Alex Veremeyenko engagement report
@alex_verem - 103K followers on X
Measured over 23 original posts from a 30-day window, last computed on September 21, 2026.
Engagement
A typical post picks up 84 interactions against 103K followers, an engagement rate of 0.082%. Measured over 23 original posts, its engagement rate beats 48% 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 10K times each, and 0.821% of those impressions turn into an interaction. That is about 9.92% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.4 posts a day over the last 30 days, though only 40% of days saw any activity at all. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 23 posts sampled, 61% carry an image or video, 52% are part of a thread and 13% link out. The account's strongest tracked post pulled 12K interactions, about 144x its own typical post.
Measured over 23 original posts from a 30-day window, last computed on September 21, 2026. Recurring tag: #hyper3d.
Compared with accounts its own size
Alex Veremeyenko's engagement rate beats 48% of the tracked X accounts closest to it in follower count (15,519 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 35% 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 0.082%, Alex Veremeyenko sits above the 25th percentile of the 157,102 accounts in this comparison. That places it in the below the median band, which runs 0.022% to 0.128%.
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 16:00 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 | 61% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 13% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 61% 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.
- 13% 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 1610 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 17, 2026144x their median
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant https://t.co/h6NzKzNpCg
- Sep 4, 202626x their median
BREAKING: Hikers had to be rescued after using AI chatbots to plan their climb up a mountain
- Feb 4, 202518x their median
I stopped using AI for writing, and at first, it felt like I lost a part of my brain. For months, AI had been my silent co-writer. Need an idea? AI. Stuck on a sentence? AI. Want a better hook? AI. So I made a drastic decision: No AI for writing. Here’s what happened 🧵: https://t.co/c1YM3yDCkp
- Sep 4, 202612x their median
Found a GitHub repo that gives your AI agent 50 marketing specialists for free. It's called marketingskills, built by Corey Haines, and it sits at 46,800 stars with 7,300 forks. The idea is simple. Skills are markdown files. Each one teaches an AI agent how to do one marketing job, with the frameworks and checklists a good marketer carries in their head. There are 50 of them, covering copywriting, CRO, cold email, pricing, SEO audits, A/B testing, churn prevention, ad creative, launch planning, referral programs, and the list keeps going. They're also wired together. One skill called product-marketing holds the context about your product, your audience, and your positioning. Every other skill reads that file first before doing anything. So the copywriting skill and the pricing skill work from the same understanding of what you sell. A few of them caught my eye. marketing-council spins up a simulated board of advisors so you get several expert takes on one question instead of a single answer. marketing-loops sets up recurring workflows an agent runs on a schedule without you re-prompting it. marketing-ideas is a bank of 139 ideas for SaaS products you can pull from when you're stuck. Installing is one command. It works with Claude Code, Codex, Cursor, Windsurf, and anything else that follows the Agent Skills spec. You type "help me optimize this landing page for conversions" and the right skill kicks in on its own. Is a folder of markdown files worth more than a $5,000 a month marketing hire?
- Aug 19, 202611x their median
Nvidia and a homebuilder want to bolt $150,000 of GPUs onto new American houses. The box is the size of an AC unit. It's liquid-cooled, runs without fans, and bolts straight onto the exterior wall. Inside sit 16 Blackwell server GPUs, 4 server CPUs, and 3TB of RAM, which puts over $150,000 of hardware on the side of the house at roughly $10,000 per GPU. The homeowner never buys any of it. Hosting is the whole arrangement, traded for subsidized electricity and internet, a smart panel, and a backup battery, usually against a flat fee of around $150 a month. Span, the startup behind the program, keeps full ownership of the GPUs and sells the compute to AI cloud providers. The pilot covers 100 new homes in the Southwest, and the stated target is 80,000 nodes pushing a gigawatt of compute by 2027. Here's why the deal exists at all. Seven in ten Americans oppose a data center near them, and New York and Texas have paused permits for the large ones. A box on a private house skips that entire fight. The suburb quietly becomes AI infrastructure. The compute value flows to the companies, the homeowner gets a discount on their bills, and the rules for compute on a private house haven't been written yet. Would you take that trade?
- Aug 19, 20264.4x their median
A PE firm hired us to run technical diligence on a SaaS target before close. The data room looked clean: $6M ARR growing 35% year over year with SOC 2 Type II in place, and the CTO walked us through their security stack for forty minutes. We asked a question the deal team skipped: how does your team use AI day to day? The CTO listed two approved tools, but when we scanned their network traffic we found fourteen. Employees had pasted customer PII into personal ChatGPT accounts, and three teams built internal workflows on unsanctioned API keys billed to personal credit cards. The security team knew about two of the fourteen tools, and finance knew about zero. The company had no AI governance policy and no record of which models touched customer data. The PE firm repriced the deal. IBM's 2026 breach report found 43% of security incidents involve unapproved AI tools, double the previous year's rate. Those breaches cost $6 million on average, a million above the $5 million baseline. ISO published new liability forms in January 2026 excluding generative AI from general coverage, and carriers are writing absolute AI exclusions into specialty lines. Buy a company with unscoped shadow AI, and the insurer may deny the claim. Your diligence team reviews code and infrastructure, but they don't scope AI usage across the org. We run shadow AI scans on every diligence now. Haven't found a clean one yet.
