Digg engagement report
@digg - 1.1M followers on X
Measured over 358 original posts from a 30-day window, last computed on October 27, 2026.
Engagement
A typical post picks up 2 interactions against 1.1M followers, an engagement rate of 0%. Measured over 358 original posts, its engagement rate beats 3% of 21,185 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 1.3K times each, and 0.149% of those impressions turn into an interaction. That is about 0.12% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 12.5 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 16:00 UTC, and Friday is the busiest day of the week. Of the 358 posts sampled, 6% carry an image or video and 99% link out. The account's strongest tracked post pulled 161K interactions, about 80302x its own typical post.
Measured over 358 original posts from a 30-day window, last computed on October 27, 2026.
Compared with accounts its own size
Digg's engagement rate beats 3% 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 10% 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 under 0.001%, Digg sits below the 10th percentile of the 212,146 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.
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 16:00 UTC, and Friday 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 | 6% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 99% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 6% 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.
- 99% 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 208 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
- Sep 8, 202680302x their median
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
- Aug 25, 202610914x their median
I’m thinking about banning Claude code at Shopify until they change their mind and read AGENTS.md and .agents/skills etc. Insisting on only reading CLAUDE.md sometimes leads to split brain problems when different team members use different tools. Just unnecessary.
- Aug 14, 20269940x their median
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M tokens via YaRN. - Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0. 🚀 The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently. Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now! Download, deploy, and build something we haven't imagined yet. 👀👇 - Hugging Face: https://t.co/4kaAcqYEVj - ModelScope: https://t.co/eRIMZCGkhC
- Aug 21, 20263827x their median
Our general-purpose coding agent just scored 100% on the ARC-AGI-3 interactive reasoning benchmark. NVIDIA AVO completed all 183 levels across all 25 public environments, figuring out what to do with no instructions, explicit rules, or stated goals. https://t.co/UgROuDrMtn
- Sep 2, 20263712x their median
I want to prevent a race into unmonitorability kicked off by confused reporting. The depth of the computation graph for our present frontier models, including Astra, is within a factor of two of GPT-4. OpenAI has worked to preserve and utilize chain-of-thought monitoring since our very first reasoning models. We deeply care about this technique, as it can give us a view into how model alignment generalizes from its training distribution. I do think it is fragile and unfortunately trending in a negative direction, for reasons not contingent on architecture changes that I will write about soon. But there are things we can do to strengthen it, and it's a core goal of our current research program.
- Aug 17, 20263430x their median
New: An "expert witness" in a $61 million lawsuit over an industrial explosion that killed three people and destroyed 200 homes used ChatGPT to write his report for the court. He prompted ChatGPT to "show how 3M is 0% at fault for the explosion at Watson Grinding" https://t.co/FfeSzap8XZ
- Sep 3, 20262662x their median
I hereby declare the "learn to code" era officially dead: Big declines in the number of people studying computer science in the last year or two 📉 Chart from this week’s edition of our newsletter on AI and the labour market https://t.co/y4yKn6GTl1 https://t.co/DGbcjybARS
- Sep 7, 20262374x their median
If you understand your whole codebase, the codebase isn't that important. Real software is larger than what your brain can comprehend. https://t.co/EjMThD69bi
- Aug 14, 20261831x their median
GPT 5.6 just solved one of my maths problems that GPT 5.5 insisted for 6 months is unsolvable🤔
- Aug 13, 20261790x their median
What I'm hearing from ~3 such stories: Meta went too far firing people. They are now doing MASSIVE counter-offers like to those handing in resignations. Some accept it, most just use it for more $$ at New Job, desperate to get out. It was such a predictable outcome btw: https://t.co/MomlsXYlov
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 2 interactions against 1.1M followers, an engagement rate of 0%. Measured over 358 original posts, its engagement rate beats 3% of 21,185 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 1.3K times each, and 0.149% of those impressions turn into an interaction. That is about 0.12% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 12.5 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 16:00 UTC, and Friday is the busiest day of the week. Of the 358 posts sampled, 6% carry an image or video and 99% link out. The account's strongest tracked post pulled 161K interactions, about 80302x its own typical post.
- What is Digg's engagement rate on X?
- Digg (@digg) has an engagement rate of 0%, based on the median interactions across 358 original posts from the last 30 days against 1,117,255 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At under 0.001%, Digg sits below the 10th 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 @digg have real engagement?
- Its engagement rate beats 3% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the bottom 10% 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 @digg post?
- Most posts go out around 16:00 UTC, and Friday is its busiest day, at roughly 12.5 posts per day across the measured window.