Hussein Nasser engagement report
@hnasr - 90K followers on X
Measured over 10 original posts from a 30-day window, last computed on September 24, 2026.
Engagement
A typical post picks up 150 interactions against 90K followers, an engagement rate of 0.167%. Measured over 10 original posts, its engagement rate beats 60% of 21,185 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 9.6K times each, and 1.57% of those impressions turn into an interaction. That is about 10.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.5 post a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 14:00 UTC, and Wednesday is the busiest day of the week. Of the 10 posts sampled, 40% carry an image or video, 30% are part of a thread and 20% link out. The account's strongest tracked post pulled 1.2K interactions, about 8.0x its own typical post.
Measured over 10 original posts from a 30-day window, last computed on September 24, 2026.
Compared with accounts its own size
Hussein Nasser's engagement rate beats 60% of the tracked X accounts closest to it in follower count (21,185 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 54% 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 0.167%, Hussein Nasser sits above the 50th percentile of the 212,805 accounts in this comparison. That places it in the above the median band, which runs 0.146% to 0.697%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.697% |
| 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 14:00 UTC, and Wednesday 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 | 40% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 20% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 40% 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.
- 20% 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 589 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 5, 20268.0x their median
In the coming few years, most engineers will heavily rely on AI to build software. Loops will produce and review code, while engineers will be out of the loop, figuratively and literally. The final product, which is supposed to be used by humans, is unrecognizable by the engineers who built it. Bugs and performance issues will arise, and the engineers will go back to the loops to fix them. The loops will fix the bugs but produce more others. Eventually, we will reach a stage where we can’t possibly know what is wrong with the system, because the agent built its own framework, libraries and abstraction that only it understands. The product will get into a state where no one can read or understand the code and only the loops can fix them. Loops need tokens, and engineers will have no choice but to shovel them like coal into the Titanic’s boilers. Very few engineers will be able to understand and troubleshoot systems. Those rare engineers who kept their skills sharp will be in high demand when we reach that state. By all means use AI but not to an extent it dulls your critical thinking.
- Apr 15, 20266.5x their median
I wrote a new book that has been in the works for years. It is called Root Cause, and it is for those who enjoy the art of backend engineering. Early in my career, 20 years ago, I built backend and database applications without fully grasping their inner mechanics. Performance issues, race conditions, bugs, and even data corruption often left me lost. Since that day, I resolved to truly understand how systems work. From networking protocols and intermediary proxies to backend services and various database engines. I made it a habit to follow every request on its journey through the dark alleys of the network, down to the bowels of the database engine, meanwhile interacting with various kernel data structures in the process at every hop, and back. I became obsessed with understanding what happens behind the scenes in software. Not just what breaks, and how but also why and what was the source of the bleed. Root Cause is a collection of the most interesting bugs I encountered, ranging from performance bottlenecks and non-deterministic crashes to subtle data inconsistencies and incorrect results. This book is for anyone curious about how production backend systems really behave under pressure, and how to debug them when they don’t. Even when you don’t have access to the source code. Root cause consists of 15 chapters, each is a story about a backend bug, with investigation, diagrams, a section of a fundamental concept until the root cause is revealed. Grab your copy here paperback or kindle ebook on amazon https://t.co/AgYMX4sWTQ
- Jun 25, 20265.5x their median
In the end, AI will produce more bloated products for us to troubleshoot and fix. If you are a good software troubleshooter who understands the fundamentals. you will be on high demand. And Your skillsets are deadly.
- Sep 14, 20264.9x their median
As I'm exploring SQLite architecture, I learned today an interesting design choice the creators had to make designing it. SQLite doesn't have a detailed schema table for all columns and their data type. Instead they only store the sql text used to create the Table. Other database platforms have verbose meta data for every column, data type, indexes, constraints and much more. This makes the altering the table trivial as you can update the metadata type to change a column data type, or an index storage. However, this also increases the footprint of the schema table and makes backward compatibility harder to maintain as the schema table itself evolves. Because SQLite is an embedded database they opted to keep it simple and store the sql string itself in the schema table, minimizing the footprint and improving backward compatibility. This however came as a price making alter table significantly challenging, as the code now has to parse the sql text to figure out what is what. Details are the in the SQLite documentation. I'm wondering how large sqlite file will get should the full schema be detailed.
