A recap of your performance in Valorant Episode 6

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We build stats profiles for various games, including Valorant. This year we decided to take on a new style of project, with Spotify Wrapped being a major influence. We wanted to bring that excitement of revisiting past experiences to our users. Motion, design and player identity were core objectives, and I hope that comes across in the final product. I'll also share some technical details for those interested. Each page is individually composited in After Effects and exported using a plugin called Bodymovin, which exports the entire scene composition into a "compact" JSON representation, which is then rendered by Lottie. We then dynamically composite each page with the player's stats and tweak any relevant colours as needed. I'm not aware of anybody else using Lottie this way and I have to say it was a huge pain in the ass. But we got it done and I'm pretty happy with the result. Rive is on our radar as well, but there will be trade-offs to make in terms of it being feature complete -- After Effects is very powerful! Here's a demo profile if you haven't played Valorant before: https://tracker.gg/valorant/backtrack/episode6/375282b4-7b56...

Visit product View on Hacker NewsJun 26, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · 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.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · 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.

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

15%15% 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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