Js

Jsonnet Course Online

Hacker News

Jsonnet Course Online

Hi HN! I'm usually a lurker here, but I wanted to share this: I'm an enthusiastic user of Jsonnet[1] to flexibly generate JSON and YAML files (eg for kubernetes configurations). I wanted to spread awareness of Jsonnet and made a course on Udemy. The first 1000 students enrolling within 5 days with this link get the course for free: https://www.udemy.com/course/jsonnet-from-scratch/?couponCod... I hope you enjoy the course (I'm interested in your feedback!) and if it makes you start using Jsonnet it will be mission accomplished :-) [1] https://jsonnet.org/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% 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: 000, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% 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
35%35% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos
9%9% predicted probability of success on Product Hunt, 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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