Changelog Generation Tool

Hacker News

It's the Easter holidays here so I decided to finish one of the pet-projects that I had on my work machine. https://github.com/bdryanovski/logchanges This small package is designed to help me generate a changelog for my other projects and keep it up to date when it comes to update and handle it. The JSON part is for the cases when I want to integrate it into Single Page Apps like for example inside https://docusaurus.io/ or https://www.gatsbyjs.org/ when I want to have a lot more control on how the data is rendered. Or even make it searchable. I will be happy to get some feedback on what other people could need or is it useful for someone. Happy Holidays to everyone.

Visit product View on Hacker NewsApr 18, 2020

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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

62%62% 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: mac, apps, single · Missing: agents, macos, agent

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

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

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% 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

32%32% 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

12%12% 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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