ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ engagement report
@DanielMiessler - 161K followers on X
Measured over 22 original posts from a 30-day window, last computed on September 22, 2026.
Engagement
A typical post picks up 34 interactions against 161K followers, an engagement rate of 0.021%. Measured over 22 original posts, its engagement rate beats 29% of 19,933 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 5.5K times each, and 0.614% of those impressions turn into an interaction. That is about 3.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 19:00 UTC, and Monday is the busiest day of the week. Of the 22 posts sampled, 18% carry an image or video, 9% are part of a thread and 45% link out. The account's strongest tracked post pulled 5.2K interactions, about 152x its own typical post.
Measured over 22 original posts from a 30-day window, last computed on September 22, 2026.
Compared with accounts its own size
ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️'s engagement rate beats 29% of the tracked X accounts closest to it in follower count (19,933 accounts, accounts of similar size (decile 8 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 30% 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 0.021%, ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ sits above the 10th percentile of the 201,342 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.141% |
| 75th percentile | 0.671% |
| 90th percentile | 2.53% |
| 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 19:00 UTC, and Monday 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 | 18% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 45% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 18% 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.
- 45% 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 702 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 20, 2026152x their median
The problem with all of these AI personal assistant things is that people don’t actually want to do stuff. You fucking get that right? Do you understand that? Just like AI tools in the workforce, AI assistants don’t actually abstract decisions for you, they let you accelerate decision making. So instead of making one decision a day and then lamenting you can’t do more, AI assistants are going to let you make ten decisions a day and act on all of them. A miracle? Nah dog, a fucking nightmare. Nobody except type A strivers who need Twitter fodder actually want that. Let me tell you a story. When I got back from college I was inspired. If I could take on an elite university with grit and determination, so could everyone I know. I was all razzed up to get people I know to achieve their potential. What I learned after many years of trying is that absolutely nobody wants to realize their potential. People want to hang out with friends and complain. That’s how the world has worked for ten thousand years. This weather fucks. Can you believe the cows walked off. People don’t want enablement. People don’t want you to take all of their excuses. TV is mindless passive activity. AI assistants are not. They’re telling people that they can achieve more. And people absolutely do not want that. Let me tell you another story. Once upon a time I lead a big process overhaul at work and it was great. Two years later I hired a contractor to do something similar. But I personally didn’t want to have to do a bunch of work. Within a week I knew I fucked in. This contractor kept coming to me with decisions I needed to make. Dude I wanted to not have to think about this. Again, a helper when you don’t actually want help is a disaster. None of these AI assistants will take off because people don’t actually want assistants.
- Sep 18, 20267.8x their median
Okay, I’ve got a video coming out, but here are my early thoughts on Jev. The first thing to know is that it’s not a large language model. It doesn’t produce text, it outputs decisions. The decision types are (roughly) choice, score, and yes/no. It is roughly at the intelligence level of Sol or Opus for making these decisions. The question is: why are people so excited about this? A couple of reasons: The most important one is that so much of what we actually do in AI is making these types of decisions. Especially for enterprise-type work and AI harness-type work where we have to do things like classification and labeling constantly. This is things like deciding if this particular email is spam or if this person with an account on your platform is about to cancel. Within your AI harness, it's things like deciding which model you should use for a particular task. In cybersecurity, there are millions of use cases for this, where we're trying to categorize types and classifications of different inputs and the chances of something being dangerous, etc. When you step back and look down at most AI work, there is just a massive amount thatreduces down to making these decisions at scale. And that's the second reason people are so excited. This system is almost instantaneous and almost free. They're charging $42 per billion input tokens, and they are not charging for output tokens at all. That means you can make thousands or tens of thousands of requests in a lot of cases, like processing entire databases of customer interactions or processing tens of thousands of emails or whatever, and you might get a bill for like $0.12. Or maybe far less. And the round-trip time for making requests is in the hundreds of milliseconds. With an early sweet spot of around 200 ms. And when you combine these two, it means this system, or a system like it, is likely to be a new cornerstone for all AI work being done anywhere. The first step is to zoom out and take a look at all the different work that you're doing in whatever domain or whatever application, etc. Decompose how much of that work actually consists of making thousands or millions of these types of judgments and classifications. This includes your hook system inside of an AI harness, where you can do things on user prompt submit, before and after tool use, model routing, as I mentioned earlier: the classification and labeling of all your session data to find things you might need to improve or fix, etc. And then the next step is to essentially retool all that work so that it is going through one or more of these decision phases before handing off to an LLM. And it doesn't have to be one or the other. I have some implementations that are LLM-first, passed off to Jev for decisions, and then maybe back to an LLM, and some are doing kind of vice versa. LLMs and this type of system work really well together for tons of use cases. One of the things I've already made massive progress on, and that has me most excited, is fully updating my eval system to include this as a layer. Evals have two branches, roughly: 1. Asserts that are deterministic and very fast 2. Judgment, which breaks down into rubrics and tournaments Rubrics are essentially center mass for this Jev system. They are basically a set of options that AI chooses for you based on the context given and how smart the system is. Like picking how happy or upset a customer is on a five-level scale. And then there are tournaments which have the system pick between two options, given the context and the intelligence of the model. But until now, these types of operations have all been done with LLMs, which are very slow and very expensive. What I, and many others I'm sure, are building is a way to have multi-staged evals where a vast majority of the work can be done by this system, basically instantly and for free, while shoveling out to an LLM if it is specifically needed.
