NF

NFC tap-to-log health tracking, just like tap-to-pay

Hacker News

NFC tap-to-log health tracking, just like tap-to-pay

Here is the ndef record so you can do this yourself for free (we have an orderable product but it's not actually needed and we sell it at-cost): ndef: { lock: false, // keeping it unlocked allows users to modify it to remove the android app link if needed // structure documentation: https://gitlab.com/gototags/public/-/tree/main/Desktop%20App... records: [ {type: 'WEBSITE', url: ' https://guavahealth.com/nfc/YOUR_ID '}, // create a random unique string ID per tag {type: 'AAR', packageName: 'com.guavahealth.app'} ] } Fancy demo video: https://www.youtube.com/watch?v=018IItVGXvA This started as my colleague Awadi hacking an NFC tag and sticking it to his water bottle and repeatedly showing us his cool blue checkmark animation. Then a couple more of us got involved and did a 5-day hackathon to make it work for all health log types in Guava, and got some stickers printed! There's a lot of variety in the choice of NFC tag material, antenna, chip, etc., and we chose the more expensive sticker tags so they'd be waterproof and work on metal. For certain use cases you could use tags that are much cheaper and look different though. One thing you can do is create a huge board on your wall with all the 20-100 (???) things you log for biohacking or whatever, and use a unique NFC tag to log each one. We wrote the software to allow configuring custom types, so it's fairly unlimited.

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2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
85%85% 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.
TrustMRRFits verified-revenue profile · Strong signals: video, users · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, 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
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
28%28% 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
18%18% 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.

Incorrect prediction on native model

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