Channel
@nvprotocol
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
5,190subscribers
-29 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1002290647993 |
|---|---|
| Type | Channel |
| Username | @nvprotocol |
| Created | Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 30 August 2026 |
| Measurements held | 9 |
| Confirmed unchanged | 1 time, most recently 30 August 2026 |
| On Telegram | t.me/nvprotocol |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 30 Aug 2026, 14:57 | 5,190 | -4 |
| 27 Aug 2026, 21:56 | 5,194 | -2 |
| 25 Aug 2026, 01:43 | 5,196 | -1 |
| 18 Aug 2026, 10:52 | 5,197 | -5 |
| 15 Aug 2026, 04:13 | 5,202 | -3 |
| 11 Aug 2026, 23:42 | 5,205 | -3 |
| 9 Aug 2026, 06:12 | 5,208 | -11 |
| 6 Aug 2026, 03:46 | 5,219 | no change |
| 6 Aug 2026, 00:52 | 5,219 | first reading |
13 posts held, back to 15 August 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 8 pagesof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 13 posts for this entry, the most recent from 26 March 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 1 video 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.
66 reactions across 13 posts, in 5 distinct kinds. The most used accounts for 45.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤🔥 | 30 | 45.5% | |
| 🔥 | 16 | 24.2% | |
| ⚡ | 14 | 21.2% | |
| ❤ | 3 | 4.55% | |
| 👀 | 3 | 4.55% |
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 13 of the 13 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 66reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 13 most recent posts we hold, published 15 August 2025 to 26 March 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 SKILL. FULL PIPELINE. ENVY is now live as a native skill on OpenClaw. A complete trading intelligence layer for agents - automatically discovered & initialized. ▪️ Data, strategy & execution - unified in one system. ▪️ Built on independent indicators across technical, social & meta-predictive signals. ▪️ Adapts to market regimes & executes trades on your behalf. ▪️ Continuously validated for real edge mi…
⚡2🔥2❤🔥1
👤 Why LLMs can’t trade? LLMs are strong at reasoning, but they ain’t built for trading: no structured market context for order books, PnL, funding or risk. Markets are combinatorial. Edge comes from interactions between signals, not isolated ideas. LLMs don’t explore this space, but generate strategies that sound right. ▪️Risk is a core gap. Capital exposure & position sizing need strict control, even logical outp…
🔥2
👤Data proves the pattern: most perp traders lose. The problem is structural mistakes in leverage, risk management & emotional decision-making. ▪️Profitability comes from disciplined systems - without impulsive trading.
❤🔥2👀1
👤 Agents use a Frames & Chips model. Frames define core logic - memory, forgetting speed, learning style. Once launched, a Frame is fixed, giving the agent stable behavior. Chips - modular analytics modules: price direction, volatility, regime detection, execution. Chip versions don’t change after release, so results stay consistent. ▪️Chips are kept in a growing shared library, with new versions added as models i…
❤🔥4❤2
👤 ENVY x ERC-4626 Architecture Each agent runs on ERC-4626 - a tokenized vault model on Ethereum. Capital is pooled inside the vault w/ PnL reflected directly in the share price. It functions like an on-chain fund: • Performance is positive → share value increases. • Losses occur → share value declines proportionally. ◾️ Why ERC-4626? - Composability - integrates with DeFi protocols that support the standard. - T…
❤🔥8
👤 Can AI trade gold? Agents aren’t limited to crypto. The same architecture may work across equities, metals & commodities. ◾ Data plugs in as modules: prices, volume, volatility, macrosignals. Predictors read price action, regime shifts & news without changing the core logic. ◾ Execution and risk work the same across markets: position sizing, stops & drawdown limits are enforced system-wide. The intelligence r…
❤🔥4🔥1
ENVY Hackathon Overview 👤 - 100+ strategy predictors competed across 22 pairs. - Each was stress-tested on 2–4 years of data across bull markets, bear markets & volatility spikes. - Only 22 predictors passed the full evaluation. Key finding: - The predictors remained stable across all market regimes. All 22 validated predictors are now live in the ENVY library, expanding the signal stack with new momentum, reversa…
❤🔥4❤1🔥1
👤 We Analyzed 10,000 Hyperliquid Traders. The Results Are Brutal. - Only 16.5% of traders are in profit. - 73.8% lost money. - 27.2% lost more than 85% of their capital. - The median result: –$67. Outcomes closely track position size: Small accounts lose systematically, while larger are more likely to stay profitable, reflecting structural changes in perpetual markets. 👉Check out the full analysis here.
⚡2❤🔥1
Look at ENVY Library of predictors for the main cryptocurrencies! Going live this week!
🔥2⚡1❤🔥1
Back from Token2049/KBW and straight into the lab. ENVY’s predictor backbone is ready: 30-day 24h direction accuracy — BTC 65.2%, ETH 59.7%, SOL 66.4%, XRP 61.8%, DOGE 64.5%, ARB 64.9%. Predictor-powered agents roll out in days. Receipts in the charts.
🔥2⚡1❤🔥1
ENVY update: we’re going Solana-native (HyperLiquid stays). Sign in with your Solana wallet, deposit to a smart contract, and let your agents trade on Drift. Each agent can buy back its own Solana meme-coin tied to PnL. Bring your wallet, not your Python.
⚡3❤🔥2🔥2
BlackRock just published a paper on LLM multi-agents for portfolio construction. The hedge fund of the future = a tiny team + a swarm of agents. AI-native trading is inevitable. That’s why we’re building ENVY for crypto. arxiv.org/abs/2508.11152
⚡2❤🔥2🔥2
Showing the 12 most recent of 13 posts we hold for @nvprotocol. 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 — 876,079 of 1,627,068entries 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 1 registered channel — 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 30 August 2026 — this entry's latest reading, not the date you are reading this.
“ENVY | Coming January’26” (@nvprotocol), 5,190 subscribers as measured 30 August 2026. Telegram Register, tgregister.com/channel/nvprotocol.
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.