Open-Source Interactive Eclipse Map

Hacker News

Link is to the Github repository, which has a link to the actual map in the readme. This was originally intended as an example to show others how to make their own, but turns out to be pretty useful in itself. I intended to accompany a couple of articles explaining the computations, but they won't be done before the April 8 2024 eclipse, so the code will have to do for now. The code is released as public domain, so feel free to do anything you like with it.

Visit product View on Hacker NewsMar 7, 2024

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4points

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Launch Intel predictions

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

87%87% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

67%67% 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 · Missing: mobile apps, ios, personal

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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