- Jul 1, 20263.6x their median
When I created this course I didn’t know it will grow and reach over 60k engineers. I honestly just shared my experience, lessons and patterns that I discovered in the backend engineering space. To this day (years later) I get emails of people who aced interviews and promotions, got more confidant building software and started polishing their foundation. They speak with grounded knowledge instead of throwing out abstract and confusing terminologies. If you have specific knowledge, share it to the World. You never know who may resonate with it.
- Apr 26, 20263.6x their median
When this news broke I really wanted to understand how the linux scheduler work. So I started to research. What I gathered so far: (of course I might be missing something) Unlike user code, the Linux kernel code isn’t usually preemptable. That is when a system call is executed or a page fault is triggered the kernel runs to completion hogging the cpu core it runs on. The current Linux 7 tip experimenting a different preemption model for kernel code, so other more critical tasks can be scheduled. With this default mode, Postgres experienced 50% dropped in performance in one test suite 96 cores, with 100GB shared buffers pool. The theory from the thread discussion is that the large buffer pool 100 GB with the default 4kb kernel vm page size, caused significant number of page faults. Page faults gets triggered to allocate physical memory on first access or on swap (I have a video detailing this). Page fault runs kernel code to do the allocation of the physical memory and update the page table data structure for the process. If page faults are being preempted, it keeps the user process code in spin lock waiting for access. Indeed Andreas, the postgres maintainer, was able to prove that even with stable Linux you could see the contention obtaining memory from shared buffers, its just not as obvious with 7.0 where kernel preemption is enabled Using huge pages significantly reduces page faults, improving the performance. My Page Faults The Backend Engineering Show https://t.co/QtcIL4nsZi Phoronix article https://t.co/hXMzz0KOi0
- Aug 26, 20262.4x their median
You wouldn’t know what a good software is until you've seen a bad one. Bugs and slowness must be experienced in software to appreciate quality efficient software.
- Sep 1, 20262.0x their median
Engineering efficient backends is interesting. Engineering efficient real-time backends is a different beast. I tackle and breakdown the challenges my real time backend course, with examples. Head to https://t.co/TQhpYQT3jb to grab a copy. I recommend backend fundamentals as a prerequisite. Enjoy the breakdown like thousands of other engineers.
- Jun 15, 20262.4x their median
https://t.co/rsO62YH2bF https://t.co/D4w6hl4FgW
- Jun 21, 20262.2x their median
Root Cause will make you look and think of system internals from a different lens. Grab your copy Amazon: https://t.co/flopo4GN73 b&n https://t.co/gz4usDugsn https://t.co/3DV2cXXy7M
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 150 interactions against 90K followers, an engagement rate of 0.167%. Measured over 10 original posts, its engagement rate beats 60% of 21,185 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 9.6K times each, and 1.57% of those impressions turn into an interaction. That is about 10.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.5 post a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 14:00 UTC, and Wednesday is the busiest day of the week. Of the 10 posts sampled, 40% carry an image or video, 30% are part of a thread and 20% link out. The account's strongest tracked post pulled 1.2K interactions, about 8.0x its own typical post.
- What is Hussein Nasser's engagement rate on X?
- Hussein Nasser (@hnasr) has an engagement rate of 0.167%, based on the median interactions across 10 original posts from the last 30 days against 90,010 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.167%, Hussein Nasser sits above the 50th percentile of the 212,805 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 @hnasr have real engagement?
- Its engagement rate beats 60% of the tracked X accounts closest to it in follower count (21,185 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 @hnasr post?
- Most posts go out around 14:00 UTC, and Wednesday is its busiest day, at roughly 0.5 posts per day across the measured window.