CubeSolver – An OpenGL-Based Rubik's Cube Simulator

Hacker News

Hello Hacker News community, I would like to share my latest project, CubeSolver, which is an OpenGL-based Rubik's Cube simulator. The simulator allows users to manipulate the cube using keyboard inputs, just like a real Rubik's Cube. I built this project for fun and to improve my OpenGL skills. However, I would appreciate any feedback or tips from the community on how to improve the project further. I am open to suggestions on new features or better coding practices. To get started with the project, you will need to have G++ compiler, GLUT library, OpenGL library, and GLU library installed on your local machine. Once you have these prerequisites installed, simply clone the repository onto your local machine, navigate to the project directory, and compile the code using the "make all" command. You can run the program using the "./bin/Debug" command, and then start manipulating the Rubik's Cube using the keyboard and mouse controls listed in the README file. If you are interested in contributing to the project, feel free to fork the repository and submit a pull request with your changes. I welcome all contributions and feedback. Thank you for your time, and I look forward to hearing your thoughts on CubeSolver. Best regards, Vinícius

Visit product View on Hacker NewsFeb 24, 2023

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

87%87% 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: hacker news, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

10%10% 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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