I'

I'm working on cataloging open source hardware designs

Hacker News

I'm working on cataloging open source hardware designs

When starting a new hardware design, I often find myself pulling up familiar boards (like Adafruit or Sparkfun's dev boards) almost as often as the chip's application note. A full reference project provides useful context, like which voltage regulator they used or how a USB port is wired. But, it's kind of an awkward process because I'll have to download the design files from Github and open it in the native CAD software (Eagle, for example). Plus, there's no easy way to search for a specific electronic component in Github and find relevant designs. I've been toying with how to solve this. I'm thinking of a "Dribbble for electronics". A searchable, visual way to explore open source hardware designs without jumping between browser and ECAD. I made a script to crawl Github for open hardware designs, then generate a schematic and interactive BOM for each design. Now, you can (hopefully) search for "ESP32"[1], "WiFi"[2], or "Bluetooth"[3] and browse relevant designs to view in browser. [1] https://www.openappnote.dev/tags/esp32/1 [2] https://www.openappnote.dev/tags/wifi/1 [3] https://www.openappnote.dev/tags/bluetooth/1

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, visual · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, 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
84%84% 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
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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