Itemingo
Photos to listing drafts and true profit, for resellers
What is Itemingo?
Itemingo is software for resellers. It follows each item from the intake photo through identification, listing and sale, and records what the unit actually earned after marketplace fees, shipping and what you paid for it.
Photograph the item and Itemingo proposes a brand, model and part number from your photos, with the evidence, for you to confirm. Approve one Master Listing and send it as a draft to eBay, Newegg and Whatnot; nothing goes live until you post it. Orders, returns and fees flow back so you see profit per unit after fees, shipping and cost. Try the live demo, no account needed: .
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
I usually go to a popular app and a similar app within the same niche and I look at the layout of their app and I take the elements from it and adapt it to my app so I don't copy it the exact same... try not to reinvent the wheel just take what's working from their app adapt it a little and change it up.
Steal Onboarding Layouts From Category-Leading Apps Then Twist Them For Your Niche
Ethan designs onboarding by lifting structure from incumbents like Cal AI and Duolingo, since their flows reflect years of A/B testing he could never afford. Adapt rather than copy verbatim, then add a niche twist so the screens feel native to your audience. Stand on the shoulders of teams who've already tested every onboarding pattern.
Tinder you know really went after the 18 to 24 demo and 24 to 30 but 18 and 24 especially in a way that nobody else really had and so in order to do that you had to kind of invent a couple things.
Tinder's Early Growth: Target the Demographic Nobody Else Addressed
Match served 35+ users, OKCupid brought that to 30+, but no online dating product had genuinely served 18-24 year olds. Tinder's core product decisions — swipe mechanic, 90-second profile setup, blind double opt-in — weren't arbitrary features; they were purpose-built for a demographic with low patience for friction. The lesson: deep demographic segmentation often reveals underserved users who'll adopt with unusual intensity, and solving for them forces product innovations that become moats.
There's a play for whatever you're stuck on.
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