Hotreload for a retro Python game engine

Hacker News

Writing games can be an enjoyable way to introduce people to programming. This works especially well if it's kept easy and with fast turnaround. For faster turnaround I wrote a library for an existing retro Python game engine (Pyxel) that adds hot reloading for both code and assets. My library is called ReloadPyxel. This means that even as your game is running, you can modify the code or graphics, and see the results right away in your running game. The link has a few animated gifs illustrating what I mean, and you can download and run them yourself if you'd like. There is also a more detailed explanation in the link at the bottom of the linked page. Since my motivation here is to use this in an educational context, I chose an approach with as little "magic" as reasonable. This is my first submission - please take a look and enjoy! Feedback is welcome but please be gentle.

Visit product View on Hacker NewsAug 31, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% 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 NewsStrong engagement from HN community · Strong signals: exist, existing, game engine · Missing: https docs, excited, just released

65%65% 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 HuntOn track for Day 1 leaderboard · Strong signals: context, code · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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