Brevis engagement report
@brevis_zk - 370K followers on X
Measured over 46 original posts from a 30-day window, last computed on September 1, 2026.
Engagement
A typical post picks up 30 interactions against 370K followers, an engagement rate of 0.008%. Measured over 46 original posts, its engagement rate beats 17% of 3,899 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 7.1K times each, and 0.42% of those impressions turn into an interaction. That is about 1.93% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 02:00 UTC, and Monday is the busiest day of the week. Of the 46 posts sampled, 72% carry an image or video and 15% link out. The account's strongest tracked post pulled 59 interactions, about 2.0x its own typical post.
Measured over 46 original posts from a 30-day window, last computed on September 1, 2026.
Compared with accounts its own size
Brevis's engagement rate beats 17% of the tracked X accounts closest to it in follower count (3,899 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 22% 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 0.008%, Brevis sits above the 10th percentile of the 37,856 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.012%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.081% |
| 75th percentile | 0.439% |
| 90th percentile | 2.10% |
| 99th percentile | 155.6% |
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 02:00 UTC, and Monday 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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 53K |
| 01:00 UTC | -2% | 53K |
| 02:00 UTC | -3% | 52K |
| 03:00 UTC | -4% | 55K |
| 04:00 UTC | -6% | 44K |
| 05:00 UTC | -4% | 43K |
| 06:00 UTC | -4% | 50K |
| 07:00 UTC | -5% | 54K |
| 08:00 UTC | -4% | 63K |
| 09:00 UTC | -3% | 72K |
| 10:00 UTC | -2% | 75K |
| 11:00 UTC | -3% | 81K |
| 12:00 UTC | -2% | 90K |
| 13:00 UTC | -2% | 98K |
| 14:00 UTC | -3% | 101K |
| 15:00 UTC | -2% | 105K |
| 16:00 UTC | -4% | 102K |
| 17:00 UTC | -3% | 95K |
| 18:00 UTC | -1% | 88K |
| 19:00 UTC | -2% | 83K |
| 20:00 UTC | -1% | 77K |
| 21:00 UTC | -1% | 69K |
| 22:00 UTC | -2% | 60K |
| 23:00 UTC | -2% | 53K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 238K |
| Monday | 0% | 302K |
| Tuesday | -3% | 302K |
| Wednesday | -1% | 257K |
| Thursday | -2% | 250K |
| Friday | -3% | 259K |
| Saturday | +3% | 233K |
Best tweets
- Aug 7, 20262.0x their median
Where do zero-knowledge proofs sit in the long history of trust? 🤔 Every era has invented a way to trust a stranger without having to. 🕯️ The wax seal, so a letter couldn't be opened and resealed unnoticed. 📒 Double-entry bookkeeping, so the numbers had to reconcile or the lie showed. 🔍 The audit, so a company's claims could be checked by someone with no reason to flatter them. ✍️ The notary, so a signature actually meant the person signed. Every one of them works the same way. It hands you something you can check for yourself, instead of asking you to take someone's word. The zero-knowledge proof is the newest entry in that line, and the first one built for computation itself. A way to prove a calculation was done correctly, verifiable by anyone in an instant, with no redoing the work and no trusting whoever ran it. It's the same move humanity keeps reinventing, now aimed at the machines doing more and more of our thinking. That's the thread we pull at Brevis 🟠
- Aug 31, 20261.9x their median
Here's a neat thing about zero-knowledge proofs. 🧩 We've talked before about how an enormous computation collapses into a small proof that's cheap to check. That's the property most people know. But that's not all they do. Proofs can also be combined. A proof can be verified inside another proof. So a thousand separate proofs can be folded together into a single proof saying all thousand of them checked out, and that one proof gets verified once, for roughly what any one of them would have cost on its own. A few things fall out of that: 🖥️ One computation, many machines. A block gets split into chunks, proved across every GPU at once, and the pieces merged back up into a single proof. It's the reason throwing more hardware at proving makes it faster at all. 💸 Shared verification. Unrelated applications can batch their proofs together and split one on-chain bill between them. 🪙 Small things become worth proving. A verification that costs real money makes no sense for a tiny action, until a thousand of them are sharing it. This is what turns proving from an expensive one-off into real infrastructure, and building that out is most of what we do at Brevis.
