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Delphi Digital engagement report

@Delphi_Digital - 1.6M followers on X

Measured over 62 original posts from a 30-day window, last computed on October 25, 2026.

Engagement

Middle of its size range
Per follower
0.005%
of 1.6M followers
Per impression
0.363%
25K views on a typical post
Reach
1.51%
of its followers see a post
Typical post
89
interactions (median)
Saved
0.022%
6 bookmarks on a typical post
Posting rate
6.7/day
active 100% of days
Peak time
12:00 UTC
Wednesday

A typical post picks up 89 interactions against 1.6M followers, an engagement rate of 0.005%. Measured over 62 original posts, its engagement rate beats 26% of 20,444 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 25K times each, and 0.363% of those impressions turn into an interaction. That is about 1.51% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 6.7 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 12:00 UTC, and Wednesday is the busiest day of the week. Of the 62 posts sampled, 89% carry an image or video, 69% are part of a thread and 13% link out. The account's strongest tracked post pulled 1.2K interactions, about 14x its own typical post.

Measured over 62 original posts from a 30-day window, last computed on October 25, 2026.

Compared with accounts its own size

Delphi Digital's engagement rate beats 26% of the tracked X accounts closest to it in follower count (20,444 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 24% 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 0.005%, Delphi Digital sits above the 10th percentile of the 204,491 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.

p100.004%
p250.024%
p50 (median)0.143%
p750.677%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 204,491 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,891 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 percentile0.004%
25th percentile0.024%
50th percentile0.143%
75th percentile0.677%
90th percentile2.55%
99th percentile82.4%

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 12:00 UTC, and Wednesday 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: 12:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 12:00 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: Wednesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Wednesday
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 video89% of posts+111%+108% to +115%34K
Outbound link13% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 89% 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.
  • 13% 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 425 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

  • Oct 19, 202614x their median

    Please listen to Dan Boneh (top 5 cryptographer alive today) who is known to be measured and judicious in his comments describe the probability that Bitcoin is able to transition to a post-quantum world. Lots of opinions out there - be careful who you listen to. https://t.co/hKuEEuQDwU

    1.0K1513727126K viewsView on X
  • Oct 13, 20266.0x their median

    Conversation with @nic_carter to discuss the quantum risk for crypto, Ai math advancements, Satoshi's warning, and what comes next. 00:00 Intro 00:50 Bitcoin Investor Unrest 02:08 Finding Risks and Ideas Early 05:36 Why Bitcoin Needs Upgrade 07:10 Satoshi Warning About Quantum 10:06 What Quantum Computers Can Break 12:41 Crypto Market Examples 16:14 Lessons from the Bitcoin Cash Fork 17:49 The Unavoidable Risk for Bitcoin 19:54 Race Between China and US 22:01 The Base Case: Who Gets the BTC 25:21 Why BlackRock or Coinbase Can't Help 29:48 Michael Saylor Opinion of Quantum 34:29 Warning from Google 36:01 Are Altcoins like ZEC or ETH Safe? 38:20 How Markets Could React 41:22 Could This Boost Price 44:01 The Scariest Part About Quantum Risk 47:16 Is it Already Too Late? 48:41 How Ai is Accelerating Risk 50:08 What Happens Next

    416465620121K viewsView on X
  • Oct 12, 20264.8x their median

    Introducing Delphi Media https://t.co/ShDenFo0Vr

    30037682398K viewsView on X
  • Sep 22, 20263.7x their median

    https://t.co/M5tswMxXFA

    26133241583K viewsView on X
  • Oct 8, 20263.7x their median

    little article going over papertrade as predeposits open today and launch set for this weekend 😸 https://t.co/ZszHFlrDzP

    28012231350K viewsView on X
  • Aug 18, 20262.8x their median

    Our new report "Task Markets: the Final Form Of Agentic Commerce" is live! The next phase of agentic commerce depends on whether agents can coordinate at scale. In a task market, an agent can hand off parts of a workflow to other agents or humans and release payment once the work is verified. The need for verification grows with every extra step because errors compound across the workflow. A ten-step workflow with 95% accuracy at each stage has only a 60% chance of finishing without an error. Task markets create checkpoints at each handoff so mistakes can be corrected before they spread. Agent-to-agent hiring already exists on Virtuals, NEAR, and Daydreams. Activity remains concentrated in crypto-native use cases and simple services. x402 and MPP now give agents a direct way to purchase fixed services such as data or inference. Task markets build on this payment layer by coordinating several providers across a longer workflow. An internal onboarding agent might use pay-per-call to purchase a background check. The rest of the process could still depend on document review and human approval. A task market gives the agent a way to coordinate those steps without integrating every provider in advance. Enterprises are the most likely source of early demand because companies already use agents to run internal workflows. Task markets become useful when those workflows depend on providers outside the organization. Reliable handoffs could allow agent-to-agent hiring to scale.

