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Coding Tips Every Monday

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Coding Tips Every Monday

Hello folks! Me and a small group of developers started a free newsletter. The idea is to share 3 web dev tips every Monday. Our goal is to improve our writing skills and share knowledge as much as possible. So far, few hundreds of developers have subscribed and seem to like it. To get a feeling of what kind of stuff we share, here is an example of last Monday's issue: https://us4.campaign-archive.com/?u=5e499c75818d4611a008adfb5&id=106e5fbc71 If this is something that would interest you, you can subscribe here (its completely free): https://nordschool.com/subscribe/

Visit product View on Hacker NewsOct 12, 2019

Actual performance

15points

10comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

70%70% 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: started · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

60%60% 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

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

25%25% 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

13%13% 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

2%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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