Kate Miller engagement report
@KateMillerGems - 1.1M followers on X
Measured over 10 original posts from a 30-day window, last computed on October 27, 2026.
Engagement
A typical post picks up 384 interactions against 1.1M followers, an engagement rate of 0.034%. Measured over 10 original posts, its engagement rate beats 52% of 21,185 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 48K times each, and 0.806% of those impressions turn into an interaction. That is about 4.22% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 10:00 UTC, and Monday is the busiest day of the week. Of the 10 posts sampled, 50% carry an image or video and 40% link out. The account's strongest tracked post pulled 29K interactions, about 74x its own typical post. Recurring topics include #ai, #solana, #token.
Measured over 10 original posts from a 30-day window, last computed on October 27, 2026. Recurring tags: #ai, #solana, #token.
Compared with accounts its own size
Kate Miller's engagement rate beats 52% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 10 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 41% 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 0.034%, Kate Miller sits above the 25th percentile of the 212,146 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.694% |
| 90th percentile | 2.61% |
| 99th percentile | 83.1% |
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 10:00 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% | 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 50% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 40% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 50% 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.
- 40% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 249 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.
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
- May 5, 202674x their median
Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. @subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.
- Apr 16, 202616x their median
you can now control things with your brain. literally. we're building the most wearable BCI on the planet, with @sabi, backed by @khoslaventures @accel @initialized & @kevinweil. we collected the world’s largest neural dataset and trained the most capable Brain Foundation Model. then we invented a new class of biosensors powered by custom ASICs. type without typing. click without clicking. a cap that lets your brain do the work. we’re sabi.
- Apr 14, 20265.4x their median
Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control – https://t.co/8TEolq14Z5
- Jun 23, 20264.3x their median
Newton Mainnet Beta is live! 🎉 VaultKit, the SDK that makes your vault’s rules actually enforceable onchain. Newton checks the rules before any transaction settles, then writes a signed attestation anyone can verify. https://t.co/5GOXFDff3i
- Apr 22, 20264.3x their median
Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!
- Apr 27, 20263.2x their median
Speed. Precision. Trust. 🏁 What happens when motorsports meets crypto? 🎙 Hosted by Fernando Lillo, Marketing Director of @ZoomexOfficial 👥 Guests: @OllieBearman, Driver of @HaasF1Team @cryptorover @wallstreetbets 💰 1,000 $USDT up for grabs Follow, RT, and Set Reminder 👉 https://t.co/Ug1O2V5nAr to share first part of 500 $USDT
- Oct 27, 20262.8x their median
The next BILLIONAIRE‘S #token is $_______ 🔥
- Oct 5, 20262.2x their median
18 months of building. Today, we’re finally ready to show you what we’ve been working on. Meet the new HyroTrader. More ways to get funded. More ways to trade. More transparency. https://t.co/3M2HGbAfxN
- May 6, 20261.9x their median
Most fintech companies started in the world's biggest markets and expanded outward. Most were built in Silicon Valley, with Silicon Valley assumptions. We started on the other side of the world, in a Melbourne coffee shop. Outsiders, by every measure. So we went deeper than anyone wanted to. Reimagining correspondent banking from first principles, not abstracting away the old system but replacing it. Years of obtaining licenses, building infrastructure, and shipping every financial product a growing business needs including intelligent finance. Bank accounts in 70+ countries. Treasury across 25+ currencies. Cards, spend management, bill pay, multi-entity, all on one platform. Built so the whole world feels like your home market. Now AI means the builders are everywhere. Global financial infrastructure has always been the hard part. Ours took eleven years to build, and it's ready for you on day one. Build the Future ↓
- Mar 26, 20261.7x their median
🇭🇰 #AlchemyPay, together with SFC-licensed HTF Securities Limited, is pleased to announce the successful uplift of SFC Type 1 license to include virtual asset trading services. With this upgrade secured, we have completed the Type 1 & Type 4 license upgrade, while the Type 9 license upgrade is still in process. We remain committed to building the bridge between traditional finance and digital assets in Asia's leading financial hub, strengthening our roots in Hong Kong. https://t.co/CvBJ0o9RxM $ACH
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 384 interactions against 1.1M followers, an engagement rate of 0.034%. Measured over 10 original posts, its engagement rate beats 52% of 21,185 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 48K times each, and 0.806% of those impressions turn into an interaction. That is about 4.22% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 10:00 UTC, and Monday is the busiest day of the week. Of the 10 posts sampled, 50% carry an image or video and 40% link out. The account's strongest tracked post pulled 29K interactions, about 74x its own typical post. Recurring topics include #ai, #solana, #token.
- What is Kate Miller's engagement rate on X?
- Kate Miller (@KateMillerGems) has an engagement rate of 0.034%, based on the median interactions across 10 original posts from the last 30 days against 1,126,764 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.034%, Kate Miller sits above the 25th percentile of the 212,146 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 @KateMillerGems have real engagement?
- Its engagement rate beats 52% of the tracked X accounts closest to it in follower count (21,185 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 @KateMillerGems post?
- Most posts go out around 10:00 UTC, and Monday is its busiest day, at roughly 0.4 posts per day across the measured window.