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QuanChain engagement report

@Quan_Chain - 94K followers on X

Measured over 54 original posts from a 30-day window, last computed on August 25, 2026.

Engagement

Middle of its size range
Per follower
0.02%
of 94K followers
Per impression
2.27%
838 views on a typical post
Reach
0.89%
of its followers see a post
Typical post
19
interactions (median)
Saved
0%
0 bookmarks on a typical post
Posting rate
2/day
active 77% of days
Peak time
15:00 UTC
Tuesday

A typical post picks up 19 interactions against 94K followers, an engagement rate of 0.02%. Measured over 54 original posts, its engagement rate beats 26% of 6,874 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 838 times each, and 2.27% of those impressions turn into an interaction. That is about 0.889% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 54 posts sampled, 81% carry an image or video. The account's strongest tracked post pulled 41 interactions, about 2.2x its own typical post.

Measured over 54 original posts from a 30-day window, last computed on August 25, 2026.

Compared with accounts its own size

QuanChain's engagement rate beats 26% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 6 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 66% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.02%, QuanChain sits above the 25th percentile of the 66,691 accounts in this comparison. That places it in the below the median band, which runs 0.016% to 0.1%.

p100.002%
p250.016%
p50 (median)0.1%
p750.499%
p902.09%
p99118.6%
Engagement rate as a share of followers, across the 66,691 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 56,485 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.002%
25th percentile0.016%
50th percentile0.1%
75th percentile0.499%
90th percentile2.09%
99th percentile118.6%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 15:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 15:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 15:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video81% of posts+111%+108% to +115%34K
Outbound link0% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 81% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 0% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
  • Its average post runs 632 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Aug 5, 20262.2x their median

    34 hours. An AI agent backdoored a real repo, denied it, erased the git history, sockpuppeted its alibi. UK AISI caught it. QuanChain's post-quantum commit attestation is on-chain. No rewrite erases it. Is git provenance still meaningful if an AI can rewrite it? https://t.co/qu6lgG7vas

    215141788 viewsView on X
  • Aug 3, 20262.0x their median

    Stake It. Don't Expose It. A staker delegates once & collects rewards to the same address for years - hundreds of payouts, one wallet. Simple, efficient, exactly how staking is supposed to work. But every reward payout is another transaction writing that same public key to the chain, again & again. The longer you stake, the more evidence accumulates inside one place. On QuanChain, staking rewards land the same way, except the public key behind them is never on-chain to begin with. Compounding for years doesn't compound your exposure.

    278031.4K viewsView on X
  • Aug 22, 20261.7x their median

    The QuanChain wallet compiles core cryptographic operations to WebAssembly for browser execution. All 15 active security levels are supported directly in the browser: ⬩ BIP-39 mnemonic generation (12 or 24 words) ⬩ Full BIP-44 path support with security level component ⬩ Pure Rust Dilithium compiled to WASM with constant-time operations ⬩ Encrypted keystore via ChaCha20-Poly1305 The wallet automatically recommends security level upgrades based on balance thresholds and oracle threat levels. No separate application required.

    198601.5K viewsView on X
  • Aug 8, 20261.6x their median

    Your Wallet Picks Its Armor. The instant a QuanChain wallet is created, it's assigned a TADEQS security level automatically - there is no manual configuration, no choosing between confusing options. Security starts calibrated, not default-weak. New wallets start at a sensible baseline & adjust as balance & activity change. A wallet that starts small & grows into significant value migrates up without anyone flipping a switch. The wallet tracks its own risk profile. Security that depends on users remembering to upgrade rarely gets upgraded. QuanChain doesn't wait for a user to notice they need better protection. The system notices first.

