Let AI Find Your Next Car

Hacker News

Hi HN, I was hoping to get some feedback on a new feature i've added to my side project cardog. I built Cardog to make finding a car easier, manually searching through dealerships websites and building out a crazy ad hoc spreadsheet to compare cars is not fun. I've just added an LLM chat feature that has access to our API using function calls. The next step is to add a RAG store consisting of vehicle recalls, complaints, reviews / blog posts and anything else. I was interested in hearing what information is paramount to customers when searching for cars. Also, given that buying a car is a huge financial commitment only made every few years, would you see any value in a system that could constantly keep you updated with price changes and new vehicles on the market that match your criteria?

Visit product View on Hacker NewsMar 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, plain · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, 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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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