Jason ✨👾SaaStr.Ai✨ Lemkin engagement report
@jasonlk - 250K followers on X
Measured over 30 original posts from a 30-day window, last computed on September 9, 2026.
Engagement
A typical post picks up 54 interactions against 250K followers, an engagement rate of 0.022%. Measured over 30 original posts, its engagement rate beats 35% of 6,874 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 8.8K times each, and 0.619% of those impressions turn into an interaction. That is about 3.52% 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, though only 33% of days saw any activity at all. Most posts go out around 14:00 UTC, and Friday is the busiest day of the week. Of the 30 posts sampled, 50% carry an image or video and 20% link out. The account's strongest tracked post pulled 510 interactions, about 9.4x its own typical post.
Measured over 30 original posts from a 30-day window, last computed on September 9, 2026.
Compared with accounts its own size
Jason ✨👾SaaStr.Ai✨ Lemkin's engagement rate beats 35% of the tracked X accounts closest to it in follower count (6,874 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 34% 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 0.022%, Jason ✨👾SaaStr.Ai✨ Lemkin sits above the 25th percentile of the 66,258 accounts in this comparison. That places it in the below the median band, which runs 0.016% to 0.1%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 119.6% |
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 Friday 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 | 50% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 20% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 50% 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 526 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 2, 20269.4x their median
There’s an alternate reality where Slack never sold to Salesforce and is operating in full founder mode, aggressively embracing AI. I’d love to see what that product would look like. And what market cap they’d be trading at rn.
- Sep 7, 20262.7x their median
Way oversimplifying, but I think in B2B today, if you have the best agent in your space, you win. We thought the fact the LLMs were open to everyone would make a lot of AI workflows in B2B something of a commodity. Everyone would just have great AI chat, receptionists, dashboards, SDRs. So there would be no competitive benefit in AI per se for many B2B apps. All the agents would do it all, well. That day may come soon enough, but it’s not remotely here today. We’re back to those crazy times, still early in a new age, when the best can win by truly being the best. But you have to be 1000% honest here on what that means. It doesn’t mean parity. It doesn’t mean evals. It means solving big customer problems with agents materially and viscerally better than everyone else in your category. Go make it happen.
- Sep 4, 20262.3x their median
Off to last coffee meeting of day https://t.co/7bZwnwLBGs
- Sep 3, 20261.9x their median
This podcast is the single most important podcast to know what is going on in tech every week. On the agenda this week: - NVIDIA Crushes Quarter and Buys Hugging Face - OpenAI Cuts Off Cursor - Instinct Hits $2.5BN Valuation and The Race for AI Assistants - Cognition Raises at $46BN, Linear $2.5BN and Clay $7BN My notes with @rodriscoll and @jasonlk below: 1. How Instinct Could Follow the Same Path as Replit and Lovable In the early days, cloning lightweight AI tools is trivial. Defensibility emerges by rapidly adding complex workflows like security automation and multi-agent orchestration. Products that start without a moat can build formidable ones over time by solving dozens of evolving customer requirements faster than anyone else. 2. Hugging Face Acquisition Explained As the maker of compute, NVIDIA benefits when AI token traffic flows through 30% gross-margin open-source models rather than 70% gross-margin closed models where platforms capture more of the economics. Driving down software margins allows a greater share of total ecosystem spend to flow directly into GPUs. 3. The Bull Case for Clay Being a $100 Billion Company Autonomous agents executing go-to-market strategies around the clock could consume 10x to 100x more tokens and software usage than human sales teams ever could. As a leader in agentic GTM, Clay is positioned to capture an enormous wave of automated outreach, campaign analysis, and global prospect engagement. 4. The Bull Case for Linear When software teams build 100x more features at 50x the speed using AI, legacy project management tools and manual Kanban boards begin to break down. Linear can become the agent-friendly system of record for coordinating, tracking, and managing thousands of issues generated simultaneously by human-agent development teams. 5. The Three Ways the Wheels Come Off the Bus for NVIDIA NVIDIA’s record-breaking momentum faces one fundamental existential threat: a sudden collapse in end-user demand for AI intelligence. Hyperscaler CapEx buildouts and complex vendor financing arrangements work only as long as customers continue aggressively buying frontier-model tokens throughout the supply chain. 6. We Are All Building Compound Startups Today AI development tools have accelerated code production dramatically, making narrow point solutions increasingly vulnerable. To survive rapid competitive convergence, software startups must embrace becoming compound companies that ship expansive, multi-module product suites covering the entire customer workflow. 7. We Did Not End Up Doing More With Less. We Did More With More, and That’s Why European Startups Fail The belief that AI would allow companies to shrink headcount and simply do more with less has not played out as expected. Winners are compounding capital and talent to do vastly more with more, putting underfunded point solutions, particularly across Europe, at risk of being overwhelmed by aggressively scaling U.S. competitors. (links in comments)
- Sep 4, 20261.8x their median
So we vibe coded our own Calendly on @Replit this week But ... why? Calendly is cheap, works well, no issues Well the Why is interesting. We wanted to do >something< we couldn't without building our own. We wanted a dynamic, 100% custom prospectus / deck to go to customers instantly, on the fly. And a meeting set up at the same time. Not a generic deck, but one build entirely based on the customer's goals in real time. A 100% custom pitch + a meeting scheduler is realistically 10x more important than a meeting scheduler alone. Could we have figured out how to do this with Calendly's API? Possibly. But it seemed easier to just build that on top of the n=1 part, the custom deck for the prospect.