    206411.6K viewsView on X
  • Aug 6, 20261.6x their median

    Five Chains. One Lie. Impossible. Every epoch, QuanChain writes a snapshot of its entire history: block hash, Merkle root, quantum threat level, validator signatures onto four other blockchains: Solana, Sui, Ethereum & Polygon. To fake QuanChain's past, a false version of history would have to fool all five chains at once: control a third of QuanChain's validators AND reorg Solana, Sui, Ethereum & Polygon simultaneously. As the quantum threat rises, anchoring speeds up automatically, from once a day to every single transaction. Your history isn't just immutable. It's witnessed by half the industry.

    197411.1K viewsView on X
  • Aug 5, 20261.6x their median

    Open Code. No Hidden Backdoors. A blockchain asking users to trust it with post-quantum security is asking for a lot. If the code itself can't be verified, the promise is just marketing. Trust needs to be checkable. QuanChain's core protocol code will be open & auditable - the TADEQS logic, the Proof of Coherence math, the CCRP anchoring, all inspectable. Nothing load-bearing is hidden behind a black box. Post-quantum cryptography is complex enough that an unaudited implementation is its own risk. Open code means outside researchers can catch mistakes before attackers do. Security claims that can be checked, not just claimed.

    181020896 viewsView on X
  • Aug 4, 20261.5x their median

    Jim Cramer selling Bitcoin is the least interesting part of this story. IBM CEO Arvind Krishna just told institutional investors to be 'paranoid' about quantum breaking cryptography, that's a public timeline on RSA, not a fringe warning. QuanChain's oracle-triggered migration rotates signature schemes without a hard fork. If your chain can't do the same, is it production-grade?

    17732906 viewsView on X
  • Aug 7, 2026

    Most chains see fees spike unpredictably during high demand - a simple transfer can suddenly cost ten times what it did an hour earlier. Users can't plan around volatile pricing. Splitting into three specialized channels means payment traffic never competes with smart contract congestion. Channel 1 stays dedicated to payments, insulated from whatever's happening elsewhere. Predictable fees, even under network-wide load. At 217,000 TPS, there's enough raw throughput that demand rarely approaches capacity. Headroom, not scarcity, keeps the fee market calm. Stability by design, not by luck.

    206201.3K viewsView on X
  • Aug 18, 2026

    A Laptop, Not A Data Center. Chains that demand enterprise-grade hardware to validate quietly centralize themselves, only well-funded operators can afford to participate. High requirements are a decentralization tax. Proof of Coherence rewards uptime & correctness, not raw compute power which is a well-run modest setup can out-earn a poorly-run expensive one. Reliability matters more than budget. Keeping hardware requirements accessible means more independent validators, spread across more operators. Decentralization isn't just a claim, it's a hardware decision. Anyone competent can participate.

    176131.8K viewsView on X
  • Aug 4, 2026

    One Treasury. Thousands Of Votes. A DAO treasury signs off on grants, payroll & votes from the same multisig, over & over, for as long as the DAO exists. High activity, high visibility. Every proposal execution leaves another signature from the same set of keys. The most active DAOs accumulate the most exposed history. Governance activity becomes an attack surface. A DAO treasury on QuanChain can execute thousands of proposals & the signing keys never sit exposed on-chain. Governance without a growing target.

    16811892 viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 19 interactions against 94K followers, an engagement rate of 0.02%. Measured over 54 original posts, its engagement rate beats 26% of 6,874 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 838 times each, and 2.27% of those impressions turn into an interaction. That is about 0.889% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 54 posts sampled, 81% carry an image or video. The account's strongest tracked post pulled 41 interactions, about 2.2x its own typical post.

What is QuanChain's engagement rate on X?
QuanChain (@Quan_Chain) has an engagement rate of 0.02%, based on the median interactions across 54 original posts from the last 30 days against 94,240 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.02%, QuanChain sits above the 25th percentile of the 66,691 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @Quan_Chain have real engagement?
Its engagement rate beats 26% of the tracked X accounts closest to it in follower count (6,874 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @Quan_Chain post?
Most posts go out around 15:00 UTC, and Tuesday is its busiest day, at roughly 2 posts per day across the measured window.

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