That’s really interesting. Was there a specific type of Note that drove most of that growth — personal stories, useful tips, opinions, or something else? 500 in a month from Notes is worth studying 👀
This is such an honest and refreshing audit about the superficial nature of Sub4Sub Notes - the engagement on them is often purely transactional, filled with low-effort replies like "done" or "subbed" that offer zero editorial depth.
That said, I think there’s a nuanced side to this tactic that deserves a bit of credit:
Algorithmic Velocity vs. Discovery: As your data clearly shows, the Substack algorithm heavily rewards this level of raw engagement. The real silver lining here isn't the vanity metric itself, but visibility. These high-activity Notes get pushed into feeds where writers and readers can discover creators they would have otherwise never crossed paths with.
The Writer’s Trap: For the person posting the S4S Note, the risk is massive. You end up saddled with an audience that doesn't care about your niche, and you feel obligated to reciprocate, clogging your own inbox with unread posts.
The Smart Reader’s Strategy (The "Useful Parasite"): On the flip side, being a selective participant in these threads can actually work wonders. If you ignore the noise and only engage with writers whose work legitimately catches your eye - or browse the comment sections of S4S posts to find hidden gems - it becomes a highly effective discovery engine. In a way, it’s like a beneficial dynamic in nature: capitalizing on a messy system to find genuine value.
Overcoming the "Zero-To-Fifty" Cold Start: I regularly come across brilliant, high-quality publications stranded at 20–50 subscribers simply because they have no distribution. While reciprocal growth shouldn't be a long-term editorial strategy, a brief stint with it can provide the initial momentum a new writer needs to get past the painful cold-start phase.
The drop in open rates is usually where the true cost of these tactics comes to light. Looking forward to Part 2!
Yes, that’s the part I find most interesting too. A number can look great, but until you understand what caused it, it’s hard to know what you can actually repeat.
That’s why I keep going back through the results, comparing what changed and trying to separate a real pattern from a one-off.
But yes, exactly. A number only becomes useful when you understand what caused it and whether you can reproduce it. That’s what made me rethink a lot of my early growth results.
Oh, I hadn’t even noticed that 😅 Must have been one of those slightly chaotic moments — sorry about that!
And yes, exactly. A strong number on its own can be exciting, but I find the real value is in understanding what actually caused it and whether that result can be repeated.
Understanding *why* something worked is usually more valuable to me than the result itself. 🤍
I also touch on this idea briefly in today’s Daily Lens, and yesterday’s free Crypto Lens includes the actual visuals and experiments I’ve been testing, so you can see some of the examples rather than only read about them:
Analyse it properly, combine several AI tools where useful, and keep refining. Those seem to be some of the biggest keys.
To test what actually works, you often have to go very broad first — experiment, compare, measure, learn — while knowing that the goal is eventually to narrow everything back down again.
I’m still going through that process very deliberately myself. I document it in the articles, Notes and books too: what works, what doesn’t, what changes, and why.
And I genuinely think there’s useful information in that process for both beginning and more advanced creators or writers. You don’t have to copy the path — but you can learn a lot from the data it leaves behind. 🤍✨
Looks interesting! I've grown to a bit over 500 subscribers in almost a month so glad to meet another new creator!
Nice to meet you—and 500 subscribers in a month is impressive.
What was your biggest acquisition channel: Notes, recommendations, or reciprocal networking? And did your open rate stay stable as the list grew?
Notes and yes.
That’s really interesting. Was there a specific type of Note that drove most of that growth — personal stories, useful tips, opinions, or something else? 500 in a month from Notes is worth studying 👀
I'm actually working on a post on this coming out later today!
This is such an honest and refreshing audit about the superficial nature of Sub4Sub Notes - the engagement on them is often purely transactional, filled with low-effort replies like "done" or "subbed" that offer zero editorial depth.
That said, I think there’s a nuanced side to this tactic that deserves a bit of credit:
Algorithmic Velocity vs. Discovery: As your data clearly shows, the Substack algorithm heavily rewards this level of raw engagement. The real silver lining here isn't the vanity metric itself, but visibility. These high-activity Notes get pushed into feeds where writers and readers can discover creators they would have otherwise never crossed paths with.
The Writer’s Trap: For the person posting the S4S Note, the risk is massive. You end up saddled with an audience that doesn't care about your niche, and you feel obligated to reciprocate, clogging your own inbox with unread posts.
The Smart Reader’s Strategy (The "Useful Parasite"): On the flip side, being a selective participant in these threads can actually work wonders. If you ignore the noise and only engage with writers whose work legitimately catches your eye - or browse the comment sections of S4S posts to find hidden gems - it becomes a highly effective discovery engine. In a way, it’s like a beneficial dynamic in nature: capitalizing on a messy system to find genuine value.
Overcoming the "Zero-To-Fifty" Cold Start: I regularly come across brilliant, high-quality publications stranded at 20–50 subscribers simply because they have no distribution. While reciprocal growth shouldn't be a long-term editorial strategy, a brief stint with it can provide the initial momentum a new writer needs to get past the painful cold-start phase.
The drop in open rates is usually where the true cost of these tactics comes to light. Looking forward to Part 2!
This is a good one👌
Appreciate it 🙌 What part stood out to you the most?
Yes, that’s the part I find most interesting too. A number can look great, but until you understand what caused it, it’s hard to know what you can actually repeat.
That’s why I keep going back through the results, comparing what changed and trying to separate a real pattern from a one-off.
I think this one was meant as a reply to me 😄
But yes, exactly. A number only becomes useful when you understand what caused it and whether you can reproduce it. That’s what made me rethink a lot of my early growth results.
Oh, I hadn’t even noticed that 😅 Must have been one of those slightly chaotic moments — sorry about that!
And yes, exactly. A strong number on its own can be exciting, but I find the real value is in understanding what actually caused it and whether that result can be repeated.
Understanding *why* something worked is usually more valuable to me than the result itself. 🤍
Exactly 😄 That’s the part I’m trying to get better at too: not just finding what works, but understanding why it worked well enough to repeat it.
That makes a lot of sense 😄 I’ve been thinking about the same kind of thing from a slightly different angle.
If you ever have a little time, come have a look at the books too. I’ve tried to turn quite a few of these lessons into practical frameworks:
https://payhip.com/simpleinvestingschool
I also touch on this idea briefly in today’s Daily Lens, and yesterday’s free Crypto Lens includes the actual visuals and experiments I’ve been testing, so you can see some of the examples rather than only read about them:
https://simpleinvestingschool.substack.com/p/the-crypto-lens-19-august-2026-what
No rush at all. If you ever have five minutes, or a bit longer 😄, I’d genuinely love to know what you think. 🤍
The data is what really matters.
Analyse it properly, combine several AI tools where useful, and keep refining. Those seem to be some of the biggest keys.
To test what actually works, you often have to go very broad first — experiment, compare, measure, learn — while knowing that the goal is eventually to narrow everything back down again.
I’m still going through that process very deliberately myself. I document it in the articles, Notes and books too: what works, what doesn’t, what changes, and why.
And I genuinely think there’s useful information in that process for both beginning and more advanced creators or writers. You don’t have to copy the path — but you can learn a lot from the data it leaves behind. 🤍✨
Exactly. The part that surprised me was that having more data wasn’t enough — I also had to understand the mechanism behind it.
A Note can look incredible in the numbers while performing for completely different reasons.
That’s probably been the biggest lesson from this experiment: measure first, then question what the metric is actually measuring.