Lenny Rachitsky (@lennysan) X analytics
@lennysan
Deeply researched product, growth, and career advice
@lennysan has 454.7K followers, a median of 32.1K views per post and a 0.62% engagement rate.
Based on 30 recent original posts (high confidence). Updated October 24, 2026.
At a glance
Key stats
- Followers
- 454.7K454,692 followers on October 24, 2026+2.7K in 7 days
- Median views per post
- 32.1K
- Average views per post
- 106.2K
- Engagement rate (by views)
- 0.62%
- Original posts per week
- 19.0
- Average engagements per post
- 661
Engagement grade
Developing
This is near the lower benchmark range for accounts with 100K-1M followers.
Profile score
74/100
- Profile completeness100
- Audience shape100
- Engagement25
- Consistency100
- Positioning clarity47
Monetization
Estimated X creator earnings
$33–$80 in the last 30 days
From 6.6M views on @lennysan's original posts in the last 30 days.
Rewards eligibility: likely
Rough estimate. X publishes no payout rate, so this assumes about $5–12 per 1M original-post impressions from reported creator payouts. Only Premium viewers’ impressions count, so real payouts can be much lower.
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Growth
Follower growth
+2.7K followers in the last 7 days. Tracked by Xholic since October 4, 2026.
| Date | Followers | Change |
|---|---|---|
| Oct 24, 2026 | 454,692 | +567 |
| Oct 21, 2026 | 454,125 | +968 |
| Oct 19, 2026 | 453,157 | +464 |
| Oct 18, 2026 | 452,693 | +738 |
| Oct 15, 2026 | 451,955 | −2,583 |
| Oct 14, 2026 | 454,538 | +1,942 |
| Oct 12, 2026 | 452,596 | +2,087 |
Timing
Best times to post
When @lennysan's recent posts performed best (UTC).
- #1 slot
7:12 PM
Monday · UTC
- #2 slot
7:23 PM
Tuesday · UTC
- #3 slot
7:34 PM
Wednesday · UTC
What worked
Top recent posts
- 01
@lennysan ·
Head of Design at Stripe on how to stop creating zombie UIs
Watch video- 107Replies
- 469Reposts
- 5.2KLikes
- 347.2KViews
10.3x average engagementHas a videoShort post (under 100 characters)Posted Fri 13:00 UTCQuote post - 02
@lennysan ·
What's a product under $250 that has genuinely improved your life?
- 1.2KReplies
- 79Reposts
- 2.8KLikes
- 1.6MViews
7.0x average engagementText onlyShort post (under 100 characters)Posted Fri 15:00 UTCOpens with a question - 03
@lennysan ·
My favorite resources for PM interview prep: 1. Insider Loops: https://t.co/DX2lHv2ikr 2. Never Search Alone: https://t.co/1KdfW2nPZR 3. AI Practice Copilot: https://t.co/JldXzYLDo0 4. Ultraspeaking: https://t.co/RsJjWnU6XN 5. How to use AI for your next job interview: https://t.co/sNYBWkN4VD 6. The definitive guide to mastering product sense and analytical thinking interviews: https://t.co/0UPX5VQ1a0 & https://t.co/3cyZiB1ZZJ
- 21Replies
- 60Reposts
- 610Likes
- 62.5KViews
2.3x average engagementShares a linkLong post (over 200 characters)Posted Thu 19:00 UTC - 04
@lennysan ·
My biggest takeaways from @AnthropicAI interpretability researcher @mlpowered: 1. Predicting the next token requires understanding the universe. Language models got better than humans at predicting the next token around 2019. Once a model masters the easy tokens (sentence structure, then basic facts), the only ones left are hard, like the final paragraph of a PhD thesis. To finish “The capital of the state that contains Dallas is . . .,” a model has to know that Dallas is in Texas and that Texas’s capital is Austin. 2. LLMs plan ahead, even though they write one token at a time. This was one of Emmanuel’s team’s most surprising findings. When a model writes poetry, it doesn’t stumble into a rhyme. Looking at intermediate thinking states during poetry generation, researchers found that as soon as the model finishes the first line, it has already picked the word that will end the second (“rabbit”), then refers back to that plan for every word after. 3. Anthropomorphizing AI can often be a useful approach when trying to predict how they will behave. Models learn from human text and develop representations of the world from it. Training on millions of dialogues is like training an actor to play every part, and your chats with Claude are literally formatted as a script (“Human: . . . Assistant: . . . ”). In Emmanuel’s words, “Something that’s mimicking a human is really close and is much closer than a program.” 4. Interpretability research looks more like neuroscience than software engineering. The job involves taking a system you did not design and running experiments to infer why it behaves as it does. You examine seemingly meaningless lists of numbers, pass the model huge volumes of text, and watch for patterns that activate from different inputs—not unlike the methods used in brain imaging studies. “A lot of what we do feels so much closer to biology than to engineering." 