Hypersplit – Like Infinite Craft but in reverse

Hacker News

Hypersplit is like Neal's Infinite Craft but turned upside down. Instead of crafting your way up from the four elements, you start big and go small, down to quarks. It's using GPT-4 to split the words, but a big difference from Infinite Craft is that I can precalculate the possible paths since they are limited (sooner or later you hit a quark or a loop). This means that I can ship it with the whole dictionary locally instead of hitting an LLM every split. While it started with just the splitting, I quickly got the urge to split things faster... which turned the app into the incremental clicker it is now. It's pretty short and starts lagging a lot once things get crazy with a lot of chain reactions (turning off Effects helps a bit), but I plan to focus on optimizations next. I also wanted to try and build something without any graphical assets. The whole game is all just emojis and CSS. A big gotcha is that you can't rely on emojis looking the same on different platforms (for example, on Windows, the explosions are misaligned).

Visit product View on Hacker NewsMar 19, 2024

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80%80% 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: using · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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40%40% 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

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.

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