Weekend project – Blackjack Game

Hacker News

Hi HN, I built a little blackjack app over the weekend. I didn't have time to implement all the features (like split and double), but it's quite fun to play! There's a little twist in the game also, in that you can pick up prizes along the way which can be swapped for cash. Interestingly, I used a reactive Javascript framework that the company I'm interning at created. It was just to get familiar with the framework itself (which was quite cool actually). Part of me wishes that they open source it! Anyway, I was quite happy with the result, and the company let me deploy it on a server, so you can give it a try at: https://osrec.co.uk/labs/blackjack/index.html Let me know what you think! Thanks!

Visit product View on Hacker NewsJun 26, 2018

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4points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

77%77% 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.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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 · Strong signals: way · Missing: mobile apps, ios, personal

43%43% 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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% 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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