Beat your friends in Cricket

Hacker News

tldr: Week 3 update of https://bowled.gg new teams, minor bugfixes, blog post on the inspiration. (Website works best on desktop). Welcome back to "The Bowled Diaries" - week 3 of building a card-based game for cricket. - In the past 2 weeks, I spoke to a lot of people asking what they thought about it and taking feedback. All the feedback have been super-helpful and motivating. - Some minor bug-fixes and code standardization. I have logged all of the bugs and requests reported, will be working on them soon. - Game engine has been left as-is (tweaking in progress). Planning to build a simulation model based on historic data from real games in the long-term. I've started a blog series and will be posting bi-weekly articles: https://blog.bowled.gg I am excited to share that I am working on this full-time now. I wake up every day excited to build and ship this beautiful dream. We'll all play a fun game to put our cricket knowledge to the test and beat our friends! See y'all in week 5.

Visit product View on Hacker NewsFeb 19, 2023

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Launch Intel predictions

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

76%76% 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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, game engine, io · Missing: https docs, just released, exist

68%68% 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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

35%35% 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

14%14% 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.

Incorrect prediction on native model

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