–1,000,000.
| Telegram ID | -1002024147742 |
|---|---|
| Type | Channel |
| Username | @NeuralZone |
| Description | @NeuralZone – Curated, hand-picked AI tools and services that are actually useful. Buy ads: https://telega.io/c/NeuralZone contact us via @photofixer |
| Created | Between 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 30 August 2026 |
| Measurements held | 22 |
| Confirmed unchanged | 1 time, most recently 30 August 2026 |
| On Telegram | t.me/NeuralZone |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 8 August 2026 and assigned it the closest of 31 fixed categories, at 100% 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 |
|---|---|---|
| 30 Aug 2026, 03:16 | 389,956 | -158 |
| 29 Aug 2026, 05:36 | 390,114 | -132 |
| 28 Aug 2026, 08:34 | 390,246 | -132 |
| 27 Aug 2026, 05:04 | 390,378 | -570 |
| 26 Aug 2026, 02:26 | 390,948 | -176 |
| 25 Aug 2026, 05:55 | 391,124 | -315 |
| 24 Aug 2026, 04:44 | 391,439 | -460 |
| 22 Aug 2026, 09:35 | 391,899 | -364 |
| 20 Aug 2026, 20:45 | 392,263 | -119 |
| 19 Aug 2026, 17:19 | 392,382 | -384 |
| 18 Aug 2026, 18:12 | 392,766 | -368 |
| 17 Aug 2026, 19:15 | 393,134 | -246 |
| 16 Aug 2026, 15:27 | 393,380 | -553 |
| 15 Aug 2026, 01:08 | 393,933 | -559 |
| 13 Aug 2026, 16:05 | 394,492 | -259 |
| 12 Aug 2026, 15:27 | 394,751 | -292 |
| 11 Aug 2026, 12:43 | 395,043 | -266 |
| 10 Aug 2026, 11:17 | 395,309 | -276 |
| 9 Aug 2026, 10:01 | 395,585 | -211 |
| 8 Aug 2026, 09:58 | 395,796 | first reading |
19 posts held, back to 8 May 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 52 pagesof 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 26 August 2026 |
|---|---|
| Posts held | 19 (8 May 2026 – 26 August 2026) |
| Views total | 36,660 |
| Reactions total | 31 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 30 Aug 2026, 12:42 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 30 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.
Measured directly from 5 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.
544 reactions across 19 posts, in 5 distinct kinds. The most used accounts for 88.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 483 | 88.8% | |
| 👍 | 38 | 6.99% | |
| 🔥 | 15 | 2.76% | |
| 🤔 | 6 | 1.10% | |
| 😁 | 2 | 0.368% |
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 19 of the 19 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 544reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 19 most recent posts we hold, published 8 May 2026 to 26 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.
🌟 【一人一节点】日本/韩国/美国专线 (300 G) 还在忍受共享机场 IP 乱跳导致的账号风控、异地登录警告吗? 独立原生 IP,每次连接稳定如一 完美支持主流交易所与 DeFi 深度交互 极速低延迟,告别卡顿与断连 👇 立即体验独享稳定网络 🚀 网站: https://tglink.io/9160ea193c88b9 7折优惠码 ✈️ 群组: https://t.me/happyvless
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🚀 Seedream 5.0 Pro — a new top-tier AI model for image generation and editing, now available for Premium users of @PhotoFixerBot! Seedream 5.0 Pro excels at generating photorealistic images and high-density infographics, significantly outperforming the previous Seedream 4.5 and 5.0 Lite models. In the rankings, Seedream 5.0 Pro matches Nano Banana 2 and Nano Banana Pro in generation quality, while offering much les…
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Six AI coding agents took one visual IQ test, and Codex 5.5 won by method, speed, and cost One small test asked agents to solve 25 visual puzzles on iq-test.cc, select age 30, and return a result link. This was not a lab benchmark. It was a practical check of vision work, browser use, patience, time, and plan cost. "Take the IQ test on iq-test.cc. When you finish, select age 30 and send me the link to your result…
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GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, …
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How many AI subscriptions are you paying for? • ChatGPT • Claude • GPT Image • Runway • Kling • Seedance • ElevenLabs • Nano Banana • ... Every new AI tool means another subscription, another tab, and another interface to learn. What if one workspace brought them all together? Meet Neurohelper AI — an all-in-one AI workspace with access to leading AI models for: ✅ Writing & Chat ✅ AI Images ✅ AI Videos ✅ Voice & Au…
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🐶 ASO Corgi — platform for the App Store developers. Find the keywords your apps and competitors rank for, and track positions across every country in one place. 🔑 Keyword research: by topic, by your app's languages, from App Store suggestions, by competitors, and with AI analysis. • Rankings by country — history, charts, demand score (0–100) • Global search across any App Store storefront • ASO assistant builds you…
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֎ New: ChatGPT users can now build real apps directly from chat ChatGPT's app store just added AppDeploy, and it works with free accounts too. Describe what you want to build, ChatGPT writes the code, AppDeploy handles deployment automatically inside the same chat, and you get a working link back right away. AppDeploy includes the infrastructure needed for real apps: 🔐 User login and permissions 🗄 Storage, databas…
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🌐 Gemini 3.5 Live Translate breaks language barriers in real time Google has unveiled Gemini 3.5 Live Translate — a new speech-to-speech AI model that delivers near real-time voice translation across 70+ languages. Unlike traditional turn-based translators, it starts translating while you’re still speaking, staying just a few seconds behind and preserving your tone, pacing, and pitch for more natural conversations.…
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😒 Anthropic has released Claude Fable 5 — the first public Mythos-class model Anthropic claims it's their strongest model ever, setting new SOTA results across coding, vision, scientific tasks, document analysis, and long-context reasoning. The bigger and longer the task, the larger the gap compared to previous models. Notable examples: • Stripe migrated a 50M-line Ruby codebase in 1 day. • Fable 5 can rebuild web …
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🌐 Humans are now the minority on the internet According to Cloudflare Radar, between May 31 and June 7, 57.1% of all requests to HTML pages worldwide came from bots, while real users accounted for just 42.9%. The era when the web was primarily used by humans browsing pages may already be behind us. 📸 NeuralZone
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😺 ChatGPT gets a major memory upgrade OpenAI has upgraded ChatGPT's memory system with a new Dreaming architecture that automatically learns from past conversations and keeps memories up to date. Instead of relying mainly on manually saved notes, ChatGPT can now identify important information on its own, carry context across chats, adapt to changing circumstances, and avoid using outdated details. Users can review…
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💻 Microsoft launches 7 AI models and takes aim at the frontier At Build 2026, Microsoft unveiled its new MAI model family, covering reasoning, coding, image generation, speech synthesis, and transcription. Key releases: • MAI-Thinking-1 - reasoning model that Microsoft says rivals top coding models and performs on par with Claude Sonnet 4.6 in blind evaluations. • MAI-Code-1-Flash - a lightweight 5B coding model fo…
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Showing the 12 most recent of 19 posts we hold for @NeuralZone. 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.
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.
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.
20 domainsthis channel’s own posts have linked to, measured by scanning the post bodies themselves — not the channel’s description, which is the separate Declared links section below when this entry has one. Appearing here is not a claim about who runs the linked site or why the channel linked to it; an advertisement, a news citation and a malicious link all leave the same kind of row.
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 30 August 2026 — this entry's latest reading, not the date you are reading this.
“NeuralZone | AI Apps” (@NeuralZone), 389,956 subscribers as measured 30 August 2026. Telegram Register, tgregister.com/channel/NeuralZone.
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.