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150+ products launched in 7 days on this fair Product Hunt alternative

Hacker News

150+ products launched in 7 days on this fair Product Hunt alternative

A little over a week ago, I was wondering if anyone would care about the thing I’d just built. Let me rewind a bit. I’m a solo indie maker, just like a lot of you. I’ve built so many projects, put in the work, and then… nothing. A few months ago, I launched something I was genuinely proud of on one of the big platforms. I was excited, hopeful. But within hours, it got buried under a pile of other launches. No one saw it. No one cared. That sucked. Not because I expected it to blow up overnight, but because it felt like I never even had a shot. And I kept thinking about how many other indie makers feel the same way? So, I decided to build something different. I coded for four days straight, juggling my 9-5 job, running on no sleep, and a lot of self-doubt. But I kept going because I believed in the idea: a platform where indie makers could get a fair shot. The idea was simple (but powerful as I see it): • 10 launches per day, max. Every product gets its moment in the spotlight. • 2 votes per user, per day. Quality over quantity. • An "Under Radar" feature to give overlooked products a second chance. • A randomized product list to ensure fair exposure and reduce voting bias. I posted about it here on Hacker News and on X, half-expecting crickets. But something incredible happened. Within hours, indie makers just like you and me started scheduling their products. And in just 7 days, over 150 products were scheduled to launch on the platform. Every time a new product is scheduled on ItsLaunched, I'm reminded why this platform needs to exist. These are products that deserve to be seen. I'm not sharing this to boast, but to thank this community. Your support, your feedback, and most importantly, your willingness to give a platform like this a chance, means the world to me. I’m still tweaking and improving the site every day based on feedback. It’s far from perfect, but it’s growing. So, if you're a maker with a product you're stoked about, come check us out at ItsLaunched.com and if you've already launched with us, a huge thank you. You're the reason this thing exists in the first place. As for me, I'm just a maker trying to create something that makes a difference. And thanks to this community, I feel like maybe, just maybe, we're doing exactly that. Here's to the makers and the dreamers. Keep building awesome things. We'll be here to make sure the world sees them.

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, hacker news · Missing: https docs, just released, lua
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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