Burner – A low cost wallet to gift crypto

Hacker News

Hi HN! I’m one of the creators of Burner, a low cost Ethereum hardware wallet designed for gifting. After creating various forms of crypto “cash” ( https://kong.cash/ , https://offline.cash/ ) we learned that gifting cryptocurrencies was consistently one of the biggest challenges for holders and enthusiasts. Burner looks and feels like a colorful gift card, but under the hood it incorporates a secure element chip. You can access its wallet with just an NFC tap, and it works entirely through our (soon to be) open source web app. There is a lot more to share, like USD II (created by https://www.bridge.xyz/ ), a stablecoin that we designed to work with Burner without gas, but for now we’re excited to push Burner out the door and start getting feedback from users.

Visit product View on Hacker NewsNov 13, 2024

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

81%81% 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.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist

62%62% 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: users · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency

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

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