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Ansible update playbook for Fedora Silverblue
Hacker NewsAnsible update playbook for Fedora Silverblue
I've been a reader of Hacker News for many years, but this will be my first post. I love using Ansible and have created a simple Playbook for updating Fedora Silverblue, along with Flatpaks and Distroboxes. This is version 1.0 and I though that some else might find it useful as well, hence why I am posting it here. https://theunix.dad/posts/updatingsilverblue/ Have a great summer :-)
Share cardActual performance
3points
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, podcasting55%55% 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: new, using · Missing: mac, agents, macos52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released42%42% 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, personal37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue16%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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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