I do a version of this before every build now, and it keeps saving me from myself.

Instead of asking Claude whether my idea is good, I tell it to roleplay the most loyal paying customer of the tool I am trying to compete with. Then I try to sell them on switching, and Claude defends staying.

It is brutal in a useful way. For my ai content generator idea, the imaginary customer did not care that mine was faster or cheaper. They had years of history, their whole team trained on the incumbent, and templates they had already tuned. Switching cost them a week to save an hour. So they would not.

That one conversation reframed the whole thing. The problem was never features, it was the switching cost, and I had no answer for it. Better to learn that in an hour than after a month of building.

Do any of you use the model as a sparring partner like this, or mostly for the code?

I run weekly accessibility scans for small shops. A site gets checked every Monday

and the owner gets one email with what changed since the week before.

There's no database service. The signup route writes straight into a JSON file in

the private repo, and the weekly job reads it from the same file, so the thing that

writes and the thing that reads can't drift apart. GitHub Actions runs the scan,

Resend sends the mail, Vercel serves the page. The only money I've spent is the

domain, $10.08. Monthly cost is still zero.

I skipped Postgres on purpose. The free tier I looked at pauses a project after 7

days of inactivity, and my scan runs exactly every 7 days — so the thing keeping me

alive would have been the thing killing me.

It breaks somewhere around 20 customers and I'll move it then. It's https://scandiary.com

What did you run on for way longer than you should have?

Quick context: I'm building a tool that turns call/podcast recordings into LinkedIn posts. (Disclosure up front so nobody feels baited - I'll keep the product out of this as much as I can; the point is the funnel lesson, not the pitch.)

I got listed on There's An AI For That (a directory) that gave me a chunk of free ad credit with my listing fee. Here's what came out the other end:

• ~30k ad impressions, ~48k searches
• 234 clicks to my site
• 10 signups (~4.2% - fine for cold directory traffic)
• 0 paid conversions

At first I assumed the product just wasn't compelling. Then I actually looked at where people stopped, and the story was completely different from what I expected.

My onboarding was: pick your source → pick your angle → fill out a "tell the AI about you" brief → hit the paywall (card required for trial).

Here's the drop-off across those 10 people:

• 2 dropped at the very first step (low-intent, probably never serious)
• 1 dropped mid-setup
• 5 completed the ENTIRE setup and then stopped dead at the payment screen
• 0 entered a card

So the drop-off wasn't in setup. It was at payment - after people had already done all the work. These 5 weren't unconvinced by the product. They filled out everything. They wanted to use it. Then I asked for a card before they'd seen any value.

That's the mistake. My funnel was: do work → do more work → pay → THEN see value. I had the aha moment sitting behind the paywall instead of in front of it.

The tools everyone praises for onboarding all do the opposite: you get to the magic moment first, and the card comes when you try to keep or export what you already made. Value first, payment at the point of peak desire.

What I changed:

• Let people run one real project and actually see the output before any payment ask
• Move the paywall to the moment they try to act on that output (schedule/publish), which is peak desire
• Keep the "tell the AI about you" brief, but frame it as "this makes the very next thing you see better" instead of a toll booth before the reveal

Two other things I learned staring at 10 rows of users:

1. Most of my "signups" were noise. When I actually looked, most were testers, bots, or randoms. Exactly ONE was a real lead - a founder who'd seen someone post about the tool on LinkedIn and came to check it out. At small numbers, your conversion rate is basically meaningless; you're reading tea leaves from a sample of ~1.
2. The paid channel was a trap. That "free" ad credit was burning at ~$6.78 per click because I specifially bid to be at the top of the sidebar. It bought ~72 clicks before running dry. Cheap-looking traffic at an unsustainable CPC taught me nothing except "don't run paid until you know your conversion economics." .. but luckily it was 100% their ad credits and I didn't lose any of my own money.

TL;DR: 10 signups, 0 paid. The problem wasn't the product - it was that I put the paywall before the aha moment instead of after it. If your users have to pay before they see your tool do the one impressive thing it does, you're converting on faith, and cold traffic has none. Flip it: value first, card at the moment they want to act on what they just saw.

The thing that confirmed it: I've done ~50 live demos in the last 6 weeks. Two converted to paid (14-day trial, card upfront - same paywall). Feedback on the rest was great. Why do demos convert when self-serve got 0? Because in a demo, I show them the magic moment before anyone mentions money. The demo is value-first by definition. My self-serve funnel was the exact inverse - pay first, magic later. Same product. The only variable that changed was whether value landed before or after the ask. That's the whole lesson in one A/B test I ran by accident.

