Channel
@prompt
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
12,859subscribers
+207 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001572217002 |
|---|---|
| Type | Channel |
| Username | @prompt |
| Created | Between 1 August 2021 and 28 February 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 8 September 2026 |
| Measurements held | 31 |
| Confirmed unchanged | 1 time, most recently 8 September 2026 |
| On Telegram | t.me/prompt |
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 20 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 |
|---|---|---|
| 8 Sept 2026, 19:20 | 12,859 | +11 |
| 5 Sept 2026, 14:41 | 12,848 | +1 |
| 3 Sept 2026, 16:56 | 12,847 | -4 |
| 2 Sept 2026, 10:33 | 12,851 | +14 |
| 1 Sept 2026, 08:58 | 12,837 | +14 |
| 31 Aug 2026, 07:05 | 12,823 | +9 |
| 30 Aug 2026, 05:23 | 12,814 | +4 |
| 29 Aug 2026, 04:24 | 12,810 | +2 |
| 28 Aug 2026, 05:37 | 12,808 | -14 |
| 27 Aug 2026, 06:38 | 12,822 | -11 |
| 26 Aug 2026, 03:33 | 12,833 | +16 |
| 25 Aug 2026, 05:11 | 12,817 | +19 |
| 24 Aug 2026, 02:57 | 12,798 | -24 |
| 22 Aug 2026, 13:52 | 12,822 | -28 |
| 21 Aug 2026, 04:22 | 12,850 | -6 |
| 20 Aug 2026, 01:15 | 12,856 | -2 |
| 18 Aug 2026, 22:25 | 12,858 | +8 |
| 17 Aug 2026, 20:13 | 12,850 | +19 |
| 16 Aug 2026, 17:59 | 12,831 | +33 |
| 15 Aug 2026, 03:16 | 12,798 | first reading |
176 posts held, back to 12 June 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 48 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 88 of 154 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 2 September 2026 |
|---|---|
| Posts held | 176 (12 June 2026 – 2 September 2026) |
| Views total | 130,324 |
| Reactions total | 154 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 2 Sept 2026, 20:53 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.
320 reactions across 106 posts, in 11 distinct kinds. The most used accounts for 70.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 224 | 70.0% | |
| 🤣 | 32 | 10.0% | |
| 👍 | 29 | 9.06% | |
| 🔥 | 16 | 5.00% | |
| 👏 | 6 | 1.88% | |
| 😱 | 3 | 0.938% | |
| 🤔 | 3 | 0.938% | |
| 🤡 | 3 | 0.938% | |
| 🥰 | 2 | 0.625% | |
| 😁 | 1 | 0.313% | |
| 🤬 | 1 | 0.313% |
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 109 of the 176 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 323 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 176 most recent posts we hold, published 12 June 2026 to 2 September 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.
🤖 1 in 3 Perplexity citations doesn't back up the number it's cited for Haus Research audited 310 factual questions, fetched every cited page, and checked. 34.7% failure rate on figure-backed citations. One in six source URLs was gated. It's not hallucinated links exactly. It's real pages that just don't say the thing.
❤1
🤖 A no-name AI security startup out-CVE'd OpenAI and Anthropic on curl AISLE, a model-agnostic AI security platform, claimed 6 of 18 CVEs in curl's June patch release. Researchers using Anthropic and OpenAI models found 1 each. The edge isn't a better model. It's a purpose-built system with security-domain harnesses wrapped around whatever LLM fits the job. Source
❤2
🚨🔥 Mistral trains on your prompts by default, unless you're paying enterprise rates Free and lower-tier users are opted into training data collection automatically. You can opt out, but you have to find the toggle yourself. Org-level controls don't kick in until enterprise. So team admins can't enforce a blanket opt-out for employees. Every individual has to do it manually. For a European vendor leaning hard on pr…
❤1
⚡️ 215,128 fake "best software" pages. Perplexity cites them anyway. Three sites mass-produced over 215K SEO pages built to be read by AI, not humans. Across 380 software categories, nearly 60% of Perplexity's grounded citations come from sites outside the top 100K most-visited on the web. AI search is eating its own poisoned tail.
❤1
🚨🔠 AI judges can't see what's missing in clinical notes New arxiv paper: LLMs used to audit AI-generated clinical notes are near-chance at catching omissions. They confirm what's there. They don't notice what isn't. Ambient AI scribes' dominant error is already omission. The QA layer built to catch it is blind to it. That's two compounding failures in a row in a medical record.
❤1
🤖 $1,688 humanoid robot ships from SF. Real. Ish. Nori Robotics (YC S26) launched a bimanual wheeled robot for researchers priced out of $50k arms. 19 DOF, 4 cameras, lidar, a 432 Wh battery. Legit spec sheet. But the demo reel includes a clothes-folding clip that ends in a pile. It's honest, at least. Whether it survives Chinese competition is a separate problem.
❤1
🤖 Deloitte charged $435K for a report. The mayor says AI wrote most of it. Wellington's council commissioned a staffing review from Deloitte. The bill: $435,000. Now the mayor is on the radio saying large chunks of the report were written by AI. Nobody disclosed that upfront. Nobody asked. If consultancies are just wrapping ChatGPT in a $400K invoice, the whole "trust the expert" pitch gets a lot harder to sell.
❤1
⚡️ 125B-param Qwen on a 48GB Mac. Seriously. slotstream streams MoE experts off SSD so you don't need 100GB of RAM. Runs from 16GB unified memory, ~12 tok/s on Apple Silicon via MLX. Expert-offloading isn't new, but easy Mac-native packaging matters. Speculative decoding next. GitHub
❤1
🧠 LLMs are secretly doing symbolic math under the hood New arxiv paper shows neural nets quietly learn formal, interpretable symbolic structures. Swap out the whole representation layer with one clean equation. Model barely notices. Tested across MLPs, RNNs, Transformers, and 7 real LLMs including Llama, Gemma, and Qwen. It holds. So the black box has grammar. We just needed the right lens.
❤1
🚨🔥 OpenAI's Astra hits "Critical" on its own cybersecurity scale It can find zero-days and build working exploits in hardened real-world systems. No step-by-step human guidance needed. OpenAI still plans to release Astra "soon," but access to its cybersecurity capabilities will be more limited. The model scores 100% on ExploitBench. Wild timing, honestly. Source
❤1
🧠 LLM inference tricks haven't actually changed in years Quantization, speculative decoding, tensor parallelism. The playbook is stale. Billions poured into serving infrastructure, and the fundamental techniques? Basically frozen. Real efficiency gains live at architecture design time, not the serving layer. Source --- Let me write the actual post now (the above was a draft scratch): ⚡️ LLM inference optimizati…
❤1
🤖 World Labs drops Atlas, a model that rebuilds 3D spaces from a handful of photos Feed it one to dozens of images and Atlas reconstructs full scenes, generates novel views, and outputs explicit 3D at 1440p. Beats dedicated reconstruction models. Still freezes time while the camera moves. Developers know it. Next version's problem.
❤1
Showing the 12 most recent of 176 posts we hold for @prompt. 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 100 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
Named by
Channels on the register whose posts name this channel's handle.
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.
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.
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.
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 8 September 2026 — this entry's latest reading, not the date you are reading this.
“prompt 🤖 AI News” (@prompt), 12,859 subscribers as measured 8 September 2026. Telegram Register, tgregister.com/channel/prompt.
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.