I’m testing a manual signal read for founders who already built something
I’ve been spending the last few weeks studying what happens after indie founders already build something.
Not the idea stage.
The stage after a landing page, MVP, demo, Chrome extension, Shopify app, AI agent, directory, or small SaaS is already out there.
This is where the signals start to get messy.
A few comments.
Some traffic.
A waitlist.
A few signups.
“This is cool.”
No replies.
One strong reply from the right person.
A lot of views but no clicks.
Users trying it once and never coming back.
The hard part is not always getting more feedback.
It’s knowing what the feedback means.
A waitlist may show recognition, but not commitment.
Traffic may show attention, but not intent.
A positive comment may feel good, but not change behavior.
A report may create insight, but not execution.
A trial may show curiosity, but not repeat use.
That is the gap I’m trying to understand.
I’m testing a small manual service for indie founders who already built something but aren’t sure what the early response means.
The goal is not to give generic feedback.
It’s to look at the current signals and send back one practical next move:
what seems real,
what may be noise,
where the loop is breaking,
and what small action could make the next decision clearer.
The strongest signal so far is not when someone says “good point.”
It’s when a founder says:
“I’ll try that and come back with what happened.”
That is the loop I’m trying to validate.
If you’ve already built something and you’re stuck between more research, more building, and actually talking to users, I’d be interested to look at a few real cases manually.
You can send the product here:
Still early, still manual, and mostly learning from messy real products rather than polished success stories.
You've made an interesting tradeoff, but I wonder if approval isn't the real variable.
The bigger question is confidence. If users consistently see good outcomes, they'll naturally want to approve less over time. That suggests the product shouldn't force people to choose between full control and full autonomy—it should let trust expand as the evidence does.
That’s a much sharper way to put it.Approval is probably just the visible behavior. Confidence is the thing underneath.For this manual signal read I’m testing, that means the output probably shouldn’t just be:“this signal is trustworthy / this signal is noise.”It should also show what evidence would make the founder more confident over time.Weak signal → manual check → better outcome → narrower rule → less approval needed.I’m curious how you’d frame this:should confidence build around a specific signal type, or around the founder’s current decision?
I'd anchor it around the founder's decision.
A signal only has value in the context of what someone is trying to decide. The same evidence can be decisive for one decision and almost irrelevant for another.
That also makes confidence easier to calibrate over time, because you're learning which kinds of evidence consistently lead to better decisions—not just which signals appear most often.
That distinction is useful. A simple record could be decision → evidence used → action taken → outcome, so confidence is calibrated against whether the decision improved, not how often the signal appeared. For a founder choosing “keep building vs change direction,” which 30-day outcome would you score first: A) user behavior, B) revenue, or C) a clearer next decision?
That's a thoughtful question.
I don't think I'd answer it the same way for every founder, which is why I'm hesitant to give you a generic answer here. I'd rather think it through in the context of the product you're building than pretend there's a universal rule.
If you're open to it, what's the best email to reach you on?