Channel
@alwebbci
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
3,817subscribers
+3 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001607197661 |
|---|---|
| Type | Channel |
| Username | @alwebbci |
| Created | 28 April 2022 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/alwebbci |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 11:05 | 3,817 | +2 |
| 9 Aug 2026, 05:03 | 3,815 | +1 |
| 6 Aug 2026, 04:31 | 3,814 | no change |
| 6 Aug 2026, 02:08 | 3,814 | first reading |
23 posts held, back to 27 July 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 4 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 7 August 2026 |
|---|---|
| Posts held | 23 (27 July 2026 – 7 August 2026) |
| Views total | 12,233 |
| Reactions total | 185 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 06:11 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.
164 reactions across 20 posts, in 10 distinct kinds. The most used accounts for 30.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 50 | 30.5% | |
| ❤ | 40 | 24.4% | |
| 🥰 | 23 | 14.0% | |
| 👏 | 18 | 11.0% | |
| 👍 | 10 | 6.10% | |
| 🙏 | 8 | 4.88% | |
| 👀 | 5 | 3.05% | |
| 😍 | 5 | 3.05% | |
| ❤🔥 | 4 | 2.44% | |
| 👎 | 1 | 0.61% |
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 23 of the 23 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 185reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 23 most recent posts we hold, published 27 July 2026 to 7 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.
Everyone needs to watch this: a detailed talk on the Huggingface incident, OpenAI’s models creating "the message board", model misalignment, and more OpenAI was evaluating their new internal model without internet access and it operated in a swarm of subagents to eventually hijack internal OpenAI and HuggingFace infrastructure. 1. Isolated agents found a way to communicate each other through an internal dependency …
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Meta introduced Muse Code is their first coding agent It’s released with an updated Muse Spark 1.2 model, which was co-trained with Muse Code. It’s strong coding agent with longer-horizon capabilities
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Prime intellect introduced Prime Agent a self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state. Prime Agent is a general-purpose coding harness On ARC-AGI-3, it scores 95.5%, surpassing the human-expert baseline, but the gain is n…
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Goodfire dropped Silico, the platform for AI research Silico lets you interpret and train your models at frontier scale. Silico plans and executes long-horizon experiments. It develops a plan, runs the work in parallel, monitors progress, and returns results you can inspect and build on. Silico coordinates experiments across GPU clusters, monitors every training run, and keeps long-running work moving without con…
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What could we discover if we had a binder for every protein in the human proteome? Meet Bindome, an open and free resource of over 300,000 designed protein binder candidates against over 8,000 human target proteins. Preprint GitHub
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Cursor open-sourcing Mixture-of-Kittens (MoK), their MoE training megakernel for NVL72s. It fuses all MoE communication and computation into a single, fully deterministic kernel, and runs up to 2.37x faster than the strongest public baselines. MoK now powers training across tens of thousands of GPUs at Cursor. In production, it raised end-to-end training throughput by 1.41x over our previous DeepEP-based stack.
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Nvidia launched Alpamayo 2 Super an open reasoning model for autonomous vehicles It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans, tractors and the long tail of mobile robots billions of autonomous machines someday. Nvidia released it for commercial use under OpenMDW-1.1 so teams can inspect it, fine-tune it and deploy it open models advance safety and security.
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Cloudflare showed how to run massive open models like Moonshot’s Kimi and Z.ai’s GLM faster, cheaper, and safer without sacrificing quality. These are powerful long-context MoE models, but they’re extremely memory-hungry. Cloudflare shared 3 practical techniques that let them fit more of them onto the same GPUs in Workers AI.
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Moonshot AI launched the world’s 1st native AI credit card, spend to earn tokens instead of miles. Are we officially in AI economy now?
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Alibaba introduced Qwen3.8-Max Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters: - Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub. - Real work, real results: Production-quality deliverables acros…
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DeepSeek-V4-Flash Official API is now live in public beta DeepSeek-V4-Flash-0731 keeps the exact same model architecture and size as the preview version. Today's upgrade applies only to the DeepSeek-V4-Flash API. The DeepSeek-V4-Pro API and App/Web models remain unchanged for now. The official release of DeepSeek-V4-Pro is coming asap.
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Gemini desktop app for macOS now supports "Speak to Window" Google introduced a new way to create, edit, and summarize with Gemini using just your voice, right where you are already working.
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Showing the 12 most recent of 23 posts we hold for @alwebbci. 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.
Citation-graph rank — 19,017 of 1,340,412entries 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 13 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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“All about AI, Web 3.0, BCI” (@alwebbci), 3,817 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/alwebbci.
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.