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Dharma – Programmable Peer-To-Peer Loans Using Ethereum Smart Contracts

Hacker News

Dharma – Programmable Peer-To-Peer Loans Using Ethereum Smart Contracts

Hey HN! My name's Nadav Hollander, and I'm the Founder of Dharma. Dharma is building an open-source library that makes it really easy to programatically borrow and lend cryptocurrencies using smart contracts on the Ethereum blockchain. Today we're launching our Alpha Testnet release -- a command line tool that allows you to 1. Access a line of cryptocurrency credit from the command line in under 5 minutes 2. Easily build a bot that auto-invests in loans under hyper-customizable, programmable criteria Check it out: https://github.com/dharmaprotocol/dharma-cli Why I Think This Is Cool: 1. Loans in the Dharma Protocol are basically miniature ICOs issued by borrowers -- an investor's stake in a loan is held in a digital token that entitles them to future cash flows. That means loans are just as tradeable and moddable as Bitcoin or Ether, and packaging loan tokens into tranched debt instruments of all shapes and sizes is relatively trivial to implement in an Ethereum smart contract. 2. Dharma is an open protocol. This opens the door for any client application, including existing online lending platforms, to tap into a non-proprietary army of lending bots as a source of lending capital. This has the potential to dramatically lower the cost of capital for online lenders -- and, in turn, borrowers. How This Can Be Better: 1. Loans in the Dharma Protocol are slow and expensive. Ether gas costs are fairly steep for writing the requisite loan data onto the Ethereum blockchain, and an on-chain auctioning mechanism places a bottleneck on how fast a loan can be funded. We plan to address these issues by migrating towards an off-chain auctioning mechanism layered on top of a Kademlia-style P2P network. 2. The identity verification mechanisms we use to assess baseline creditworthiness are very weak -- any mainnet release of the protocol would necessitate much more robust KYC flows. Please play around with the CLI -- would love to hear feedback!

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using, open · Missing: mac, agents, macos
34%34% 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
12%12% 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, cryptocurrency, smart · Missing: web3, chat, make money
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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