I made a no-code and AI platform to create serious games

Hacker News

For 2 years, I had clients that needed serious games and created such games from scratch for each projects. I got tired, so I decided to make a platform to be able to create gamified experiences with no-code and also using AI. With Ludiz, you can either create a game from scratch, using an easy to use editor, add steps, use many game blocks such as MCQ, Sort, Puzzle, Fill-in-the-blanks... Or you can use AI by describing the game you want to create and it will create the game for you. AI creation was a bit of a challenge because I needed to generate a long JSON object respecting a complex schema, and OpenAI schema support is kind of restricted. So I had to break this process into many steps, and carefully make the schema respect the OpenAI restrictions. After you created a game, you can publish it to your users and collect statistics to see who has the best score, was the fastest... Creating this platform took me a lot of time, and some big french clients agreed to test it while it was still in early stage. This allowed me to get their feedback and polish the product. Now they are happy customers and use the platform everyday !

Visit product View on Hacker NewsMay 20, 2025

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best fitHighest predicted score across all platforms for this description.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

44%44% 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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

17%17% 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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