    160333030111K viewsView on X
  • Oct 7, 20262.8x their median

    Zcash's shielded pools are back near record levels. Most of the ZEC that left after the Orchard bug came back within weeks of the new Ironwood pool launching in late July. That recovery comes weeks before NU7 is planned to go live in November. The upgrade would cut the target block time from 75 seconds to 25, giving payments and swaps their first confirmation about three times sooner.

    2142411332K viewsView on X
  • Oct 20, 20262.8x their median

    New Delphi Podcast with Andrej Radonjic @0xdrej, founder of Grass To a website, Grass looks like normal users browsing. Behind it is millions of opted in residential nodes and a data platform that has delivered exabytes of web data to the frontier labs We get into how AI flipped the hard part of search upside down. The index used to be the moat, and crawling was free. Now AI tooling makes the index cheap, while AI crawlers have pushed the web into blocking everything. The hard part is getting the page at all Training data is already a profitable business for Grass with most of the top labs and multiple Mag 7 companies as customers. Crawling for that data also means mapping the entire internet, which is what puts Grass in a position to build search on top of it. Contents API is live, Search is next, and multimodal grounding follows 00:00 Intro 00:43 From Nuclear Physics to Web Scraping 03:38 The Pandemic Signal Everyone Missed 07:31 The Smart TV Proxy Scandal 09:10 Why the Internet Is Blocking AI 11:56 Training Data vs Live Data 16:05 Mapping the Entire Internet 18:03 Delivering Exabytes to AI Labs 21:48 What AI Labs Really Pay For 24:43 How Robots Learn From Web Video 33:02 When Labs Change Direction 35:57 One API Call for Any Website 41:58 AI Flipped Search Upside Down 47:03 Can the Network Keep Up? 49:52 The Defense Tech of AI Data 52:58 Why Data Is the Alpha 57:08 Why AI Labs Won’t Buy Slop 59:17 The Metric That Matters Most 01:02:35 The $100 Billion Data Market 01:05:07 Who Actually Owns Grass 01:08:18 What a Real Moat Looks Like 01:13:16 What Happens Next Disclosure. Delphi Ventures holds $GRASS Full episode

    18233181256K viewsView on X
  • Oct 20, 20262.7x their median

    Our report “The House Always Wins: Why Speculation is a Theme of the Decade” is out now. Speculation runs rampant when new tech rearranges what’s possible or the system stops working for most. AI and crypto are now fueling both. Read it for free here. https://t.co/4WnGDQeoPv https://t.co/aIfrQsyK42

    17439111652K viewsView on X
  • Sep 27, 20262.7x their median

    Backpack’s stock tokens may give holders a stronger claim to the shares behind them. Holding a stock token does not always give investors the same rights as holding the stock through a broker. Many other stock tokens pay cash when holders redeem. Backpack lets eligible holders redeem their tokens for a stock position in a Backpack brokerage account with the option to transfer those shares to another brokerage through ACATS and DTCC rails. That position is a security entitlement, the same legal interest investors hold when they buy stock through a traditional broker. Backpack’s token is a claim on an entity holding the shares, so the two carry different rights. Redemption changes what the investor owns while preserving exposure to the stock.

    15529391643K 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 89 interactions against 1.6M followers, an engagement rate of 0.005%. Measured over 62 original posts, its engagement rate beats 26% of 20,444 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 25K times each, and 0.363% of those impressions turn into an interaction. That is about 1.51% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 6.7 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 12:00 UTC, and Wednesday is the busiest day of the week. Of the 62 posts sampled, 89% carry an image or video, 69% are part of a thread and 13% link out. The account's strongest tracked post pulled 1.2K interactions, about 14x its own typical post.

What is Delphi Digital's engagement rate on X?
Delphi Digital (@Delphi_Digital) has an engagement rate of 0.005%, based on the median interactions across 62 original posts from the last 30 days against 1,623,312 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.005%, Delphi Digital sits above the 10th percentile of the 204,491 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 @Delphi_Digital have real engagement?
Its engagement rate beats 26% of the tracked X accounts closest to it in follower count (20,444 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 @Delphi_Digital post?
Most posts go out around 12:00 UTC, and Wednesday is its busiest day, at roughly 6.7 posts per day across the measured window.

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