Seven products on Product Hunt, going back to June 2021. SnapPoint, Makers Page, SQUAD IN SYNC, Product Hunt Wrapped 2025, Cross Write, CoLaunchly, PilotFisc. Different problems, different stacks, same one-person team. None of them took long to build.
That's not a brag. It's the actual problem.
For a long time I treated "it's live" as the finish line. Ship the thing, post about it once, wait for people to show up. They didn't, not because the product was bad, but because posting once isn't a distribution strategy, it's a formality.
Building software stopped being the hard part a while ago. An AI coding agent will scaffold the database, wire up auth, and ship the landing page faster than I can decide on a color palette. What it won't do is make a stranger notice any of it exists. That part hasn't gotten one bit easier, and going by what's actually being talked about in indie hacker circles right now, most solo builders are running into the exact same wall: the tools compressed the build phase down to nothing and left the getting-someone-to-care phase exactly as hard as it always was.
Scroll Indie Hackers or Reddit on any given day and you'll see the same shape of post over and over: someone drowning in generic distribution advice, someone posting forty times to land one sale, someone admitting their marketing problem was actually a product problem all along. AI didn't cause that pattern. It just removed the excuse that used to sit in front of it, which was "I haven't shipped yet." Once shipping stops being the constraint, whatever's actually broken gets a lot harder to hide behind.
It's not a marketing budget. Most of us don't have one. It's not a growth hack either, those mostly work once, on someone else's audience, right before the platform notices.
What it actually is: a channel you understand well enough to predict, and an audience that comes back without being re-pitched every time. That's it. Not glamorous, just repeatable. The failure mode isn't "didn't market enough," it's "never built a channel, just made noise in one direction and called it marketing."
A channel you understand well enough to predict looks boring from the outside. It's knowing that a specific kind of post, on a specific day, gets a specific kind of response, consistently enough that you can plan around it instead of hoping. Most growth content skips past that and sells the exciting part instead: a hook, a hack, a thread format. The boring part, actually watching what happens and adjusting, is the part that compounds.
Makers Page exists because of this pattern repeating across every launch above. It's a product directory with free listings, so a new product gets discovered instead of sitting alone on a domain nobody's indexed yet, plus a Marketing MCP that plugs into AI coding agents like Claude Code and Cursor: it finds relevant conversations worth joining, drafts the social posts, and publishes to X, with the founder approving everything before it goes out.
The insight behind it wasn't clever. It was just noticing that I kept solving the same "now what" problem after every single launch, and that the actual bottleneck had never once been the code.
It doesn't solve distribution by itself. Nothing does. What it does is remove the part of the "now what" problem that's just busywork: finding where a relevant conversation is already happening, and getting a draft in front of you instead of a blank compose box. The judgment still has to be yours. The tool just gets you to the decision faster.
This site's own X account is the clearest example, because it's the one place I've actually instrumented instead of guessed at.
Two weeks, same account, same content quality, different posting volume: one week averaged 5.9 posts a day, the next dropped to 3.7. Impressions fell 54%. Net new followers fell 86%, from 438 down to 51. What surprised me is that engagement rate didn't dilute with the extra volume the way I expected it to. The three highest-volume days had the three highest engagement rates of the whole stretch. The one lowest-volume day had the lowest rate. Posting more wasn't just more reach, it was also better reach.
X open-sourced its actual production ranking code on August 13, 2026, so a lot of this stopped being guesswork. The real weights are public: a post someone copies the link to and shares elsewhere is worth 40 times a like, the single highest-value action on the platform. A report, on the other hand, costs the algorithm roughly as much as 468 likes' worth of upside. One bad post can undo weeks of good ones, and it's not a vibe, it's a number in a file.
Content shape mattered more than I expected too. Posts that generalized a lesson past this one app's own internals averaged 5.64% engagement across a small sample. Posts that stayed narrow, specific to a bug in this exact codebase with no wider takeaway, averaged 3.81%, including the single worst-performing post of the batch. Specific detail as evidence beats specific detail as the entire point.
The account also lost a tailwind partway through this without anyone announcing it. Accounts under 1,000 followers, under 24 hours old, under 1,000 impressions get inserted into a guaranteed discovery slot in relevant feeds, a deliberate cold-start boost for new accounts. This one crossed that threshold a while ago and sits around 4,510 followers now, so the boost is gone and isn't coming back. Any strategy built on the numbers from when it was active would quietly stop working, and from the outside it would look like the algorithm turned against the account. It didn't. The rules that applied changed because the account did, and the only way to catch that is to keep measuring instead of trusting last month's conclusion.
None of this is impressive on its own. What it demonstrates is that distribution responds to measurement the same way code responds to tests. You don't fix what you haven't looked at.
This is still the same experiment as the first note from this newsletter: write down what's actually true, not the version that reads well after the fact. Building stopped being the bottleneck a while back. This is what happened when I finally treated the actual one like an engineering problem instead of a feeling.