Pioneer Launcher

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Pioneer Launcher

Hi HN -- I work on Pioneer. It's a kind of pre-YC network for people building projects that might become companies. You apply with your idea, accumulate points by making progress on your project, meet friends, etc. We recently built an experiment called Launcher (https://pioneer.app/launcher). The idea is to implement repeated startup advice in software. Turn the sea of startup blog posts into a list of checkboxes and progress bars, a HUD for pre-launch companies. Kind of like Peloton or a 5x5 lifting apps do for fitness. We'd be very curious to get HN's advice on two things -- (a) Is this a worthwhile endeavor? (And if not, what is the snap-to-grid adjustment that we should enact?) (b) Where should we take this next? What are the other adjacent "courses" we should implement in software? Thank you!

Visit product View on Hacker NewsFeb 15, 2021

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

61%61% 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: apps · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, 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

50%50% 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 · Strong signals: apps, fitness · Missing: mobile apps, ios, personal

36%36% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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