I

I made a non-custodial crypto DCA protocol

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I made a non-custodial crypto DCA protocol

The why I had been searching for a way to dollar cost average into ETH on-chain for a while. However, the current solutions involve locking up your funds or using some kind of weird token wrappers. I didn't want either. I wanted to be able to store keep the funds in my wallet with no restrictions in their native form (no wrappers). Since the FTX debacle and the crypto public's draw away from CEXes, I figured more people would be interested in using something like this. Introducing dca.cash! How it works The protocol uses Gelato network to recurringly execute swaps on Uniswap. Users choose the tokens, amounts they want to trade and frequency of swap. Users have to approve the token they want to swap. Then users initially have to deposit 10 MATIC to cover for gas costs. Security Although the users approve the max amount, the approval is locked by TimedAllowance which releases only the amount specified by the user and at only the specified interval. As additional protection, the function that executes the swap can be called only by a "dedicated msg.sender" that Gelato decides. This essentially means that no other msg.sender can modify the balance of any user wallets that approve. How you can use it Easy! You can head over to https://dca.cash and start DCA-ing now! Please do reach out to me for questions or feedback!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: users, way · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
40%40% 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, using · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
8%8% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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