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Self hosted CI tightly integrated with Gitea

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Self hosted CI tightly integrated with Gitea

I recently started using Gitea to host all my small projects. This has been great so far, however the lack of an inbuilt CI system like (github / gitlab) ended up hitting me hard. I turned to drone CI / woodpecker CI / agola CI but they all required me to set up a server / runner etc so that they could run. That's why I build Jaypore CI: - Runs on your laptop / remote cloud - CI status is directly posted in pull request description - Uses docker to run everything so we can have caching / db services / publishing packages etc - Configuration language is normal python. This allows job matrix, complex conditional jobs, multiple languages for each job https://github.com/theSage21/jaypore_ci

Visit productView on Hacker NewsDec 3, 2022
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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
79%79% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
66%66% 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
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: dock, using · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
42%42% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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