Robin Delta engagement report
@heyrobinai - 87K followers on X
Measured over 9 original posts from a 30-day window, last computed on September 27, 2026.
Engagement
A typical post picks up 168 interactions against 87K followers, an engagement rate of 0.192%. Measured over 9 original posts, its engagement rate beats 63% 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 22K times each, and 0.769% of those impressions turn into an interaction. That is about 25.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 11:00 UTC, and Sunday is the busiest day of the week. Of the 9 posts sampled, 89% carry an image or video, 22% are part of a thread and 22% link out. The account's strongest tracked post pulled 678 interactions, about 4.0x its own typical post. Recurring topics include #3dmodeling, #ai3d, #aicoding.
Measured over 9 original posts from a 30-day window, last computed on September 27, 2026. Recurring tags: #3dmodeling, #ai3d, #aicoding.
Compared with accounts its own size
Robin Delta's engagement rate beats 63% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 7 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 34% 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 0.192%, Robin Delta sits above the 50th percentile of the 205,374 accounts in this comparison. That places it in the above the median band, which runs 0.143% to 0.678%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
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 11:00 UTC, and Sunday 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 | 89% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 22% 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.
- 22% 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 490 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
- Sep 24, 20264.0x their median
fine-tuning open-source models without this step is just burning GPU credits. inspect your data first. catch duplicates, empty outputs, bad rows. an AI agent can build the checker for you in minutes. i tried it in Qoder. i gave its desktop agent one task: build a local checker for instruction/output JSONL, let me fix flagged rows, and export the valid ones. on 12 sample rows, it caught: >2 duplicate instructions >2 empty outputs >1 instruction over my 300-character review threshold i fixed one empty output. the valid count went from 7 to 8. Qoder then verified that the exported JSONL contained 8 clean rows. duplicates aren't always wrong, and 300 characters isn't a model limit. those are reasons to inspect a row before training, not blindly delete it. Qoder planned the task, built the app, opened it in the browser, and checked the result. that's a useful tool to have before starting a fine-tuning run. i used Qwen3.8-Flash, which showed 0× Credits in my run. it's free through September 30. Try it through my referral page before downloading Qoder: https://t.co/qTwgCrScZ3 You’ll get 600 credits: 300 from the 14-day Pro trial and 300 from my code.
- Sep 25, 20261.9x their median
vibe coding made software cheap to build but nobody talks about the deployment tax think cloud bills higher than your MRR for a hosting setup you barely understand infrastructure should be cheap, boring, and invisible Hostman starts at $4/month and deploys straight from GitHub tutorial below:
- Aug 25, 20261.7x their median
ai slop UGC ads are quietly destroying your ROAS.. the brands scaling hardest in 2026, like IM8, immi and obvi, use real creators, faces and stories. everyone else is racing to the bottom with ai slop. Icon rebuilt its entire workflow around real creators after realizing cheaper AI content doesn’t automatically perform better. tutorial:
- Aug 25, 2026
ai video isn't slop anymore wan3.0 just solved the #1 problem in ai video.. character consistency it just dropped on magnific, and people are already calling it the most consistent ai video model out there.. 1080p, synced audio, and the same character throughout an entire story here's how: > https://t.co/I3Ui2zaviu
- Sep 21, 2026
this is insane.. I gave GPT-6 a robot photo and asked it to turn it into an interactive 3D website with Hyper3D Rodin MCP. GPT-6 calls Hyper3D Rodin MCP to generate the detailed, textured 3D model. Then Codex builds a showroom where you can examine the robot from every angle and inspect its individual parts. the same asset can then become a playable character in a browser game. tutorial below:
- Sep 14, 2026
crazy.. the first AI Design Agent Team just dropped. you give OJO one rough idea → its AI Design Agent Team turns it into product logic, interaction design, visual systems, motion, a working prototype, and dev handoff.. all on one canvas even a Steve Jobs perspective is just another stackable skill Tutorial below:
- Aug 21, 2026
https://t.co/q6DZU0MP9k
- Sep 17, 2026
After way too many hours building and testing live voice agents, here's the thing nobody warns you about: Your agent rarely breaks because the LLM is dumb. https://t.co/CBXlhfIiRi It breaks because the transcript is still moving while it's trying to think. > https://t.co/Cn8buhTckI > https://t.co/OvVFVRi73z You can have the smartest model in the world sitting at the center of your stack, and it will still feel broken if the words underneath it keep shifting.
- Jul 14, 2026
Everyone keep talking about how LLMs are good enough now. The LLMs have always sounded confident. I don't buy it. After years running and building document workflows @nutrientdocs (formerly @PSPDFKit), the breakage is in the numbers. A model confidently hallucinates answers from a filing table it never fully saw because a 512-token window chopped the table in half. So we open-sourced grounding-en, a 0.4B model on @huggingface that scores whether a claim is actually entailed by the source. On our open benchmark our two models sit at #1 and #2. The real gap is number grounding: .92 AUC vs .45–.66 from the usual NLI models and we run 8,192 tokens so the table stays intact. Apache-2.0, with a free demo and a benchmark anyone can run. A document workflow gets trustworthy not when the model sounds confident, but when the claim can be checked. If it can't be grounded, it doesn't ship.
- Aug 19, 2026
this is terrifying.. ai can now find where a photo was taken from the pixels alone > upload a single photo from Instagram, even if the GPS and metadata have been removed > GeoSpy analyzes visual clues most people would ignore: buildings, roads, vegetation, terrain, shadows, even the angle of the sun > it connects those clues to real-world geospatial data and predicts where the photo was taken > seconds later, it returns the coordinates and drops the estimated location onto a 3D map > on clear, detailed images, its enterprise model claims up to meter-level accuracy > which means one harmless photo outside your home, office or hotel could reveal far more than you intended > this used to require skilled OSINT investigators and hours of manual work.. now AI can do it almost instantly two years ago this felt like science fiction.. now the pixels alone can give you away
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 168 interactions against 87K followers, an engagement rate of 0.192%. Measured over 9 original posts, its engagement rate beats 63% 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 22K times each, and 0.769% of those impressions turn into an interaction. That is about 25.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 11:00 UTC, and Sunday is the busiest day of the week. Of the 9 posts sampled, 89% carry an image or video, 22% are part of a thread and 22% link out. The account's strongest tracked post pulled 678 interactions, about 4.0x its own typical post. Recurring topics include #3dmodeling, #ai3d, #aicoding.
- What is Robin Delta's engagement rate on X?
- Robin Delta (@heyrobinai) has an engagement rate of 0.192%, based on the median interactions across 9 original posts from the last 30 days against 87,277 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.192%, Robin Delta sits above the 50th percentile of the 205,374 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 @heyrobinai have real engagement?
- Its engagement rate beats 63% 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 @heyrobinai post?
- Most posts go out around 11:00 UTC, and Sunday is its busiest day, at roughly 2.1 posts per day across the measured window.