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GitHub Actions Goat – Deliberately Vulnerable CI/CD Environment

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GitHub Actions Goat – Deliberately Vulnerable CI/CD Environment

GitHub Actions Goat is an open-source educational project that simulates common security attacks and vulnerabilities in a GitHub Actions CI/CD environment and shows how to defend against such attacks. This project is designed to be hands-on and interactive, offering ten meticulously crafted tutorials for security practitioners and developers. You can delve into the intricacies of GitHub Actions Security by forking the project and learning by doing. All you need to follow the hands-on tutorials is your GitHub Account.

Visit productView on Hacker NewsJul 17, 2023
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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
76%76% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
54%54% 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: education · Missing: mobile apps, ios, personal
40%40% 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
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
11%11% 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
9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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