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I built an offline VIN decoder using the NHTSA vPIC dataset

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I built an offline VIN decoder using the NHTSA vPIC dataset

I wanted to open source the VIN decoder I built for the car ownership platform I'm building. It uses a custom variant of the NHTSA vPIC database https://vpic.nhtsa.dot.gov/api/ ported to sqlite, its fully offline and works right out of the box, I got the <300MB SQL Server database down to about 46MB. Hope this helps anyone looking for a VIN decoder, most use the NHTSA api but AFAIK this is the only version thats offline and using a custom version of the db. For a v2 I wanted to generate a dataset of basically every 'nano vin' (the check digit in position 9 and last 6 digits are arbitrary) and the corresponding make model year trim mappings. I ran some estimates and it would fit pretty comfortably in a modern VMs memory. Hope this is useful for anyone working in the space.

Visit productView on Hacker NewsMay 27, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, using, code · Missing: mac, agents, macos
82%82% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% 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
16%16% 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.

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