Race Timing with Integrated Replay

Hacker News

Instead of playing video and trying to display timing information next to it, how about we build a simulation based on timing data and become able to determine the state of the race at any given point in time based on the few 'radar blips' of sector loop passes we have? This is that. It takes the video and timing material of a 24h race (The IMSA Weathertech Sportscar Championship ROLEX24 at Daytona 2024) - 4 videos hosted on YouTube and a big CSV of laptimes and reimagines how a race replay might look like. It comes with a few non-standard visualizations, and a lot of possibilities for navigation, including navigation based on race events such as flag states, overtakes, pit stops etc. It's a zig program based on raylib running in the browser thanks to emscripten, with a bit of js glue code to control the YouTube player. Ultimately, controlling replays is just one of the goal features of the tech stack. Live-Timing, Live-broadcast, and broadcast of sim-racing are other application areas where I see this being beneficial. Race fans, Enjoy, I know I certainly do!

Visit product View on Hacker NewsJan 20, 2025

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

67%67% 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: visual, code · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

46%46% 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

13%13% 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.

Correct prediction on native model

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