- Sep 17, 20263.8x their median
https://t.co/3q8ScyoDVU
- Sep 3, 20262.8x their median
https://t.co/plUo0Y2CY4
- Sep 17, 20262.2x their median
We were approached by a PE-backed healthcare RCM company whose senior billing analysts were answering the same denials questions by hand every week, from spreadsheets pulled off the ERP. Six weeks later, their technology leader was testing a working AI agent hosted securely on their own cloud servers. The agent answered 59 out of 60 test questions perfectly, matched their most important financial calculation to the penny, and gave the analysts back 15 hours a week. No private patient data ever left their secure system. The build ran in these seven steps, in this order: 1. Define what is correct before giving the AI instructions. We collected 60 real questions the analysts receive, paired each with the correct answer from the company's reports, and checked them by hand. Every change from that point on was measured against this fixed scorecard. 2. Lock down the data with read-only access to the database and a strict list of tables the AI is allowed to look at. This ensures the AI cannot accidentally change or write over official records. 3. Remove personal identities before the AI sees anything. Patient details were hidden at the database level rather than just asking the AI to ignore them in the prompt, which is the best way to ensure strict healthcare privacy compliance. 4. Force every answer into a strict format. If an answer doesn't fit the required layout, the system blocks it instead of sending an unpredictable, unstructured response to an executive. 5. Automatically grade every test run. We set up a system that checks the AI's output against our original scorecard, so a software update on Tuesday can't silently break the answers on Friday. 6. Keep the AI model easy to replace. We set up the system so the client can easily swap to a different AI provider if needed, and we built a dashboard to track costs, which run about $200 to $300 a month at this size. 7. Let the client do the final testing. The technology leader ran the tests himself, asking the hard questions he thought would break the system, and the AI held strong at 59 out of 60 correct. Plugging in the AI model was actually the fastest part of the project. Building the safety, security, and testing rules around it is what took the full six weeks. That is the Velocity Framework applied to a daily business process instead of just software development.
- Sep 15, 20261.9x their median
A PE firm reached out to us last month to get them out of a consulting engagement. Six figures spent on AI consulting across seven portfolio companies, and all they had to show for it was a team chat subscription. They needed real EBITDA impact by December to underwrite their 2027 exits. Instead, they had a bloated 150-hour discovery deck and a dev vendor billing extra "AI productivity" fees with zero boost in shipping speed. PE funds are buying AI advice far faster than that advice turns into working software. Across portfolio companies, the same four gaps consistently show up between legacy consulting promises and actual engineering needs: 1. Verifiable Proof vs. Slide Decks Consultancies spend weeks on interviews to sell a maturity assessment. We spend week one building a 'golden set', which includes 20 to 60 real operational cases with pre-agreed correct outputs. Grant Thornton found only 9% of PE leaders can prove their AI works to a buyer within 90 days. A golden set is definitive proof. An assessment is just a slide deck promising it. 2. Production Infrastructure vs. Abstract Roadmaps Instead of selling 'phased journeys', we stand up a live foundation directly inside the portco's Azure tenant within three weeks. We lock down read-only data access, strip PII/PHI, and set hard per-seat cost caps. The client owns everything from day one. If we leave, nothing breaks. 3. Lean Automation Pods vs. Billable Headcount Traditional firms add $350-an-hour bodies to grow the account. We deploy a lean $17K/month, month-to-month pod: one senior engineer, automated AI development harnesses, and a fractional architect. As our harness handles more of the load, our human footprint shrinks over time instead of growing. 4. Internal Ownership vs. Vendor Lock-In Unscrupulous vendors keep client setups obscure to protect billing. We embed the portco's product owner from day one, deliver a 90-day self-sufficiency plan, and leave telemetry running. True success means the portco ships its own updates without calling us. By deploying a reusable harness in the first portco, we can roll directly into the next company in weeks rather than months. Consultancies sell funds a vision of where AI could take them. We ship the first working agent and let the CFO decide if the vision was real.
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 84 interactions against 103K followers, an engagement rate of 0.082%. Measured over 23 original posts, its engagement rate beats 48% 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 10K times each, and 0.821% of those impressions turn into an interaction. That is about 9.92% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.4 posts a day over the last 30 days, though only 40% of days saw any activity at all. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 23 posts sampled, 61% carry an image or video, 52% are part of a thread and 13% link out. The account's strongest tracked post pulled 12K interactions, about 144x its own typical post.
- What is Alex Veremeyenko's engagement rate on X?
- Alex Veremeyenko (@alex_verem) has an engagement rate of 0.082%, based on the median interactions across 23 original posts from the last 30 days against 103,175 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.082%, Alex Veremeyenko sits above the 25th percentile of the 157,102 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 @alex_verem have real engagement?
- Its engagement rate beats 48% 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 @alex_verem post?
- Most posts go out around 16:00 UTC, and Thursday is its busiest day, at roughly 4.4 posts per day across the measured window.