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Plantbase, a file-first plant care database

Hacker News

Plantbase, a file-first plant care database

Hey, I wanted to share my hobby project: a file-first houseplant and plant care database. I originally built it with a fully-fledged API (using Rust Axum) + Postgres database to store and serve plant care data to a frontend. It worked, but quickly felt like overengineering for a read-only reference site. So I rebuilt it as a file-first static site: each species/genus/family is a markdown file with structured metadata. This is the 'database'. A static site generator then builds all pages from these files. Example for the 'Impatiens' genus: --- name: Impatiens familia: Balsaminaceae common_names: impatiens, jewelweed, touch-me-not, snapweed, patience, balsam or busy lizzie. etymology: The word Impatiens comes from the Latin word impatiens, meaning impatient/intolerant. This refers to the seed capsules of this plant which burst when mature, dispersing seeds up to several meters away. tags: - balsaminaceae --- The project is open source, easy to extend (just add a markdown file), and designed to stay simple: no accounts, no user data, no monetization, no roadmap with tons of features. Just a clean, free, searchable houseplant reference with open data. This project was a lesson to myself to keep things simple! On to the next one. Repo: https://github.com/plant-base/houseplantguide.org

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, open · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
47%47% 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
39%39% 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
20%20% 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.

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