Si

Simple gitignore generator on the web – GUItignore

Hacker News

Simple gitignore generator on the web – GUItignore

I created a simple gitignore generator for the repository mantained by Github ( https://github.com/github/gitignore/ ), though I know there is an alternative solution by toptal, the source looks like it hasn't been updated since Jun 2023 (About 2 years ago). My solution is sort of just a frontend to the github repository. It takes the list of .gitignore files from the repository and makes it easily available for search and concatenates multiple gitignore files into one. This can be specially useful when starting a new project. Its kept up to date with the Github repository (in the footer you can see the commit hash that its using). Source code: https://codeberg.org/0x00cl/guitignore (Its just an HTML file with the gitignore submodule)

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Actual performance

2points
Did not reach leaderboard

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
48%48% 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
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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