Maximilian engagement report
@maxedapps - 69K followers on X
Measured over 43 original posts from a 30-day window, last computed on September 18, 2026.
Engagement
A typical post picks up 155 interactions against 69K followers, an engagement rate of 0.226%. Measured over 43 original posts, its engagement rate beats 62% of 15,519 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 11K times each, and 1.38% of those impressions turn into an interaction. That is about 16.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.4 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 06:00 UTC, and Thursday is the busiest day of the week. Of the 43 posts sampled, 9% carry an image or video and 7% link out. The account's strongest tracked post pulled 1.8K interactions, about 12x its own typical post.
Measured over 43 original posts from a 30-day window, last computed on September 18, 2026.
Compared with accounts its own size
Maximilian's engagement rate beats 62% of the tracked X accounts closest to it in follower count (15,519 accounts, accounts of similar size (decile 5 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 49% 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 0.226%, Maximilian sits above the 50th percentile of the 156,839 accounts in this comparison. That places it in the above the median band, which runs 0.128% to 0.604%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.003% |
| 25th percentile | 0.022% |
| 50th percentile | 0.128% |
| 75th percentile | 0.604% |
| 90th percentile | 2.32% |
| 99th percentile | 83.4% |
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 06:00 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 | 9% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 7% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 9% 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.
- 7% 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 352 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 11, 202612x their median
A fucked thought: We are still in the punch cards phase of AI. Year 3.
- Sep 18, 20268.1x their median
Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms
- Sep 6, 20267.6x their median
The more AI news you consume, the more you start feeling like the world is moving 100x faster than you. Every day there is a new model, new benchmark, new agent, new this changes everything thread. Slowly you stop learning because you are curious and start learning because you are scared of becoming irrelevant. It happened to me too. For the last 2 months, I’ve barely followed any AI or tech updates. No daily model wars, no chasing every release, no trying 20 new tools every week. And when I look back, I can hardly find anything that fundamentally changed what I should be doing. That’s when I realised most people are not behind in AI, they are just overloaded with AI news. Stop treating every update like an emergency. Build more. Think more. Master fundamentals. The truly important changes will survive the hype cycle and reach you anyway…..
- Sep 13, 20264.8x their median
Here we go, QuickGUI 0.1 pre-alpha release (only tested on macOS atm) https://t.co/LNvhH9LGb1 https://t.co/yn7NZCr2Ag
- Sep 11, 20264.6x their median
experimental: pnpm v12.4 can install Rust and Python dependencies too. So in a multi-language repository you get js packages, crates, and pypi packages downloaded concurrently. https://t.co/L3laZrA832
- Sep 17, 20263.8x their median
Jev will unlock many new use-cases and businesses. And we're all in the top 0.001% right now! I think Jev is that kind of release that truly marks another huge evolution step in this (arguably noisy) world of AI. Think of what you can do with Jev + LLMs. Jev + (finetuned) small models. Jev without LLMs at all. We're super early. And imagining that there is so much more to come - with future versions of Jev, competitors. Of course also LLMs, new models. Absolutely mindblowing.
- Sep 15, 20263.6x their median
Cloudflare + Alchemy (@alchemy_run) + Effect (@EffectTS_ ) is such a winning stack with AI agents, it's not even real. Provides pretty much all the primitives you need for building any kind of application and ensures proper guardrails & rules to keep the agent on track. And all the infra lives in code - which is how it should be with AI agents.
- Sep 14, 20263.4x their median
A crucial skill when developing with AI is to know which "bugs" to tackle or ignore. Because AI models will *always* find "bugs". You can run 10 cycles of code reviews, they will still find something. That includes false positives BUT also *a lot* of esoteric, niche issues that will likely never matter for what you're building. For example, I find that Astra loves to find weird pretty made up racing conditions like "if you bulk import emails and after the first 10 records were written, an email address changes, it will silently overwrite it". Stuff like that. Which may never matter for what you're building. If you just let AI review + blindly accept its findings and let it fix those findings, you'll just start adding layers of complexity and turn your codebase into a mess. Don't let that happen. Use your brain, use your understanding of what you're building - don't outsource your thinking.
- Sep 10, 20262.8x their median
It's really interesting how new releases of React / Angular etc are really not that interesting anymore. A few years ago, I used to wait for them eagerly! I watched the GitHub repos to anticipate what would change months in advance (obviously mostly because I wanted to update my courses as soon as possible). Now it doesn't matter that much anymore. NOT because new versions or features don't matter - they do. But simply because it's mostly about pointing the AI at the docs / changelog AND reading it yourself to know if there's something specific you absolutely want the AI to use in your project. So the knowledge still matters - but you don't really need to get that super deep understanding of those features, syntax changes etc. Because you're not writing the code anymore.
- Sep 11, 20262.8x their median
I know it's not super sexy or hot. But I'm making really good progress with: - projects with clear guardrails & guidelines - in-depth research & planning - setting up task-specific self-verifiable loops - running tasks in parallel (worktrees) if possible but also often working sequential - code reviews that focus on the general shape and critical areas + AI-generated descriptions and chatting with AI No software factory, no "graph engineering" here. Just proper context, the right harness, tools etc. Collaborative planning (me + AI) and then hands-off implementation by the AI. This doesn't allow me to have agents run 24/7. But it does allow me to work on 3 or more projects at a time, with 1-4 agents per project. And, most importantly, it works for me.
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 155 interactions against 69K followers, an engagement rate of 0.226%. Measured over 43 original posts, its engagement rate beats 62% of 15,519 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 11K times each, and 1.38% of those impressions turn into an interaction. That is about 16.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.4 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 06:00 UTC, and Thursday is the busiest day of the week. Of the 43 posts sampled, 9% carry an image or video and 7% link out. The account's strongest tracked post pulled 1.8K interactions, about 12x its own typical post.
- What is Maximilian's engagement rate on X?
- Maximilian (@maxedapps) has an engagement rate of 0.226%, based on the median interactions across 43 original posts from the last 30 days against 69,139 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.226%, Maximilian sits above the 50th percentile of the 156,839 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 @maxedapps have real engagement?
- Its engagement rate beats 62% of the tracked X accounts closest to it in follower count (15,519 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 @maxedapps post?
- Most posts go out around 06:00 UTC, and Thursday is its busiest day, at roughly 2.37 posts per day across the measured window.