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Cardog – AI interface for vehicle ownership

Hacker News

Cardog – AI interface for vehicle ownership

Hi HN! I wanted to share something I've been working on after an interesting pivot. Last year I built a vehicle search tool that ran into legal issues with major listing sites. That experience led me to tackle a different problem - making the entire car ownership experience more accessible and data-driven. Ended up building an AI interface that helps research any vehicle, access documentation, and manage ownership - think having a car expert, market analyst, and personal assistant rolled into one. Core features: - Natural language interface to research any vehicle, parse manuals, and search relevant web/YouTube content (think perplexity for cars) - Monitor market values and listings across North America - Track maintenance, service records, registration dates for your garage - Store ownership documents, recall info, service bulletins Live demo: https://cardog.ai | Example: Ask about reliability ratings for the latest SUVs or "What should I look for when buying a used Model 3?" Would love to hear what aspects of car ownership you find most frustrating.

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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 HuntOn track for Day 1 leaderboard · Strong signals: model, perplexity · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
41%41% 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
11%11% 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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