I made a simple software licensing platform for developers

Hacker News

Hey HN, A few months ago I realized licensing my app wasn't as straightforward as I expected. There was too much setup just to create a license, and the customer experience was not great. All I wanted was for licenses to be generated automatically when I got paid, and for customers to manage their own licenses. So I built Keyforge, a simple software licensing platform for developers. It integrates with Stripe so that licenses are created automatically when someone purchases or subscribes to your product. There's also a self-serve customer portal where users can manage their licenses, devices, and download invoices. Would love to hear what you think. Nicholas

Visit product View on Hacker NewsMay 31, 2025

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Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

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

90%90% 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, stripe · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

70%70% 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: month, users · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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