Ansible Crash Course

Hacker News

Hi Everyone. My name is Mike. I've created a free Ansible Crash Course. It's a course aimed at people who are new to Ansible and want to get up and running quickly. It goes into a fair amount of detail on most topics, but should something be missing just let me know and I'll add it in. Any and all help would be greatly appreciated! Just note an email address is required to register for the course - this is a limitation of the platform I'm using to host the course. Sorry about that! The course can be found over here: https://www.thecloud.coach/ansible-crash-course Any and all feedback is greatly appreciated! Thanks a bunch, Mike.

Visit product View on Hacker NewsSep 29, 2019

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, using · Missing: mac, agents, macos

75%75% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

63%63% 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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

47%47% 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, revenue

19%19% 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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