Channel
@dappexpert
On this record: Growth · Engagement · Reactions · Posts · Cite this entry
615subscribers
+1 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001316486815 |
|---|---|
| Type | Channel |
| Username | @dappexpert |
| Created | Between 1 March 2018 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 22 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 22 August 2026 |
| On Telegram | t.me/dappexpert |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 22 Aug 2026, 03:58 | 615 | -1 |
| 14 Aug 2026, 13:28 | 616 | +2 |
| 7 Aug 2026, 16:12 | 614 | no change |
| 7 Aug 2026, 08:48 | 614 | first reading |
20 posts held, back to 3 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 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. It is computed over the 1 of 20 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 7 August 2026 |
|---|---|
| Posts held | 20 (3 August 2026 – 7 August 2026) |
| Views total | 106 |
| Reactions total | 3 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 08:48 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.
3 reactions across 1 post, in 3 distinct kinds. The most used accounts for 33.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 💯 | 1 | 33.3% | |
| 🤗 | 1 | 33.3% | |
| 🥰 | 1 | 33.3% |
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 1 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 3reactions 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 3 August 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.
One of the most interesting features of MapleStory N isn’t the NFTs. Nor is it the NXPC token. What’s more interesting is the very attempt to transform a single game into an entire ecosystem where different applications share a common economy and digital assets. We’ve broken down how this model works. #Dapp_Expert #MapleStoryUniverse #GameFi #NXPC #Web3Gaming #Blockchain #CryptoGaming More details here
Most gaming blockchains address the same problem: Making transactions cheaper. But that’s no longer enough. The main question today is different: Can the infrastructure make the blockchain completely invisible to the player? Because mass adoption of Web3 games won’t begin when yet another token is launched. It will begin when the technology becomes virtually invisible to the user. We analyzed Quasarchain Arena and it…
Most games tell the same story. Even if the player has freedom of choice, the number of possible scenarios is limited from the start. But what would happen if the plot were no longer static? If NPCs began to remember past events. What if the world reacted not according to a pre-written script, but based on each player’s actions? It is precisely around this idea that a new direction is taking shape—the AI Narrative La…
Everyone is talking about blockchains. What’s discussed much less often is what happens after a transaction is recorded. Because the transaction itself is just the beginning. It still needs to be found. Processed. Linked to other events. Passed on to applications, AI agents, or analytics services. This is exactly where the new Web3 infrastructure layer is taking shape today. Not blockchains. But data processing syste…
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GameFi has come a long way. At first, the market tried to sell players the opportunity to earn money. Now, more and more often, the main question is phrased differently: Would people still want to play if you removed the token? It is at this stage that it becomes clear where the financial model ends and the actual game begins. Dogeball Arena is interesting precisely as such an experiment. Fast-paced PvP matches, comp…
GameFi has come a long way. At first, the market tried to sell players the opportunity to earn money. Now, more and more often, the main question is phrased differently: Would people still want to play if you removed the token? It is at this stage that it becomes clear where the financial model ends and the actual game begins. Dogeball Arena is interesting precisely as such an experiment. Fast-paced PvP matches, comp…
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With every new cycle, the market promises the same thing. More data. More analytics. More AI. But there’s so much information now that the main challenge is no longer finding it. It’s the ability to quickly separate what’s important from the noise. That’s exactly why AI platforms for on-chain analytics are gaining more and more attention. But this raises a much more important question: Can artificial intelligence tru…
It seems to me that the main question surrounding Dogecoin has long since ceased to be about price. It’s phrased differently: Can one of the most famous cryptocurrencies go beyond being just a payment network? Layer 2 projects like Dogechain are betting on exactly this scenario. Not to change Dogecoin itself. But to build a new economy around it with DeFi, smart contracts, and Web3 applications. If this model proves …
A strange situation has long existed in DeFi. Technology is getting faster. Liquidity is increasing. But users are still overpaying for every transaction. That’s why more and more projects are trying to compete not only on speed but also on transaction costs. Zero-fee DEXes are one of the most talked-about approaches. But a logical question arises: If users don’t pay fees, how does the platform itself stay afloat? Th…
Each new cycle in the crypto market creates a new narrative. But not every narrative becomes new infrastructure. DeFi transformed finance. DePIN is an approach to physical infrastructure. Now, DePAI is being discussed more and more frequently. The idea behind it seems much broader than just another AI project. What if computing power, data, devices, and AI agents could function as a single decentralized economy? In s…
Showing the 12 most recent of 20 posts we hold for @dappexpert. 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.
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 22 August 2026 — this entry's latest reading, not the date you are reading this.
“DAPP.EXPERT” (@dappexpert), 615 subscribers as measured 22 August 2026. Telegram Register, tgregister.com/channel/dappexpert.
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.