5. Models can tell you about only a small slice of what they’re thinking. Emmanuel’s team found that just a small subset of a model’s internal representations, which they call the “J space,” can be reported back if you ask the model what it was thinking about. They compare it to a theory of the human mind where most processing happens below the surface. In one experiment, they swapped “Spanish” for “French” inside the J space. Asked what language a passage was in, the model said French. Asked to simply continue the passage, it kept writing Spanish. 6. Studying models closely often raises more questions than it answers. Emmanuel notes that the confident claims that LLMs are “just X” or “nothing more than Y” almost always come from people who haven’t studied their internal workings. His own experience is the inverse: studying the models closely has increased his uncertainty, not reduced it. The complexity that emerges from training is consistently underestimated from the outside. “I find that the more you look inside, the less certain you are about how they work.” 7. Even the people who build AI models don’t fully understand how they work. As Emmanuel describes it, models are grown, not built. “To make them is not to understand them.” Engineers write the code, pour enormous amounts of data through it, and a model’s capabilities emerge. That’s why his team exists. Their job is to open the model up and figure out what it’s actually doing between your prompt and its answer. 8. Interpretability as a field is small, young, and open to newcomers. Emmanuel estimates the field at hundreds of researchers, split between major labs and academic groups. Because it is so new, getting started doesn’t necessarily require deep prior expertise. He points to platforms like Neuronpedia, which hosts models and lets anyone run interpretability experiments directly. “It’s such a new field that the on-ramp is mostly self-studying and reading some of the key papers. There’s so much to explore that if you’re at all curious, you should just give it a try.”
- 48Replies
- 84Reposts
- 696Likes
- 54KViews
1.2x average engagementText onlyLong post (over 200 characters)Posted Mon 12:00 UTCQuote post - 05
@lennysan ·
"There are some people in your org who are doing way more exploring than others. They are your early adopters. They're running Astra and Fable on the weekends on personal projects, to find things they might bring to work. The problem is that they can be a massive f*cking distraction. So the key question to ask yourself if you're running a product org is: how do I harness these early adopters without distracting everybody else?" @danshipper
Watch video- 120Replies
- 36Reposts
- 581Likes
- 56.1KViews
1.0x average engagementHas a videoLong post (over 200 characters)Posted Sat 12:00 UTC - 06
@lennysan ·
Hot off the presses: A plug-and-play moat check skill Share the company you're considering joining, it'll help you understand if they have a moat. Get it here: https://t.co/amx1RTf5XU Here's me asking about @tryramp 👇
- 18Replies
- 24Reposts
- 319Likes
- 43.3KViews
1.0x average engagementHas an imageMedium length (100-200 characters)Posted Tue 21:00 UTCQuote post
Latest
Recent posts by Lenny Rachitsky
The latest 30 original posts from @lennysan, newest first, as of October 24, 2026. Replies and reposts aren't included.
@lennysan ·
Share your best AI workflow and get featured in my newsletter! We're all sick of overhyped AI demos and workflows. Stuff that looks/sounds amazing but doesn't actually work. This project we're calling "Hype(less)" aims to collect and share the most real, impactful, and repeatable AI workflows that have created real value for you and your teams. You have until Friday, Oct 30th to submit your entry: https://t.co/yKNLfSq7HY
- 64Replies
- 12Reposts
- 202Likes
- 21.3KViews
@lennysan ·
What's a product under $250 that has genuinely improved your life?
- 1.2KReplies
- 79Reposts
- 2.8KLikes
- 1.6MViews
@lennysan ·
I think Dario has been right about most timelines so far, often too conservative even
- 26Replies
- 18Reposts
- 283Likes
- 42.7KViews
@lennysan ·
Canva the frontier image model 😮 Nice work over there @themaninblue and team 👏
- 15Replies
- 14Reposts
- 248Likes
- 35.9KViews
@lennysan ·
"20 years from now, the only people who will remember that you worked late are your kids."
