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PaperMETAR – An E-Paper Aviation Weather Display for Pilots

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PaperMETAR – An E-Paper Aviation Weather Display for Pilots

Hi HN! I created an ESP32 driven weather display for pilots that looks like a cockpit instrument. The goal was to move METAR off the phone screen and onto the desk where it is always visible. It’s a dedicated, "always-on" desk accessory that fetches real-time aviation weather and renders it onto a high-contrast e-paper display. It also has a quiz mode to help student pilots learn and test METAR decoding. Jumpseaters: Adding Personality to the Data Raw METARs can be dry, so I implemented a feature called Jumpseaters. These are selectable personality profiles that transform the device into an opinionated flight crew. Instead of just seeing "OVC001," you might get a report from Downwind Donnie, who sarcastically judges your "personal minimums," or Checkride Charlie, a virtual DPE who delivers weather updates with the uncomfortable silence. It was a fun to map these "personalities" to specific weather triggers in the firmware. ---- The Technical Details Controller: ESP32-S3. I chose this for the native USB support and the power efficiency needed for long-term deep sleep. Display: 4.2" E-Paper. It uses an open-source library https://github.com/martinberlin/CalEPD Power: Deep sleep cycles keep the current draw low, allowing it to run for weeks on a small LiPo. I am continuing to optimize the power usage because I feel I can get it to last many months even after the daily screen refreshes. Tech Stack: ESP-IDF/C++ for the microcontroller. Flutter/Dart for the iOS/Android mobile apps. Nextjs for the website and API. --- I have learned a lot about hardware devices during this process. It has been fun programming for the E-paper display. It has it's own unique challenges because of the nature of the technology (seconds for each refresh vs. refreshes/second for traditional screens). I really enjoy when it refreshes. The Kickstarter is live and 38% funded with 15-days to go. One thing I wish I had done a lot more of is prelaunch marketing. Ads now have had good conversion driving to the Kickstarter page, but the volume takes time. On the same prelaunch theme, the other major lesson learned is that I wish I had asked for more feedback publicly (I do have early testers, but all local) and shared more before working on the Kickstarter. Once you say Kickstarter, all the mods put it in the commercial category and don't allow posting ANYTHING related to it. Check it out at https://papermetar.com or https://www.kickstarter.com/projects/charlie29r/papermetar

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · 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: apps, coding, open · Missing: mac, agents, macos
70%70% 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
61%61% 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: mobile apps, ios, personal · Missing: entrepreneurs, video, google
40%40% 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
31%31% 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
17%17% 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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