Discord GitBot

Hacker News

Discord GitBot

Hey HN, I've co-founded a consulting company, we're growing and I consider it a success, but everyone here wants to move into products more, and not just doing general React coding. Our end goal is either to have our own things that we're attached to, or have clients where we can be more involved. So we figured that we will learn more about product lifecycle by releasing whatever we can, and this is our first step: a simple bot that notifies you on Discord whenever something happens on GitHub. I'd love to hear your feedback! Or if you've been on this road before and you have some insight.

Visit product View on Hacker NewsJul 4, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: ide · 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.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

25%25% 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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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