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NativeSwap – Low cost cross-chain swaps without wrappers or bridges

Hacker News

NativeSwap – Low cost cross-chain swaps without wrappers or bridges

Hi HN, We’ve been working on NativeSwap.io, a low-cost decentralized cross-chain swap platform. It lets users swap native assets (e.g. BTC <--> DOGE) directly, without using wrapped tokens or centralized bridges. Our goal is to make cross-chain swaps cheaper, simpler, and safer. The platform is fully non-custodial and supports both regular and streaming swaps. Under the hood, it leverages liquidity from protocols like ThorChain and Maya Protocol. Currently, We support the following blockchains: - Bitcoin - Ethereum - BNB Smart Chain - Ripple - Dogecoin - Litecoin - Bitcoin Cash - Zcash - Base - Avalanche - Arbitrum - Dash - Radix - ThorChain - Maya Protocol More are on the way. We currently support Metamask and CTRL as browser wallets, as well as keystore files. Support for additional wallets is in progress. We built this because most solutions either rely on wrapped assets or require trusting centralized bridges - both of which introduce risk. Many existing interfaces also charge high fees. Would love feedback on the product, UI/UX, performance, security assumptions or anything else. Still early, but happy to answer questions and improve! Thanks for checking it out

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
83%83% 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: exist, existing, 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.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos
44%44% 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: introduce, smart · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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