Oasis – An open-source, 3D-printed smart terrarium

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See the website for a demo video: https://oasis-terrarium.com This project is an enclosure for growing plants. Features include: - high-power LED lighting - a built-in water tank and mister - fans for airflow - temperature and humidity sensing - wi-fi connectivity and a web-based interface for control and monitoring (see https://oasis-terrarium.com/docs/usage_guide/#web-interface) The entire project is open-source: - electronics: designed in KiCad - 3D models: designed in CadQuery - software: written in Rust I initially started this project during COVID and built a working prototype using a Raspberry Pi and off-the-shelf parts. The first prototype worked, but was pretty rough. Several months ago, I picked up the project again and began a complete re-design. After two rounds of circuit board design and countless iterations of the 3D-printed parts, I'm finally happy with the results. Along the way I learned a lot about electronics design and fabrication, 3D modeling, and Rust. Happy to answer any questions - thanks for checking it out! Here are some direct links to parts of the project you may find interesting: - demo video: https://oasis-terrarium.com - detailed build guide with pictures: https://oasis-terrarium.com/docs/build_guide/ - interactive 3D model: https://oasis-terrarium.com/docs/3dmodel/ - detailed info on electronics design: https://oasis-terrarium.com/docs/electronics/ - 3D-printable models: https://www.printables.com/model/1315117-oasis-smart-terrarium

Visit product View on Hacker NewsJun 24, 2025

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

84%84% 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: model, models, using · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, month, way · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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