Christina Cacioppo engagement report
@christinacaci - 242K followers on X
Measured over 1 original post from a 30-day window, last computed on September 30, 2026.
Engagement
Early reading. We have captured 1 original post for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.
A typical post picks up 26 interactions against 242K followers, an engagement rate of 0.011%. Posts are seen about 9.2K times each, and 0.284% of those impressions turn into an interaction. That is about 3.79% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.03 posts a day over the last 30 days, though only 3% of days saw any activity at all. Most posts go out around 17:00 UTC, and Saturday is the busiest day of the week. The account's strongest tracked post pulled 4.0K interactions, about 152x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
Measured over 1 original post from a 30-day window, last computed on September 30, 2026.
Where this sits in the catalog
At 0.011%, Christina Cacioppo sits above the 10th percentile of the 201,449 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.141% |
| 75th percentile | 0.671% |
| 90th percentile | 2.53% |
| 99th percentile | 82.5% |
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 17:00 UTC, and Saturday 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 | 0% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | -3% | -3% to -2% | 54K |
- 0% 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 109 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below 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
- Mar 30, 2026152x their median
The loudest story about AI is a lonely one. One person with an army of chatbots. Other humans are friction. That gets the future wrong. The best things aren’t built alone. In a moment of change, we want to remind the world (and ourselves) what Notion stands for: — Think Together
- Apr 29, 202641x their median
It took us two years to grow from $10 mm to $100 mm in Annual Recurring Revenue and 15 months to reach $200 mm. Nine months later, we crossed $300 mm. Vanta’s growth rate increased each of the past four quarters – compounding really is the eighth wonder of the world! “But wait,” you might be thinking. “How does a software company founded before 2022 *increase* its growth rate?” Narrative violation!
- Jul 23, 202541x their median
Today, we’re announcing @TrustVanta's Series D and new valuation – $4.15 billion – led by @Wellington_Mgmt alongside @sequoia, @ycombinator, @CrowdStrike, @GoldmanSachs, @jpmorgan, @craft_ventures, and @Atlassian. Vanta’s mission is to help businesses earn and prove trust. https://t.co/xepp8RZIX6
- Jul 31, 202530x their median
XBOW is now the #1 hacker on HackerOne, globally. For the first time, our autonomous AI pentester tops the worldwide leaderboard. Next week at #BlackHat, we’re taking it live: We’ll run real-time on HackerOne programs—come see XBOW find vulnerabilities. 📍 Booth 3257 https://t.co/9f1gXvdU8x
- Nov 4, 202526x their median
1/ We @sequoia have always said that we’re only as good as our next investment. It’s also true that we’re only as good as our next generation @Alfred_Lin and I get to work with the best generation yet:
- Aug 3, 202325x their median
this guy John in Dover, OH has dedicated his life to vacuum cleaners and I think that’s nice https://t.co/zwLRm86dOa
- Jul 15, 202516x their median
BREAKING: Brex's CMO Scott Holden has been traded to Vanta (@TrustVanta) https://t.co/CTMwFAb5T6
- Jul 17, 202514x their median
Bret Taylor (@btaylor) talked about why AI is still in its Yahoo! directory phase, how to go enterprise-first as a startup if your name isn't "Bret Taylor," and what agentic *actually* means (it's not a bag of floating-point numbers) in the latest Frameworks for Growth. https://t.co/ZOWMwBRUdJ
- May 20, 20269.2x their median
I’ve been building with Claude Cowork lately, and I figured I’d share my thoughts so far. I chose Cowork over Claude Code to see what it’s like to write code but never read it. Some early thoughts: 1. I’m using natural language, instead of code, but it’s still software development. I don’t need to worry about syntax, but the concepts underlying “traditional” software still matter: observability, client-server splits, pipelines, databases, logging, etc. Non-developers still need to think like engineers to be successful vibe coders – the syntax is gone, but the judgment isn’t (yet?) 2. The primary difference between vibe coding and “traditional” software is non-determinism. Traditional software teaches deterministic thinking – you can run the same function with the same inputs as many times as you want, and the output will always be the same. (When I was learning to code, I found this one of the stranger things; most other experiences in the world aren’t so deterministic!) Agent-driven software is not deterministic – ask an LLM the same question several times, and you will get several answers. The models’ laziness and tendency to cut corners – especially when inference is scarce – adds another twist. Understanding where to demand determinism from your software agents requires judgment. 3. I borrowed the concept of zero trust from security – the principle that an actor or system is only trusted after verification and never by default – to figure out when to demand determinism. I presumed model outputs unreliable until verified with human-in-the-loop checkpoints, retry logic with comparison (LLM as judge), or (deterministic) verification scripts. I realized observability, logging, and verification have to be first-class features of a vibe-coded system if it is to be reliable. Memory doesn’t cut it. 4. I did a lot of debugging through the models’ reasoning traces, which wasn't ideal. I imagine these interactions will improve a lot. If I were building an IDE for agentic coding, I’d start here. 5. What counts as a database? Cowork uses the local file system as the database, which is lazy and convenient and makes for fast prototyping, and comes with tradeoffs: the file system doesn’t enforce data schemas, and laziness means corners get cut. The input and output checks I built at each pipeline stage were repetitive but functional. If I kept pushing, I’d end up rebuilding versions of data integrity, normalization, indexing, query optimization, etc. over the local file system. 6. The tight coupling of the local machine as the client, server, database, etc. works when developing for yourself. To share work with others, you need a better client <> server split. When I asked Cowork to port my system to the cloud, it immediately suggested moving everything to Google Drive – swapping my local file system for a cloud file system. I had to coax it toward the architecture of a simple web app. 7. Cowork doesn’t use or expect version control, which makes tracking changes and multi-agent work near impossible. Developing without version control makes clear why we invented it. Version control is alive and well in the age of software agents. It feels like there’s a new iteration of systems design to be uncovered when building with agents. It’s not wholly new, and most of the primitives and principles of the past are still useful, but they need to be reassembled when some steps are deterministic and others aren’t. I’m excited to see what we uncover.
- Aug 17, 20268.8x their median
Today we're launching Computer Use for the @TrustVanta Agent. Of the 650,000 images uploaded to Vanta in the past six months, 36% were screenshots because evidence collection outside an API required it. The Vanta Agent can now use a computer to capture screenshots with scoped access, one-click approval, and a full record of every artifact created. Screenshot evidence collection has been one of our biggest requests, and we couldn't have built it well until recently. The last couple of months have been all about finding the jagged edge of CUAs and translating it to our customers.
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 26 interactions against 242K followers, an engagement rate of 0.011%. Posts are seen about 9.2K times each, and 0.284% of those impressions turn into an interaction. That is about 3.79% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.03 posts a day over the last 30 days, though only 3% of days saw any activity at all. Most posts go out around 17:00 UTC, and Saturday is the busiest day of the week. The account's strongest tracked post pulled 4.0K interactions, about 152x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
- What is Christina Cacioppo's engagement rate on X?
- Christina Cacioppo (@christinacaci) has an engagement rate of 0.011%, based on the median interactions across 1 original posts from the last 30 days against 241,604 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.011%, Christina Cacioppo sits above the 10th percentile of the 201,449 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 @christinacaci have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @christinacaci post?
- Most posts go out around 17:00 UTC, and Saturday is its busiest day, at roughly 0.03 posts per day across the measured window.