- 107Replies
- 68Reposts
- 1.4KLikes
- 85.4KViews
@lennysan ·
My favorite resources for PM interview prep: 1. Insider Loops: https://t.co/DX2lHv2ikr 2. Never Search Alone: https://t.co/1KdfW2nPZR 3. AI Practice Copilot: https://t.co/JldXzYLDo0 4. Ultraspeaking: https://t.co/RsJjWnU6XN 5. How to use AI for your next job interview: https://t.co/sNYBWkN4VD 6. The definitive guide to mastering product sense and analytical thinking interviews: https://t.co/0UPX5VQ1a0 & https://t.co/3cyZiB1ZZJ
- 21Replies
- 60Reposts
- 610Likes
- 62.5KViews
@lennysan ·
Loooved this episode of The Daily. They chatted with a math professor at Cornell University (Steven Strogatz) about how he and math community are feeling about AI's impact on their world. Lots of tears, and profound takes. Listen to the episode here: https://t.co/6Z1V9UpxR7 Or here: https://t.co/oZFrZEnZmH
- 26Replies
- 4Reposts
- 102Likes
- 17.3KViews
@lennysan ·
This surprised me: "AI progress looks like big jumps from the outside, but internally it's actually been a very smooth progression."
Watch video- 44Replies
- 3Reposts
- 52Likes
- 12.3KViews
Show all 30Show fewer
@lennysan ·
.@mlpowered shares one of the strangest model behaviors he's debugged at @AnthropicAI: "Slowly it would start descending into madness, writing in all caps, literally saying, 'I'm possessed by a demon." Full conversation: https://t.co/I3txx9wBvK
Watch video- 34Replies
- 8Reposts
- 58Likes
- 15.2KViews
@lennysan ·
My biggest takeaways from @AnthropicAI interpretability researcher @mlpowered: 1. Predicting the next token requires understanding the universe. Language models got better than humans at predicting the next token around 2019. Once a model masters the easy tokens (sentence structure, then basic facts), the only ones left are hard, like the final paragraph of a PhD thesis. To finish “The capital of the state that contains Dallas is . . .,” a model has to know that Dallas is in Texas and that Texas’s capital is Austin. 2. LLMs plan ahead, even though they write one token at a time. This was one of Emmanuel’s team’s most surprising findings. When a model writes poetry, it doesn’t stumble into a rhyme. Looking at intermediate thinking states during poetry generation, researchers found that as soon as the model finishes the first line, it has already picked the word that will end the second (“rabbit”), then refers back to that plan for every word after. 3. Anthropomorphizing AI can often…
- 48Replies
- 84Reposts
- 696Likes
- 54KViews
@lennysan ·
Emmanuel Ameisen (@mlpowered) has spent the last 3 years looking inside LLMs like Claude to figure out what they're actually doing. "It took me years to master SQL. I got Claude to learn it, and it was immediately better. And I'll never be better than Claude at SQL ever again. It goes against so many of our intuitions. There's two reactions you can have. There's denial, and there's curiosity. Denial is this is just not possible. And curiosity is the road I've ended up choosing. Which is, let's open it up. Let's look inside. My optimistic take on this is that I think we're going to learn a lot about the nature of intelligence itself."
Watch video- 35Replies
- 5Reposts
- 76Likes
- 15.7KViews
@lennysan ·
Anthropic AI researcher: “A lot of what we do feels so much closer to biology than to engineering.”
