Channel
@auranw
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
1,278subscribers
-8 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001627647516 |
|---|---|
| Type | Channel |
| Username | @auranw |
| Created | Between 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/auranw |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 06:29 | 1,278 | -8 |
| 10 Aug 2026, 16:51 | 1,286 | -1 |
| 7 Aug 2026, 13:15 | 1,287 | +1 |
| 7 Aug 2026, 02:16 | 1,286 | first reading |
20 posts held, back to 5 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 1 pageof 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 4 August 2026 |
|---|---|
| Posts held | 20 (5 June 2026 – 4 August 2026) |
| Views total | 264 |
| Reactions total | 5 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 02:16 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.
43 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 65.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 28 | 65.1% | |
| 👍 | 9 | 20.9% | |
| 🔥 | 6 | 14.0% |
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 20 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 43reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 5 June 2026 to 4 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.
Governments are no longer treating digital infrastructure as a technology initiative. They're treating it as strategic national infrastructure. Around the world, countries are investing in AI, digital identity, tokenized financial markets, digital public infrastructure, and next-generation payment systems The next era of economic competitiveness won't be defined by GDP alone. It will also be shaped by how nations b…
❤3
The RWA market is growing rapidly, and new tokenized assets continue to enter the market. But increasing supply alone won't drive adoption. Discovery, distribution, and usability. These are becoming the real bottlenecks. It will be defined by how easily people can discover, access, and interact with them.
👍1
The Infrastructure Behind OpenA OpenA → AI agent layer for intent, analysis, coordination, and workflow execution Aura Pay → programmable payment layer for AI-driven transactions and settlement Aura Network → RWA chain providing on-chain infrastructure for tokenized assets and real-world use cases AuraScan → verification layer for transparent, auditable transaction records Together, they form the infrastructure …
❤1
As AI agents begin interacting with financial systems, the challenge is building trusted execution. The Bank of England recently pointed to governance, authorization, and execution controls as key requirements for AI operating in payments and financial markets. The next generation of AI infrastructure won't just optimize intelligence. It will ensure every action is governed, auditable, and accountable.
❤1
AI Agents Are Entering Commerce Visa, Mastercard, Stripe, and PayPal are all investing in the infrastructure behind AI commerce. That tells us something important: AI commerce is no longer about smarter assistants. It's about enabling AI agents to participate in real economic activity. The next generation of commerce will include people, businesses, and AI agents.
❤2
Securitize Brings Its Shares On-Chain Securitize has debuted on the New York Stock Exchange under the ticker SECZ, while simultaneously launching tokenized versions of its shares on-chain. It's another signal that the RWA conversation is moving beyond experimentation and toward mainstream financial adoption.
❤1
Tokenized assets are just one application of programmable economies. The broader opportunity lies in enabling AI agents to interact with digital and real-world economic systems. OpenA is being built beyond asset discovery and portfolio management. It's an open agentic layer for AI-native commerce, tokenized real-world assets, programmable payments, and real-world coordination. Building toward a future where AI agen…
👍1
AI becomes far more valuable when it can interact with assets, not just information. Tokenized real-world assets bring ownership, financial value, and economic activity into programmable infrastructure—creating entirely new opportunities for AI agents. Rather than simply analyzing markets, AI can help discover, manage, and coordinate interactions around real-world value. RWA gives AI a purpose beyond information.
🔥1
The value of tokenization isn't measured by how many assets move on-chain. It's measured by how easily people can access and use them. As the tokenized economy grows, users will face more choices, more data, and more complexity. AI has the potential to become the interface that makes tokenized assets easier to discover, understand, and interact with. Tokenization expands access. AI expands usability.
❤2
OpenA and the Future of AI-Native Economies Every economy is defined by its participants. People create value. Businesses exchange value. Governments regulate value. The next evolution introduces a new participant: AI agents. As autonomous participants, AI agents can coordinate economic activity, interact with tokenized assets, and transact across digital and real-world ecosystems at a scale beyond manual operatio…
❤2
AI agents don't just need to think and act. They also need to transact. Aura Pay is being built to provide programmable payment infrastructure for AI-driven commerce. From paying for services to settling on-chain transactions and interacting with tokenized assets, payments become an integrated part of every intelligent workflow. Building the payment layer for the AI-native economy.
AI agents can analyze information, make decisions, and execute on-chain actions. But participating in the economy requires one more capability: the ability to pay. Paying for data. Accessing applications. Purchasing tokenized assets. Settling transactions. Without payment infrastructure, AI agents can't complete these workflows. To participate in the on-chain economy, AI needs the ability to transact.
❤5
Showing the 12 most recent of 20 posts we hold for @auranw. 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 — 392,661 of 1,350,102entries 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.
Named by 4 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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“Aura Network News” (@auranw), 1,278 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/auranw.
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.