- Sep 4, 20261.8x their median
Eight months ago the market decided that AI was going to eat B2B software. A rough, rough start to the year. Roughly $2 trillion of market cap came out of the group. Then it came back. Just not ... evenly. https://t.co/bL6CopWtiC
- Sep 5, 20261.6x their median
"The #1 mistake founders make in partnerships and business development? They don't hire anyone full-time to manage the partnership. As CEO, you are great at forging that relationship. But maintaining it? That's a full time job." https://t.co/PRWAIM4xUB
- Sep 5, 20261.6x their median
I got handed off this week by an AE I never talked to a CSM that never responded I totaly get it It's just a $20k ACV deal
- Sep 8, 2026
You think your Big Co Customers are old & established, And you are new & tiny Yes it's true But as the years go by, you'll still be there And your champions at the BigCos will move on You have to re-sell Big Customers every 2 years or so
- Sep 4, 2026
Snowflake just blew out the quarter -- and reaccelerated at a $6 billion run rate The reason, of course is AI. But not mainly from AI native customers. Third straight quarter of acceleration: 30% → 34% → 37%. Sequential product revenue added: Q4 FY26: +$68M Q1 FY27: +$108M Q2 FY27: +$158M They raised the full-year guide from $5.84B to $6.07B in one quarter, moving implied growth from 31% to 36%. #1. About half the acceleration came from AI products The other half came from those products pulling more core consumption behind them. AI went on the same consumption meter customers already had, not into a separate SKU with its own quota. #2. CoCo, their coding agent, is on 9,100 accounts out of 14,554 customers, and added 2,000 in the quarter Its cost-management skill is a top-10 skill. Snowflake shipped an agent that helps customers spend less on Snowflake and consumption accelerated anyway. #3. They gave up gross margin on purpose Product gross margin went 76% to 75%, and they guided the year down to 74% on AI workload mix. Then they raised the operating margin guide from 13.5% to 14.5%. Revenue grew 35%, opex grew 17%. The AI bill came out of headcount. #4. NRR 126% 49 customers crossed $1M this quarter against a $158M sequential add. At this scale the base is the engine. #5. Total RPO grew 30% while current RPO grew 42% In consumption, RPO tells you when a customer signed. Revenue tells you when they burned it. -> Still a $263M GAAP operating loss, with stock comp at 29% of revenue. GAAP breakeven is targeted for Q4 FY28. Two points of gross margin for seven points of growth. Stock went from $306 to roughly $370. Almost double its low, and almost back to its peak.
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 54 interactions against 250K followers, an engagement rate of 0.022%. Measured over 30 original posts, its engagement rate beats 35% of 6,874 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 8.8K times each, and 0.619% of those impressions turn into an interaction. That is about 3.52% 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, though only 33% of days saw any activity at all. Most posts go out around 14:00 UTC, and Friday is the busiest day of the week. Of the 30 posts sampled, 50% carry an image or video and 20% link out. The account's strongest tracked post pulled 510 interactions, about 9.4x its own typical post.
- What is Jason ✨👾SaaStr.Ai✨ Lemkin's engagement rate on X?
- Jason ✨👾SaaStr.Ai✨ Lemkin (@jasonlk) has an engagement rate of 0.022%, based on the median interactions across 30 original posts from the last 30 days against 249,929 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.022%, Jason ✨👾SaaStr.Ai✨ Lemkin sits above the 25th percentile of the 66,258 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 @jasonlk have real engagement?
- Its engagement rate beats 35% of the tracked X accounts closest to it in follower count (6,874 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 @jasonlk post?
- Most posts go out around 14:00 UTC, and Friday is its busiest day, at roughly 1.6 posts per day across the measured window.