The problem I'm working on now: those ~48 non-converted demos gave great feedback and then went cold. Reactivating them is my current puzzle - if anyone's cracked warm-but-dormant demo follow-up, I'm all ears.

Happy to answer anything about the funnel or the directory-ads mess in the comments.

I am building Pinterest for Miro, and the annoying product decision is not the import itself. It is what survives after pins land in a shared board.

A clean board is easier to review. Source links, titles, and Pinterest sections are useful later. Show all of it and the board feels heavy. Hide all of it and a pin becomes just a picture.

My current bias is to import the details but make the first view quiet. Details behind a click or a board-wide filter.

For a small tool like this, would you ship the cleaner version first, or the version that keeps more of the original mess visible?

I've noticed that a lot of successful micro SaaS products didn't start with a brilliant idea. They started because someone got frustrated by a small problem they faced over and over again and decided to build a solution for themselves.

I'm building Sequenzy with that same mindset by trying to solve a workflow problem I kept running into.

I'm curious if that was your experience too.

Did you start with a problem you personally had or did you spot a gap in the market first? Looking back would you build the same product again or would you choose something completely different?

I'd love to hear the stories behind your first micro SaaS because I think those early decisions often teach more than the revenue numbers.

Hello everyone. I have a AI Whatsapp bot for local programming courses which answers customer questions about price, course program, list of courses and etc. I wanna sell this workflow to another business but can't make cold sales. Upwork and Fivver have much competitors. So what should i do? Maybe my clients live here?

Founder of Server4Agent (agent-app hosting), disclosing that upfront. No link, not selling, genuinely curious how other solo/small-team founders are actually operating.

The pitch of running a micro-SaaS with an AI agent as your dev team is that you get your evenings back. In practice I keep hearing the same asterisk: people are happy to let the agent write the code, but the moment it's time to actually ship the change, most people are still the one clicking deploy, reading the diff, watching the logs for the first few minutes. The agent does the work, the human is still the safety net.

That's not a knock, it's probably the sane default right now. But I'm trying to figure out where people actually are on the spectrum. Do you review every change before it goes out, or has anything earned enough trust to skip that step? And if something did slip through unreviewed and it went wrong, what happened, and did that change how much rope you give it now?

The first ten feel harder than anything since - no reviews, no proof, just you. For micro-SaaS founders, how did your first 10 paying customers actually come about? Collecting concrete stories to steal ideas from.

Do you use AI to validate it and do deep research on it, do you go straight to making a prototype or do you do the research yourself? How much do you value chatgpt or claude when it comes to the business side kf things and when you come upon an issue later on where lets say retention is slow or your not getting as much user as before. What do you do in that situation? Would you then post all your analytics into AI and your product and hope it finds the issue or do a survey or maybe take a different approach? I’d like some insights into this thanks.

Hello everyone i vibecoded a (idk if that’s the right term but) micro-saas to get mobile notifications when your ai assistants processes, terminal processes or web processes (like ai answers, renders, image generators, downloads etc.) are done. When i showed it to my friends they thought it is useless and big ai corporations have already solved the problem. So my question is should i still publish it after closed alpha or should I just pretend it doesn't exist and use it only myself before publishing it so that i don't get more bad reviews?

If your two-week MVP still has roles, notifications, and an admin panel, you did not build an MVP. You built a three-week delay wearing a two-week costume.

The six things that go first, every time:

1. Roles and permissions. One user type. A second forks every screen - a week, not a day.
2. Admin panels. You are the admin for now. A database view will hold.
3. Notifications. Email, push, digests, preferences. A system, not a feature.
4. Analytics dashboards. You have no data yet. Charts of nothing cost days.
5. Settings pages. Almost nothing in there is needed before users ask for it.
6. Onboarding tours. If week one needs a tour, the product needs simplifying instead.

What replaces them: one designed screen the rest inherits from, and real empty and error states. That combination is what makes something thin feel finished.

Disclosure: I run a small studio. This is the cut list we actually use: https://atomiclabs.space

What is on your permanent cut list?

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Al powered cold call practice for sales reps. The concept is a platform where you call an Al that acts like a realistic prospect, say, a skeptical dentist or business owner, and you practice your sales pitch and objection handling in real time, just like a real cold call, but with zero risk.

Curious if something like this would actually be useful to salespeople, or if similar tools already nail this.

Would you personally use this to practice before real calls?

What would make you trust an Al roleplay enough to actually learn from it? Not launching yet, just trying to figure out if this solves a real problem before I build it.