ADS-B Scope – Portable ADS-B and mesh radio on a LilyGo T-Display-P4

Hacker News

I built a portable 1090 MHz ADS-B receiver and Meshtastic mesh radio on a LilyGo T-Display-P4 (~$100). An RTL-SDR dongle plugged into the USB Host port receives aircraft transponder signals, decoded in real-time on the ESP32-P4's dual RISC-V cores. The onboard SX1262 LoRa radio runs Meshtastic-compatible mesh networking simultaneously. Live demo / companion app: https://adsb-scope.offx1.com (WebSerial — Chrome/Edge, connect via USB) Questions? Feedback? hello@offx1.com

Visit product View on Hacker NewsMar 25, 2026

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3points

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

71%71% 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: code · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

19%19% 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

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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