Mixing C++ with Angelscript for runtime introspection and debugging

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Hey everyone, I created D0 for myself to enable faster iteration and introspection while working on my C++ projects. Particularly, it annoyed me that the simplest change of adding a printf() or trying to view an object required application restart and recompilation. The project is still in it's infancy and I'm looking for initial feedback. I'm currently working on adding a way to override built-in API's with your own definitions to be able to easily define how e.g types like std::vector should be viewed in the UI. I've also recently released a Visual Studio integration via an extension and eventually, I want to move as much functionality as it is possible to VS/VSCode/other code editors so that you can access all features from the editor you use to write code.

Visit product View on Hacker NewsMay 12, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code · Missing: mac, agents, macos

92%92% 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: created · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, 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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

13%13% 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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