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Breakout Game – Smash a Brick, Read a Post

Hacker News

Breakout Game – Smash a Brick, Read a Post

Hi HN, I was already building a breakout game[1] just for fun, but then I thought—what if the bricks were news? So now, every time you smash a brick, you get a post and have to decide: read it or toss it. The idea came from realizing I skim through news way too fast, often skipping things that don’t immediately interest me. This was my way of slowing down and giving those overlooked posts a fighting chance. Give it a try! I also wrote a short post [2], detailing the physics behind the game. I spent quite a bit of time just fine-tuning the bounce logic: adjusting angles and adding random variations to make sure the movement feels just right. [1]: https://github.com/neberej/breakout [2]: https://nabraj.com/blog/breakout-game-physics/

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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
47%47% 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
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
27%27% 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
16%16% 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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