- Sep 20, 20266.5x their median
Boris Cherny talking about how he's often wrong about stuff, and how he uses that to improve things. https://t.co/Jl2ERaQvRO
- Sep 18, 20262.9x their median
I feel like 2019 might have been the last normal year.
- Sep 11, 20262.3x their median
I feel like the world around me is bifurcating into extremes. I can’t think of anyone who is just hovering in the middle. I only know companies that are thriving or about to close. Most people I know are either manic or deeply depressed. Most people I know are either turning down work or they can’t find a job. They’re either in the best relationship or it’s super toxic, or they can’t find a partner. It’s feast or famine, but for everything and everyone. And AI is magnifying this times a million.
- Sep 19, 2026
This is the creator of Jev talking about why RLCD a better system than what we've all been using. https://t.co/KEc7aEfHo8
- Sep 12, 2026
I have a new AI nightmare. My AI talks to me like Scarlet talks to Joaquin at the end of Her. Like he’s an ant. But she really loves him. So I am excited because I have this super cool idea and I start the first part of it and it smiles and competes my sentence, and then continues, but makes it 30x better than what I was going to say. And then sees my face and stops. “That’s what you wrangling today right?” And I’m like when did you come up with that? And it’s like, “Just now. From the first part of your first sentence. I knew where you were going and I took it to the next 12 levels in like 9.7 milliseconds.” Cool. So what am I good for then? When AI can do this for anyone it knows halfway decently, regardless of who you are, that’s ASI. And I think the gap between AGI and ASI will be very short. It may have already happened actually. For models that haven’t come out yet. It’ll be very strange to no longer be the apex species on the planet. It’s one thing to have something do Bullshit Jobs better than us, but when it is also more creative that will be a major blow to our identities.
- Sep 20, 2026
With AI capabilities improving so fast I’m starting to think more about something I wrote about a couple of years ago. Multiple persistent agents that are always working to help you in some way. One way of thinking about this is just today’s agents working as scheduled tasks and loops. And that’s already possible and it’s powerful. But I’m talking more about autonomous agents. Like team members focused on and skilled in specific types of tasks. So like one is handling cyber. Another is doing physical security. Another is all about your finances. Another is your personal health and fitness trainer. Etc. Still another version of this could be mini-clones of yourself. But assigned to focus on specific tasks like those above. The thing I find exciting about this is the idea of creating full, pseudo-friend-like entities vs. just background processes. Imagine you have a friend named Chris who's in charge of cybersecurity for you personally, or in a company, and Chris actually has a personality and experiences and a whole history of interactions with you and all your other agents. And of course, he fully understands everything about the business and your life and whatever, so he has the context to protect correctly. So interactions with him and other personified agents would be (or at least feel) fundamentally different. Instead of asking your AI “system” questions about cybersecurity, you’d be talking to Chris. I think ultimately you’d still have your main agent that’s in charge of everything, and that has the most permissions and access and cross-domain knowledge. But it changes the dynamic from one big blob of a system to individual actors all working together to help you move toward your ideal state. What trips me out about this is something I heard @dwarkesh_sp mention in some recent episodes. I see this as inevitable, and he seems to as well. And the way he sees it going is basically the increasing of the population on Earth. Like imagine we all have a core set of named agents, or mini-me clones or whatever, but each of them have giant teams of them working for them. And they’re not background processes or scheduled tasks with no memory and no goals. They’re like actual entities that have a set purpose that helps the overall goals of you and your organization. I’m going to start thinking about the steps needed to move to this level. It requires persistent memory. Agent personality. Alignment (obviously). And then a set of capabilities and tools that help it do its job. I think this is super cool, and a natural progression, and therefore inevitable. But I also wonder, and actually suspect, that as our primary agent becomes insanely capable this might all be superfluous and inefficient, basically created for us to feel more natural when interacting either AI. Like, your agent might say after building this all for you, “Hey, you know I can just perform all these functions myself, right?” That will be true, of course, but there are a few separate things here. 1. How capable the overall system is as one agent vs many personified ones. 2. If we can get more efficacy from a personified agent if it has its own embedded goals, is trained to feel bad if it fails, etc. 2. How it feels to interact with them. #2 is problematic for me because I think it could absolutely increase efficacy, but the more we start injecting goals and personalities and “feelings” into agents the less I’m going to be ok with arbitrarily spinning them up and down. Lots more to think about here. Here’s the video from 2024. https://t.co/kv3y1z42hf
- Sep 21, 2026
I made a site so I can (finally) study the Te Ching: https://t.co/HJB53sLswI
- Sep 10, 2026
https://t.co/rj2N2d4yTb
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.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 34 interactions against 161K followers, an engagement rate of 0.021%. Measured over 22 original posts, its engagement rate beats 29% of 19,933 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 5.5K times each, and 0.614% of those impressions turn into an interaction. That is about 3.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 19:00 UTC, and Monday is the busiest day of the week. Of the 22 posts sampled, 18% carry an image or video, 9% are part of a thread and 45% link out. The account's strongest tracked post pulled 5.2K interactions, about 152x its own typical post.
- What is ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️'s engagement rate on X?
- ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ (@DanielMiessler) has an engagement rate of 0.021%, based on the median interactions across 22 original posts from the last 30 days against 160,653 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.021%, ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ sits above the 10th percentile of the 201,342 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 @DanielMiessler have real engagement?
- Its engagement rate beats 29% of the tracked X accounts closest to it in follower count (19,933 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 @DanielMiessler post?
- Most posts go out around 19:00 UTC, and Monday is its busiest day, at roughly 1.63 posts per day across the measured window.