GigNGood. List and Bid on Services(Gigs) and Items(Good) for free

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I was a little bummed out that something like Exec, TaskRabbit, AirTasker, etc. wasn't available in my area (Atlanta) as I'm broke and could use the spare cash, so I spent the past few weeks creating GigNGood. People can list and bid on services worldwide in an auction style format. The lowest bid wins. This naturally extended itself to items, so I added those in there as well, for those things you want to sell in an auction format but don't want to pay ebay listing fees. I've really strived to keep the interface as drop-dead simple as possible. I am open to constructive and destructive criticism before I start promoting this heavy, so fire away!

Visit product View on Hacker NewsNov 9, 2012

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

64%64% 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 · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

17%17% 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

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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