I

I Launched a Newsletter

Hacker News

I Launched a Newsletter

Always set yourself a deadline even when you feel not good enough. I have a lot of doubts in myself. That's why I set real deadlines and targets to do. For example, 6 months ago, I said myself, If I reached 1,000 follower on X, I would start a newsletter, I reached it, here I am. Set a deadline, announce it to your friends, relatives, family. And then make it happen!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
60%60% predicted probability of success on TrustMRR, 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 · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000 · Missing: https docs, excited, just released
48%48% 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
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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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