marcus engagement report
@marcusyul - 553K followers on X
Measured over 24 original posts from a 30-day window, last computed on October 15, 2026.
Engagement
A typical post picks up 370 interactions against 553K followers, an engagement rate of 0.067%. Measured over 24 original posts, its engagement rate beats 64% 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 206K times each, and 0.179% of those impressions turn into an interaction. That is about 37.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.7 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 14:00 UTC, and Thursday is the busiest day of the week. Of the 24 posts sampled, 96% carry an image or video, 17% are part of a thread and 46% link out. The account's strongest tracked post pulled 33K interactions, about 91x its own typical post.
Measured over 24 original posts from a 30-day window, last computed on October 15, 2026.
Compared with accounts its own size
marcus's engagement rate beats 64% 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 13% 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 0.067%, marcus sits above the 25th percentile of the 204,880 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.
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 14:00 UTC, and Thursday 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 | 96% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 46% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 96% 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.
- 46% 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 466 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
- Aug 25, 202691x their median
HAN CREADO UN CAPCUT GRATIS Y SIN MARCAS DE AGUA, Y YA TIENE 62K STARS EN GITHUB CapCut te mete marca de agua, te bloquea funciones y encima te cobra suscripción. Un grupo de devs se cansó y construyó la alternativa open source y gratuita. Se llama OpenCut. Te explico todo: Un editor de vídeo open source que están construyendo desde cero, con arquitectura basada en plugins. La idea: una alternativa real a CapCut, pero abierta. Sin marcas de agua, sin paywalls, sin suscripciones. Lo que crearon: → Editor completo con línea de tiempo y multipista → Plugins nativos para expandir lo que puede hacer → Una sola app para web, escritorio y móvil (núcleo en Rust) → MCP Server, automatizaciones y soporte para agentes de IA → Licencia MIT: puedes hacer lo que quieras con él Cómo instalarlo: → Clona el repo desde GitHub → Instala las dependencias → Ejecuta la versión clásica (disponible ya) → Sigue la nueva versión en https://t.co/wa5VTQYLn1 Es exactamente lo que CapCut debería haber sido desde el principio. Enlace abajo👇
- Sep 16, 202635x their median
THIS CHINESE TRADER USED CLAUDE TO BUILD AN AI TRADING BOT And recorded this tutorial where he shows how to replicate it from scratch 23 minutes. Step by Step & Free. Save this before you forget it https://t.co/e6bbH7Grmr
- Aug 24, 20265.2x their median
I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching https://t.co/twx6etZb3X (@agentsky_dev), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.
- Sep 17, 20264.0x their median
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- Oct 13, 20263.9x their median
Wan 3.0 is now UNLIMITED on Higgsfield for 7 days. 60% OFF on Higgsfield API. > Advanced motion design, animation and live-action realism > Consistent characters across every shot > Top-tier quality at an affordable price Available now at Higgsfield. Limited offer. https://t.co/ncpqpOg4NX
- Sep 16, 20263.3x their median
RIP other vibe coding platforms. We just launched Macaly Cloud. We built it without the most expensive part of a vibe coding platform: the coding agent. You already pay for ChatGPT or Claude. Let them write the code. We provide: → database, auth and hosting → domains and payments → SEO, analytics and security checks → image generation and web scraping Free until October 1. Then $10/month. https://t.co/guFkkIdkSW
- Oct 13, 20263.1x their median
Introducing Selfdrive. The AI growth team for your iOS app that gets you users while you sleep. It handles Apple Ads, ASO, SEO and UGC creators for you. All of it. https://t.co/2UCiidgwrq https://t.co/FxTrvkR25n
- Sep 4, 20263.0x their median
In mid-2024, Pocket FM was at $200M ARR, growing 50% YoY, and we decided to kill its business model. Revenue flatlined for six months, we turned cash flow negative, and investors were writing us off. But then, Pocket's ecosystem exploded. Our reasoning and why this happened: What constrains Netflix's growth is content. They spend $17B+ on content production yearly, but many shows don't make it to the final stage. Of those that do, only a few become blockbusters. And those few are the reason subscribers stay on Netflix. So we started thinking. Pocket was growing nicely. However, what limited us was how many new users we could add and retain at scale. The only solution we came up with was increasing the number of blockbusters. Blockbusters drive user growth and retention at the same time. To get more blockbusters, Pocket FM needed more production. That's why we pivoted to only AI Content production. This did a number of things: 1. More content gets produced because we removed a lot of friction from doing so. It's easier for writers to maintain daily production. And for writers who still haven't found their hit show, testing and pivoting becomes less time-consuming. From taking several months to test even one show, you can now test 3-4 shows in one month. 2. Content quality increases. The more content gets produced and consumed on Pocket, the better the engine gets. We continuously monitor retention and other engagement metrics. When we detect something is working, our writing copilot learns it. 3. Unlocked hundreds of thousands of writers; Pocket's writing copilot is built to help writers without storytelling training. This is in contrast to Netflix's model. While their budget gets allocated among a limited number of studios, Pocket's supply side is getting flooded with writers. Over 500k writers are producing on our platform. 4. Made it easier to cross-create ads to attract users. Better shows are easier to sell to people who aren't yet using Pocket. And the same data that feeds our copilot goes on to feed the engine we use to create ad videos, which is also self-reinforcing. _____________________________ After making the change, ARR flatlined for six months and we started losing money. It was extremely painful, but then growth and the whole ecosystem exploded. - Hours of content produced per year went from 35k to ~2.5M. - New titles added yearly more than tripled from 120k to 400k+ - Millions of new users joined Pocket. The obvious risk was opening the gates to AI slop. That's why we have an extremely rigorous feedback and moderation process Ultimately, the only thing that matters is whether users are enjoying the content that's being uploaded. Metrics don't lie: - 12-month revenue retention went from 44% to 76%. - Avg daily streaming time went from ~25mins to 150+ mins. - DAUs/WAUs from 30% to 51%. I think we're extremely early, and I'm excited to see how Pocket's flywheels continue to accelerate from here.
- Sep 16, 20262.6x their median
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- Sep 17, 20262.5x their median
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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 370 interactions against 553K followers, an engagement rate of 0.067%. Measured over 24 original posts, its engagement rate beats 64% 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 206K times each, and 0.179% of those impressions turn into an interaction. That is about 37.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.7 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 14:00 UTC, and Thursday is the busiest day of the week. Of the 24 posts sampled, 96% carry an image or video, 17% are part of a thread and 46% link out. The account's strongest tracked post pulled 33K interactions, about 91x its own typical post.
- What is marcus's engagement rate on X?
- marcus (@marcusyul) has an engagement rate of 0.067%, based on the median interactions across 24 original posts from the last 30 days against 552,865 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.067%, marcus sits above the 25th percentile of the 204,880 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 @marcusyul have real engagement?
- Its engagement rate beats 64% 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 @marcusyul post?
- Most posts go out around 14:00 UTC, and Thursday is its busiest day, at roughly 3.67 posts per day across the measured window.