Hg

HgLab 1.0 Released - Mercurial Server for Windows

Hacker News

HgLab 1.0 Released - Mercurial Server for Windows

This is the thing I've been busy with for the past 18 months, over which it has grown from a skunkworks project into a real-world product. This is going to be my first microISV and even if it's a colossal commercial failure, I'm looking forward to the experience of product development. To ship HgLab, I first effectively rewrote Mercurial Core in C# (https://bitbucket.org/hglab/hgsharp), and then built on that to first deliver a bare-bones read-only repository browser and then a full-fledged Mercurial Server. I'm open to any kind of feedback. And wish me luck!

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
68%68% 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: open · Missing: mac, agents, macos
62%62% 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
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
34%34% 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
30%30% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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