Liquid Auctions – Instant liquidity powered by incentivized bidding

Hacker News

GM HN! Working on a novel way to auction non-standard tokenized assets. Two main quirks with the way our auctions work 1. Bid-to-earn model that encourages bidders to participate actively, bidding high and early. Incentivizing people to bid even though they may not want the underlying asset. 2. Instant Liquidity: Sellers get to tap into the liquidity from the bidders by paying a fee that gets redistributed to bidders based on how much bid "risk" they take (the standard rate is set at 3%) Note: Sellers can cancel the auction anytime, but they will need to repay whatever they took out plus an additional fee, which is paid to bidders. Canceled auctions effectively become loans. Unlike p2p laon protocols, our auctions optimize for the sale (default) instead of the repayment. Video showing how to get a loan/advance for a tokenized Pokemon card on Liq: https://www.loom.com/share/5bd2d2fd4e7f4ff58a7227379720e2d2?sid=ab9429d1-30a2-41e6-82ac-15afc47a2835 If you want to bid or play around with what we've built so far: https://beta.liq.auction *Ignore the Request access button on the page and click "Login" on the top right to get access. To bid, you'll need some USDC on Polygon. The Pokemon cards being auctioned off are physically backed and redeemable cards vaulted by Courtyard (a startup backed by YC) Happy to answer any questions and listen to feedback!

Visit product View on Hacker NewsDec 7, 2023

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

82%82% 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: ide, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, physical · Missing: mac, agents, macos

35%35% 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: active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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