I turned algae into a bio-altimeter and put it on a weather balloon

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Hi HN - My name is Andrew, and I'm a high school student. This is a write-up on StratoSpore, a payload I designed and launched to the stratosphere. The goal was to test if we could estimate physical altitude based on algae fluorescence (using a lightweight ML model trained on the sensor data). The blog post covers the full engineering mess/process, including: - The Hardware: Designing PCBs for the AS7263 spectral sensor and Pi Zero 2 W. -The biological altimeter: How I tried to correlate biological stress (fluorescence) with altitude. - The Communications: A custom lossy compression algorithm I wrote to smash 1080p images down to 18x10 pixels so I could transmit them over LoRA (915 Mhz) in semi-real-time. The payload is currently lost in a forest, but the telemetry data survived. The code and hardware designs are open source on GitHub: https://github.com/radeeyate/stratospore I'm happy to answer technical questions about the payload, software, or anything else you are curious about! Critique also appreciated!

Visit product View on Hacker NewsNov 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, physical, using · Missing: mac, agents, macos

78%78% 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting

75%75% 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, io, including · Missing: https docs, excited, just released

55%55% 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 · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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 · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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