My

My preparation before scaling up my product

Hacker News

My preparation before scaling up my product

Hello HN, I'm Rully, an Indie maker who loves to build something. I wanna share my little story from one of my products. a little story, a long time ago before I'm know and using VPS. I'm using Shared Hosting for my hobby and small business. But, the problem was: 1. I can't get hosting with unlimited bandwidth 2. It's not easy to find a hosting provider that supports NodeJS, Ruby, Python, and other services in 1 bundle 3. Usually, when point 2 is achieved. The price is too high and I can't afford it. So, that's the reason why I'm launching JustAHost. What is next? Scaling up by adding more server locations. The first serve location was in USA. Then, slowly but surely I can add more server locations to cover up my client's needs, especially for Europe that has GDPR regulation. Before I do that, I don't wanna take a rush. I wanna make sure I can implement the same features and services for all regions. So, I'm starting by creating a boilerplate/framework/architecture to achieve this. Then, I can safely add more server locations. And now, I've 7 server locations with the same features, benefits, and services. Cheers, - Rully

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
90%90% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% 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 · Strong signals: host · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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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