Cardog – Finding a car made easy

Hacker News

Hi HN, I just finished building the first version of Cardog, a platform that makes finding a car online simple. There is currently over 240,000 vehicles to search and filter from. Currently I only have vehicles from Canada but am working on adding 20,000 dealerships from the US. Searching for cars online is an awful experience, most dealerships websites are littered with popups, ads and bugs. This leaves users with the large vehicle listing platforms, which suffer from the same problems of failing to make searching simple. That's why I built Cardog, I saw most dealerships use the same few website providers, so I built a system to fetch the data and serve it in one simple to use platform. Everyday the data is fetched and updated so there isn't any stale listings. Its my first major project and I was hoping to get some feedback from the HN community. I was also hoping someone from Google might be able to speed up the OAuth verification process. I'm really excited to share this with the community and would love to hear any feedback.

Visit product View on Hacker NewsJan 10, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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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.

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

75%75% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

67%67% 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: google, users · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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

13%13% 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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