HortusFox – FOSS system for houseplants with enterprise-scale features

Hacker News

HortusFox is a free and open-source management, tracking and journaling system for indoor and outdoor plants. It's 100% FOSS software under the MIT license, but still has grown to a scale that can be considered enterprise-scale. That was possible because of the great community that evolved around this leafy project, that kept me motivated as well as supported me in a very kind and lovely way. After a few months of no new version release, a few days ago I've published v5.3 of HortusFox, which provides some cool changes to help empower your plant parenting. You can see a very detailed changelog in the 5.3 release page on GitHub. Overall 25 issues have been resolved, where some of them were actually quite big. If you're new to HortusFox: the system offers you a large number of features to support your plant parenting (or gardening) journey with plant details, locations, photos, default and custom plant attributes, inventory system, tasks system, calendar, history (to keep a memory of your leafy friends), collaborative group chat and a few opt-in features such as weather forecast or plant identification (via photo). It also offers an extensive REST API, backup feature and themes (if you want your workspace a little more personal). I have to say, for me it's the perfect blend of two things: The passion for software development as well as the fondness for nature. I'm really grateful for that. Thanks for reading and have a wonderful and leafy day.

Visit product View on Hacker NewsOct 29, 2025

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

95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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