- Aug 11, 20261.9x their median
There's a property that makes zero-knowledge proofs feel like magic. 🪄 Take a computation as heavy as you want: ▶️a year of on-chain history ▶️thousands of users at once ▶️an entire AI model run. The proof that all of it was done correctly stays tiny, and verifies in milliseconds. huge computation ⟶ small proof ⟶ checked in an instant No matter how large the computation gets, the proof stays small and checking it is fast. That gap only widens as the work gets heavier. There's a name for this: succinctness. It's why a blockchain can rely on a computation it could never afford to run itself and still be certain the answer is right. That property is one of the foundations we build on at Brevis.
- Aug 17, 20261.9x their median
Three common misconceptions about zero-knowledge proofs: 1️⃣ "It's all about privacy." Privacy is just one use. The bigger one is verification: proving a computation was done correctly without anyone having to redo it. Most ZK running in production today is there to make results checkable at scale. 2️⃣ "It hides everything." You choose exactly what to reveal and what to hold back. Prove you're over 21 while showing nothing else, or prove a balance covers a loan without ever revealing the number. Selective, down to the detail. 3️⃣ "It's too slow to be real." That was true for years. Today a zero-knowledge proof of an entire Ethereum block finishes in seconds. The engineering finally caught up to math that's been around since the 1980s. We walk through all of this properly, from blockchain basics up to real-time proving, in our From 0 Knowledge to Zero Knowledge series. Start from part 1 👇 https://t.co/zs9msoJdv8
- Aug 19, 20261.7x their median
For years everyone in crypto wanted to be "decentralized." It was the word that made a project sound legit. Half the time it meant three guys and a multisig. The word that actually matters now is "verifiable." ✅ "Decentralized" is a claim about who's in charge, and you mostly just take their word for it. "Verifiable" means you can check it yourself. No faith required. The projects still standing a few years from now will be the ones you can actually check, end to end. That's the reality Brevis believes in. 🧡
- Aug 6, 20261.7x their median
Gm, we ran the tests and it turns out the smallest unit of trust is one Brevis. 🔬 https://t.co/VymJzA5to5
- Sep 1, 20261.6x their median
gm. growth takes patience and a good pattern. 🌾 https://t.co/65EAproZwT
- Aug 20, 20261.6x their median
Gm. https://t.co/EFD4SO8I4G
- Aug 28, 20261.5x their median
gm. finish friday strong, then go touch grass. 🌱 https://t.co/v4Mp9HTH17
- Aug 28, 2026
We spend most of our time here talking about proving. Tonight let's look at the other side of it, the verifier. 🔍 A verifier is the thing that does the checking. It's a piece of code that you give it a result along with the proof that came with it, and it tells you whether the proof holds up. Checking is quick. The verifier runs a short fixed sequence of math and returns true or false. It never redoes the original work and it never needs the private data behind the claim. Confirming something is far cheaper than doing it. 📱 Because that check is so cheap, a verifier can run almost anywhere. A server, an app on your phone, a browser tab. On a blockchain it's a smart contract, which is where most people first meet the idea, though plenty of zero-knowledge proofs get verified nowhere near a blockchain. ✅ In practice this is far less complex than it sounds. If you wanted to check a proof yourself, you'd open a page, upload the proof, and wait a moment for a true or false to come back. That's the entire experience. Which is a big part of why zero-knowledge proofs are so powerful. Anyone, anywhere, at any time can confirm something is true, and they need nothing from whoever produced it.
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 30 interactions against 370K followers, an engagement rate of 0.008%. Measured over 46 original posts, its engagement rate beats 17% of 3,899 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 7.1K times each, and 0.42% of those impressions turn into an interaction. That is about 1.93% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 02:00 UTC, and Monday is the busiest day of the week. Of the 46 posts sampled, 72% carry an image or video and 15% link out. The account's strongest tracked post pulled 59 interactions, about 2.0x its own typical post.
- What is Brevis's engagement rate on X?
- Brevis (@brevis_zk) has an engagement rate of 0.008%, based on the median interactions across 46 original posts from the last 30 days against 369,833 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.008%, Brevis sits above the 10th percentile of the 37,856 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 @brevis_zk have real engagement?
- Its engagement rate beats 17% of the tracked X accounts closest to it in follower count (3,899 accounts), which puts it in the bottom quarter for its size 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 @brevis_zk post?
- Most posts go out around 02:00 UTC, and Monday is its busiest day, at roughly 1.57 posts per day across the measured window.