My

My online food ordering start-up

Hacker News

My online food ordering start-up

For the last few months I've been working on an online food ordering startup, and our initial site (www.couchster.com) is nearing completion. Initially, I wanted to do more than just food ordering (I also wanted to offer products like spa and salon reservations, grocery ordering, etc) but we decided to focus on a core product initially. Anyhow, I'm curious what people think and what jumps out as something to improve. We actually do have some cool technology in place that's not immediately apparent (our payment system is way better for businesses than Grubhub's or Seamless' for example), but I'm sure there are plenty of things we should work on.Additionally, we're currently running a Crowdtilt campaign to try and raise some funds to build an app and improve our site a bit further. To date, I've entirely self-financed, but I've only been able to stretch that budget so far and I can't cover an app as well, as much as I'd like to. Any help here, even if it's just helping spread some awareness of our campaign, would be hugely appreciated!

Visit product View on Hacker NewsSep 17, 2012

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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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.

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

49%49% predicted probability of success on Product Hunt, 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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, 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 · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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