Matrix, a fast and flexible blogging engine

Hacker News

Still pretty rough ... Emphasizes: 1) Simple, no-frill dashboard for managing posts (files). 2) Lightweight markdown editor 3) Relationships: taxonomy (file tagging) and fast linking (content to other posts) Some of the important key bindings I have are ctrl+c for code, ctrl-L for linking text to other posts. Right now this is baked in but I'd want to allow users to customize it. It's still pretty beta but let me know what ya'll think! Here's the github page if you want to help out: https://github.com/keera/matrix

Visit product View on Hacker NewsMay 14, 2014

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Hacker NewsStrong engagement from HN community · Strong signals: 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: user, code · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

29%29% 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.

Correct prediction on native model

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