DoxyJS. A for-fun and homemade symbolic regression app for the browser

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Dear HN: Here's a side project I've been tinkering with for awhile. There's an explanation in the PDF doc. If you've ever used Eureqa, it's a bit like that. Although it's not the most fine-tuned example of this type of genetic code, and JS is hardly the ideal way to chew through lots of data, it does work, and it does things in its own way fairly well. I'll leave it up to the fine folks here to mess around with. This is absolutely a hobby project, but it may actually help you, so I'd like to put it out there before it's too late. I have a couple similar projects; another involves Markov chains to let you predict the future while telling stories. That one requires server power, so I'm keeping it private. I've been asking myself: Why make this public? It's not for money. Not really for acclaim. I'd like to be a little like _why ...I'd like to be someone you associate with being skilled at what I do. I thought maybe I should bulk up some social media presence before giving this out, but, who cares. Here's a cool piece of code, have fun with it. Math.js deserves most of the credit, anyway.

Visit product View on Hacker NewsJan 16, 2023

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

71%71% 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 · Missing: supports, reddit linkedin, podcasting

61%61% 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.

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

39%39% 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

34%34% 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

14%14% 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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