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Cardog – AI car companion for transparent ownership

Hacker News

Cardog – AI car companion for transparent ownership

Hi HN: I'm Sam, a 22-year-old founder who spent the last 8 months building Cardog after a frustrating car buying experience where I felt completely outgunned by information asymmetry. What it is: An AI platform that gives car buyers and owners the same data transparency that dealers have had for years. Core features: AI chat that answers vehicle questions by synthesizing multiple data sources Price analysis that shows if listings are fairly priced based on market comps Digital garage that tracks your car's value and maintenance needs over time The problem I'm solving: Car buying requires 15+ hours of research across fragmented sources, yet consumers still lack access to comprehensive market data. Dealers know vehicle history, true market values, and maintenance patterns - buyers get marketing copy and hope for the best. Technical approach: The platform combines structured automotive data (specs, pricing, reliability) with LLM-powered natural language processing. Users ask questions like "Should I buy a 2019 Honda Accord with 60k miles for $23,000?" and get data-backed analysis considering market pricing, maintenance costs, depreciation trends, and model-specific issues. What I need: I'm looking for a small group of testers (aiming for ~50 people) to try the iOS TestFlight beta and provide honest feedback. The core user flows take about 2 minutes to test: - Ask the AI a car question to see how it handles complex queries - Upload a vehicle listing (or use our sample ones) to test the price analysis - Add a vehicle to your garage to see the tracking interface Feedback I'm looking for: - Does the AI provide genuinely useful answers vs generic responses? - Is the price analysis accurate for vehicles you know well? - What features are missing that would make this valuable for you? I built this solo while studying CS at University of Toronto. The goal isn't to replace car enthusiast knowledge, but to democratize access to market data and help people make better decisions. TestFlight waitlist: https://cardog.app I'll personally onboard everyone who signs up over the next 48 hours with a quick demo and will be around to answer questions here. A few technical notes for the curious: - Backend handles real-time data from 15+ automotive sources - Custom ML models for price prediction trained on historical transaction data - iOS app built with Expo / React Native Thanks for taking a look. Always excited to get feedback from the HN community.

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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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
96%96% 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 HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, personal, month · Missing: mobile apps, entrepreneurs, apps
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, ide · Missing: https docs, just released, exist
24%24% 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
15%15% 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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