I built a digital wrap tool for Tesla owners

Hacker News

A while ago, I showed some friends a Tesla digital wrap I'd made in Photoshop. These wraps change how the car model looks on the Tesla screen and in the app. They asked me how I'd made it, so I shared a tutorial with them. But they weren't familiar with Photoshop and eventually gave up. I also saw other Tesla owners online running into the same problem. So I built Tesla Wrap Studio using the official templates from Tesla's GitHub repository. It has a gallery of ready-made wraps you can download for free, plus an online editor for creating your own designs. You can work with your own images and text, see the results in 3D as you edit, or use AI to generate a design.

Visit product View on Hacker NewsSep 16, 2026

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

72%72% 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.

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% 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: model, using · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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