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Traffic Champ – Beat traffic,find parking spots easily

Hacker News

Traffic Champ – Beat traffic,find parking spots easily

Hi HN, I am Gilbert co-founder of TrafficChamp https://traffichamp.firebaseapp.com/ . Traffic congestion costs the United States $3bn each year and a subset of that is people trying to look for parking. We own a car but after finally making it through traffic, another nightmare of looking for where to park kicks, and we spend money and lots of time looking for spots. But what if you could instead exchange your car to another driver going somewhere and they return your car on-demand when you need it? That's why we are building Traffic Champ. How it would work; List your car on our site and we will find other car-owning drivers who are interested in using it Drivers pick exchange spots which they use to drop off there cars to other drivers and you simply go do whatever you want. In case you need your car back, you simply request it back. Feel free to reach out for any feedback or questions.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
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
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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