Watch video- 25Replies
- 4Reposts
- 38Likes
- 13.4KViews
@lennysan ·
"To make them is not to understand them." Emmanuel Ameisen (@mlpowered) has spent the last 3 years doing interpretability research at @AnthropicAI, looking inside LLMs like Claude to figure out what they're actually doing. As he puts it, models are "grown," not built. We discuss: 🔸 His discovery that models plan many tokens ahead 🔸 Why anthropomorphizing AI can actually be the right approach 🔸 What the "J-space" reveals about how models think 🔸 How interpretability connects to AI alignment and safety Listen now 👇 https://t.co/qEYdKfpkab
- 55Replies
- 23Reposts
- 223Likes
- 181.8KViews
@lennysan ·
"There are some people in your org who are doing way more exploring than others. They are your early adopters. They're running Astra and Fable on the weekends on personal projects, to find things they might bring to work. The problem is that they can be a massive f*cking distraction. So the key question to ask yourself if you're running a product org is: how do I harness these early adopters without distracting everybody else?" @danshipper
Watch video- 120Replies
- 36Reposts
- 581Likes
- 56.1KViews
@lennysan ·
"Refuse to confuse done with good." @lil_dill
Watch video- 28Replies
- 13Reposts
- 187Likes
- 17.8KViews
@lennysan ·
Head of Design at Stripe on how to stop creating zombie UIs
Watch video- 107Replies
- 469Reposts
- 5.2KLikes
- 347.2KViews
@lennysan ·
Katie Dill (@lil_dill)'s talk from the Lenny & Friends Summit is now my third most viewed YT video of all-time 👏👏👏
- 31Replies
- 2Reposts
- 95Likes
- 13.9KViews
@lennysan ·
There’s way too much AI hype right now. So @noamseg and I are launching "Hype(less)," a research project to showcase the most genuine, impactful, and repeatable AI workflows that have created real value for you and your teams in product, design, and engineering. We’ll showcase the best in a special three-part newsletter series in the coming months. Here's our ask: If you're a PM, engineer, or designer, please share your most valuable AI workflow here: https://t.co/yKNLfSq7HY (by 10/23). By sharing your workflow, you'll help others cut through the AI hype, and share what you've learned with the world. Here's the form: https://t.co/yKNLfSq7HY (Please RT and share with your colleagues so we can collect as many great examples as possible).
- 81Replies
- 41Reposts
- 376Likes
- 42.4KViews
@lennysan ·
Here it is: Benchmarks to measure a given company's moat Image 1: KPIs to look at, by potential moat. Image 2: What "good" and "great" looks like. Note, these are rough and meant to be interpreted directionally, because there is so much variance by industry and business model. For a deeper exploration, see links in the PDF here: https://t.co/kbxEnO5fzU And for a lot more context and advice, don't miss @danhockenmaier's full post: https://t.co/jMdyoPpeF6
- 41Replies
- 23Reposts
- 302Likes
- 31.2KViews
@lennysan ·
Head of @GoogleLabs: "Great ideas rarely from design sprints, or brainstorms, or the time you set aside on a calendar. You can't microwave good ideas."
Watch video- 31Replies
- 11Reposts
- 141Likes
- 16.1KViews
@lennysan ·
Hot off the presses: A plug-and-play moat check skill Share the company you're considering joining, it'll help you understand if they have a moat. Get it here: https://t.co/amx1RTf5XU Here's me asking about @tryramp 👇
- 18Replies
- 24Reposts
- 319Likes
- 43.3KViews
@lennysan ·
Head of Google Labs, Gemini app, and AI Studio on the skill he's hiring for right now: 1. "Unlearning" rate 2. Explosive endurance Full conversation: https://t.co/wdZm7MK2mo
Watch video- 14Replies
- 9Reposts
- 138Likes
- 15.4KViews
@lennysan ·
"Look at people's eyes. That's the metric. When you're showing people early prototypes to see if we have PMF, what I'm always doing is looking at people's eyes. It is not about DAU, or DAU/MAU. It's none of that. It's literally like, hey, Lenny, did you see this? What do you think about this? If your eyes light up, if you lean in, these are the signals you're looking for." Full conversation: https://t.co/D4BOgLoiAu
Watch video- 19Replies
- 12Reposts
- 128Likes
- 20KViews
@lennysan ·
.@JoshWoodward: "Watch this space. We hear you loud and clear.”
Watch video- 7Replies
- 1Reposts
- 47Likes
- 14.9KViews
@lennysan ·
Distribution is not a moat. Execution is not a moat. Brand is not a moat.
- 68Replies
- 19Reposts
- 282Likes
- 135.8KViews
Formats
Content mix
- Text only
- 23%
- With media
- 63%
- With links
- 10 posts
- With hashtags
- 0 posts
Creator style: High-reach broadcaster
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- Analytics are inferred from public profile and recent post data. They do not include private impressions, profile visits, link clicks, or follower-source data.
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