A fair Product Hunt alternative

Hacker News

Over the past few weeks, I’ve been developing Simple Lister, a platform built to support indie product creators and give them a fair shot. If you’ve launched on Product Hunt recently, you might have noticed that only featured products get the spotlight, while others struggle for visibility. Why Simple Lister? Simple Lister aims to fix this by offering a more transparent and fair approach for product launches. Here’s how we do it: • No favoritism: Every product gets an equal chance, and we don’t play favorites. • Daily Underdog Feature: Each day, we highlight one underdog product to give them extra visibility and support. • No hidden fees: There are no surprise costs. We have a simple submission fee, and that’s it—no pay-to-play or hidden charges. Also we have a long to do list to do better. Why does this matter? After launching on Product Hunt ourselves, we realized how tough it is for smaller creators to get any attention unless they’re featured. Simple Lister is here to champion those indie products that deserve to be seen by a wider audience. The platform is new and evolving, and I’m constantly working to make it better. If you’ve got feedback or questions, don’t hesitate to reach out! Thanks for your support, and I’d be happy if you submit your products!

Visit product View on Hacker NewsOct 16, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

91%91% 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: new · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% 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

19%19% 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.

Incorrect prediction on native model

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