I built a climbing-based VR puzzle game

Hacker News

In each level, the goal is to climb to the top of the wall. Each wall has a unique layout of handholds, magnets, hooks, and elevators, and only one path to the top. The challenge is finding the right combination of moves to get you there. After each unsuccessful attempt, you can view the entire wall from afar to plan your route and strategize before trying again. I just released this on App Lab and would love to get some feedback and see how much interest there is in the concept. It’s listed at $3, but if anyone here wants a free key, just email me at john@solvethewall.com. I also set up this Discord for feedback: https://discord.gg/KVQfhcbZ .

Visit product View on Hacker NewsJul 15, 2024

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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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, io · Missing: https docs, excited, exist

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.

Product HuntUnlikely to reach the leaderboard · Strong signals: email · 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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