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Snipplle – an open-source snippet manager

Hacker News

Snipplle – an open-source snippet manager

Hi HN, I’m the author of Snipplle. Snipplle is an open-source snippet manager built to help developers organize and reuse code snippets across projects. This is an early beta. Right now the focus is on core workflows: managing snippets, organizing them into collections, and pulling them into local projects via a CLI. I’m especially interested in feedback around real-world workflows: how you currently store snippets, what’s missing, and what would make a tool like this actually useful long-term. The project is fully open source here: https://github.com/snipplle/snipplle There’s also a cloud at https://snipplle.com

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Actual performance

2points
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Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
13%13% 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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