Visualizing Code in Virtual and Augmented Reality

Hacker News

Thanks for peeking in to this post. I have been an avid fan of the push in the recent years for true visualization of code. If one searches HN for "visualization", more than 10 years of links, articles, documents, research, and projects come up with all sorts of wonderful metaphors for seeing your code in new ways. I am a much simpler, much dumber man. I just wanted to see the code - literally, like long sheets of paper - floating in air so I could walk up to it and just.. interact with it. Move it. See what its next to. Then, maybe, use the same tools that present the code to me in this new way to introspect it visually, without relying on complicated or impossible hard-form algorithms that can do things like "show me related entries" or "all the classes that have function Z". Reducing the complexity of that question and answer down to "look to your right and left for the things that are now highlighted in purple" is my goal here. To that end, I have an incredibly amateurish video to share with the group - pardon that it is a massive video file hosted on my private Proton Drive account. (If anyone has a suggestion or hosting videos that HN appreciates, I'll gladly take it!) It's about 6 minutes long, and is a basic demo of the project I have working on that tackles the things I mentioned above. I'm posting this to HN because I need help . Motivational help, implementation help, and everything in between. I am absolutely convinced there are like minded people interested in ideas like this floating around the community, and it is to them (you!) that I am reaching out. To all the above I also say: I appreciate your patience and willingness to hear an amateur software developer out. https://drive.protonmail.com/urls/7NH8Y81Y9C#SlPidzB4EF0t

Visit product View on Hacker NewsApr 12, 2022

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

78%78% 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.

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

62%62% predicted probability of success on Product Hunt, 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

48%48% 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 · Strong signals: host · Missing: plus, platform, intuitive

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

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

39%39% 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

11%11% 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.

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