A Comic Book Marketplace (with a few twists)

Hacker News

I've just launched my comic book marketplace into public beta. At first glance, it's a fairly standard marketplace. Now, those twists I mentioned: First, we aim to create liquidity in markets that traditionally do not have it. Our users can add an item to a "wantlist", which when a particular item reaches a significant amount of wants a feed is sent to a list of dealers. They compete to claim the 'bounty' of the item based on how many wants it has. Additionally, our transaction platform is decoupled from the site listings... users need not go through our site to track a transaction using our platform and feedback tools. External transactions can be brought in (from forums, blogs, craigslist, whatever) for free. This is my first startup (man, the hours!) and any and all feedback is greatly appreciated! For tech: the site was built with Laravel 4 (my first project with Laravel). Link: http://comicswap.com/

Visit product View on Hacker NewsOct 15, 2013

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

75%75% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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

28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · 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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